The 16% of African tech. A framework for segmenting talent

Hello again dear ones,

First, let me start by thanking everyone who read and reacted/responded to my earlier article on Sub Saharan Africa’s tech talent problem. I learned so much from your reactions and the resulting conversations. I hope that at the very least the article gave you a new perspective on the topic.

I thought I should share a few more ideas that should hopefully help the conversation along.

This article was inspired by my thoughts and experiences over the years working with tech talent in different capacities as well as conversations with some of you in the last few weeks.

A framework for segmenting talent

I would like to introduce/propose a framework for use in the conversation about technical talent. This framework is based purely on a series of hypotheses inspired by the work popularized by the likes of Everett Rogers in his 1962 classic, Diffusion of Innovations. If you have not read Rogers’ book or any of its derived works, I highly recommend it. At the very least read the Wikipedia article.

My hypotheses are as follows:

Firstly:

For a given skill like software engineering ( designing, developing, maintaining, testing, and evaluating computer software), if you could measure and plot the levels of passion or enthusiasm (note that these are closely related but not the same) for the skill in a social group (organization, a community, a country, a geographical region, etc) against the number of people with that level of enthusiasm/passion, it will describe a bell curve similar to the one below.

Talent Enthusiasm Bell curve

Secondly:

The bell curve can be divided into different segments by percentage based on their enthusiasm/passion for the field as follows

Level 1 (2.5% of the population with the highest passion/enthusiasm levels for that particular skill):

1. Made up of people with an ultra-high level of passion and enthusiasm that are ready to do whatever it takes to learn the skills and grow a career in that skill even to the point of being underpaid or even working for free just for the opportunity to practice their profession in its purest form.
2. Most likely to show up. People at this level tend to take risks more readily and are the most venturesome when it comes to learning and discovering methods.
3. This group will seek out learning sources themselves and are good at self-directed learning and research leveraging whatever resources are available online and offline.
4. This group is most likely to achieve expert level proficiency in the skill if they stay consistent and are exposed to the right opportunities
5. This level along with level 2 is usually an inspiration for the levels below

Level 2 (the next 13.5% of the population):

1. Made up of people who have a high level of enthusiasm for the field though not as much as the level one folks.
2. People in this category may also take a pay cut for an opportunity to learn or practice but will not inconvenience themselves if there is a better-paying opportunity available.
3. While this group can also make progress through self-directed learning, they do best when this is supplemented by being around peers or having peers, mentors, and role models who can “show them the way” and encourage them.
4. Most members of this group will end up attaining an advanced level of proficiency in the skill short of becoming true experts.

Level 3 (the next 34% of the population):

1. These are the people with a moderate level of enthusiasm for the skill.
2. They will likely not build a career in this industry if they can earn more money in another field.
3. This population will likely not engage as much in self-directed learning and usually require some kind of instruction.
4. They may go above and beyond the curriculum to deepen their knowledge if they think it will help them achieve their goals faster.
5. They are likely to build up an intermediate level of proficiency for the skill

Level 4 (34% of the population):

1. These are people who have a less than moderate level of passion or enthusiasm for the skill.
2. They are interested in the skill purely because it meets their current economic and career needs and will just as easily switch to another skill/industry/ career if they think the benefits will be more.
3. This group requires direct instructions and some degree of hand-holding to enable them to acquire the skill and will rarely if ever go beyond the taught curriculum to learn more.
4. They will likely only ever attain a basic level of proficiency in the core skills required in that industry.
5. They will usually work in an area of the industry that does not require a lot of the core skill to excel in.
6. They may even be experts in another skill that has an application in the main industry in question. E.g One could be an expert IT manager in a software engineering company and get by with very modest programming skills.

Level 5 (16% of the population):

1. This group has little or no passion or enthusiasm for the skill in question and similar to the level 4 folks are interested in the skill because it meets their current career/economic needs and they could just as well be working in another field.
2. This group cannot acquire the skill without handheld instructions and will need extra motivation to even keep up with the curriculum.
3. The most they can ever attain in terms of proficiency in the skill is that of a novice/beginner. e.g. a human resource professional in the software engineering industry may have little to zero passion for software engineering even though they are an experienced and highly skilled HR professional

Note that

1. Level of passion/enthusiasm does not equal the level of expertise. It means the level of passion/enthusiasm for a skill which with consistency at the highest levels can lead an to increase in expertise
2. In this context passion/enthusiasm does not mean the mushy / gushy / emotional feelings that we tend to think of when someone says they are “passionate” about something. In this case, it simply means how much self discipline the person in question is willing and able to exhibit in building the required skills for the given area and how much they are willing to sacrifice for an opportunity to practice the given profession. For example, Gabriel Batistuta, one of the most lethal Argentine football (the one played with the feet) strikers, did not really like football. For him it was just a profession and he was a professional which meant the highest levels of dedication and self discipline.
3. Also, while related, passion/enthusiasm in this context should not be confused with the so-called “natural talent”. While I believe there is such a thing as natural talent, this article is focused purely on self discipline and sacrifice put into acquiring a skill.
4. The reason or motivation for the passion / enthusiasm is not in the scope of this article. In my personal experience, I have found that it could be from any one (or combination) of a number of things including: poverty, affluence, peer pressure, family pressure, community / societal norms, etc
5. While there may be a relationship between the two, belonging to one level or the other does not guarantee a position in the career hierarchy in that industry. An extreme example is that it is possible for an accountant to raise to a C-level position in the software industry as a CFO or similar even though they do not possess any software engineering skills
6. Lastly, there is mobility between groups. i.e it is possible to move from one group to another as your enthusiasm and level of self discipline towards that skill increases or reduces relative to others

How the various levels of tech talent acquire software engineering skills

Now let’s look at one way this framework can be applied.

