Let the data do the talking in your CV and cover letter: Mastering the art of quantifying achievements

I recently came across a great CV and was reminded of the power data has to make someone stand out. It’s one thing for example to say you were responsible for social media marketing vs creating a social media campaign that resulted in X number of impressions, leads or engagement, or that you were a food delivery entrepreneur who had a 5 star rating in customer satisfaction. The latter evidence demonstrate the value you bring.

In today’s competitive job market, it’s important to be able to quantify one’s achievements and articulate them in our CV and cover letter. It lends credibility to your claims and helps you stand out from the competition. In addition, it helps showcase your value to potential employers. However, putting this into practice can be challenging, especially when you can feel you haven’t accomplished anything significant. In this blog post, we explore practical strategies to help effectively quantify achievements and present them confidently in your application.

1. Focus on measurable outcomes:

Instead of dwelling on grand achievements, shift your perspective to measurable outcomes. Think about the impact you have made, regardless of its scale. Consider the following approaches to quantify your achievements:

  • Demonstrate improvements: Highlight instances where you have made improvements in your work, even if they seem small. For example, if you streamlined a process, communicate how it resulted in time or cost savings.
  • Showcase growth: Emphasise personal or professional growth you have experienced. This can include acquiring new skills, completing relevant courses, or taking on additional responsibilities.
  • Highlight positive feedback: If you have received positive feedback from colleagues, clients, or superiors, incorporate their testimonials as evidence of your skills and contributions.

2. Utilise percentages, numbers, and timeframes:

Numbers have a powerful way of communicating impact and value as I shared in a previous post. Even seemingly insignificant figures can provide context, demonstrate your ability to quantify results, and set you apart from other applicants. Consider the following examples:

  • Increased sales by 10% within three months.
  • Managed a project that reduced customer complaints by 15%.
  • Completed a task ahead of schedule, saving two hours per week.

3. Emphasise transferable skills:

Quantifying achievements is not limited to tangible results. Highlight transferable skills that can be measured, such as:

  • Effective communication: Mention instances where your communication skills have led to successful collaborations or improved team dynamics. Don’t just say you are an effective communicator and leave it at that.
  • Problem-solving: Provide examples of how your problem-solving abilities have contributed to resolving issues or implementing innovative solutions.
  • Leadership: Describe situations where you successfully led a team or project, highlighting specific outcomes such as meeting deadlines or exceeding targets.

4. Use qualitative data:

While numerical data is valuable, qualitative information can also be impactful. Incorporate testimonials, accolades, or recognition you have received, even if they are in the form of written or verbal appreciation from colleagues or clients. These qualitative indicators can demonstrate the value you bring to the table.

To conclude, quantifying achievements in your CV and cover letter may seem daunting, however, by shifting your focus to measurable outcomes, utilising percentages and numbers, emphasising transferable skills, and including qualitative data, you can effectively showcase your value to potential employers. 

Remember, it’s not always about grand achievements, but rather about presenting your experiences and contributions in a quantifiable and confident manner. 

Unsure how to showcase what you have done? As a Data Analyst professional, I can help. Feel free to reach out.

GenAI prompts to help you identify your IKIGAI, what you want to do in Life

You might be at a crossroad in your life or are still searching for your dream job or perhaps wondering if what you are doing is what you are meant to be doing. In a previous article, I explored the concept of IKIGAI or identifying what you want to do in life. I presented the diagram below as a way to identify what you want to do by looking at the intersections of what  you love, enjoy, care about, what you are good at, what the world needs (…that you can give) and what you can be paid for.

Ikigai diagram, adapted from Source

In the article, I shared a manual process I had undertaken that includes writing down lists under each theme and then finding intersections between the four lists and analysing them to come to a conclusion. While this was very fruitful, it was still a manual process that could become challenging with longer lists.

With their ability to access vast information about the world and find intersections, in this blog post I explore the use of textual Generative AI (GenAI) tools to support the process of finding what to do in life. Textual GenAI tools allow you to interact with them in a natural language manner and have been trained on a vast amount of information that they are able to analyse, process and provide new insights. I share a process and set of prompts I used to reach, not so surprisingly, similar conclusion to what I had come to manually but I reached there faster and found a few things along the way, like ideas for side hustles. I also include Screenshots from one of the tools I used, Google’s Gemini. You can also test the prompts out in Open AI’s ChatGPT.

Prompt: As an expert IKIGAI coach, you are going to help me find my Ikigai, the intersection of what I love to do, what I am good at, what the world needs, and what I can get paid for.  

Here is the list of what I love, enjoy and care about.

  • Travelling
  • Empowering people
  • Watching good tv shows and movies
  • Listening to music
  • Dancing 
  • Critical thinking 
  • Data analysis 
  • Baking
  • Spending time with my family 
  • Bringing out the best in people 
  • Writing blog posts that share new insights
  • Conducting research and problem solving on problems that I care about
  • Having a good laugh
  • Making functional arts and crafts items
  • Reading books especially crime related or courtroom dramas
  • Being creative and coming up with solutions
  • Organising meaningful events

Here is then the list of what I am good at:

  • Baking
  • Event management and planning
  • Big picture thinking
  • Being disciplined and seeing things through
  • Adapting and being flexible 
  • Staying cool under pressure
  • Good eye for visuals
  • Connecting people
  • Being proactive
  • Being creative and generating ideas
  • Teaching
  • Critical thinking
  • Research, gathering data, evidence
  • Creating visually appealing visualisations
  • Braiding hair
  • Supporting and inspiring people
  • Learning new skills
  • Data analysis

What are the intersections between what I love and what I am good at?”

Screenshot 1 in the Gallery below shows the output with a list of intersections that it found. The first and last two options were more aligned with I want to do. I entered the following prompt after that.

Prompt: Looking at the Creative problem-solving and Data storytelling, what are the problems in the world that need solving where those two things would be applicable.

Screenshot 2 gives a list of problems that are aligned with my skills and what I enjoy.

Prompt: List the ways in which I can make good money from solving those problems and how. Include suggestions of who also could pay for it.

As I have been thinking of going more on the entrepreneurial route, I also asked:

Prompt: What about entrepreneurial options. What businesses can i build to solve those problems.

Many of the ideas that were given in the output align very much with what I have been thinking about and actually had started doing, e.g., see HERE. From these I can delve further and narrow down on what to try.

From these prompts, you can begin to form a picture. Naturally GenAI tools are limited to your context or understanding of your environment. However, they can provide general thinking points to  continue and generate ideas on how to start and create a road map for yourself.

Enjoy the journey of exploration and discovery!

3 simple reasons why being data-driven matters for your value proposition

Harnessing the potential of data-driven approaches can revolutionise how organisations, projects, and individuals achieve their goals. However, despite the abundance of data available, many struggle to fully utilise it to drive success. In this blog post, we will delve into three simple yet crucial reasons why implementing data-driven approaches can bring significant benefits whether at an organisational, project or person level.

Let’s first establish a common understanding of data. Simply put, data is a representation of information that can manifest in various forms, including numbers, text, images, or sensor readings. Now, let’s explore how data matters, using concrete examples at three perspectives.

  1. Data provides proof of value proposition. Data holds the power to provide tangible proof of the value and impact of your endeavours . By analysing data, you can uncover insights that showcase the effectiveness of your offerings. Let’s illustrate this with examples:
    1. Organisation level: An ecommerce business can analyse customer purchase patterns, feedback, and engagement metrics, to demonstrate the value of their products or services brought to customers. By understanding customer preferences and tailoring their offerings accordingly, they can increase customer satisfaction and retention rates.
    2. Project level: A non-profit project that provides clean water taps to rural areas collects data on usage patterns, customer testimonials, and health improvements. By analysing this data, they can showcase the positive impact of their project, attracting funding and support for expansion.
    3. Personal level: An individual who offers freelance graphic design services analyses client feedback, project success rates, and portfolio performance. By leveraging data to demonstrate their track record and client satisfaction, they can attract more clients.
  1. Unlocks new opportunities for transformation. Data and insights from the data can reveal new opportunities and help you transform your offerings. Building upon the previous examples:
    1. The ecommerce business: By analysing the purchasing patterns, market trends, and customer preferences, they can identify upsell or cross sell opportunities to increase revenue generation. 
    2. The non-profit: By analysing the usage patterns could make a stronger case for expansion.
    3. Similarly, at a personal level, the freelancer can use project rates, competitor insights, and client feedback to inform a more effective pricing strategy.
  1. Empowers decision-making and confidence – When you have data, it acts as armour, empowering you to make well-informed decisions. As demonstrated with the examples above, it instils confidence to take calculated risks at all levels.

Remember, data itself is just raw information until you give it meaning. By using data to provide proof of your value proposition, uncover new opportunities, empower decision-making, and give meaning to data through effective storytelling, you set yourself up for success. 

Want support in collecting, analysing and communicating your impact with Data? Read more on what I can offer and reach out.

A step-by-step data-driven approach to job search and preparation

Transitioning careers or entering the job market can be a daunting task, but with a data-driven approach, you can streamline your preparation and increase your chances of success. In this article, I provide practical steps to help one identify the skills in demand, prioritise your learning, and make yourself visible to potential employers. Included is also a prompt that can easily be used with for example OpenAI’s ChatGPT or Google’s Gemini to easily conduct the analysis. Naturally other analysis methods can be applied. While the examples are largely tailored to a career in Data Analysis, the underlying steps are applicable to other fields as well.

Step 1: Identify the skills needed for your target career

To begin, it’s crucial to identify the skills that are most sought after by employers. As a Data Analyst, I have often received questions as to whether to study python or R, while I can give my opinion based on my workplace, it is beneficial to leverage data to guide the decision. Start by analysing job boards such as Jobylon or LinkedIn Jobs, and search for positions related to your desired career. This will help you discover the most in-demand skills in your desired location. Filter by location if that is a factor.

Step 2: Prioritise skills based on market trends, benefits, and location

Now that you have a list of skills, it’s time to prioritise them based on various factors. Start by analysing the correlation between specific skills and advertised benefits or salaries. This will help you identify skills associated with higher earning potential, aligning your learning with your career goals. Additionally, analyse the growth trends of these skills over time to stay ahead of the curve and focus on those with increasing demand.

Step 3: Acquire skills and make them visible

Once you have prioritised the skills, it’s time to acquire them and make your expertise visible. Search for online courses, tutorials, and bootcamps that cater to these specific skills on platforms like Udemy, Coursera, edX, or university websites. Filter the learning resources based on your current skill level, ensuring you find materials suitable for beginners, intermediate learners, or advanced professionals.

To showcase the real-world applicability of your skills, consider engaging in projects with non-profit organisations or participating in competitions. These experiences not only help you grow your skills but also demonstrate a social impact. Additionally, leverage hobby projects, summer jobs, or internships to gain practical experience.

Step 4: Tailor your CV, resume, and LinkedIn profile

Now that you have acquired the desired skills and gained some practical experience, it’s time to tailor your CV, resume, and LinkedIn profile to highlight your expertise. Incorporate the keywords you discovered during the analysis of job postings into your skills, experience, summary, and header sections. This will make your profile more relevant and attractive to potential employers.

Step 5: Build your network based on your findings

They say your network is your net worth. Leverage the findings from your analysis to build a relevant professional network. Perform a LinkedIn search based on the identified keywords and connect or follow individuals who are active in those areas. Follow companies related to your desired field and participate in skill-related events organised by those companies. Building a strong network, regardless of the country you are in, can open doors to opportunities and valuable connections. 

Find below an example of a process that you can adapt for your own use.

In summary, while a data-driven approach is a valuable tool in building your career path, it’s important to consider your personal interests, career goals, and learning preferences as well. Combine the insights gained from data analysis with your passions to make informed decisions about your learning journey. With the right skills, a tailored profile, and a strong network, you’ll be well-prepared to pursue your dream job and achieve professional success.

Process:

1.Create an Excel with job descriptions in one column. Collect as many relevant positions as possible.

2.Enter the below prompt in ChatGPT or Gemini or another tool. I would recommend trying this on multiple platforms as for instance ChatGPT provides insights based on available data up until 2021. For Tech jobs at least, the job market changes fast. Therefore, it’s essential to supplement this analysis with up-to-date information and stay informed about the latest trends.

Prompt:  “Please analyse the keywords from the  job posts that are shared below and provide insights on their frequency, benefit correlation, and market trends for a career in [Data Analytics]

First, Identify the most frequently occurring keywords in these job posts related to [Data Analytics] and provide a ranked list of these keywords based on their frequency of occurrence.

Secondly, analyse the correlation between the specific skill keywords and the benefits of the job posts. Identify keywords that show a strong positive correlation with higher salaries in the [Data Analytics] market. Provide also insights into the skills or keywords that are associated with higher earning potential in [Data Analytics].

Thirdly, analyse the growth trends of specific skills or keywords over time in [Europe]. Identify skills or keywords that have seen a significant increase in demand over the past [3 years]. Provide insights into emerging skills or keywords that are in high demand and can give professionals a competitive edge in [Data Analytics].

Lastly, Please present the results in an organised manner, including the frequency-ranked list of keywords, a summary of benefit correlations, and an analysis of market trends. Additionally, if there are any notable findings or recommendations based on the analysis, please include those as well.

[Attached are the job descriptions in csv file/Below are the job descriptions]”

Below are screenshots of sample results from Google’s Gemini with the above prompt.

Screenshot of sample results from ChatGPT

4 Superpowers to stay relevant in the AI Age

I often wish for the ability to think of something like sending an email or WhatsApp message and it actually sends, especially as my time in front of devices has diminished considerably. However with advancements like Elon Musk’s first Neuralink Telepathy product, that future does not seem that far away now. Thus it becomes essential to equip ourselves with the right qualities or skills to stay relevant.

Inspired by a keynote presentation on “AI and the Future of Humans” by futurist Niclas Hermansson at a recent event by Avaus called AI beyond the Hype, this blog post explores the four superpowers that can help us remain relevant in the Artificial Intelligence (AI) age: Critical Thinking, Collaboration, Creativity, and Communication. These also made me think as a parent, how I encourage these in my kids for the AI world they are growing up in. Not only as a parent, but how I also stay relevant in my career when automation is going to handle a lot of what I do, from data cleaning, advanced analysis, visualisation, and storytelling, which in itself is beneficial. But it does force me to be more creative and expand my big picture thinking, looking at what else and, how to create additional value.

Critical thinking – In an era of abundant information and increasing disinformation, critical thinking becomes a vital skill. According to Hermansson, AI will compel individuals to become smarter and exercise source criticism. As the sea of information deepens, it becomes crucial to step back, comprehend the bigger picture, and discern reliable sources from misleading ones. Developing critical thinking skills empowers us to make informed decisions and navigate the complex landscape of AI-driven information both at a personal and professional level.

Collaboration – The ability to work effectively  with others becomes a superstrength in an AI-driven world, especially as the gig economy grows. Hermansson highlights that high emotional intelligence and the capacity to cooperate are increasingly valuable in the workplace. While technology has reduced the demand for high IQs, the power of human connection and collaboration remains unparalleled. Embracing collaboration allows us to harness collective intelligence, combine diverse perspectives, and tackle complex challenges in the AI age.

Creativity – Despite the advancements in AI technologies like Sora, Midjourney, Gemini, and ChatGPT, among many others, creativity remains a superpower. In fact, Hermansson predicts that by 2050, 40 percent of today’s professions will be automated, while others will require creative problem-solving skills. Standing out and finding innovative solutions to complex problems become essential for professional success. Cultivating creativity enables us to adapt, think outside the box, and leverage our unique human capabilities alongside AI-driven tools.

Communication – In an era of information overload, effective communication becomes a superpower to cut through the noise as well as consume the right information. Hermansson highlights that our brains process a staggering 100,000 words daily, but we only remember a mere two percent. Therefore, becoming a skilled communicator allows us to convey our ideas, insights, and value in a compelling manner. Mastering communication ensures that we can effectively share our knowledge, connect with others, and make an impact in the AI age.

The AI age is undeniably here and it is becoming more and more accessible to the wider public as well as becoming more user-friendly. By honing these skills, we can remain relevant, adapt to the changing landscape, and leverage the potential of AI to enhance our personal and professional lives.

How are you preparing?

Product innovation as a means for self-financing in nonprofits

Product innovation is something that you rarely hear  non-profits discuss as a way to generate funds. Yet nothing stops nonprofits from productising their services/offering as a means of self-financing. 

I recently listened to a presentation on Business skills that make millions by Myron Golden.  He shared three skills that businesses need to generate a lot of money. The presentation  emphasised  product innovation, and also provided a framework for thinking about how to create products that have value – that are desirable in the marketplace. It made me reflect on its applicability in the nonprofit world where funding is always a challenge.  

The framework outlines three main skills, product innovation, revenue generation (which includes promotion, marketing, selling) and asset allocation (which includes pricing, payment collection, payment gateways). In this blog, I focus on the first skill, product innovation, in the context of nonprofits. As defined by the Harvard Business School, product innovation is the “process of creating a new product—or improving an existing one—to meet customers’ needs in a novel way”.

Myron emphasises product innovation rather than service innovation as a way to generate wealth because a service needs time to fulfill. Because time is limited, he recommends disconnecting the revenue generation from time as much as possible. This is especially relevant for nonprofits, as from observation, we tend to invest more in services than products. Yet time is even more scarce especially in volunteer-run nonprofits like Think Africa.

In Mylon’s framework, product innovation has three characteristics:

  1. Desirability: A product is desirable if it offers a solution to the marketplace, if it has value. Myron shares three situations that cause people to value things and that could make your offering/product desirable.
  • Past perceived voids create present pursued value: If someone grows up without something but  desires it, when they have a chance to get it, they will. To create something valuable, you can then find a large group of people with a void and design a product  that fills that void.
  • Present perceived virtues create present pursued values: When someone perceives something as good now, they are going to pursue it. 
  • Future perceived visions create present pursued values: When someone perceives something with the vision that it will pay off in the future they are going to get it e.g., paying tuition fees for a degree.
  1. Measurable: The promise of your product needs to be measurable, life before your product and after the product. The greater the transformation of your product, the more you can charge for it.
  2. State-able in a soundbite:  You need to figure out how to say what you do for people in an easy-to-understand sentence.

Now, it’s easy to say or know that you should pursue product innovation, but even that takes time which nonprofits do not have. However, in some cases creating a desirable product could take the same amount of time it takes to write and submit a public grant, or organise an event or project. The advantage of investing in product innovation is that once you have created a desirable product then you don’t have to keep reinventing the wheel. Even the process can become a template for new innovations. Whereas with a grant, you might end up having to submit to several different funders to get a positive answer, if at all.

A few examples of low hanging fruit that nonprofits could explore include innovation around membership fees and services that are already being offered. Many nonprofits charge membership fees in return for benefits. This is already a product where more value could be created to attract members who are happy to renew each year. One could for instance innovate to also have a specific offering for those that can afford €200 and offer that to 5,000 people/companies and that is €1 million annual revenue. 

Most nonprofits are also run by people with a certain expertise that makes them the best people to fulfill the organisation’s mission. Those same experts could spend time writing a book, newsletter, online guidelines that provide value (something that is desirable for many) and sell those assets. Your creators could potentially sell more digital products within  an hour  than you could deliver a service in that same period.

I hope this ignited some thoughts around product innovation in nonprofits. Do you have examples of nonprofits that have succeeded in  product innovation and t could inspire others? Please share.

Unlocking Finland’s African Non-Profit Ecosystem

Collaboration, representation, and the celebration of the diversity of cultures are among the key values of Think Africa. To enable that, an overview of the ecosystem, the players, history, and their location is essential.

Finland is home to a thriving African diaspora community that constitutes around 1% of the population (57 496, Statistics Finland 2020). Non-profit organisations like Think Africa and many others provide essential services important for the Diaspora and the Finnish Society, from supporting integration, well being, information sharing, cultural understanding, and bridging of Finland and African countries. 

The mapping of the ecosystem of nonprofits in Finland reveals that there are at least 340 registered organisations in the Finnish Trade registry system. Majority of them, 69,7% (237) are focused on a specific African country. The mapping reveals a rich variety of missions, activities, presence across Finland and impact areas. Gain insights into the ecosystem players, oldest, and new. Whether you are a new-comer in Finland or seasoned, the mapping expands your networks, reach and collaboration opportunities. 

The mapping has been captured in an interactive Dashboard that allows one to explore and discover all the organisations that have ever been registered, when, where, their mission and web pages where available, if they are still active, as well as whether they are transnational, regional, national, or ethnic focused.

See Dashboard here Ecosystem mapping of Africa-related non-profits in Finland | Tableau Public

Insights overview

According to the ecosystem mapping, we’ve identified 340 African diaspora and Africa-focused non-profit organisations across Finland. With the majority of the organisations being registered in Helsinki. The highest concentration of organisations can be found in Helsinki, which also has the highest concentration of the African diaspora in Finland (38,5% = 22,138). The number of transnational organisations that represent the whole continent, similarly to Think Africa are 52, the number that are still active, i.e., have a web presence are 32 (61,5%)

A majority of these, 237 (69.7%) constitute organisations that represent a specific African country or ethnicity in a specific country (40 organisations). These represent 34 African countries, out 54 that are present in Finland as per Statistics Finland 2020. The oldest of these is a Moroccan organisation called Suomalais-Marokkolainen Yhdistys ry that was registered in 1964. However it does not seem to still be active from the lack of any web presence. Moroccans constitute the fourth biggest diaspora population according to Statistics Finland 2020 and we identified 17 organisations representing Morocco. Considering that the first person of African descent was a Namibian, Rosa Lemberg, who came to Finland in 1888, it is surprising to see that the earliest Namibia-focused organisation was registered in 1975.

African countries represented by the organisations – See full visualisation of the data here ->Ecosystem mapping of Africa-related non-profits in Finland | Tableau Public

Expectedly, as Somalia and Nigeria constitute the biggest diaspora population, with 22,534 and 4,150 people respectively, the data shows a higher number of organisations with 68 and 25 respectively. Surprisingly Sudan which has a relatively smaller diaspora population, 2,013, has almost as high a number of organisations as Nigeria.

The data perspective also reveals the absence of representation of certain African nations, such as Burundi, Ivory Coast, Lesotho, Mali, Chad, among others.

Activeness and collaboration among organisations

I have heard people say that Finland is a land of nonprofits as it is easy to start one. Just in 2023, 11 organisations were registered. Unfortunately it is not easy to also maintain operations especially as many of the non-profits are run on a volunteer basis.  Out of the 340 identified registered non-profits, 117 (34,4%) have a web presence and are considered for the purpose of this analysis active. However, it does not mean that having a web presence means one is still operational and vice-versa. Looking also at the list of active organisations, many are active through Facebook pages rather than their own websites.

Organisations have been registered across 38 cities around Finland, with the majority of organisations existing in the bigger cities of Helsinki, Espoo, Vantaa, Turku and Tampere.

Through experience, one of the questions that often gets asked among the diaspora organisations is the need for an umbrella organisation as that would also facilitate collaboration and unity. Looking at the missions of the organisations, there are umbrella organisations, some who are country focused. However, among those with a continental focus, there has only been two that describe themselves as such; The African Civil Society (ACSF) and Finnish African Diaspora Platform for Development (FADP) ry, with the latter seeming not to be active anymore and the former not having any fresh content on their website, i.e., content within the past year. The oldest of the transnational organisations is the Finnish African Society ry that is still active as within the last year.

DATA COLLECTION

The data was collected through an initial listing of organisations within Think Africa’s network that focus on the African diaspora or Africa. The list was expanded through a search in the Finnish Trade registry system with keywords that include Africa*, Diaspora, Afrofinn*, names of African countries represented in Finland (See Tableau Dashboard). Hence it’s possible that some organisations focusing on some ethnicities that did not get returned through the search could have been missed.

Moreover, only those non-profit organisations registered were included. Other civil societies like Good Hair Day, Ubuntu Film club, or UEF Joensuu African Students Organization (ASA), among others, that focus on people of African descent in Finland but are not in the registry were not included.

CREDITS

Ecosystem mapping was produced by Myriam Munezero, Data Analyst by profession. Data collection was supported by members and volunteers of Think Africa Aderemi Fayoyiwa, Olivia Alfred, and Salma Gheita. Think Africa recently captured the actions of organisations in this video -> Celebrating the Impactful Actions of the African Diaspora in Finland.

For any corrections, contact Myriam at mdoucem[at]gmail.com

The equation for behaviour occurrence. A Framework for assessing the likelihood of desired behaviour

At the heart of any behaviour change or habit formation is discipline. Whether you want to improve your health, master a skill, manage your time better, or start and run a business, it all comes down to your discipline.

I recently listened to an interview with entrepreneur Steven Bartlett (link) by Chris Williamson,  where he talked about the equation for discipline and behaviour change. Steven Barlett through his Diary of a CEO podcast has interviewed and gathered a lot of information on discipline, behaviour, psychology, and many other related topics. As a data analyst, I couldn’t help but be curious about the applicability of the equation, scenarios in which it works, and how it could be leveraged. I like formulas because they give you guidelines for data collection and prediction.

The equation

According to Steven, the equation for discipline is as follows: Discipline = Subjective importance of goal (why the it matters to you, how much it matters) + Psychological enjoyment that you get in the pursuit of the goal – Psychological cost, how much pain, effort, friction do you experience. Simplified as Discipline = Importance + Enjoyment – Cost, this equation offers insights into factors influencing behaviour. If the equation is positive it will occur, otherwise not.

Expanding the equation

To make the equation really represent discipline, I believe there has to be a time component as discipline involves something happening over time. Thus I see this equation more as a behaviour occurrence equation. In addition, I would propose adding another component, Priority of the goal now, which is influenced by time. This is because due time constraints, not all behaviours can happen. The equation thus becomes: Behaviour = (Importance * Priority) + Enjoyment – Cost

In the interview, Steven and Chris discussed a few scenarios to assess the effectiveness of the equation. I tried it as well and what I like is that it gives you an idea where to focus your efforts if you really want a behaviour to occur. For instance, I have been wanting  to start a business for a few years now but it has not happened yet. And running this behaviour through the equation, by assessing each variable on a scale of  1 to 10 and priority on a scale if 1 to 3 (*note the scales are still a work in progress), the Importance is really high = 8, the Cost is also very high = 9, and Enjoyment is low = 3. The priority of the goal at this moment of my life is also not so high 1. This results in a +2 overall. 

Even though the answer is positive,currently the friction/cost is too high and priority too low and hence the desired behaviour does not happen. To make the behaviour happen, I would need to reduce the friction/cost or increase the perceived enjoyment from pursuing the goal. This is also where James Clear’s four steps on habit formation and creation processes to achieving goals come in handy (see the summary). 

Exploring a use case of the equation: Data collection and event attendance

Another intriguing application use case that came to mind when I was assessing this equation was how, with data collection, to assess and predict event attendance before an event. Drawing inspiration from my volunteerism work at Think Africa, and our recent event, Think Africa Week. I contemplated how we could gather data on the equation variables to predict and influence attendee behaviour. As it often happens in many free events, there are many people who register and do not show up and as an organiser you are often left wondering why, is it the time of year, is it the content, what could you have done differently, what could you do better next time.

If we look at a person coming to an event as the desired behaviour that we want to occur. Could we collect data along the equation variables that could help us assess whether this behaviour will happen or not? And could we use the data to affect the behaviour? 

I still need to test this but I imagined incorporating the variables into a registration form, almost like levers that a potential attendee moves in a simple way not to prevent registration, to indicate at what level they assess:

  • The importance of the event or similar ones to them
  • The perceived enjoyment they will get from the event or they get from similar ones
  • The level of friction that might prevent them from coming 
  • Level of priority they give to attending the event, other things considered

With the answers, we could obtain a likelihood score of someone coming to the event. Such insights would be valuable for planning purposes, enabling us to make informed decisions and improve future events. 

My next steps are to try this with a few real events and share the results.

What do you think of this framework and what other use cases come to mind?

3 ways ChatGPT can save your nonprofit time and resources and boost productivity – From fundraising, finding partners, marketing, etc.

Having served as the Chairperson of a nonprofit for five years, I know too well the operational challenges that come with limited time and resources. Especially when the nonprofit is largely volunteer based, as the one I was heading, Think Africa. Being from an IT background, I am always open to trying out technologies that can support by automating tasks and thus lessen the burden. In this post I share 3 ways where I see ChatGPT being beneficial for nonprofits, especially in those areas that I have found to be time consuming but necessary to keep the nonprofit going.

Many of us have probably by now heard of ChatGPT since its launch late last year. ChatGPT, which is short for Chat Generative Pre-trained Transformer, is an “advanced natural language processing tool trained to provide information and assemble content together based on prompts entered by a user. With the ability to continuously learn and adapt to new information, ChatGPT can remember past conversations and constantly iterate based on user prompts.” (Source). It is developed by Open AI, and is just one of the many existing tools utilising advances in large language models (LLMs) – “advanced Artifical systems that are designed to understand and generate human-like text based on the patterns and information they’ve learned from vast training data. These models can be used for various tasks, including natural language understanding, text completion, language translation, question-answering, text summarization, and much more.” (Source)

ChatGPT is currently free for anyone to use over the Internet. I do see the irony in writing this post as anyone could type ‘Write a blog post on how AI-powered technology like ChatGPT can benefit nonprofits.’ and you would get the list but I do supplement the points with context, which hopefully will lead you to formulate your own prompts. It is definitely an exercise in asking clear and succinct questions. 

An advantage I find to using this over searching over Google is that you get the text ready rather than piecing search results together yourself.

This post touches on the following benefits: 

  • Grant writing and Fundraising
  • Marketing
  • Event planning

Grant writing and Fundraising

At the nonprofit that I was heading, we for instance wrote and submitted close to 10 grants in the past year. This is a time consuming task and it’s a gamble, in the sense that you put in the effort with no guarantee that you will get it, but yet if you do not do it, you wouldn’t get enough funding for all the projects. ChatGPT is not going to do your job fully but it can help you write an outline for a work plan, find statistics and data to put into the application which is especially important for problem formulation. It can also help in suggesting an action plan to solve a particular problem. In addition, it can suggest upcoming grant opportunities for a particular project. 

If starting a project from scratch you could use ChatGPT to first find a problem to solve, e.g. “What are the top three challenges that the African diaspora are facing in Finland?” 

(Screenshot of result, find the full results and chat here)

Once you have found the problem you would like to work on, you could narrow it down again with ChatGPT to get facts and statistics on the problem e.g.,”How many people are unemployed and underemployed among the African diaspora living in Finland?” (Note this prompt did not produce results as ChatGPT is still limited in the data that it has access to) but you could ask for instance “List five problems that are caused by unemployment in Finland among minority groups”. Then delve further to ask “List 5 actions that can be taken to address unemployment among the African diaspora living in Finland”. 

(Screenshot of result, find the full results and chat here)

You can continue to ask about the budget of one or more of those actions, refine the language so that it is clear and concise, and eventually you can piece all the above together to create a work plan that can be submitted. 

What I also found handy is that you can ask ChatGPT to “List grant providers in Finland focused on economic integration of immigrants”. Note, it might help to put this prompt in Finnish as many of the grant pages are in Finnish.

Other than grant writing, there are other ways ChatGPT can help with Fundraising, from giving you ideas on Fundraising to creating Fundraising campaigns. For example, continuing from our previous prompts, one of them  by using the following prompt “We need to raise 5000€ for a one day thought leadership event on the economic integration of immigrants in Finland. Can you give me five ideas on how to do this?’’. And Voila, you get ideas for activities. But often in a nonprofit it’s not ideas that are lacking but the ability and resources to execute those ideas effectively. For that you could also use ChatGPT more specifically, for example by prompting “Create an action plan for … (describe the activity)”.

(Screenshot of result, find the full results and chat here)

Fundraising can also be done through partners and sponsors. However it’s not always easy to identify those companies that would be best to partner with. Here ChatGPT can help you identify relevant potential partners. For example, in some of Think Africa’s projects we have been interested to know the Finnish companies that are operating in Africa and thus could be potential partners for some activities. Using ChatGPT, you can enter a prompt like “Give me a list of 10 companies or organisations that are based in Finland but also have operations in Africa”

(Screenshot of result, find the full results and chat here)

Marketing

There is a lot that ChatGPT can help you with here. Marketing is essential for any civil society, yet it is a demanding task. Although there are more, here are three areas where it could save you effort.

Creating a marketing plan. Continuing from our previous prompts from the grant writing, you can create a marketing campaign for the project by providing the topic, the platform, and any additional details you can offer. For instance a prompt like “Create a marketing plan for a one day thought leadership event on the economic integration of economic integration of immigrants in Finland. The campaign will last two weeks, promoted on LinkedIn and has the goal of creating awareness and driving registration

Getting hashtag ideas. Prompt: “Give five compelling hashtags for the above event”

Optimising subject headers. One of the most disappointing things is when you spend a lot of time designing e.g., a newsletter only to see very opens. Of course there are other things that affect, but the subject impacts a lot. Same goes for emails. Chat GPT can help you brainstorm on creative headers, “Give 5 examples of compelling subject headers for (describe event)”

Blog writing – Blogs are a great way to keep content on your website fresh. Old content often gives the feeling that the organisation is inactive. But it is not always easy to find time to write new content. ChatGPT can help you in that area and whatever it produces can be read for tone of voice and accuracy before it is posted. Prompts could for example “Write a 300 word blog post on the Finnish government’s efforts on the economic integration of minorities”.  Read it through for tone and accuracy before publishing.

Event planning

At Think Africa we organise over 50 events every year. Events are great for connecting and sharing information and general brand awareness, but you also need new ideas to keep them fresh and attract an audience. Not only ideas as those ideas also need execution, ChatGPT can help you in planning the event and in thinking of the logistics. With a prompt like the below, you can obtain a plan for an event: “Generate an interesting program for a one day thought leadership event on the economic integration of immigrants in Finland. Create also the event plan”. You could also ask for ideas to get volunteers for the event.

(Screenshot of result, find the full results and chat here)

There are many other uses of ChatGPT that can save time in a nonprofit, from writing operational policies, speeches, thank you messages, fundraising request emails, volunteer or job positions, analysing meeting minutes, translations, the list goes on. If you have not had a try with it yourself, you can access it here – chat.openai.com. There is also a paid version available.

If you have used it before, what are the best tips for getting the most out of it? Feel free to get in touch on this.

In my next post, I will share other AI technologies that I find would also support nonprofits.

Persistence: The invisible superpower

I was glad to recently be part of the Harambee podcast, one of the products of Think Africa’s members initiative that is still going strong. In the podcast, I talk about leadership and the legacy I leave after serving as the Chairperson of the organization for five years.

One of the questions I have often received during my Chairmanship is how I manage to do everything I do (even though I always feel like I could be doing more). This is because I have been a very pro-active and involved Chairperson, from fundraising, building partnerships, doing marketing and sales, event organization, community building, you name it. And by no means by myself, but I was always actively involved, thinking and looking for ways to drive all those efforts in order for the organization to carve out its niche and make an impact.

I think underneath that question of how I do everything that I do is how I do it on a volunteer basis. With salaried work, there is a clear reward model. However, with volunteer work, the reward is not regular nor guaranteed, and the threshold for quitting when things get challenging is very low.

In the chat with Cucu, I reflected that more than passion and belief in Think Africa’s mission, it is persistence, the ability to continue the course of action in spite of difficulty or opposition, that has actually kept me going as well as become good at things I didn’t know.

Being persistent. That is my superpower. And it is a quality that I even wish to instill in my children – especially as I see my daughter giving up easily on things when they get challenging.

To give a concrete example: in my first year of being the Chair, one of Think Africa’s long-term members gave compelling reasons why the organization needs to collaborate closely with the Africa department at the Ministry for Foreign Affairs of Finland (MFA).

I wrote several emails to the office, including the director of the department as well as made phone calls, but all went unanswered. One day I was at work and happened to see that the director was speaking at an event that afternoon that was organized by the Finnish-African Association.

Luckily it was a day when I had no meetings, so I jumped on the tram and went to the event. After the director’s speech, I noticed she was leaving and not staying for the networking session. I followed her and while she was putting on her jacket, I introduced myself, Think Africa, and why I believed it was important that she meet with us.

I think I was six or seven months pregnant at this time. After this initial meeting, I immediately followed up with an email and asked for a meeting, and Think Africa got invited to meet with her and the other Africa regional departments at MFA. And to this day, we still have a good partnership with the Africa department at MFA and have been honored to have many office members participate in our events, including the Minister for Foreign Affairs. 

And it’s this kind of work, the necessary work, that is also the invisible work. The giving up of your time to attend something, the preparation of the one-minute, two or three-minute sales pitch, the follow-ups, the preparation should you get that meeting, the not giving up if your emails or calls do not get answered, etc.

I have so many persistence stories like these. From coordinating a big event like Think Africa Week each year with close to zero budget at the beginning but with the vision that it must get better each year, to spending nights writing grant applications for them to be rejected, but not quitting until you find a way to learn, pivot and make it happen (read more on my advice when it comes to grant applications here). 

In all this, what I have come to learn is that being persistent is worth it in the end.

In the podcast, I share a few achievements, but there are many, many more which add fuel to the persistence 🙂. 

What is next for me after leaving the Chairmanship? The empowerment of African people is my calling I believe and the next venture will definitely revolve around that. I will, of course, continue to be involved as a dedicated active member of Think Africa and supporter of the good work, and I hope that many come on board to support

Let me also know what your superpower is. What keeps you going?

How does Finland compare with other EU member countries in terms of non-EU foreigner employment and social inclusion?

It is expected that in any country, immigrants will fair off worse on several indicators than the natives. In this post, using the European Union Migration integration Statistics (available here), I created an interactive Tableau Dashboard to look at how EU countries perform in comparison to each other in terms of the employment and social inclusion indicators, with a particular interest on Finland and it’s performance when it comes to those foreigners with a non-EU background. Indicators that interest me a lot but also ones we (Think Africa) often focus on when we look at integration.

“Successful integration of migrants into society in the host country is a key element for maximising the opportunities of legal migration and making the most of the contributions that immigration can make to development.”

Source: eurostat: Migrant integration statistics – labour market indicators

The EU Agenda has in the recent years placed great importance on the integration of non-EU migrants, since successful integration and the fight against poverty and social exclusion is necessary for maximising the economic and social benefits of immigration for individuals as well as societies (Migrant integration statistics: labour market indicators and at risk of poverty and social exclusion).

Data used is that collected from EU member states in 2020, with partial information or limited reliability in some member states. The employment and at risk of poverty and social exclusion data is available according to Country of background and Gender. Country of background is divided into three categories; Natives, EU foreigners, and non-EU foreigners. Unfortunately the data does not go further to give the specific country background which would have allowed me to look at e.g., differences among the non-EU foreigners from different continents.

Interactive Dashboard is available >> here

Employment rates:

Similar to the experiences found in Finland, in many of the EU member states migrants make up a significant part of the workforce. Many arrive with skills that are highly needed in the labour markets but they often face difficulties in having them valued and finding jobs that reflect their skills level (Action plan on Integration and Inclusion 2021-2027 ).

According to the Eurostat data, in 2020 the EU unemployment rate for working age people (defined in the statistics as people aged 20 to 64 years) was 13.9 % for those born outside the EU, 8.1 % for those born in another EU Member State and 6.1 % for the native-born population. More than double the rate of the non-EU foreign unemployed rate in comparison to natives.

Observations from the Dashboard:

In Finland, the employment rate is 78,1% for Natives, 77,3% for EU foreigners and 62,7% for non-EU foreigners. For the two former values, they are above the EU averages, while for the non-EU foreigners, the figure is below that of the EU average = 66,82%. This trend of a lower employment rate among non-EU foreigners is observed in nearly all EU member states for which data are available (no data for Romania).

As expected, the gap between the employment rates for the Native-born population and persons born elsewhere in the EU was often smaller than that observed between the native-born population and those born outside the EU. However, an analysis of Finland confirms that the gap is among the wider ones when compared to the other EU countries. Looking at the gap between the Natives and EU foreigners, Finland is 9th from the bottom with a gap of 14,6 percentage points. While the gap between EU-foreigners and non-EU foreigners is 5th from the bottom with 15,4 percentage point difference (see Figure 1). Noticeably, many of the Nordic countries are in the bottom five. Czechia is the only country where the employment of non-EU foreigners is higher than that of Natives. Will be interesting to understand why.

Figure 1: Screenshot of the Euorostat Employment rates data, with Finland highlighted. Dashboard

At risk of poverty and social exclusion:

Inclusion for all is about ensuring that all policies are accessible to and work for everyone, including migrants and EU citizens with migrant background.

Source: Action plan on Integration and Inclusion 2021-2027

Many EU countries have inclusion strategies to ensure that every citizen may fully participate in society (Migrant integration statistics). Unfortunately many citizens of foreign background, are not yet participating in society in their fullest potential.

The at risk data also pertains to those of working age (20 to 64 years), analysed according to Country of birth and Gender.

Observations from the Dashboard:

In terms of at risk of poverty and social exclusion rates, Finland performs better than it does on employment when it comes to foreigners. Looking at the gap between the Natives and EU foreigners, Finland is 14th from the bottom with a gap of 16,7 percentage points. While the gap between EU foreigners and non-EU foreigners is 10th from the bottom with 13,9 percentage point difference. In comparison to the employment data, when analysing the gaps between EU and non-EU foreigners, only two Nordic countries are in the bottom five (Norway and Sweden) versus the four that we saw (Sweden, Denmark, Norway, and Finland (see Figure 2).

Among people in Finland, 15,4% of Natives, 18,2% of EU foreigners, and 32,1% non-EU foreigners faced the risk of poverty or social exclusion in 2020. That is the risk of monetary power was approximately twice as high for non-EU foreigners. Generally, a higher share of foreign citizens were at risk of poverty or social exclusion in 2020 when compared to natives with Czechia and Hungary seeming to be exceptions. (incomplete information for Romania). In comparison to the EU average, Finland performs better than the average for Natives (18,39%), EU foreigners (23,09%), but approximately the same as the average for non-EU foreigners (32,41%).

Figure 2: Screenshot of the Euorostat at risk of poverty and social exclusion rates data, with Finland highlighted. Source

From the observations above, we can see that among the Native and EU foreigners, Finland performs well in terms of labour market integration and social inclusion, but there is still a lot of improvement to be done among those with a non-EU background.

Chart portraits of my daughter in Tableau

Here is a look at my latest Tableau project, building images of my daughter using charts with a little bit of interactivity >> See it HERE

Always cool learning the capabilities of Tableau and visualisation of data.

I borrowed from the work done by Alexander Varlamov (on coolbluedata.com) and tailored it to images of my daughter through time.

Steps: 

1. Prepare the images and put all in the same dimensions

2. Convert the photos to data with a python script and save to a .csv file

3. Connect the csv file to Tableau and prep the data

4. Build the visuals and dashboard and publish 🙂

Screenshot from Dashboard

Visualizing the independence of African countries over time with Tableau

Africa Day celebrated annually on the 25th May, commemorates the foundation of the Organisation of African Unity on May 25, 1963. As we at Think Africa, the organisation I am chairing, are organising a few exciting events to celebrate this day, I wondered about the independence of African countries and if it would be possible to have a visualisation showcasing how that happened over time and from whom, especially as I had not seen something like that.

I always like those time-based motion videos that show the development of something over time, but I had never created something like that. Thus this musing of mine, also gave me an opportunity to learn something new 🙂

Creating the Dashboard in Tableau was not difficult, but creating the video was more involved than I had anticipated. But thanks to Google, I found some good guiding materials that I share here.

Here is the finished video.

A clarification on the data: French Cameroon got its independence on 1st January 1960, while British Cameroon or British Cameroons became part of Cameroon on 1st October 1961.

Unfortunately, Tableau does not have a great way for creating animations that automatically play when the page loads, and as I got to learn, when you save an animation to Tableau Public, you loose the functionality of the animation and the user has to click to make transitions. Here is the Dashboard on Public Tableau.

I thus used this helpful tutorial on recording videos with VLC player to record my screen and capture the transitions. I then used this free tool called, online video cutter, to speed up the video, and that was it!

If you spot any errors, please let me know 🙂

Not time management but Self management

Lately I have been finding myself feeling very busy. My mind is always occupied with tasks to complete, issues to address, emails and WhatsApp messages to reply to, meetings to attend and most importantly relationships to nurse so I don’t die a lonely chatless death :). And of course this is while raising a young family. The To Do list is never ending. Many can relate to this I am sure. I have also been noticing this heavy feeling of not having made progress towards my mission, despite all the busyness, I am left with this feeling at the end of the day that I didn’t move one step closer to my goal nor do I have the feeling of being in the moment, that I have experienced my day. I am basically finding myself being run by the day rather than me running the day. 

And true to what I wrote in my previous article, I started asking myself, what do I need to sow to feel that I am in charge of my day, so that at the end of the day I feel satisfied with what I did and what I chose to do with it. A few of the things I have done to address these are;  Reading and listening to time management guidelines, I am a firm believer in arming yourself with knowledge and these readings gave me tools to work with. One of the works I really liked is from Rory Vaden, self-discipline strategist and New York Times Bestselling author, he pointed out that it’s not really about Time Management but about Self-Management (video). His view is investing a bit of time on assessing yourself, your day and where your time is going, because everyone has the same amount of hours in a day, and time will pass whether you manage it or not. It’s more about managing yourself, and what you decide to put into the hours. 

In this post I share the three things that I have learned. Three simple steps that from experience are not easy to implement because they require discipline and persistence. But nothing good comes easy, right?

Blocking off time – This is a technique that promotes focused deep work and encourages you to be intentional with your time whether it’s towards your work, personal projects, learning, family time, friends etc. With the technique you divide the day into blocks of time with each block dedicated to accomplishing a specific task or activity and focus on that during that blocked time. This post gives more detailed information on the technique and how to use it. I was attracted to the technique because it promised what I needed, i.e., to improve focus and concentration on tasks / activities at hand and minimize my procrastination. It was not easy for me to get in the habit of doing it. But I persisted, always remembering the words of Aristotle “We are what we repeatedly do. Excellence, then, is not an act, but a habit.” I had to learn to close off all email and messaging apps during those blocked times and refrain from checking, as knowing myself, I would respond or start thinking of responses or acting up on the requests. I had to also learn to do the blocks realistically and flexibly so that I don’t get disappointed and give up if I did not complete something in the allocated block. For instance, I know that I am most creative in terms of generating ideas in the morning and more in the mental space for design work on quiet afternoons or late evenings. Thus it does not serve me to put these tasks at the wrong time of the day. The trick is also to keep re-assessing at the end of the day, and plan better the next day and not give up if it does not go according to plan as it does when e.g., my daughter is ill at home. Start with planning a day, then a week, then a month, and a year (admittedly harder). 

Learning to multiply your time – As you plan your day, learn to invest in spending time on things that will create more time for you in the future. Ask yourself, what can I do today that can make tomorrow better or give me more time tomorrow? For example, could I automate something or delegate it or say no to it, should I for instance, spend a few hours searching for a good cleaning company that will save me hours every week, time that I could put towards something else that could maybe earn me more than what I am spending on the cleaning? Rory Vaden gives nice guidance on how to multiply your time.

Being present for the experiences – I have had so many instances where I was cooking, taking a shower, on holidays, or playing with my daughter and I couldn’t describe to you what those experiences were like, because in my head I was somewhere else, planning a reply to an email or message, or writing a blog post, thinking about something I could have done better. While to me it felt like I was being efficient (doing many things at once), I have however started to notice that there are things that are suffering. Thus one of the biggest learning in self management was learning to be present for the things I choose to spend my time on, when I am at work, work, when I am playing play; when I am on holidays, really be on holidays. Life is about experiences. When you are more present, you are also more appreciative of having the experiences, and happier.

While there are many more other tips out there, these were the most meaningful for me in leading me to feel like I have control of my day. 

Before I close off, I also have to say that it was not lost on me that the majority of the time management advice came from books authored by males. From just Googling ‘Time management books’, 44 out of the top 51 book results were by males (that is 13,7% of the books were by females). Even just from this 15 best time management books list, only 1 book is written by a female. And not that there’s anything wrong with that, but it made me reflect on that there is a lot of time in the day for a woman with kids that is not her own to manage, from taking care of the kids, house, family, etc, that even finding that 15 – 20min to plan the day is elusive in some instances – but it is also why it’s more important that as women we persist and allocate that time in the day to work on what we find important to ensure we reach our goals.

Are you sowing what you want to reap in life?

Many of us have probably heard the Bible verse that says “For whatever a man sows, this he will also reap’ or more commonly as a proverb, ‘You reap what you sow”. I have heard this many times but it’s only recently when I heard a talk by Jim Rohn on becoming the best version of yourself that it kind of hit home. He presented this same fundamental idea from a different perspective that rang true to me and made me just stop and think. He said ‘Whatever you reap, is what you have sown’… and it made me ask myself, Am I sowing what I want to reap in my life? This year, I turn 38 and as the big 40 lurks around the corner, I keep getting this growing heavy feeling of needing to account for my time, my place in this world. Which is probably what led me to Jim Rohn, who is a great motivational speaker. 

This view of life I could grapple with, certainly more than the mysteriously abstract question of purpose. It made me think about my actions, what I am spending time on, my relationships, and asking myself if I am really putting in what I need to to get the results that I want. You see at this stage of my life, I at least have the clarity of knowing what I want to do with my life as I previously wrote. Trust me this was not always the case but, now that I have this knowledge, what have I been doing to make it a reality?

It made me think that now that I know what I want to do with my life, am I sowing what I need to make it a reality? What can I start preparing now?

In as much as I want up-wards mobility at my work at the moment, am I sowing what I need to get that promotion? Am I improving my skills? Am I adding more value? I might need the promotion, but I will not get it just because I want it. What is my game plan? :).

I have also been feeling like my health needs improving from sitting all day in front of the computer, what am I sowing to reap good health? I must do something, may that be walking or running during the day or standing at my desk.

This question, Am I sowing what I want to reap in my life?, gave me a new perspective. It made things actionable for me because I am the person that can most affect what is sowed. Yes there are things I may not be able to control that might affect the harvest, but really what this taught me was to start making a plan, looking at what I need to sow, and assess when and where the conditions are most favorable. And even when I harvest and the results are not as expected or desired, it’s good to realize that there would be no harvest at all if I didn’t sow anything. 

And when you realize that you have a big influence on what you reap, then you can get to work 🙂

Happy sowing everybody!

Developing a Storytelling Tableau Dashboard for Mobile. Case: Women in parliament correlation with the Happiness and Human development indices

I had heard that it was possible to tailor Tableau Dashboards for mobile but I had never tried it. I often created larger, landscape sized Dashboards that colleagues access on Desktop or laptop. However, I believe that many who read my blogposts published either through my website or Medium access it on mobile devices. This made me think about how I could start providing a better mobile experience for my Dashboards.

Inspired by the International Women’s day and wishing to explore and tell a story on this day, I embarked on a journey to create a mobile-friendly Tableau Dashboard. Motivated by the fact that I was born in a country that now ranks number one in the world for the most women in Parliament (Rwanda)and that I live in a country that is ranked the happiest in the world (Finland), I explore if there is any relation between Women in Parliament and the Happiness Index. Not only on the happiness Index but the Human Development Index as well.

The magic was actually just at the Dashboard creation stage, i.e., the worksheets are created normally (of course keeping in mind that any visualization you create is being accessed on a much smaller screen). Then when you reach the stage of creating the Dashboard, you define the layout for different platforms. This was a good tutorial I followed.

Check it out HERE. Note: This version is best viewed on Mobile.

Why we should raise our children to be data literate

Data is all around us, is only increasing, and the ability to understand, use it, and communicate has become a valuable asset to have. One can say that it has actually become just as important as reading and writing. I recently came across a video showing how one mother was helping her kids collect, analyze and communicate using data and I found it so interesting, to see how we as parents could support our kids to think about data and with data at an early age. At work and in my field of data analysis, I and colleagues often talk about the benefits of data-driven decision making, creating data cultures and strategies, and empowering employees with data. It has been researched and found that employees who are for instance able to use data, are more successful in their work. With all this, I thus wondered why we don’t hear much about data literacy and data-driven decision making in our personal lives, and how data literacy could empower us to make more informed decisions. In particular, I wondered how I could already support my three year old child to be on a path where she becomes data literate and gets to reap the benefits that come with it such as; increased curiosity (ability to ask questions and seek answers), increased experimentation and exploration; empowered to make decisions, and increase in logical reasoning. Another benefit I also see is that parents will trust their kids to make more informed decisions.

Just like any skill, being data literate can be developed through practice. Children like adults are collecting, observing, and measuring information in their own way, and we can support them in being aware of it, how to use it and communicate with it. In this post, I wanted to share something small I started doing with my daughter to start the process and hopefully I will continue sharing more examples and results.

My little girl, probably like many three year old girls out there, likes dresses, particularly those that make her feel like a ballerina. The first exercise we did as with any data project was data collection. I took her dresses and laid them out so that she could take pictures of them. She likes to take pictures nowadays and is actually not bad at it either. I uploaded the pictures to a blank PowerPoint slide and we started exploring the dresses. I asked her questions and she responded based on what she could see.

Her taking pictures of the dresses as part of the data collection.

The questions I asked her included asking her to point out the order in which she liked the dresses. This was not an easy one when she had many dresses on one slide, but when I reduced them to just three, she was able to say her favorite, second favorite, and least. This is a simple sorting methodology she is learning through this exercise.

Another exercise we did involved learning to group similar objects together. As she is now learning her left from right, another question was asking her to put the dresses that had pink in them to one side and those that didn’t to another and counting the number in each side, or ones that had long sleeves together, medium sleeves in another group, and no sleeves in another and counting how big each group was. Through this she learns techniques of grouping and classification.

Her selecting the dresses with pink

We didn’t manage to go through all that I had planned as she found something more interesting to do but we will definitely continue. My plan is to expand this and include clothes of different seasons and connect the clothes to the temperature outside. In this way, I hope that when we check the weather in the morning and we see that it feels like it’s -10, she will be able to realize which clothes to put on by herself, connecting different data points to make an informed decision. 

The above is a simple exercise one that could even have been done with just the dresses themselves, i.e. no need to take pictures. But it was interesting to see that just from the interaction, the questions, she was realizing the similarities and differences she had in her dresses which she might have known but at least had not communicated. Now when she asks for a dress, she is able to say whether she is looking for a short or long sleeved dress, aspects of the dress she was not using before.

And I believe as we continue these exercises, her reasoning of things, her communication of things will improve, just as we see of employees in companies where data literacy is encouraged.

If you get to try this with your children, do let me know 🙂

How much Trade does Finland conduct with Africa and other regions?

In my last post I presented tips on designing dashboards, one of them was stealing like an artist, which I have done in this one :). I came across one Tableau Zen Master’s work and loved how their dashboards popped, although I can’t for the life of me remember their name. The dashboards were simple and elegant, and I wondered how he got to create such beautiful dashboards. I downloaded some of his dashboards, did a bit of reverse engineering to understand how he had done it. After a bit of time and tinkering, voila! this is my result. A simple but elegant dashboard that showcases the amount of trade Finland does with different regions around the world.

I remembered the tips from the last post, I used the grid, avoided clutter, used big ass numbers, etc.

In my learning, I always try to use data sets where I am also genuinely curious about the data. One of the things that Think Africa does is look at enhancing the collaboration between Finland and African countries and currently how we can be more involved in the Finland-Africa Strategy. For us to do this well, we need to understand the amount of the current collaboration, which also includes the Trade.

I used the trade statistics data from tuuli.fi. From the dashboard, we can see that out of the total exports of Finland, 1,15% is to Africa at a rank of 4 when comparing the continents (1 = Europe, 2 = Asia , 3 = North America, 4 = Africa, 5 = South America, and = Oceania). Majority of the Exports are also with North Africa when compared to the rest of Africa. Looking at the Imports, Africa ranks 5th, at 0,59%.

Thus from these numbers, there is still a lot to do in increasing Trade between Finland and African countries.

In Future this Dashboard will be improved to include a filter for time to see whether there are any improvements over time.

How to improve Dashboards for Readability and Impact

As a Data Analyst, it is my job to take data, manipulate it and articulate it so that it delivers answers to the questions the audience has, in such a way that they have a pleasurable experience while getting the answers. This is a tough ask, one that requires combining technical ability with User Design experience.

Recently I got reminded that I still have a long way to go in creating Dashboards that are pleasant to look at and impactful. In a recent work project, I got to work with the UX team on designing a Tableau Dashboard. I thought I was ok with designing beautiful and functional dashboards, but after their presentation I realized I still have some improving to do.

Inspired by this realization, I started watching a few webinars from experts to pick up best practices. One of the memorable webinars I watched was the seven part series titled Building dashboards that persuade, inform, and inspire hosted by Andy Cotgreave, one of the authors of The big book of Dashboards. I liked these series as they gave very practical advice with examples. There were a few key tips that I picked up and wanted to share here. They are easy to follow and have a big impact. I give an example at the end how I applied these tips to improving one of my publicly accessible Dashboards.

Fortunately, the tips can be applied to improving any Dashboards, whether its Tableau, Google studio, Salesforce, etc.

Design to a grid

Grids help organise content on a dashboard. Many of design tools have the option to show grid lines, similar to the image below. Not every box has to have content and the grid does not have to be just vertical or horizontal, it can also be diagonal.

Research also shows that it is best to have the key information in the top left corner or at the top row of the dashboard.

Source: The Big Book of Dashboards

Avoid Clutter

On any dashboard, show only the needed information. A dashboard can only really answer a few key questions and when you try to put everything, it can easily become cluttered. Make white space your friend as it can help a dashboard look more cleaner, give it room to breath. Below is an example of a cluttered Dashboard (left) versus one that has been stripped down for easier readability (right).

Source: The Big Book of Dashboards

Use of color

Use of color is also another important thing to keep in mind. Use color to communicate information as well as create pleasurable experiences. Use colors minimally as more colors add mental overload. There are 5 key guidelines to using color depending on the type of data you have:

If you have quantitative data you can use sequential (e.g., to show sales or population in regions) or diverging (e.g., profit or loss) color palettes as shown below. While if you have qualitative data, you can use categorical palettes. And under this, you can use a color to highlight and draw attention to a piece information, or you want to alert about something in the data.

When working with combination of colors, be mindful of color blindness. Use colors where even those that are color blind, still show well. Luckily tools like Tableau offer palettes that are friendly for persons that are color blind.

Font contrast and BANS

To reduce the cognitive load in Dashboards, the Webinar advises to have three levels of fonts; the top, middle, and low. Simplify the typographic structure. This guideline also helps one be more disciplined if you just keep to few things.

Source: The Big Book of Dashboards

They also advise to use BANs, which refers to Big Ass Numbers. These are quite common nowadays in Dashboards, especially executive ones. Most people are looking for key numbers – KPIs. But more than just numbers is if one can indicate if these BANs are good bad. And a good place to put them is in the top row grid.

Source: The Big Book of Dashboards

Collaboration


Just as I started this story, collaboration, especially with knowledgeable people who have different perspectives can help you iterate and evaluate your dashboard until it is more functional and clear. For Tableau users, there are a lot of opportunities to find collaborators, from Tableau user groups, Tableau public, to Blog contributors. Thus do not be afraid to collaborate.

Steal like an artist


This is one of my favorite methods for learning something. When I was younger and learning how to draw, I would trace or use number block drawing approaches and I actually felt bad like I was cheating. But as long you are not taking someone’s work and passing it on as your own (plagiarism) then this is a good way to learn, to have a starting point, to find inspiration, to reverse engineer a process. By that process of copying, you learn the process, and you can slowly start to infuse your own creativity, and become an expert.

Source: The Big Book of Dashboards

With these tips, I went back to one of my recent public dashboards on Integration Statistics in Finland to see how I could improve it. The first thing I improved was the use of the grid. Before, my visualizations were misaligned but now they are aligned to the grid. I reduced the number of details and colors to just the essentials color. Have a look and let me know how it now looks 🙂 -> Integration Dashboard

How are persons of African Descent fairing compared to Asians or other Europeans living permanently in Finland: Integration statistics data visualized

Originally published here.

In this day and age of data, making decisions based on data is important. As a data analyst, I understand the vast benefits data and the use of it for decision making has for organisations.

However in order to make decisions based on data, you need the data first and sometimes this is not so easy to acquire or it’s not available, especially when it comes to data pertaining to persons living in Finland that are of African descent i.e., African diaspora. This has often posed a challenge for us at Think Africa as we need such data whenever we are designing programs or activities. Data to for instance help us understand the scale of employment or unemployment or where the majority of the diaspora reside in Uusimaa and around Finland.

I was thus glad when I saw that Statistics Finland has made available economic and integration data at the level of the country of birth (source). The Statistics are available until 2019. Using my data analytic abilities, I downloaded the data in addition to the Population data, cleaned it up and created this visualization as a Tableau Story for anyone to access the data in a visual format -> Here 

The visualization includes data of persons whose background is Africa, Asia, Finland and the rest of Europe. The Tableau Story consists of two pages. The first shows the economic data that is available. This is available for persons who have permanent residence in Finland up to the year 2019 or 2018. The second page shows data pertaining to those who reside in Finland regardless of whether they have permanent residence or not, up to the year 2020.

Based on the data and from the Think Africa perspective, I summarize here a few initial observations, with more in depth analysis to be conducted over time. Many of them are sort of already known, but it’s also good to have the numbers.

– In 2019, 71.2% (38 765 out of 54 450) of those with an African background had permanent residents.

– The largest diaspora group is of Somali background, the second is from Nigeria.

– Majority of the African diaspora live in Helsinki, then  in particular. Knowing this also helps us identify the cities we can expand to.

– In which occupation groups and levels are Africans employed in? At the top level 1, we see that most African diaspora are employed in Elementary occupations and service and sales work and very few are at managerial levels when you compare the percentages among those with Asian or European background. When looking at the level 2, we see in particular that the diaspora is employed  . When you look at which group particularly under there, the majority are in cleaners and helpers, personal care workers, drivers and mobile plant operators groups.

– The largest unemployment levels are seen among the African diaspora group, in particular, among the females.

– Similarly to all residents regardless of background, of those that are employed, majority receive a salary or wage as opposed to self employment. Noticeably, females of African background do not seem to pursue entrepreneurship when compared to females of Asian, European or Finnish background.

That’s it for now. Is there something more you wish me to add to this visualization? Any insights you gain from it? Or recommendations of further analysis to conduct? Drop me an email at mdoucem@gmail.com

Identifying what you want to do in life that: you enjoy, are good at, has an impact on the world and you can get paid for

There are people who at an early age know what they want to do with their life and go on to do it. For me, and perhaps for many, it has always been a challenge. A constant exploration of what it is that I really want to do, what I am good at, what I enjoy, and what I want to contribute to the world. This especially became more challenging when I moved to Finland and my ‘world’, the landscape, the society and community changed.

In my search, I have explored a lot of approaches and models but none have spoken to me or have given me the answers I sought as did the Japanese concept of Ikigai. Ikigai is made up of two words: iki which means life and gai, which means value or worth. Putting these words together, we have — “value of life” or as described, “a reason for being” [source]. Ikigai brings you joy in life (not just your work). This version of the  “Ikigai” diagram captures the basic idea, although it does not fully represent the Japanese concept of Ikigai, which is more about valuing life everyday, a topic I will not delve into here.

However, when I saw this diagram presented at an event, I immediately connected with it. I saw  it as something that could help me identify my purpose as the diagram intersects four aspects that I find important. It consists of four circles and at the intersection of all four is where you are meant to find the sweet spot between what you love to do, what you are good at, what the world needs from you and what you can be paid for. The diagram also offers what one might feel at the other intersections where not all four are met.

Ikigai diagram, adapted from Source

“Taking the time to invest in yourself, understanding yourself and what is important to you, is the most important time you’ll ever take in your life.”

In this post, I share how I used the diagram to identify what I want to pursue. There are many posts out there that talk about the Ikigai diagram but many just present the model generally with no concrete examples. I want to share how I actually used it, my approach and thinking, and perhaps this will bring insights into how you can use it. You can also use it if you currently have a job or career but wish to become more specialized, change or just need reaffirmation.

“A job is not your permanent address”

Let’s start with each circle and list down the answers to each.

What you love, enjoy, care about

Under this, list down all the things you love to do, that you enjoy doing, find interesting, exciting and motivating, that you lose yourself doing for hours. This shouldn’t be a hard list to come up with – if you love it, you must be doing it in some way, or you miss doing it. When writing this list, leave money out of the equation, just think about what you enjoy. It can  end up being like 10 pages and that is alright, it will get narrowed down.

What you are good at

This relates to talents, skills and competencies. This is not an easy one to answer, as we are often not very good at assessing our own competencies. We either overestimate or underestimate our abilities. But leaving ego, modesty and self-doubt aside, and again not considering whether you are getting paid for this or not, think about what you are good at, what you do effortlessly, what people come to you for, what you can be good at with just a little bit of training, what qualities are your strengths? Write it all down.

What does the world need (…that you can give)

The world needs a lot, but this list is more about what the world needs that you can give. It can be your skills, passion, perspective, culture, etc. Think about which problems in your society would benefit from your passion / skills for solving them. What problems do people come to you to solve for them? Give you their money? The world here can be a community, society, nation, etc. Try to also think long term, what will the world need from you a decade from now, 20 – 30 years from now?

What you can be paid for

Under this circle we can now think about money. We have to think about how to get paid for what we do, to put food on the table and more. Preparing this list requires some research. Look among the things that you are good at and the things that you love to do, list down the ones that you can get paid for, as well as those that you could be paid for if you improved your skills in them. If you are already doing some work and getting paid for it, can you continue getting paid for it? If yes, list it down. Are other people with similar skills and competencies getting paid for them? If yes, list it down. If your list is empty, then it is time to invest in upping your skills or value.  People will always pay for value.

My list (not an exhaustive list)

Here is a snippet of my list and what falls under each of the four circles. I am presenting a relevant snippet of it for readability purposes.

Finding the ‘sweet spot’

Once you have listed down the answers to the four questions, the next step is to look at the intersections. My approach was to take first what I love and what I am good at and find the common things there. Then I intersected that with what the world needs, then finally identified from that list, what I can get paid for. From this exercise, those highlighted in different colors (table above) are what made it to the intersecting section in the diagram.

It might not be easy to find the intersections, but keep digging, revising and rethinking what could be common.

Then look at the items that make it to the list and try to come up with ONE sentence that encapsulates the items. Drilling it into one sentence gives your focus and makes it easier to sell and brand yourself with that one sentence.

If you have many sentences, prioritise and select one, and dedicate your energy to make that one succeed. Of course you can still have your hobbies and side hustles.

For me, my one sentence was “use data analysis, research, and activities, to creatively support people realize and fulfill their potential”. What I also find helps is to come up with a hashtag that represents this sentence. Mine is #actualizedlife. This hashtag can then be used as part of your personal branding for those that need you to find and know you.

To make the one sentence actionable, I advise you to go one step further and find out who can pay you for what you want to do. This can be an entity in the private or public sector, it can be a foundation that gives grants for what you want to do, it can be individuals (customers) that buy your services or products. Think of who can pay you and then start making plans for how to get closer to them, how they can know what you are offering and the value you bring, and then how to actually offer your service to them.

That’s it. If you get to use the above, I would like to hear how you used it and the results in the comments.

“The two most important days in your life are the day you are born and the day you find out why” – Mark Twain

Correlation between Olympic Results and Annual GDP of Countries

The Tokyo2020 Olympics ended on the 8th August and it’s always interesting to see the final results table with the winners. Looking at the list, it’s easy to notice that countries with money are at the top, which is not really a surprise as these countries are able to invest more into sports. 

In my curiosity, I wondered what the data actually showed, i.e., the correlation between the medals won by a country and its annual GDP. This also gave me a chance to learn some new data exploration techniques. 

I created a Tableau Dashboard looking at the trend between the medals won and the annual GDPs of countries, for the 2020, 2016 and 2012 Summer Olympics. The Dashboard can be found HERE.

Looking at the 2020, 2016 and 2012 Summer Olympics: 

– We can see a clear trend, correlation between the number of medals won and the annual GDP. This same trend is visible even when looking at the GDP per Capita, though I did include this in the Dashboard.

– The visualization also shows the distribution of the medals won across the countries and the continents. From here we see that the USA and China lead in the numbers.

Data collection and cleaning

GDP data and the Olympic results were collected into an Excel Workbook, with different sheets containing the GDP data as it was in the respective Olympic year, and the Olympic results were recorded in three separate Sheets. Python’s Pandas was utilized to clean the data, and combine them into a format that I could easily import and use with Tableau.

Cleaning the data involved resolving some country information, for example in the Tokyo 2020 Olympics, Russia was not officially participating, but they had a team there under the name Russian Olympic Committee (ROC). GDP data is usually not recorded for Great Britain, but the United Kingdom.

Data sources:

Annual GDP data: 2021, 2016, and 2012

Olympic data: 2021, 2016, and 2012

About me:  http://myriammunezero.com/

#continouslearning  #dataanalysis #data #visualization #python #Tableau #excel #actualizedlife

Three important tips to help you succeed in your non-profit grant applications

Being able to write winning grant applications is one of those skills that is invaluable. The benefits of it are manifold, whether you are running a non-profit, a profit-making business, or wishing to carry out research or a project. 

I worked as a postgraduate researcher for over 10 years, first as a PhD researcher then as a Postdoc. In that time I wrote several funding applications, but was never as successful in my pursuits as currently with Think Africa. As the Chairperson of the non-profit organisation, I have been entrusted with the responsibility to ensure financial stability. This is not an easy feat and for the past three years of my chairman-ship, the team and I have had our share of rejections and wins. However, over the last nine months; we have made strides of wins, totalling six (6) successes out of seven (7) grant applications. 

Writing grant applications in a team of knowledgeable people and receiving constructive feedback from funders have been a meaningful learning experience and an enlightening process.

As the application period for many grants begins again in the Fall (in Finland), I wanted to share the following tips that I have gained along the way.

Place yourself in the funder’s shoes (mindset)

If you had money and were deciding who to give it to; how would you decide or who would you choose as the recipient? 

Most applications request similar data input, i.e., the purpose of the application, the problem being addressed, nature of the problem, solution focus, other players and why you are unique, your target audience and action plan, indicators of success, risk assessment, and budget. This is common information that is very similar to what you would find in most business plans.

Many funders also have their “business plans”, they have a problem or need to address, they have objectives that they aspire to fulfill by providing funding to non-profit entities, and indicators of successful implementation, etc. 

Thus, in your application, there is a need to align and strongly indicate anticipated results and how they contribute to the fulfillment of their objectives. 

Budget is also an important aspect of the application process. Funders are audited and most likely have limits to what they can spend, how and where. These restrictive limitations serve as guidance to what is permissible and/or not as far as the funding is concerned. It’s your task to make a funder’s decision as easy as possible, by formulating your budget clearly, precisely, transparently, and as realistic as possible. 

Reduce funder’s risk

Probably the biggest risk the funder takes is that they give you the money and you do nothing with it, or you spend it but do not achieve any results. Thus one thing to look at is, how can you reduce this risk for them?

Being transparent and having a clear and realistic plan is a must. Transparency ensures trust and increases credibility. By outlining a solid implementation plan shows that (when given the money), you have thoroughly thought through the initiative; and are ready to implement it.

However, nothing speaks volumes like past behavior. They indicate that you managed to deliver substantial results, and thus, guarantee the possibility to receive future grants. In this regard, funders have evidence of what you can achieve with reduced risk.

This is also why it’s important for organisations to continue having activity. I remember when I joined Think Africa in 2018, the previous year had been a very quiet one with hardly any activities. When we applied for grants, many asked for the previous year’s annual plan, financial statements, and audited reports. When those are empty, there is hardly any way you will win a big grant. This is also understandable, if there is no past behavior, how can they trust that you can get the job done. 

This was one of the main reasons we invested in showing traction, we organised a lot of activities (just have a look at our 2018, 2019 annual reports) on a volunteer basis. We applied for small grants that have lower thresholds, which we used to amplify our volunteer efforts. This allowed us to have traction and evidence of what we could achieve given the opportunities. Starting small also allows you to learn what needs to be in place in order to actually manage big grants, from accounting and audit processes, monitoring and evaluation, human resource, and a whole lot of things.

Forming good partnerships is also one way to help reduce the risk of funders and add value to your implementation and achievement of sustainable results. 

Do your research

In order to successfully accomplish any of the above, you need to research, research and research. It is also necessary to understand the objectives of funders exactly in terms of requirements. 

Conduct research on previous projects that they have awarded grants to and study their past criteria and relatively associating them with their current, past and future requirements is an advantage.

Always contact the responsible person in charge of the grant by giving them a call about your application proposal. It is also necessary to seek clarification and inquire, if your organization qualifies and meets the requirements. This might give you significant insights into improving your application and make you aware of the reality of your status and situation and save you time. 

Inquire about the success rate of the applications, this will help you understand the competitive environment. 

The grant application landscape is getting tougher and having a good application does not naturally guarantee success.

Have a good understanding of the problem you are solving, the scale and the target audience. Don’t assume funders know anything about the problem you wish to solve, or anything about you and your ability to solve the problem. Frankly, assume they know nothing and write from that perspective. It’s your job to provide sufficient and convincing information.

This aspect is often a challenge for our applications, because of the lack of adequate publication and research to confirm and support the existence and scale of the problem; especially to those who are not aware of the phenomenon. 

One way we have tackled this is through sampling and small scale surveys. Conducting a small research on a sample of the target audience to highlight the magnitude of the problem and its potential to escalate exponentially.

There you go! 🙂 My three tips. 

My other small tips include: 

Soliciting an external party to proofread your application proposal . An outsider often notices things that you may have missed. 

Use easy to understand language, be precise and concise. Verbosity in grant applications does not work in your favor. 

Choose your scope carefully and be realistic in what you can achieve – under promise and over deliver – not the other way round.

But trust me, the more you do it, the easier it gets :).

Feel free to reach out to me through email mdoucem@gmail.com or myriam@thinkafrica.fi if you need feedback or consultation with your grant applications. 

Happy grant writing!

The power of numbers: A guide to communicating achievements and tips on how to add them in your CV or Cover Letter

In this blog, I will attempt to highlight some tips on how to communicate your achievements and reflect them into your Curriculum Vitae (CV) and/or Cover letter effectively. 

Why is it important to precisely communicate your achievements numerically? 

Numbers are objective; and easily understood across cultures. Many people, myself included have often fallen prey to applying for jobs and focusing on listing tasks and responsibilities. The truth is that a list of previous tasks and responsibilities do not convincingly communicate what you are able to achieve. 

Instead, employers want to know about what you have achieved, what value you have contributed, and why they should prefer you over tens or hundreds of applicants who have performed similar tasks. 

I was recently on the other side of the table as a recruiter, as part of my role in the non-profit organisation Think Africa, conducting the interviews for a hiring position. The importance of being able to articulate results and achievements clearly became apparent to me and I believe that this is a skill that all applicants should have. 

The purpose of a CV and cover letter are to communicate and exhibit to a potential employer what you have done and achieved. This gives your future employer an idea of the impact you can possibly make should you be hired. 

So how do you position your achievements into measurable numbers that can easily be understood?

Use numbers to quantify your achievements.

I will use an example in marketing to show what I mean by putting a number. If you say that you designed and implemented a marketing campaign that saved your employer x€ or increased the revenue by x%, this gives a visual of the scale or magnitude of your achievements. Whereas if you had said that ‘I was responsible for designing and implementing a marketing campaign’ all you have communicated is what you did, but with no indication of how well you did it or what was the impact. Anybody can design a campaign, but how do you show that you can be impactful in what you do? 

This is not always easy and in some disciplines or professions , it can be challenging or easier than in others. 

I share here my thinking process for translating achievements and work done into numbers. I use three real cases from three varied jobs that I have done. With each job I ask myself what I did well and why it’s considered good. I compare it to the average, and try to assess how much better my achievement was. The number of people I supported. What my colleagues said was good and how many said it.

Example job 1: Strawberry picking – This was a summer job I had for three years in my first years of being in Finland. I want to use this to show that even part-time field work can be communicated with numbers to showcase your qualities. In this role, my task was to pick strawberries for roughly one month. I got paid by how much I picked. 

If I had to include this in my CV, I would think about communicating my achievements as follows:

  • In terms of volume and money: I brought in 50% more than the average picker per day. I would then translate that into a money value by multiplying it with the market value of the strawberries to show how much more revenue I generated for the farmer. 
  • In terms of showcasing how hard working I am: I would highlight how I was one of the top 5 berry pickers for 3 seasons. 

Example job 2: Researcher – I worked as a researcher for over 10 years in different projects and universities. Putting my achievements into numbers for this role was a bit difficult, as most of my achievements were done as part of a team thus making it harder to single out what was so impressive with what I did. But leaving modesty behind and digging deeper, this is how I would communicate some of the impact: 

  • In terms of publications: If I said that I published over 10 publications. This is really not a good use of numbers as there is no indication of whether this is good or not to a potential employer. But I can say that I was awarded the best paper award twice in top tier forums.
  • In terms of the number of people I supported: I supervised 8 students to successfully complete their school projects or thesis, 50% of these graduated with the best grades
  • In terms of funding that I brought in: If I had succeeded in winning funding applications, I would include the amount here. 

Example job 3: Data analyst – As I am currently still in this role, I will not say too much or put exact figures but one of the achievements so far that I would give would be:

  •  In terms of time saved: I created a Tableau report that automatically reads in data from our Data lake and summarizes the effectiveness of campaigns on customer behavior. This work was normally done manually by a colleague for each of our partner customers. Thus, by doing this report, I saved the colleague at least 180 work hours per year, thus, reducing financial cost .

Employers focus on different things depending on their strategy and goals. Some examples include revenue generation, market awareness, customer attraction, customer happiness, company growth, employee happiness, cost reduction, and process efficiency1. Naturally with each number, you have to tie it to what the potential employer is looking for. The important step is to start thinking of how you can show your impact, and numbers are great for demonstrating your achievements and impact.

Leave a comment to share how you use numbers in communicating your input impact or share alternative tactics that you use.

How smarter machine learning reduces churn

Written by me and originally posted by F-Secure -> https://blog.f-secure.com/how-smarter-machine-learning-reduces-churn/

Machine learning can make a lot of things better. But as everyone who deals with artificial intelligence knows, a model is only as good as the data used to train it.

At F-Secure, we’re not only applying machine learning to the cyber security domain, we’re using it to help improve the relationships that we and our service provider partners have with the customers we serve.

What we’ve learned about churn

Keeping customers is cheaper than acquiring new customers. According to the White House Office of Consumer Affairs, “it is 6–7 times more expensive to acquire a new customer than to retain an old one.” This is why many companies invest in building prediction machine learning models that can help identify the customers that are about to leave, reasons as to why they are leaving, and help build suitable retention strategies to keep customers, as well as improve services and customer satisfaction.  The models support companies to pre-act rather than re-act.

For the past few years, I have been working on building churn prediction models using machine learning. In each project, the data set has been different, capturing different perspectives of the customer. This also leads to models that perform quite differently. As many people in this field know, that data used to train machine learning algorithms is the crucial part, and unfortunately, sometimes the data that companies have is not sufficient for the purpose of predicting customers who will churn.

“When a model fails to predict something, it’s because the information used to train it lacks predictive power,” Max Pagel explained. “Having a fancy or complex algorithm wouldn’t help either if you do not have the right data.”

In this post, I look at data points that should be collected for those thinking about building accurate churn prediction models.

How to train your churn model

The first step of course is to define what churn means. Is it that an end user uninstalls a product, stops making payments, cancels a subscription or does not renew, or something else? When that part is clear, the second step is to collect data that captures who this user is and how they interact with the product and company, among many others.  The data should reflect the end users / customers reality – cause that’s what models are, they are abstractions of the reality.

From my experience, the best way to identify the data needed is to look at the reality and capture that as much as possible while respecting consumer privacy. Consumers need to be made aware of the data being collected as well as give consent to their data being collected.

With the above in mind, the reality could be captured by asking the following six questions:

Who are our customers, really?

Answering this question gives you information such as the age, gender, residence, occupation, income, ethnicity, education, etc. of your customers. Many companies might already have this information which is mostly used for segmentation. Besides predicting churn, this type of information allows you to do personalization of services and engagement which also helps retain customers.

Which product(s) do the customers have?

For each customer, you need to know which of your products they have, whether they got it for free, with a promotion, or at full price; as a standalone product or in a bundle with other products; the number of licenses they bought, on which platform, from which channel, are they making annual or monthly payments, at what price etc.

How do customers use our product?

Getting the answer to this question is very important. Customers stay with companies or products where they see value. The answer provides an overview of the behavior of the customers, e.g., how often they log in, time spent with the product per session, what features they access, amount and type and frequency of errors they encounter, etc. For digital products, the data collection for this type of data is easier as it can be implemented into the product.

How are customers engaged?

While this question might be similar to the above, it captures something different. It captures the engagement between the company and the customer online or offline. Highly engaged customers often demonstrate also more loyalty. This can include information such as emails or newsletters sent, events hosted, etc.

How do customers feel about our products and us?

This question gives you data capturing how your customers feel about your products, their user experience. For instance from ratings, feedback, social media posts, or customer care interactions. Negative sentiments, emotions or opinions from customers are big indicators of churn. However, this information is not available for all customers.

What other external factors influence our customers?

Sometimes customers just leave for reasons beyond your control. For example, many customers have been adversely affected by Covid-19 and have unsubscribed or cancelled several services. There are also other external factors such as new competitors in the market, network coverage, etc., that might drive your customers away. Capturing this information is difficult but if possible, it is valuable to a prediction model.

Respect the data and users’ privacy

The above six questions can give you a vast amount of information to help you identify the customers that stay or leave and most importantly understand why.

With the available data it is the task of the data scientist to then use all or some of this information to perform feature engineering to capture trends and patterns over time that might be informative to churn modeling, like whether a customer increased the number of features they interact with or decreased login activity, and so on. With this information, customers tend to stick around for the best possible reason—their needs are being met by the service.

Capturing and recording this information, requires companies to have a data strategy and invest in data architectures that will enable long-term and short-term storage to enable historic and current analysis to be performed. Naturally, privacy concerns will hinder data collection or the ability to connect all of these data points. However, wherever possible, aim to collect as much as data as customers knowingly and willingly share that would give you a model that closely reflects reality.

What is the formula for success? Success = 2*Preparation + Opportunity

The turn of the year always forces me to sit down and take stock of where I am and what I want to accomplish in the new year. And as I get older ‘living a fulfilled life’ is something that is usually at the forefront of my thoughts, especially in terms of my career. Living a fulfilled life to me means being satisfied and happy because I am developing my abilities and character to the best that they could be

Being able to do this requires me to sit, think and plan about what I want in life, what I am good at (my abilities), what I enjoy, and how to make a living out of those. Achieving this to me is success.

There is no exact formula to success but I want to share my formula – a formula I derived from two quotes that I embrace.

The first one is from Frank Underwood, the character played by Kevin Spacey in House of Cards. House of Cards, especially Season 1 has to be one of my favorite TV Shows and I have been re-watching it in the last few weeks. In episode 12 Frank says “Success is a mixture of preparation and luck.” And it is precisely because of his preparation, his calculated planning in reaching where he wanted to reach, that I enjoyed the show.

When Frank said the above, it reminded of the second quote: “Luck is what happens when preparation meets opportunity”, often attributed to Seneca the Younger (L. Annaeus Seneca, 4 BC – AD 65). Many people have often quoted this from the likes of Oprah to Denzel Washington.

I suppose because of my analytic and mathematical background, these quotes became equations in my mind and I was able to connect them together as follows:

Equation 1: Luck = preparation + opportunity

Equation 2: Success = preparation + luck

And these two lead to the below formula of success: 

Success = preparation + (preparation + opportunity) = 2x preparation + opportunity

Success thus requires two times more preparation than luck, which at least seems to make sense when I reflect back on times I felt success vs luck. For instance if I look at my transition from academia to industry, this was something I put a lot of preparation into to getting myself ready and positioning myself, for when the opportunity came along.

I have to also add that the more preparation one does, the more opportunities avail themselves.

Articulating this formula has thus defined my 2021 and future years resolution. For me to achieve success in my career and life goals, for me to live a fulfilled life, I have to double my efforts on preparing myself, while continuing to open myself up to opportunities.

#lifegoals #fulfilledlife #selfdevelopment

How to get the most out of a mentorship program as a mentor or mentee

Originally posted on F-Secure’s blog: https://blog.f-secure.com/how-to-get-the-most-out-of-a-mentorship-program-as-a-mentor-or-mentee/

I have been privileged to be part of mentorship programs from varying perspectives, from being a mentee, mentor, to an organizer. The first time I took part in a mentorship program was in 2017 as a mentee. It was at a time when I was looking to transition from Academia to Industry and I wanted to network and get guidance in my transition process. 

I got matched to a lady who was the head of an IT department at a transportation company. Although the relationship didn’t last long, she gave me valuable insights that I still carry with me today. She helped me improve my cover letter as well as my CV by pointing out that I needed to always communicate value and results rather than listing the responsibilities that I had. And to try as best as I could to put that value in numbers, which was not an easy thing to do with academic positions.

That additional perspective plus experience is what mentorship programs offer. Mentorship programs, when done well, allow you to reflect on how you work and find ways to improve yourself.

Fast forward to 2020, I am now part of another mentorship program, an internal program at my workplace that is meant for all the Fellows (F-Secure employees) across all the different units and offices where we operate. In this program, I got to participate as an organizer, a mentor, and a mentee. I learned something new and valuable in each of those roles and I wanted to share some of these learnings.

Naturally, both the mentee and mentor have to be ready to commit to the process. It usually helps to have a written agreement signed by both to show this commitment. Once you do decide to commit, here are some tips or guidelines.

As a Mentee:

  • Understand that you are the driver of the relationship. It’s your responsibility to set up the expectations, arrange the mentoring meetings, the meeting frequency, etc. 
  • Understand that the mentor is not there to do the work for you. You are responsible for your development. This means you should also say when it’s not going well.
  • Set a realistic goal and communicate it well to the mentor. I find it useful and more valuable to set a goal that challenges you and will have an impact on you once you go through the program. For example, as I shared earlier, I had the goal of transitioning from academia to industry. This was a challenging goal which has had a huge impact on my career .
  • Depending on the length of the program, break that goal into few manageable and measurable objectives and try to tackle one at a time over one or two meetings. In my last mentoring program, which lasted over 5 months, I had 3 objectives and the mentor also had 3 objectives. And we tried to tackle one objective in each meeting.
  • Prepare for the meetings, that way you and the mentor get the most out of the meetings. Even though the meetings can have the atmosphere of a casual chat rather than a serious meeting, preparing a focus for each meeting helps in achieving the objectives. I usually agreed on the agenda of the next session at the end of each meeting. Preparing or giving a purpose to each meetup also reduces the common feeling of meetings of “I am wasting the mentor’s time”.
  • Lastly, try to include personal topics for discussion and fun activities, this way you will get to build a more open and trustful relationship.

As a  Mentor:

  • As a mentor, you are usually there to guide and not be a problem solver. It’s easy especially for technically orientated persons to be problem solvers, but the idea is to help your mentee figure out the solution. This is also where good listening skills come in handy.
  • Put in the effort and take time to also prepare. You shouldn’t be too comfortable that you are the one with the knowledge and that you are just going to pass it on. It’s best to learn how your mentee learns and adapt your guiding process to them. You have to learn how to transition from being an expert to a leader.
  • Get rid of the imposter syndrome – I remember when someone asked me to be a mentor, my first thought was “was I qualified enough to be someone’s mentor?” and I have heard many mentors ask themselves this. Key is to be confident that you are in the position you are in for a reason and that you have something to contribute to a person who wishes to grow. Have healthy confidence in your abilities.
  • Have and show empathy – Be able to put yourself in your mentee’s shoes and see whether the advice you give relates to that person. For instance, if your mentee is in some way disadvantaged  it doesn’t help to tell them,” work hard”, without taking into account or discussing the existence of possible structural barriers that prevent that person from advancing as they would like, no matter how hard they work. 
  • Lastly, set your own objectives in the program. I feel that that helps build a more mutually beneficial relationship. 

I just also recently started participating in another mentorship program. This one is a cross company program where people from different companies are paired together. Under this program, I will be focusing on developing myself as a more valuable and insight-focused data scientist (more on that to come) and I will surely be applying many of the above tips. 

Write and let us know what other tips you have.

Beauty in Data: Experiences from a data visualization competition

I have written before about the importance of visualizations in communicating insights (here). Having the skill to create beautiful and insightful visualizations is a great arsenal to have in one’s pocket and I always wish I had more time to hone that skill. Although creating visualizations is part of my everyday data analysis work, from using them to explore the data, look for trends and correlations, to reporting insights and findings, I don’t really get a good chance to be creative. And when I say creative, I mean this or this or this. Thus, when our Data Strategy team at F-Secure created a visualization competition titled “Beauty in Data”, I got excited about the opportunity to try something new.

The competition was part of a bigger strategy to move the whole company towards being more data driven. It ran in June and one had two weeks to make a submission. It unfortunately was not great timing for me.  I was so busy during those two weeks that I almost changed my mind about participating. But I wanted to take part, having never competed before in a data analysis related challenge, I wanted to challenge myself to be creative and also observe what other submissions would come up with given the same dataset. Thus I entered the competition knowing very well that I wouldn’t win. 

We were given four data sets containing information on customers, customer activity data, sales, and product usage. The data was based on real data models and processes and utilized real terminology. However, the data was 100% fabricated and did not relate to reality in any way. The task then was to find insights from these four data sets, things that could be interesting and then create a set of visualizations to communicate these insights. Although the insights were important, it was the style of the visualizations that was key. The audience/judges in this scenario were business leaders about to make an “investment” decision based on our findings, but with little knowledge. One got extra points if they used F-Secure’s brand colors in their visualization. 

We were allowed to use any tool of our choice. I chose to use Tableau as it’s a popular business Intelligence tool, with a strong community and has interesting features that I don’t get to try in my everyday work. I use Tableau as part of my work usually to create reports. But I find that many of the reports I create tend to be similar and basic, and just communicate what needs to be communicated. But one can do so much more interesting visualizations with Tableau, just look at some of the examples here.

For the competition, I decided to try the Story feature of Tableau, which is a sequence of visualizations that work together to convey a narrative. It’s not a complicated feature to work with, but it forces one to think of an interesting narrative you want the visualizations to tell and then create that. 

I didn’t win, as expected (good visualizations do need time) but I was pleasantly surprised by the feedback that I received from the judges. I was told that it was a favorite among some of them as it “provided clear insights to a clear problem.” Adding that my submission was “a textbook example on how one should do visualizations”.   

This screenshot was shared with me where the judges pointed out that “with 1 click, the visualization directly provides insights in regards to a specific product and how its sales have performed.” 

A close up of a map

Description automatically generated
Screenshot from my submission

Although I was happy with the feedback, I realized I still had to put more effort into getting out of my comfort zone and thinking out of the box with visualizations, especially after I saw some of the other entries (see below): 

Screenshot of a submission entry
Screenshot of a submission entry

So although my submission did not display so much beauty, I learned a lot. The challenge helped me learn a new Tableau feature, and from seeing the other submissions as shown above, I learned what was possible with using other tools such as Splunk, which I have never used before.  

This is how I usually learn new tools or features, by using a fun problem that inspires and challenges me to solve it. And if you are anything like me and you want to learn something new, I would advise to look for fun problems that interest you and motivate you to solve them with features and tools that you want to learn.  

Let’s see what next year’s competition will inspire me to learn 😊 

 

Experimenting with Python’s Pipeline Class for Churn Prediction

In this post I share the latest in my quest for continuous learning. This time, I experimented with the Pipeline class by scikit learn to see on its advantages. I applied it to a machine learning task that I previously worked on, churn prediction.

From my reading, using Pipeline is supposed to be simple, yet a powerful way of designing sophisticated machine learning systems [source]. I had not come across many analysts that use it (though now after searching for it, I have come across more examples where its used). The class is set up with the fit/transform/predict functionality, similarly to other estimator classes like LinearRegression. One of the advantages is that you can fit a whole pipeline to the training data and transform to the test data [source]. This means that, you do not need to carry out test dataset transformation if you already did it with your train features – this is taken care of automatically [source]. Hyperparameter tuning is also made easy, new parameters can be set on any estimator in the pipeline and refit – all in one line. Its also easy to use GridSearchCV on the pipeline to find the best parameters [source].

Below I run through the exercise I did where I used Pipeline, Logistic Regression, Standar Scaler, Principal Component Analysis, and GridSearchCV for churn prediction. Code is also available on github. I make use of the telco-churn dataset available here.

First I loaded the necessary libraries, dataset, nothing special there. I then did a bit of data exploration, looking at missing values, distribution of the target class, removing irrelevant data, and changing data types. Normally I would do more data cleaning and exploration, but that was not the point of this exercise.

As seen in the next steps, the pipeline object is created by providing a list of steps. The steps are a list of tuples consisting of a name and an instance of the transformer or estimator that are chained, in the order in which they are chained. The names can be anything, but the final item in the tuple list should be an estimator. In this exercise, I provided Standard Scalar, Principal Component Analysis, and Logistic Regression as the steps.

Doing grid search over this is also quiet easy.

And then do the predictions.

From this small exercise, I find that using the Pipeline class provides convenience in that you do not have to apply fit and transform methods twice, i.e., to the train and test sets. Its also easy to read. In addition, as mentioned also by [source], it “enforces a desired order of application steps which in turn helps in reproducibility and creating a convenient work-flow.” Will probably start using it more often in my work.

6 values to raise your kids with, in this complex world – Advice from Risto Siilasmaa, Chair of the Board of Directors of Nokia and F-Secure.

Having recently become a mother, I spend quite a bit of time thinking of how I should raise my child – the values to instill, the advice to give, and the habits to encourage – such that she is enabled to survive and succeed in the challenges life throws at her as well as enjoy life. I recently listened to Risto Siilasmaa’s talk at the HundrED Innovation Summit (video) which touched on many of the values and ideals I had been thinking about. In his speech, Risto presents six values or practices that we as parents should encourage our kids to have, which I share in this post. As Risto Siilasmaa also points out, these values also apply to running companies or leading people – areas that also interest me greatly.

So what are these six values?

1. Instill a sense of ownership: When you have a sense of ownership, you care, you act, and you do things that are beyond what is expected. Risto uses rental vs own car to illustrate this – when you rent a car you hardly ever take it for a wash, you feel that that is somebody else’s problem, but when it is your own, you care for it. The same principle applies to many things. Risto advises that we should encourage our kids to have a sense of ownership about the world, society, environment, people close to them, and the work that they choose to undertake – in that way, they will care and be encouraged to act.

2. Take a long-term view, not just look at the short-term: We should teach our kids to understand that the short gains maybe long-term losses. We should encourage kids to stop just looking at the tips of their shoes but lift up their heads and look to the horizon. Encourage them to ask themselves, “Am I being too short-sighted, should I be looking longer into the future?” To me, this ability to look ahead also allows them to create alternatives for their lives (see value no.5). Not only that, but also be able to take calculated risks.

3. Have respectful suspicion: Risto advises that we must nurture a strong and healthy suspicion in our kids, help them think that things could be done better than the way they have been done in the past, that perhaps the way things have been done before is not optimal. Encourage them to challenge themselves and others to come up with better ways of doing things. And if we can instill that in our kids, they will challenge us as well and help us improve.  Here I would add that, not only parents but teachers as well should encourage kids to have respectful suspicion and not see it as disrespect, i.e., that teachers or elders should not be questioned.

4. Be a paranoid optimist: Being an optimist means that one approaches life thinking that there is always a way out, things will work out, you will survive no matter the challenge faced. While being a paranoid, one thinks forward, plans for what can go wrong, prepares, mitigates, and has alternatives. Although it might seem contradictory as Risto admits, its a balance we should teach our kids to have. As a paranoid you are worried about what might go wrong and plan for that – and because of that, things will go wrong less often because you plan and do small things everyday to reduce things going the wrong way. This increases the likelihood of things going well and this experience of things going well, gives you fuel for the optimism. “Being a paranoid optimist as a life philosophy, creates good balance.”

5. Demand alternatives: “There should always be alternatives”, Risto says. “If you are making a decision without any alternatives then you are not making a decision”. We should encourage our kids to imagine other futures and choose which one you want to take, rather than seeing or thinking that there is only one way available. Having alternatives and choosing which way to go can create value.

6. Experiment: The world is changing fundamentally. With the speed of change and advances in technology the world is becoming a complex place, what Risto explains is a combination of complications and unpredictability. How then do we then teach our kids to survive in this complexity – by experimenting. We have to help them build an inclination to experiment. They should demand alternatives which means they need create alternatives which means they experiment. In this way they learn – evolve.

Identifying vs finding your passion

“Doing what you love or are passionate about does not feel like work” – In the past, I had always envied people who could say that. Of course I know it does not mean that everyday is a bed of roses, but I envied them because they knew what they were meant to do, and  at the time, I did not have the same feeling .

Finding one’s passion or purpose are popular statements with enough books and articles written that give advice and guidelines on how to reach this. They often come with  a set of questions to ask oneself and then an evaluation process follows with a eureka moment in sight. I have followed a few of these discovery routes but for one or other reason, I always ended up where I started, frustrated and unsure of what to do. I even tried ‘doing something new for 30 days’ to see if I like it and if it’s really what I want, but that did not work either.

So, the task of finding my passion has not  always been clear, but I continued to feel it was important for me to continue digging because I want to focus my life hours on doing what is meaningful to me and others. It was not until some years back, I read an article (and unfortunately now I cannot seem to locate that same article 🙁 ) that made me realize that a passion wasn’t something to be found – because if I was passionate about something then at this age, I would already be doing it somehow.

My perspective shifted from looking at it as ‘trying new things or finding something that I did not already have’ to ‘I am already doing what I am passionate about’ – I just had to realize it.

Thus finding my passion, involved me looking backwards at what I spent and liked spending my time on, what I prioritized, what I liked talking and reading about, what I could do for hours out of my own will without any expectations. Something I was already doing without even putting much thought on. I cataloged everything. With each item, I dug deeper into what actually made me enjoy those things and wrote it down. I repeated this process a few times, each time asking myself what is it really that I enjoy in this activity, why do I drop everything to do this, why can I spend hours on this and not something else, and so on.

And through this self reflection, I realized that the common thread was that I enjoyed helping people to be in a better position and helping them solve problems, in particular those that enabled them to advance in life. There was a deep desire within to make a positive impact in people’s lives. All I need is a call for help and I will do it gladly, and it does not really feel like work.

After I came to this realization, I wanted to focus on it more and make it the central part of what I was doing with my day. When I realized it, my self-confidence and drive to to hone those skills increased and it also gave me clarity and focus to set goals and carry out activities that are in line with what I enjoy. Thus, when I looked for employment, I looked for work that included this aspect and utilized the skills that I already have. It’s also the reason I commit my time to Think Africa’s work.

When you realize what you want to spend your day on, eventually the money question arises (and I firmly believe it should come last.), ‘how can I make money from this?’ And the answer is that, just like any venture, you have to make yourself valuable enough that someone is willing to pay you to do what you do. And if money cannot be made, learn to be content to have it as a hobby or pro bono :).

“Your work is to discover your world and then with all your heart give yourself to it.”

Buddha

Only 34% of organisations highly trust their data and analytics for decision making

As a data analyst, one of the satisfaction I get from my work is to see the analytics results get utilized and that decisions are made based on my work. From talking with other data scientists, this however is not always the case. As also pointed by a  recent study with data scientists at a software company, one of the biggest challenges of the job is to actually ensure that the results get utilized (Riungu-Kalliosaari, 2017). Reasons such as business stakeholders being not fully convinced or confident about the results, are among the contributors for this.

In a survey conducted by Forrester Consulting for KPM for their Building Trust in Analytics report, it was identified that about 34% of organisations have a high level of trust in their own organisation’s use of different types of analytics. This is a disappointingly low number, considering that many companies want or are on the path of being data- or analytics-driven and the need for data scientists in companies continues to grow.

Trust in analytics and the results are important for its effective use. There are several reasons as to why there might a trust problem. Ramaloo in his post on Hidden Insights, acknowledged that in most businesses, there is a disconnect between the data scientist teams and business units. “Business units often don’t understand the predictive models and how they can really add value to the organization. This can be hugely frustrating, especially for the data scientists, who have often invested a lot of time and effort into developing these models”.

There was one webinar I attend which tried to provide reasons why there is an analytics ‘trust gap’. The facilitators pointed out the following top reasons why this happens:

  • Incomplete data,
  • Data is not clean,
  • No single version of truth,
  • Understanding output of analytics: This goes to how the output of analytics is presented. If there is failure to talk of the results in terms of business, there is a low chance of adoption.
  • The mindset of decision makers – experience vs data: Since sometimes analytics and data do not give the complete picture – some high management might feel that their experience is better.

There are several strategies proposed for instance in the KPM report and in the webinar about how to bridge this trust gap but I will not detail them here. From my experience so far I believe a lot of the trust problems can be addressed by having data scientists be not too far removed from the decision making process and end beneficiaries of the results. Data scientists should work early on with the business units making the decisions as a way for the business stakeholders to gain understanding and confidence in the data, the process, and the people doing the analysis and hence the results – as well as for data scientists to better develop more effective, consumable, actionable and domain-guided analytics.

What is your take? How is the trust between analytics and decision making in your organisation and how have you managed to this trust gap be bridged?

Transitioning from Academia to Industry: My personal experiences

There are those decisions you make and its almost like something changes in the universe, in your whole outlook and attitude to ensure that that decision becomes a reality. It was the beginning of 2017 when I made a hard decision to transition from Academia to Industry. Having been in Academia for several years (since 2010 to be exact), I knew I wanted a change. As much I liked the research work and the projects I worked in, I started feeling like something was missing – the ability to see more the real impact of my work.

With this feeling, I sat down and thought of the kind of work I wanted to do, the experience I wanted to gain, the skills I wanted to use and build on, and more importantly the impact I wanted to make. Looking at those things I knew I wanted a data scientist position, not just crunching numbers, but use data to solve problems that I care about.

At that time, some of the skills I needed in order to perform that job well were not up to par, so I also knew what I needed to do next. I made the decision to use my spare time to learn as much as possible. I made more effort to improve my programming skills, took up data science courses, and started networking more in the circles I wanted to work in. I also narrowed down the types of problems that interested me. I took the opportunity to also gain some business, project management and leadership skills. By beginning of 2018, I was ready, at least ready enough to confidently communicate and demonstrate my abilities.

In a period of 4 months, I went on a total of 13 data science related interviews from 8 companies. Reached 2nd stage with 7 of them, was selected by 3 (not at the same time), and finally landed with one. With this experience, I wanted to share my experiences, just in case they might be beneficial to someone.

Lessons Learned (in no particular order):

– Data science is a hot field, but it is also big and combines various fields. And like me, you will probably feel like there are people out there who can do the job better than you. One can never know everything, but you should show that you are willing to learn and are continuously learning. I took several courses and did small projects that I could point to as evidence. But you should be good at something in this big field. You should have an answer to the “Why you?” question.

– A degree can play in your favor or against. Having a PhD and with only academic experience, I knew going into the interviews that there would be advantages and disadvantages with that. An advantage I observed is that there is a respect that comes with completing the PhD and the fact that you are exposed to new techniques and methods. The disadvantage is that some interviewers might think you are not ready for the fast-paced industry world. Luckily most of the research projects I had worked on were with industry partners, practical in nature, i.e., not just theoretical, and I had done some consulting work that I could point to and show that I can handle the business world.

– It is your practical abilities and attitude that seem to make a big difference. As data science is a practical field, more than the education, its what you can do. Thus many of the interviews included homework tasks that I had to perform and submit. Here I was glad I took the time to improve my programming skills. It also goes without saying that, in this field, as in many IT fields, having a github account or portfolio with examples of projects is essential.

– Find a sweet spot. As mentioned, Data science is big, with companies looking for a variety of skills, some more programming skills than others, some more statistical and mathematical knowledge than others, others more business skills. I found my sweet spot at the center of business and analytic skills, the ability to translate business problems or questions into data science problems, use data science to solve business problems, and the ability to communicate the above effectively.

A few tips to get the interviews:
– Attend events and network – This is a practice emphasized everywhere. But this is something that came handy in my process. Majority of the companies I got to interview with, were the ones where I had sent in an application through a contact I got during data science events. With each company, I sent in an application through the contact and if they had an open position open on their website, I sent in application through there as well. The advantage with the person to person contact is that you also get better feedback on your application than the automatized rejection email versions.

– Following from the above point, don’t just apply for jobs posted on company websites. Read about companies you are interested in or ones that might benefit from the skill you are offering. Identify people from those companies that are mostly likely to take your application seriously, find their emails and send them your application with an attention grabbing email subject header and cover letter. I got one interview through this approach.

A few tips during the interviews:
– Be confident, you also have the deciding power.

– Learn and adapt from each rejection. This is part of the journey. After a rejection, I found it helpful to speak with the interviewer and find out the reasons why they did not select me. This helped me identify my weaknesses and things I needed to improve. But it also informed the jobs I applied to next.

– Take the interviews as learning opportunities. You get to learn a lot about yourself, what kind of work, people and company you want to work for.

That’s all! Would also love to hear your experiences, thus feel free to share.

Interactive visualization of the Shark Tank deals: a Shiny App

In one of my earlier post, I analyzed the Shark Tank data to gain some insights on the  distribution of deals among industries and presenters’ genders. In this post, I want to present a more visual exploration of the data as a way to also gain more practice using R’s Shiny package.

There is nothing sophisticated in the visualization App itself, there is a UI and a Server part that allows you to visualize information regarding the deals that have been made on Shark Tank.

The App has been made available here, and the code for the App can be found here.

The right-hand side of the App displays a scatter plot showing all the companies that have gotten deals. The y-axis shows the deal amount, while the x-axis shows the equity that was given in the deal.

Upon hovering on each point, you get more information, including the Name of the company that got the deal and the Valuation amount. The deals are also colored by the Industry it belongs to.

Screenshot of App

On the left-hand side, the App allows you to select:
– The maximum deal amount – whereby the App responds to show you all the deals made under that amount.
– The Season – the App shows you only the deals made in that season. I have only added 3 seasons worth of data, but more seasons can easily be added to the csv file and will be reflected accordingly in the App.
– The Gender – the App shows you only deals where the presenters are of that selected gender.

As future work, it will be good to allow the user to make a selection and see deals made by a particular Shark on the show.

Importance of Communication and Visualization Skills in Data Science

Last week, I lead the second session of the Finland-Uganda Data Science Meetups. The topic for this session was data import, cleaning and pre-processing, and visualizations.

In the session, we welcomed senior data scientist, Jukka Toivanen, who shared with us the importance of good communication and visualization skills in data science. With the image below, he outlined to us the three spheres that make up the major part a data scientist’s work.

Data scientist’s work areas (source: Jukka Toivanen)

In those three areas, he pointed out that there is a tendency of data scientists to focus on the comfort zones, i.e., the blue and purple spheres. With the red sphere often not being focused on so much.

“Effective communication is undervalued but important” – Jukka Toivanen

But communication is important and it often means the use of good visualizations, as they can have remarkable effect in spotting patterns and conveying them effectively to other people.

I also recently got to learn the importance of this skill in an interview with a company for a data scientist position. The company gave me a task that involved developing a predictive model on a dataset, with the additional task of presenting key findings to a CEO. Guidelines were that the presentation should  be maximum 10 slides, be visually appealing and should be top notch. My performance on the task would be based (not solely) on my presentation, i.e., communication skills.

Although, I have undertaken several presentations in my career, this task did challenge me. As an audience, executives are a special group. They have limited time and want to hear meaningful results quickly. Thus, my first two slides had to already catch their interest. In addition, with visualizations, I had to communicate two to three important findings, insights and action points that had the most business value. To do this well, required learning as much about my audience’s business as I could and their potential business model. This paid off in the end as I was commended on my impressive presentation.

Often though, good visual presentations is not something that is emphasized in many data science school programs, let alone computer science programs. But it is important to learn how to effectively communicate and visually connect the technical approaches and results to the business value. This skill I believe will take data scientists a long way in creating impact.

Part II: Data analysis of the Africa, Europe, and Asia-Pacific 2018 Index of Economic Freedom data

This is Part II, a continuation of the Data analysis of the Africa, Europe, and Asia-Pacific 2018 Index of Economic Freedom data – Part I

In the first part I explored the:

1. distribution of nations in Africa (where I am from), Europe (where I am currently living) and Asia (where the economic freedom leading countries are) among the 5 categories of freedom, and

2. correlations between economic variables to see if any insights can be gained from this data.

In this post, I explore the

3. relationship between economic freedom, government expenditure and unemployment rates.

and summarize the findings and give recommendations.

The aim of this data exploration is to obtain insights and identify potential areas for improvement, especially for African nations. For the analysis, I used python. Full code is available on github.

Relationship between economic freedom, government expenditure and unemployment

Reducing unemployment is something that personally interests me. Thus in this post I focus on the relationship between the economic freedom, government expenditure and unemployment rates as a percentage of the labor force. Previous work has stated that government spending should go towards improving economic situations including employment levels (read John Maynard Keynes’ work).

According to the 208 Index data, the average unemployment rate is 10.08% in Africa, in Europe it is 9.8% and in Asia-Pacific it is 5.1%, the lowest among the three regions. The graphs below plot the economic freedom score vs unemployment rate data points and show the trend as well as their distributions.

Trend in Africa. As can be seen in the graph below, there is a slight upward trend between the economic freedom score and unemployment rate in Africa which is not the case in the European and Asia-Pacific graphs (see below). In the graph we also see several countries with similar economic freedom scores (scores between 50 and 60) but have very varying unemployment rates. The highest unemployment rate is present in Gambia at 29.7%. while the lowest is in Benin at 1%.

Trend in Europe. In the graph below, we see that there is a downward trend between the economic freedom score and unemployment rate in Europe. A bit interesting, the lowest unemployment rates is found in Belarus at 0.5%  but Belarus is categorized as moderately unfree. The highest unemployment rate of 26.7% is in Macedonia.

Trend in Asia-Pacific. Asia-Pacific also has a downward trend between the economic freedom score and unemployment rate. The region has some of the lowest unemployment rates when compared to the Europe and Africa regions. However, the highest unemployment rate is 31% in the Solomon Islands, the highest rate also when compared to those nations in Europe and Africa. The lowest unemployment rate is 0.3% in Cambodia.

Now, looking at the government spending, the average African government spending (graded on a scale of 0 to 100 in the Index) is 72.69, whereas it is 45.05 and 68.51 in Europe and Asia-Pacific respectively. Thus, the government spending in Africa is considerably higher. Moreover, the average government spending as a share of the GDP per person (an indicator of the size of government across countries (source)) is at 25 in Africa, which is higher than both Hong Kong (18) and Singapore (17.7) which are the first and second nations respectively in the economic freedom rankings.

Thus looking at the level of spending in Africa, and other variables such as unemployment rates or the economic freedom score, we can see that it is not the amount of spending that only matters to improve the economic freedom but the quality of the spending. “Things like the rule of law and a relatively corruption-free political culture—is more important than their size.” Stephen Gordon. As was pointed in the Index report, African nations need to strengthen their rule of law, protection of property rights, and curb down on cronyism and corruption.

Thus, spending itself is not the solution to reducing unemployment rates or improving the economic freedom score. The best results are likely to be achieved instead through strengthening and encouraging more business freedom, trade freedom, investment and financial freedom, aspects that create opportunities.

Summary

Part I and this post have explored the 2018 Index of Economic Freedom data, in particular looking at the Africa, Europe and Asia-Pacific specific data. The findings have revealed relationships, similarities and differences among the three regions. The aim of the exploration was to gain insights and identify potential areas for improvement, especially for African nations.

From looking at the correlations, it was possible to identify areas that could be worked on by African economies. This can include creating policies to improve regulatory efficiency (business freedom, labor freedom, and monetary freedom) and  create market openness (trade freedom, investment freedom, and financial freedom), policies that can provide the best chance of translating opportunity into prosperity.

In future, I aim to expand on the analysis to include more data sets (public and private sector) that can hopefully provide even more insights.

Data analysis of the Africa, Europe, and Asia-Pacific 2018 Index of Economic Freedom data – Part I

According to the 2018 Index of Economic Freedom, released by the Heritage Foundation, the world is moderately free. The Index, which was launched in 1995, evaluates nations on 12 aspects that affect economic freedom. These are grouped into four broad policy areas; 1) Rule of law (property rights, judicial effectiveness, and government integrity); 2) Government size (tax burden, government spending, and fiscal health); 3) Regulatory efficiency (business freedom, labor freedom, and monetary freedom); and 4) Market openness (trade freedom, investment freedom, and financial freedom).

Each freedom variable is a value between 0 and 100. Values of these 12 freedoms are averaged to create an overall economic freedom score. Based on this score, nations can be classified into five categories: free (>= 80), mostly free (70-79.9), moderately free (60-69.9), mostly unfree (50-59.9), and repressed (< 50).

Those economies that are rated free or mostly free enjoy higher per capita incomes than the many other nations that are not economically free. Examples of these free nations include Hong Kong and Singapore which have been leading in the rankings for several years.

Using the 2018 Index data, I explore the:

1. distribution of nations in Africa (where I am from), Europe (where I am currently living) and Asia-Pacific (where the leading countries are) among the 5 categories of freedom,

2. correlations between economic variables to see if any insights can be gained from this data, and

3. relationship between economic freedom, government expenditure and unemployment rates.

The aim of this data exploration is to obtain insights and identify potential areas for improvement, particularly for the African nations. The analysis on the Finland-Uganda Data Science Meetup, Session 1 that I instructed on the 29th March. The session explored the relationship between African nations’ government expenditure and unemployment rates using simple linear regression.

I have split the analysis and results into two posts. This post, Part I, covers the 1st and 2nd exploratory items mentioned above. Part II covers the 3rd item and summarizes the findings and gives recommendations. For the analysis, I used python. Full code is available on github.

Categorization of nations

As mentioned, the economic freedom score can be used to categorize the nations. Figures below show the distribution of the nations into the five categories of freedom.

World nations categorization. A total of 180 economies were ranked in the 2018 Index, and have an average score of 61.07 (Moderately free), a slight improvement of 0.17 points from the previous year. This is also the highest score in the 24-year history of the Index. From the chart below we can see that majority of the World’s nations fall in the moderately free and mostly unfree categories.

African nations categorization. Total of 53 economies were ranked in the 2018 Index, and have an average score of 54.46 (Mostly unfree), a slight drop of 0.49 points from the previous year (54.95).  The graph below shows the distribution. Visible is that the distribution skews more to the right with majority of the nations being mostly unfree. Noticeably there are no African nations that are considered free. Mauritius is the only mostly free nation, with Botswana falling short of that category by only a 0.2 point drop from 2017, causing it to drop into the moderately free category.

European nations categorization. Total of 45 nations were ranked in the 2018 Index, and have an average score of 68.78 (Moderately free), a slight improvement of 0.80 points from the previous year (67.98). Majority of the nations in Europe are moderately free. Noticeably, Europe has zero nations that are considered repressed.

Asia-Pacific nations categorization. A total of  43 nations were ranked and have an average score of 61.05 in 2018, a bit higher than the average of 60.43 in 2017. Asia-Pacific leads in the number of countries that are considered free, with a total of four nations. Majority of the countries are however mostly unfree.

Correlations among the 2018 Index economic variables

The 2018 Index data includes 28 numerical variables which include the 12 freedoms, as well as other variables such as GDP per Capita, Unemployment rates, inflation, or public debt.

The heatmaps below reveal the correlation coefficients, i.e., the relationship between these variables in the African, European and Asia-Pacific regions. Looking at the heatmaps, we can see those variables that are positively and negatively correlated.

Correlations of variables in African nations (download image)

 

Correlations of variables in European nations (download image)

 

Correlations of variables in Asia-Pacific nations (download image)

From the heatmaps, what is immediately visible, is the higher amount of positive correlations on the Europe and Asia-Pacific heatmaps. In all three regions, there is higher positive correlation among the Rule of law economic variables, i.e., property rights, judicial effectiveness, and government integrity. With the Asia-Pacific and European heatmaps, one can see that higher positive correlations also exist in the Regulatory efficiency (business freedom, labor freedom, and monetary freedom) and Market openness (trade freedom, investment freedom, and financial freedom) variables. In Asia-Pacific particularly, these three categories are all highly correlated to the economic freedom score. Of course correlations do not imply causality, but this does highlight areas such as regulatory efficiency and market openness that African nations can work on and create policies that have a positive impact on the economic freedom score.

An aspect that further interests me is the relation between economic freedom and employment opportunities for citizens. In the next part, I particularly look at the relation between economic freedom and government expenditure and how these relate to unemployment rates in African, European and Asia-Pacific nations.

-> Continue to Part II

 

 

Differentiating between Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text

In April 2014, my colleagues and I published an article in the IEEE Transactions on Affective Computing journal titled “Are They Different? Affect, Feeling, Emotion, Sentiment, and Opinion Detection in Text.” In writing the paper, I didn’t know that it was going to end up being one of  one my most  read and cited paper. 

The paper made a case that the following five subjective terms should be differentiated for their effective detection in text: affect, emotion, feeling, sentiment, and opinion. In the field of subjective detection, a major limitation in the automatic detection of affect, feelings, emotions, sentiments, and opinions in text is the lack of proper differentiation between these subjective terms and understanding of how they relate to one another. This lack of differentiation, also influences real-world text analysis applications.

In the paper, I tried to bring clarification to the terms and highlight the distinctions and relationship between them, first at a definition level, and then at a more structural level so as to allow for the detection of those terms in text. The definitions and structural differences that were identified are summarized below.

Affect is non-conscious and is difficult to conceptualize in language, revealing that what can be detected from text is the conscious expression of affect, which was found to be feelings and emotions. What is normally or can be detected in text is rather the affective reaction expressed towards something.

Feelings are conscious phenomena that have been labeled and they can be detected from text.

Emotions are complex psychological phenomena that are near impossible to detect in totality from text. What we are able to detect is the written conscious experience of five factors (appraisals, feelings, physiological reactions, expressive behavior, and readiness to act in a certain way), which constitute emotions. I also found that the use of words to convey emotions is influenced by culture. Thus the paper further recommend the inclusion of ethnographic studies to answer questions such as “What role does culture play in the linguistic expression of emotions?” Answering that would enable natural language processing researchers to create more robust emotion detection algorithms.

Schematic structure of an emotion

Sentiments are enduring emotional dispositions that have developed over time about particular objects. Conclusions about sentiments in text have to be performed for a period.

Schematic structure of sentiment

Opinions are personal interpretations of information, which may or may not be associated with an emotion or sentiment. The paper gives examples of such instances.

Schematic structure of an opinion

From the definitions and structures above, it is clear to see the differences between the subjective terms. The terms are however related to each other as the figure below illustrates.

Relation and differentiating factors between affect, feelings, emotions, sentiments and opinions.

In summary, to reach their full potential, natural language processing techniques focused on detecting subjective terms must be able to capture the subtle differences that reflect personal, cultural, and societal signals within the subjectivity terms. In the paper, I only focused on five terms, but there exists others that are related such as mood and attitude.

The existence of these related concepts is one of the reasons I did this research. In particular, I wanted to use the results as building blocks for building a common framework for capturing all the subjectivity terms in text. This is something I still have the desire to do and something that is still an open question on whether its possible or even needed.  Perhaps, the current advances in word embedding techniques would be one approach to capture not only the semantic relations but the affective relations between words, sentences, or documents. Thus if there are any interests out there on investigating this common framework for detecting subjectivity in text, please drop me a line.

 

What assumptions and limitations do the theories of emotion pose on the detection of emotion in text?

One of my areas of interest and expertise is emotion detection. In this post, I look at the existing theories of emotion, in particular the assumptions and limitations they set on the detection of emotion in text. In the literature on emotion detection, no unified or generally accepted theory of emotion exists. However, there are six theories that have had a significant impact on the field of emotion detection research.  I briefly review each of the six theories, then look at the assumptions each theory contributes particularly to the natural language processing of emotions in text. For a more thorough discussion, see my dissertation (Chapter 2).

Theory 1: Darwin’s evolutionary theory

The first theory of emotion can be traced to Charles Darwin’s evolution theory of emotion in 1872 [1]. Darwin’s theory focuses on the nature of emotion expression, and it states that non-verbal communication, such as body language, movements, and facial expressions, are not only used to communicate meaning but have also been genetically retained because they were useful to ancestors. Darwin also suggested that emotional expressions are initially learned behaviors. The main emphasis of the theory though is the “survival value” of emotions, particularly their universal similarity across races and cultures (illustrated in Figure below).

Sequence of Darwin’s theory emphasizing  the survival factor leading to emotion expression.

Theory 2: James-Lange theory

In his 1884 article, “What is an Emotion?”, James argued that bodily changes must come first and that it would be impossible to have emotions without these bodily changes [2]. Similarly, Carl Lange, a Danish professor, emphasized the influence of vasomotor changes to emotional experiences [3]. Because the two scientists similarly emphasized that physiological arousal precedes emotions, their two theories were combined to form one theory, known as the James-Lange (J-L) theory. The theory states that physiological arousal occurs first, and when this arousal is perceived or interpreted, emotion is experienced (illustrated in Figure below). In other words, a stimulus triggers physiological changes in a person’s body, and a person’s brain interprets these physical changes into the appropriate emotion [4].

Sequence of J-L theory emphasizing the importance of physiological
arousal for an emotion experience (adapted from Walsh [5])
Theory 3: Cannon-Bard theory

The Cannon-Bard theory argues against the J-L theory and states that physiological arousal, such as sweating and trembling, occurs simultaneously with emotions. The theory argues that the thalamus is a necessity for experiencing emotion. According to the theory, the thalamus sends messages to the cortex for an interpretation of the emotion, which then generates the subjective feeling of emotion, and simultaneously sends them to the sympathetic nervous system for the appropriate physiological responses, thus producing arousal at the same time [6, 7] (illustrated in Figure below).

Sequence of the Cannon-Bard theory emphasizing the simultaneous occurrence of physical arousal and emotion (adapted from Walsh [5])
Theory 4: Schachter-Singer theory

Like the Cannon-Bard theory, the Schachter-Singer theory [8] acknowledges that the same pattern of physiological arousal can occur for different types of emotions. Similar to the J-L theory, the Schachter-Singer theory also states that physiological arousal occurs first and provides important feedback for interpretation; however, rather than simply perceiving or interpreting the arousal, Schachter-Singer’s theory suggests that a reason for the arousal must be identified before being able to experience an emotion (see Figure below).

Sequence of the Schachter-Singer theory emphasizing physical arousal
and cognitive labeling of the arousal to experience an emotion (adapted from Walsh [5]).
Theory 5: Cognitive-Appraisal theory

The focus of the cognitive-appraisal theory is that thought and emotion are inseparable [9]. According to this theory, to experience an emotion and respond to it, one must think about the situation they are in. The theory is often believed to provide the missing link that explains the interpretation or perception in the J-L theory. Interpretation is thus explained by cognition, more particularly by appraisal, a term coined by Arnold [10] to represent sense judgments, which are “direct, immediate, nonreflective, nonintellectual, [and] automatic.” Theorists of this perspective, pointed out that depending on the significance for the individual, the appraisal of a situation will automatically trigger an emotion and physiological response as an appropriate response to the stimuli, which can either be immediate, imagined, or remembered [11] (illustrated in Figure below).

Sequence of the Cognitive-Appraisal theory emphasizing the importance of cognition (appraisal) before emotion experience and response (adapted from Walsh [5])
Theory 6: Social constructivist theory

Social constructivists view that “emotions are not just remnants of our phylogenetic past, nor can they be explained in strictly physiological terms. Rather, they are social constructions, and they can be fully understood only on a social level of analysis.” [12]. The theory focuses on the systems of culturally specific rules that govern how, when, and by whom particular emotions are to be experienced and expressed [9] (see Figure below). From this perspective, emotions fulfill a social purpose by regulating interactions between individuals. Although the theory differs from Darwin’s and James’ theories, those who support Darwin’s theory have acknowledged the role of culture in regulating emotional displays [13].

Sequence of the Social Constructivist theory. emphasizing the importance of culture and social norms in emotion expression.

Discussion on the assumptions each theory contributes to emotion detection in text

Each of the above six theories agree that emotions are triggered by a stimulus or event (external or internal) that is deemed important to the organism. The theories can be grouped into five main categories: evolutionary, physiological, neurological, cognitive-appraisal, and social constructivism [14]. Although the theories seem to contradict one another, they actually focus on different perspectives of emotions, which Cornelius [14] summarized as follows:

“Neurophysiologists are interested – almost by definition – in the neural organization of emotion, Darwinians are interested in the evolutionary organization of emotion, Jamesians [those following the James-Lange theory] are interested in the bodily organization of emotion (for want of a better term), cognitive-[appraisal] emotion theorists are interested in the psychological organization of emotion, and social constructivists are interested in the social-psychological and sociological organization of emotion.”

With these theories, a natural language processing researcher in emotion detection has to determine which theories and their assumptions provide the most suitable basis for describing emotions in the context of their research.

With the Darwin’s theory, it can be assumed that there are universally recognized facial expressions present when experiencing emotions. It can also be assumed that there are body movements or reactions that are primary in every living being [15]. From this perspective, and considering a textual environment, evidence of facial expressions would be present in texts in descriptions of the face, i.e., “I have sad face” or “she is smiling at me,” or in the use of symbols, e.g., emoticons. Otherwise, there would be no evidence of facial expressions in a textual environment, and thus the emotion could not be identified in accordance with the theory. Hence, the Darwinian Theory is more suited to studies that have access to visual signals.

From the perspective of the J-L theory, it can be assumed that an emotion experience is accompanied by unique patterns of physiological activities, i.e., changes in the autonomic nervous system. In the textual environment, expressive and descriptive words may provide evidence of physiological arousal, as it is not possible to use equipment to measure physiological signals. For example, descriptive phrases such as “my palms are sweating” in reference to nervousness or “I am finding it hard to breathe” in reference to panic provide self-physiological activity descriptions; however, the theory is limited to only subjective feelings which is the component of emotion that the J-L theory focused on [16].

Moreover, based on the neurophysiological perspective, which was represented by the Cannon-Bard theory in the review above, it can be assumed that there are activities in the nervous system that cause some of the emotion experiences and the accompanying physiological arousals. In particular, the emotion experience can occur without an awareness of bodily changes. That is, people can react to the emotional significance of a stimulus before fully understanding the stimulus [17]. The theory also assumes that there are neural circuits that have developed evolutionarily [18]. From a text perspective, this theory makes it more difficult to determine how the nervous system’s activities make assessments of a stimuli because it would be overly complicated to use circuit models as a means to differentiate one emotion from another in text.

From a cognitive-appraisal perspective, which includes the Schachter-Singer theory, it can be assumed that each emotion experience has its own corresponding and unique pattern of appraisal, thought, and mental activity. More specifically, following from the Schachter-Singer theory, it can be assumed that an emotion has been labeled and recognized by an author of a piece of text. As cognitive-appraisals arise from personal conceptions of a situation, identifying an emotion experience becomes a complex task to perform because the uniqueness of each pattern makes it difficult to evaluate it across different people [19]. This challenge is particularly difficult in a textual environment because detailed information is not often available.

Furthermore, from the social constructive perspective, it can be assumed that there are cultural and social factors in play during emotion experiences and expressions. Social processes and cultural norms play significant roles in specifying when emotions are felt and how emotions are expressed [19]. Some emotions are directed towards other people and arise from interactions with them. Although it is apparent that social and cultural norms do affect emotion expression and that there is a need to study emotions in a social context, it is a challenging task in a textual environment because the textual environment might not offer enough background information to obtain accurate results when adopting this perspective.

In summary, each theory presents a different perspective which can guide but also has implications regarding the approach one can use to detect emotions in mediums such as text, audio, video, body or facial movements, etc.

 

References:

[1] C. Darwin, The expression of the emotions in man and animals, Vol. 526, (University of Chicago press, Chicago, US, 1965). (Originally published in 1872).
[2] W. James, “What is an emotion?,” Mind 9, 188–205 (1884).
[3] C. G. Lange, “The mechanism of the emotions,” in The Emotions,D. Dunlap, ed. (Williams & Wilkins, Baltimore, MD, USA, 1885), pp. 33–92.
[4] W. James, The Principles of psychology (Dover, New York, NY, 1890).[5] J. Walsh, “Theories of emotion,” (12/15/2013), Khan Academy, https://www.
khanacademy.org/video/theories-of-emotion (visited on 2017-03-12).
[6] W. Cannon, Bodily Changes in Pain, Hunger, Fear and Rage: An Account of Recent Researches Into the Function of Emotional Excitement (Appleton-Century, New York, NY, 1929).
[7] P. Bard, “On emotional expression after decortication with some remarks on certain theoretical views: Part I,” Psychological Review 41, 309–329 (1934).
[8] S. Schachter and J. Singer, “Cognitive, social, and physiological determinants of emotional state,” Psychological review 69, 379–399 (1962).
[9] R. R. Cornelius, “Theoretical approaches to emotion,” in Proceedings of the ISCA Workshop on Speech and Emotion (ISCA, 2000), pp. 3–10.
[10] M. B. Arnold, Emotion and personality (Columbia University Press, New York, NY, 1960).
[11] P. C. Ellsworth and K. R. Scherer, “Appraisal processes in emotion,” in Handbook of affective sciences, K. Scherer and H. Goldsmith, eds. (Erlbaum, Mahwah, NJ, 2003), pp. 572–595.
[12] J. Averill, “A Constructivist View of Emotion,” in In Emotion: Theory, Research and Experience, R. Plutchik and H. Kellerman, eds. (Academic Press, New York: NY, 1980), pp. 305–339.
[13] P. Ekman, W. V. Friesen, and P. Ellsworth, Emotion in the Human Face: Guidelines for Research and an Integration of Findings: Guidelines for Research and an Integration of Findings (Pergamon Press Inc., Elmsford, NY, 1972).
[14] R. R. Cornelius, The science of emotion: Research and tradition in the psychology of emotions (Prentice-Hall, Inc, Englewood Cliffs, NJ, 1996).
[15] N. H. Frijda, The emotions (Cambridge University Press, New York, NY, 1986).
[16] K. R. Scherer, “What are emotions? And how can they be measured?,” Social science information 44, 695–729 (2005).
[17] J. E. LeDoux, “Emotion, memory and the brain,” Scientific American 270, 50–57 (1994).
[18] K. R. Scherer, “Psychological models of emotion,” The neuropsychology of emotion 137, 137–162 (2000).
[19] R. A. Calvo and S. D’Mello, “Affect detection: An interdisciplinary review of models, methods, and their applications,” IEEE Transactions on affective computing 1, 18–37 (2010).

Only 2 universities and zero computer science departments in Africa are in the top 200 in the QS rankings

I distinctly remember last year when the Head of the Computer Science Department at the University of Helsinki gathered majority of the departmental staff to celebrate the department being ranked 1st in the Nordic region and coming 101-150 around the world – according to the QS World University Ranking. The University of Helsinki as a whole comes in at number 102 globally in the QS ranking and 90 in the Times Higher Education World University Rankings. While standing there,  I naturally wondered which African universities and especially computer science departments appeared on this same list.

To answer my question, using the same QS ranking system (which judges institutions along the core missions of research, teaching, knowledge transfer, and international outlook), I visualized the top 5 universities and top 5 computer science departments across all the continents. In the analysis, I chose to separate the Middle Eastern countries for further comparison.

So what does the QS ranking list show?

3 of the top 5 universities on the African continent are in South Africa and the other two are in Egypt.

(Full size)

As can be seen in the map, the first African university to appear on the QS ranking is the University of Cape Town, in South Africa, coming in at no. 191.

Looking solely at the ranking of African universities, I found that the top 15 universities on the continent constitute:

7 universities in South Africa,  5 in Egypt, and 1 in Uganda, Ghana and Kenya respectively. 

With the Times Higher Education ranking, Makerere University in Uganda, comes in fifth rather than 12 as ranked by the QS ranking [source].

In addition, I looked at the ranking of computer science departments around the world and found that:

Only 2 computer science departments in Africa appear in the rankings, one in South Africa and one in Egypt.

(Full size)

The QS ranking system uses six metrics to evaluate universities, namely: Academic reputation, employer reputation, faculty / student ratio, citations per faculty, international faculty ratio, and international student ratio [1]. Other ranking systems like the Times Higher Education and the Shanghai ranking use different metrics for their evaluation. In majority of these rankings, the top 10 spots are dominated by universities in the USA and UK, though in recent years, a few Asian universities, mostly South Korean have climbed up to be in the top 20.

When it comes to the ranking of universities, there are debates on whether these rankings say anything of the actual quality of the institutions. For instance, those universities that have sufficient resources can game the ranking system. I tend to agree with Ward [2] that these indicators and metrics “tend to greatly overvalue the ‘haves’ at the expense of the ‘have nots’.”  Many of the metrics used emphasize strong research results and these results are strongly influenced by the amount of resources reserved for research and international cooperation by universities. Research is expensive, publishing in top journals is expensive, and resources (and a good reputation) are needed to attract international students and leading faculty – which rules out a lot of institutions in developing countries.

Recently, there has been talk of creating ranking systems exclusively focused on Africa, like The Times Higher Education African ranking that was piloted in July 2015. This ranking uses metrics that take into account the developing context of the continent. However, I don’t know if this captures quality either and I do not see how this will help the institutions compete with the rest of the world.

When it comes to ranking African universities, another issue pointed out by former UN secretary-general Kofi Annan at the African Higher Education Summit in Dakar in 2015,  is that there is a chronic lack of data available about Africa’s universities – data is missing to accurately rank and compare universities [3]. This was also supported by the World bank and Elsevier report [4]. Missing data, of course can lead to miss-ranking of universities, which can affect resource allocation and the awarding of grants and projects. It has been reported that Africa accounts for 1% of world research outputs [4], but this number could, in reality, be higher. But it is probably not as high as it should be. Research breeds innovations, and for me, improving research (also linked to resources) is a key outcome that would make a difference. On top of that, there is also a need to strengthen the data collection for accurate reporting and decision making – both areas interest and motivate me greatly.

 

Sources:

[1] QS Top Universities (June 2017), QS World University Rankings Methodology, https://www.topuniversities.com/qs-world-university-rankings/methodology

[2] Steven C. Ward (October 2, 2014), What do world university rankings actually mean? The Conversation, https://theconversation.com/what-do-world-university-rankings-actually-mean-32355

[3] Damtew Teferra (September 3, 2015), Ranking African universities is a futile endeavour, The Conversation, https://theconversation.com/ranking-african-universities-is-a-futile-endeavour-46692

[4] Blom, A., Lan, G., & Adil, M. (2015). Sub-Saharan African science, technology, engineering, and mathematics research: a decade of development. World Bank Publications.

*The maps were created with R’s leaflet and map packages.

Any increase in the number of people of color in the 2018 Oscar nominations?

Continuing from my last post about the Oscars, in this one I look at this year’s Oscar nominations. In particular, I focus on the number of people of color that have been nominated in the “big” categories (i.e., Best actor / actress, best supporting actor / actress and best director). I analyze the changes in the context of my previous analysis where I looked at the diversity in the 2017 nominations compared to the 2016 and 2015 nominations. 2016 was the year when steps were publicly made to diversify and increase the number of Academy Award voters, especially in light of the #OscarSoWhite hashtag that trended after the 2015 and 2016 nomination announcement as well as the Oscar boycott in 2016.

Last year as well, new voters were ushered in, as a way to continue diversifying the voting committee (Read the full report on the Oscar class of 2017). So what did this change bring in terms of nominations for people of color? Based on the Oscar nominations that were released on the 23rd January, we can see that:

Total number of people of color nominated reduced from 7 to 5

In the 2017 nominations, we saw a big rise from zero people of color nominated in 2015 and 2016 to a total of 7 in the five big categories (i.e., Best actor / actress, best supporting actor / actress and best director). From the figure below we see that in 2018, this number decreased to 5 – which is still better than the 2015 and 2016 nominations. In addition,  this year people of color are represented in the best actor, best director and best supporting actress categories.

A woman this year has been nominated in the Best Director category

In light of the women movement in Hollywood, I also looked at the gender distribution in in the best director category. Here, we see that in this year’s nominations, one woman was nominated. Although it is only just one, it is an improvement from 2015, 2016, and 2017 where zero women were nominated.

This year’s nominations thus showed a decrease but still a presence of people of color in the big actor categories. In addition, it showed an increase of one woman nominated in the best director category. As more changes and movements keep happening, I look forward to seeing how these impact the diversity in nominations and opportunities in Hollywood.

 

Who has appeared the most with whom in Oscar nominated movies? – this and other results

The following movie analysis post was driven out of curiosity. I distinctly remember that when I was watching the movie Joy starring Jennifer Lawrence, and Bradley Cooper’s character appeared, I wondered about the exact number of times that these two actors have actually appeared together. I know they were together in Silver Linings Playbook, Serena, as well as American Hustle.  While pondering about that, I continued to wonder which other actors in Hollywood have often appeared together and exactly how often. So this year, I finally got around to doing the analysis and satisfy my curiosity. This post shares some of the analysis results. The analysis code and data are available on github.

Data collection 

As the number of movies is enormous and I did not want to go through every movie that has ever been made in Hollywood, I restricted the analysis to only movies that have received an Oscar nomination in the predominant categories of Best and Supporting Actor and Actress and Best Director and Producer, from the time the Oscars have been awarded, i.e., 1928 to 2017. Of course this restriction removes certain actors that I know appear frequently together like Adam Sandler with David Spade, Rob Schneider or Luke Wilson,  or Vince Vaughn, with Ben Stiller and Will Ferrell,  whose movies are usually not nominated for Oscars.

So first a list of the Oscar nominated movie titles in each of the 6 categories (i.e., Best Actor / Actress, best supporting actor / actress, best producer, best director ) was automatically collected from Wikipedia into a csv file along with the year of nomination. In total, I had 1,266 movies in the dataset (Data available here).

Then, with that movie title and year, I wrote a R script to search Google and extract the IMDb URL of the movie, with the aim of getting the IMDb ID of each movie.

Next, with the URL and another R script, I extracted the ID and searched the IMDb page of the movie to get the full cast of the movie.

Lastly, With the full cast data set, I was thus able to explore the data and answer my curiosities. This is what I found out:

Results

Meryl Streep has appeared 23 times in Oscar nominated movies

In total, 15,689 actors have appeared in these movies. From the analysis, I found that Meryl Streep is at the top of the list with the highest number of appearances in Oscar nominated movies, with 23 appearances.  Of these 23, she has won an Oscar on 3 occasions, for the movies Kramer vs. Kramer (1979), Sophie’s Choice (1982), and The Iron Lady (2011).

Bette Davis (deceased) comes in second with 19 appearances. I also noticed that some of my favorite actors such as Robert Duvall, Jack Nicholson, Jeff Bridges, Robert De Niro, Ed Harris, Tom Hanks, Cate Blanchett, and Leonardo DiCaprio are high on this list.

In addition,

Meryl Streep has also the highest appearance with other actors

Not surprising, Meryl Streep has acted with a lot of actors. The analysis shows in total that she has acted with 437 other actors.

Among those actors that Streep has appeared the most with in the Oscar nominated movies, Margo Martindale appears to be at the top. They appeared together in the movies Marvin’s Room (1996), The Hours (2002), and August: Osage County (2013).

Although Streep has the most appearances, she is not the one leading the list of those who have worked with the same actor the most. There are a total of 23 actors such as Anna Q. Nilsson (deceased), Beah Richards (deceased), Eric Phlmann (deceased) –  who have acted with one actor 5 times, 5 being the highest. As a possible explanation, these actors were in movies in the early part of the 20th century – where the number of actors and actress was not as large as it is currently. Thus of those 23 actors, only two are still alive, Hal Holbrook and Jane Alexander.

Bradley Cooper has worked the most with Jennifer Lawrence in Oscar nominated movies.

With my original curiosity, I got to confirm that in total Cooper and Lawrence have appeared together 3 times in Oscar nominated movies.

As a side note, Cooper did not win an Oscar in any of those three movies, Lawrence however did win for Silver Linings Playbook.

So that’s it, my curiosity for now is satisfied. All code and datasets are publicly available here. If you notice something that I did wrong there or reported here, please drop me a line. Otherwise, if time allows, I might extend this analysis to also include directors. I know for instance Leonardo DiCaprio has collaborated often with Martin Scorsese“, as well as Tom Hanks with Steven Spielberg and Robert Zemeckis, and there might be other co-appearances with directors that might be interesting for some future post.

Black Mirror shows us how simple technological decisions can go humanly wrong

Ever since a friend recommended the Black Mirror anthology series some years back, I have been a fan. So when Season 4 came out on December 29, my New Year’s holiday day was spent binge watching the whole season – I couldn’t help myself!

Although this was not the best season, many of the episodes were very intriguing to watch. [Spoilers ahead]. After finishing all the 6 episodes, I observed a sort of common theme around the technology, i.e., technology allowing for full immersion to the point of transferring consciousness and or allowing for shared experiences – with the exception of the Metalhead episode (Ep. 5) which really did not provide much background information. We observe for instance, that the technology in episode 1: USS Callister gives the lead actor the ability to fully immerse himself in a digital environment that his consciousness could even be trapped in it, and in episode 3: Crocodile, we see a device that makes sharing of memories possible.

Furthermore, the Season 4 stories particularly show us how seemingly simple technological decisions can go humanly wrong. This is more apparent in episodes 2 and 6.

In episode 2: Arkangel – we observe a child getting lost and the mother naturally getting very worried for the child’s safety. With her love and concern for the child, the mother makes the decision to get a rather advanced tracker implanted into the child. A device I can see many mothers seriously considering. Beyond what current trackers are capable of, i.e., knowing where someone is, this tracker can track body vitals and conditions, see what the child is seeing, with the option of even filtering out what the child is seeing, among other capabilities. But we soon see how such capabilities, i.e., constantly knowing what your child is looking at, having the power to filter and even know body conditions before the child does, goes wrong for both the mother and daughter. We see the daughter want to experiment with things, start to need her privacy as well as lie as teenagers would do, and we see the mother struggle with the need to know, control and filter the daughter. In a way, I actually think the mother became addicted to the device and the constant need of knowing what was the child was doing.

Addictions and sharing of experiences were also some of the topics that were touched on in the three stories told in episode 6: Black Museum. There is the doctor who is able to share the pain of his patients. Although at first he is able to help his patients through this shared experience, we soon see him become addicted to the pain and fear, that he eventually commits murder to get his fix. Then, we also see the ability to transfer consciousness in two of the stories. There is the story with the comatose wife and the other with the death-row in mate whose consciousness gets transferred into a hologram.

In the story of the comatose wife, we can perhaps all understand why the husband and wife would make the decision to have the wife’s consciousness transferred in the husband. Who would not give their loved one the chance to talk, see and hug their child if they could? But having someone constantly commenting and criticizing what you look at, what you do, how your body reacts – all this on top of your own critical voice is a recipe for disaster, and we see in this story how it all goes wrong.

In addition to the above stories of season 4, there are notably other Black Mirror seasons that show us how the technology they present does go wrong, e.g., season 1, episode 3. That is one of the things I like about Black Mirror,  that it  always reminds us that as technology advances and permeates many aspects of our lives, as we immerse ourselves in it and it allows us to have shared experiences – there are several ways in which these technological advancements can go humanly wrong.

Motivational words for 2018

I believe good motivational and encouraging words are always needed – to give us that extra push when and if we need it. Thus my first post in 2018 is going to be just about that – some motivational and encouraging words for the year ahead.

The motivational words are not mine but I like them a lot (had posted it here as well). They are from a montage motivational video, called Sacrifice by Les Brown, Eric Thomas and Ray Lewis. I find the words inspirational, and as the video itself does not have subtitles, I have included the words below just in case you are like me and like to have motivational words posted on the wall, work desk or laptop so that you can reflect and visualize them often.

Wishing you all a Happy and Successful 2018!

 

More women who pitch on Shark Tank get the deal – Shark Tank Analysis Part I

Ever since Shark Tank aired in 2009, I have been a fan of the show – from the varied business ideas that get pitched, the background stories of the presenters, to the deals that get made or not made. After several seasons of watching the show, I became curious about certain statistics such as the actual number of females vs males that come to pitch, how many people of color pitch and get deals, what industries gets the most deals, how many of those that got deals shed some tears, etc.

I was thus glad to find a full Shark Tank dataset being kept and maintained by angel investor, Halle Tecco – bit.ly/STankData. With the dataset, I was thus able to explore some of my curiosities. As there are many, I will explore them over two posts. With the first post, I will look at gender distribution, focusing on the eight seasons that are complete. Season 9 is currently ongoing and the analysis below will be updated to reflect that once its completed.

The number crunching was done using R programming, and I have made the R script available on github.

So what did the analysis reveal?

As expected, men constitute the biggest proportion of the presenters: 

Looking at all the eight complete seasons, a total  of 707 ideas and businesses have been pitched. Of those, 422 were made by Males, 177 by Females, and 108 by a Mixed team.

This trend is apparent from season to season, where we also observe that the show has been having a higher number of seasons since the first three seasons. More specifically, season 2 had the lowest with 36 presenters, while seasons 5, 6, and 7 had the highest with all a total number of 116 presenters each.

Naturally, with the higher proportion of Males in the show, majority of the deals also go to men:

As shown in the figure above, the highest number of females to receive deals were observed in seasons 1 and 2, with 37%. Mixed teams were highest in season 5, at 23% of the total presenters.

 

Looking at those who got deals, on a good note, more women who pitch get the deal in comparison to males:

Considering just gender specific categories, the data reveals that:

– Out of 177 females that pitched, 99 got the deal (56%)

– Of the the 422 males that pitched, 211 got the deal (50%)

– Similarly to females, 56% of the mixed teams that pitch get a deal (61 out of 108).

 

The ‘Food and Beverage’ industry has the highest number of presenters:

There are several Industries that are represented on the show and I am always interested to see if there are gender differences in the fields that men and women pitch in. Overall, among the 707 presentations, ‘Food and Beverage’ and ‘Fashion and Beauty’ had the highest representation with 20.8% and 19,8% respectively. The lowest fields represented were ‘Automotive’, ‘Green and CleanTech’,  ‘Travel’, and ‘Business and Services’  with 0,99%, 1,41%, 1,41%, and 1,84% respectively.

Notably, Men tend to pitch in ‘Food and Beverage’ and ‘Lifestyle and Home’ industries, while Women are more in ‘Fashion and Beauty’ and ‘Food and Beverage’:

Results reveal that 19,4% (82 of 422) and 16,4% (69 of 422) of males pitched in the ‘Food and Beverage’ and ‘Lifestyle and Home’ industries. While 31,6% (56  of 177) and 22% (39 of 177) females in ‘Fashion and Beauty’ and ‘Food and Beverage’ industries respectively. Similarly, mixed teams were mostly in 24,1% (26 of 108) ‘Food and Beverage’ and 19,4% (21 out of 108) in ‘Fashion and Beauty’.

Notably, no females are recorded to have pitched in the Green and Clean Tech and Automotive fields.

As I work in Tech and constantly hear and observe the low female representation in Tech fields and startups, I was interested to see how this fact is reflected in the Shark Tank data. The data revealed first that the ‘Software and Tech’ industry is fifth in the list of the most presented in industry for men and  8th for women. For some reason, I thought the industry would be higher on the list.

Moreover, of the total females that pitch, 3,39% pitch in the ‘Software and Tech’ industry, while 9,24% of the males pitch in that industry. Roughly half of those who pitched in the Tech field got deals irrespective of gender.

 

********  That’s it for Part I  ********

The above results will be updated to also reflect incoming data for Season 9.

Stay tuned for Part II, where I will explore other factors such as how many people of color have pitched and got deals from season to season.

Guidelines to creating a chatbot

With all the hype around chatbots, I was glad to get a chance to actually get some experience in developing one. A few weeks ago, within our Immerstive Automation project, we had Nick Diakopoulos, an Associate Professor in computational and data journalism, lead a workshop on creating chatbots. I was very excited to get to build my first chatbot.

Image source – https://i.ytimg.com/vi/YeLh-Fzm5Eg/maxresdefault.jpg

During the workshop, we were introduced to one tool, Chatfuel, for developing Facebook messenger chatbots. It is used by many companies and newsrooms (shown here https://chatfuel.com/bots/) and is easy to use for beginners.  Examples of chatbots include those that give you weather updates when you ask, ones that help you pick out and order groceries for the week or news bots that alert you when something interesting happens. For many of these companies, chatbots provide several advantages, as they are able to handle tasks automatically, thus reducing manual hours.

In the workshop, we were teamed up in groups of three. In my group we ended up creating a chatbot that serves you a poem when you ask from it. Nothing fancy as we just implemented a few conversational rules and we didn’t publicly release it. Future plans include experimenting with something more advanced using machine learning, as Chatfuel is rule-based and has limitations in conversational depth.

When designing chatbots, however, here are a few guidelines we were given in the workshop that can guide you on creating a chatbot:

1) Select a specific problem or opportunity that you want to address.

2) Define audience and goal(s) of audience interacting with bot. What will the bot do? Does it solve a problem? Who will use it? Does it serve a particular demographic or niche? In what context will the audience use it?

3) Create a persona for your bot: what are the bot’s goals and behaviors? Will it have a name or personality? What kind of tone will it take?

4) Define the interaction: How will your bot interact? Can you describe a scenario that walks though those interactions step by step? – Good practice here is to try and improvise the conversation: Act out the conversation.

5) Check if there is any data or knowledge-base that your bot needs in order to work?

Lastly, always have a fall back, a user friendly way for your bot to react when it does not ‘understand’ user input.

That’s it! Feel free to share any interesting chatbots that you have created or your favorite tools and approaches, etc.

 

Six New Publications: in NLG, OSS, and Experimentation

In academia, they say that you either publish or perish, thus it was good news to know that this year, I was not going to be falling under the latter. In June and July, I received news of six publications that were accepted, putting me in an even better mood before going for summer holidays.

Below is a summary of the papers and their highlights.

Natural language generation

Data-driven news generation for automated journalism: Leppänen, L., Munezero, M., Granroth-Wilding, M. and Toivonen, H., The 10th International Conference on Natural Language Generation (INLG). Santiago de Compostela, Spain, September 2017. To appear.

Highlights: In this paper, we explore the field and challenges associated with building a journalistic natural language generation system. We present a set of requirements that should guide system design, including transparency, accuracy, modifiability and transferability. Guided by the requirements, we present a data-driven architecture for automated journalism that is largely domain and language independent. We illustrate its practical application in the production of news articles upon a user request about the 2017 Finnish municipal elections in three languages, demonstrating the successfulness of the data-driven, modular approach of the design.

Finding and expressing news from structured data: Leppänen, L., Munezero, M., Sirén-Heikel, S., Granroth-Wilding, M. and Toivonen, H., 21st International Academic Mindtrek Conference. Tampere, Finland, September 2017. To appear.

Highlights: This work proposes approaches for automatically finding news or newsworthy events from structured data using statistical analysis and for providing added value to audiences. Utilizing a real natural language news generation system as a case study, we demonstrate the feasibility and benefits of automating those processes. In particular, the paper reveals that through automation of the news generation process, a large amount of news can be expressed in digestible formats to audiences, at varying local levels, and in multiple languages. In addition, automation makes it easier to provide interactivity to the audience allowing them to tailor or personalize the news they want to read.

Open source community analysis

An exploratory analysis of a hybrid OSS company’s forum in search of sales leads: Munezero, M., Kojo, T. and Männistö, T., In Empirical Software Engineering and Measurement (ESEM), 2017 ACM/IEEE International Symposium on. IEEE, Toronto, Canada, November 2017 – To appear

Highlight: The paper presents ongoing work utilizing text analysis techniques to analyze the content of forum posts of a hybrid open source company that offers both free and commercial licenses, in order to help its community manager gain improved understanding of the forum discussions, sentiments, and automatically discover new opportunities such as sales leads.

The many hats and the broken binoculars: State of the practice in developer community management: Mäenpää, H., Munezero, M., Fagerholm, F. and Mikkonen, T., The 13th International Symposium on Open Collaboration (OpenSym), Galway City, Ireland, August 2017 – To appear

Highlight: The paper investigates the varied tasks that community managers perform to ensure the health and vitality of their communities. We describe the challenges managers face while directing the community and seeking support for their work from the analysis tools provided by state-of-the-art software platforms. Our results describe seven roles that community managers may play, highlighting the versatile and people-centric nature of the community manager’s work.

Continuous experimentation

Introducing Continuous Experimentation in Large Software-Intensive Product and Service Organizations: Yaman, S.G., Munezero, M., Münch, J., Fagerholm, F., Syd, O., Aaltola, M., Palmu, C. and Männistö, T., Journal of Systems and Software, 2017 – In Press

Highlights: This article presents a multiple-case study that aims at better understanding the process of introducing continuous experimentation into an organization with an already established development process. Our findings indicate that organizational factors may limit the benefits of experimentation. Moreover, introducing continuous experimentation requires fundamental changes in how companies operate, and a systematic introduction process can increase the chances of a successful start.

Notifying and involving users in experimentation: Ethical perceptions of software practitioners: Yaman, S.G., Fagerholm, F., Munezero, M., Mäenpää, H. and Männistö, T.,  In Empirical Software Engineering and Measurement (ESEM), 2017 ACM/IEEE International Symposium on. IEEE, Toronto, Canada, November 2017 – To appear

Highlights: This paper examines how ethical issues involved in experimentation are currently understood by practitioners in software development.  We conducted a survey within four software companies, inviting employees in different functional roles to indicate their attitudes and perceptions through a number of ethical statements.  We observed that employees working in different roles have different viewpoints on ethical issues. While managers are more conscious about company-customer relationships, UX designers appear more familiar with involving users, and developers think that details of experiments can be withheld from users if the results depend on it.

 

 

Beginner’s Guide to Doing Text Analysis: Links and Courses

During the Tech Research Showcase I attended a few weeks ago, someone asked me to guide them to some materials to start learning and doing some natural language processing and text analysis as a beginner. Text analysis, text mining, and natural language processing (NLP) are fields that all deal with mining and analyzing textual data to discover interesting patterns, extract useful insights, or learn more about the structure of language. Sometimes the terms are used interchangeably, even though the fields have different goals and work at different levels of language analysis. In addition, they all include techniques from linguistics, mathematics, statistics, and computer science.

 

In this post, I will not go into introducing the fields as they has been covered in several resources, and frankly, they are too huge to cover well in one post. They include a vast amount of techniques and methods for performing language detection, translation, document summarization, sentiment analysis, part-of-speech tagging, topic modeling, question answering, etc. A good background book which is practically oriented is the Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications.

As large fields that have gained increased interest in both Academia and Industry, there are several sources and ways one can start. However, for someone who wants to just get their feet wet with text analysis, I would recommend the following links and courses to get practically started. From these, you also get to understand what is involved and helps you identify what questions or what insights you might want to get from the analysis or mining of the text, e.g., what is the sentiment in the text? What is the genre? Is it spam? among many others.

 

Recommendations:

-Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit – a practical introduction to NLP. You will learn by example, write real programs, and grasp the value of being able to test an idea through implementation.

Python for text analysis – A practical course in Python, geared towards those who want to get some hands-on experience working with language data.

Apart from python based, there is also R based practical courses that make use of the tm: Text Mining Package, for example, Introduction to Text Mining with R for Information Professionals.

For online course which provide both theory and practical materials

Introduction to Natural Language Processing – Provides an introduction to the field of NLP. The programming assignments are in Python.

Text Mining and Analytics – The course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort.

Besides understanding the field itself, recently it has become important to also understand how machine learning models are powering NLP applications. Recently, deep learning approaches have obtained very high performance across many different NLP tasks.

Deep Learning for Natural Language Processing – Online course with lectures and reading materials – Lecture videos are available here.

Oxford Deep NLP 2017 course – An advanced course on natural language processing containing lecture slides.

I will continue updating this page as I come across better or additional materials. If you have other good suggestions for beginners, please feel free to share.

 

 

 

There should be more research pitching events

Last week, I took part in my first Tech Research Showcase. As a researcher who is interested in entrepreneurship I wondered afterwards why I had never attended a pitching event where the sole focus was my research. The tech research showcase event was organised by Icebreaker.vc at the startup hub Maria 01.

Icebreaker invited tech researchers from around Finland to come and give a five minute pitch on their research to an audience of business people, developers and designers. The event was motivated by the world-class research conducted in Finland and by the several companies that have started as a result of it, for example, Relex, Beddit, and Callstats.io.

I was attracted to the event, first because one could pitch their research even if you were at the state where no commercialization value had been identified – the aim of the event was to put researchers in a room with experienced people that have the ability to collaborate, point you in the right direction or give some business  advice. And since commercialization is something that has come up in my current work project Immersive Automation, I wanted to gauge the interest and potential of our research. Secondly, I was challenged by presenting my research in five minutes. Normally, I have done research presentations in conferences, seminars, lectures, etc., events where I have anywhere between 20 to 1 hour, significantly more time. But 5 minutes, in front of people who might not know the field was a challenge that I wanted to undertake.

For the pitch itself, we as the presenters were advised by the organizers to follow a format where we presented the following:

  1. The problem that is being solved – put in a way that it could be understood by a non-expert.
  2. How the problem is solved today. What is the state-of-the-art?
  3. What it is you have discovered in your research. What is the new idea?
  4. What the results are using your method compared to the best solution today.
  5. What you are looking for. For example, particular skills, co-founders, or guidance. This should be as concrete as possible.

In total we were 12 researchers pitching, with majority of the presenters being from Aalto University and University of Helsinki. Machine learning, as might be expected was the dominating theme among the presentations and the presentations ranged from ideas to already launched startups.

Having participated in a few business pitching events, one advice I could give to fellow researchers, myself included, would be to participate in more training on selling ones research work and ideas. A few presentations went into too much detail about the technicalities of the research such that it was difficult for some of the audience members to fully grasp the potential of the idea. A good training course that is focused on how to sell research work and ideas is the one provided by the brothers Andy and Steve Langdon. The two-day course is practically oriented, one learns a lot of selling techniques, and its organised in a fun and relaxed way.

Actually, I think many universities should internally organize events like these as a way for researchers to learn and practice selling their work, a skill I believe is needed in today’s competitive world. I found having to think and prepare a five minute pitch interesting and it does make you look at your research from various other angles besides research publications.

Continuous experimentation for personal development

A few weeks ago, my colleagues and I published a book titled “Continuous Experimentation Cookbook: An introduction to systematic experimentation for software-intensive businesses”. The book is a result of several years of work within the Empirical Software Engineering research group and with software development teams in medium and large companies that were part of the Need for Speed project. The book provides an introduction to continuous experimentation, which is a systematic approach to continuously testing product or service value or whether a business strategy is moving in the right direction.

Continuous experimentation as a development and decision-making approach has always been one that I found not only to apply to software or businesses development. It could actually also be applied to personal development. Just like in business and product or service development, experimentation, through small, fast, and cheap probes, can allow us to gain deeper insight of what we really want, what makes us happy or what works for us in terms of achieving our goals.

As the cookbook lays out, to conduct successful experiments, a few key elements should be understood (captured in Figure 1). Although these elements are specific to software development in the book, I will use a personal development example to illustrate how this approach might be applicable (see Table 1).

Figure 1: The continuous experimentation cycle that links decision-making to systematic experimentation (Adapted from [1]).

Table 1: Key elements in continuous experimentation applied to a personal development example (Example narrative is in Orange color).

 

Notably, not all the experimentation elements that are presented in the cookbook or in Table 1 will be necessary for every personal development experiment. In addition, I think that for personal development experiments, there is more flexibility on how scientific they should be. For instance, in experimentation, one must introduce the change and then hold everything else constant to observe cause and effect, but in life it is hard to hold things constant and this might interfere with the results.  But I believe a clear hypothesis and good execution plan can mitigate those factors, and can help you make informed decisions where you need them or just want to be certain.

To conclude, continuous experimentation, just as with software or business development, can also work for our personal development, by helping us test our assumptions about what we want or think is true about ourselves, and then help us make informed decisions and changes. Of course this approach is one of many other approaches to decision-making. As highlighted in the cookbook, one of the pitfalls to avoid is making decisions solely from the collected data. As humans, we still have our intuition and experiences to guide the decision making process (see discussion of this in a previous post). But of course decision-making is guided by having access to factual information.

Would definitely love to hear your experiences on how or where you would apply this approach to your personal development.

Source:

[1] Munezero, M., Yaman G. S., Fagerholm F., Kettunen, P., Mäenpää H., et al.: Continuous Experimentation Cookbook: An Introduction to Systematic Experimentation for Software-intensive Businesses, DIMECC Publications Series No.15, Helsinki, Finland, 2017.

Credit:

Post edited by Fabian Fagerholm –  https://www.linkedin.com/in/fabianfagerholm