I would like to put forward the following chart for discussion and feedback. It is a chart mapping the various levels against the means through which they typically ( in my experience) acquire skills in software engineering.

The main point here is that people at the different levels of enthusiasm are able to utilize different ways of learning to varying degrees. For example, people at Level 1 are so driven that they are willing to leverage whatever resources they can find through research as well as trial and error to get the knowledge they need. Many of the folks in Nigeria who learned to code on their own in the pre-internet or early internet days fall into this category. I know of people who started on their own by coding on paper or who would spend the night at the cyber cafe to download and print out websites that taught programming. Folks like Elvis Chidera who learned to code on a Nokia feature phone also fall into this category.

Community workshops and events have always been a crucial part of the growth of the tech ecosystem. The key things they offer are a group of peers and mentors who share advice, resources, learnings, inspiration, and motivation with each other and so if you are in Level 2 and have not been able to push through and figure things out on your own, you have a group of people who you can fall back on when the going gets tough and who can keep you up to date on the latest and greatest.

As we probably already know , the best way to learn stuff like programming is by doing and that is where the coding internships come in. Now I use the word internship in its broadest sense to mean any opportunity (paid or not) where you can get your hands dirty with code while “on the job”. “Job” here could refer to anything from a dummy website at a hackathon to a full-fledged application that someone is paying money to use. Folks at levels 1 & 2 tend to jump at such opportunities even when they do not have the skills to begin with because they know that somehow they will come out on the other side more knowledgeable and experienced. Andela utilized this to great effect in their early days to get access to the best talent in the market at the time. The legendary piscine of the 42 school is another great example of this as is the HNG internship.

While the 3 methods I listed above are great, they have the shortcoming of being relatively unstructured and purely dependent on the motivation of the person in question to push through. So basically folks who need to be taken through step by step typically struggle. There is also the fact that with the above 3 methods, it is easy to miss out on some basic knowledge and end up knowing “how” without knowing “why”. This is where structured learning comes in. This is where there is a set curriculum and you go through it step by step and come out fully baked. First, there is the self-paced learning as you find with the likes of Udacity, Open Classrooms , Datacamp, etc and then there is the instructor-led as you would expect to find in a good computer science university, Developer boot camps or the likes of NIIT franchise offered in Nigeria back in the day to power the generation of computer programmers and IT savvy staff in Nigeria of the 90s and early 2000s. I have found that people in Level 3 are able to find enough discipline and motivation to go leverage self-paced courses as a means of learning and make it to the promised land while the Level 4s and Level 5s cannot make any progress without an instructor guiding and prodding them (most times with the threat of “failure”). This means that instructor-led training is the best hope for them.

Of course in reality none of these learning methods work in isolation and you will often see these combined in various ways to achieve certain results. It is possible that using the above chart, we may be able to predict the outcomes in terms of what kind of resources we get based on the mix of learning methods that we deploy. For example, Google and Facebook working with Andela applied a combination of community events and structured self-paced learning to drive learning outcomes that could not be achieved with just community events or self-paced courses on their own.

Applying the framework to the Nigerian tech space

Now that I have introduced the model/framework and shown one way it can be applied, let us try and make it even more real by attempting to apply it to the Nigerian tech talent space to see if/how it plays out at a macro level.

In this case, the social system would be Nigeria, the total sample size could be the young people (15–24 years old) who could potentially build a career in the tech industry (estimated at about 16 Million). But let us narrow it down a bit more to 600,000. This is the number of students who graduate every year from all of the tertiary institutions in Nigeria according to this news article. If we assumed that 30% of that number would be interested in a career in tech to the point of at least reading a tech job posting then we have a sample size of 180,000 to work with for our back of the envelope calculation.

Applying the model to our sample population that means that we should be able to break it down into the following:

• Level 1 = 2.5% of 180,000 = 4,500
• Level 2 = 13.5% of 180,000 = 24,300
• Level 3 = 34% of 180,000 = 61,200
• Level 4 = 34% of 180,000 = 61,200
• Level 5 = 16% of 180,000 = 28,800

The hypotheses and numbers above (though a product of guesstimation ) make for some very interesting conversation starters for example:

It would appear that given the predominance of community and self driven learning approaches in the Nigerian ecosystem, most of the current talent in the space are the level 1s & 2s ( with a sprinkling of level 3s), which would explain the high rate of churn/attrition in the space since a motivated Level 1 / Level 2 developer provided with the right opportunity can double their skill level every 6 months thereby enabling them to seek higher paying opportunities at home and abroad ( I referred to them as ninjas in my previous article)

Does Nigeria (or even Sub Saharan Africa) have the right mix and scale or learning methods available to harness up to 50% of the available graduates every year to fuel its tech ecosystem aspirations? As I discussed extensively in my previous article, the bulk of the load is currently being carried by the community initiatives and structured self-paced learning but this is not yet at a scale that can enable us fully harness even all the level 1s and 2s (approx 30,000 graduates) on a yearly basis. This is at the core of the reason for the talent problem. The current talent utilization would probably be closer to what is shown in the diagram below

Hence my call for some supercharging by corporate organizations that have a lot to gain by doing so if we are to enable up to 30,000–90,000 skilled developers a year. Crazy I know, but very feasible. Think what that could do for the ecosystem over the next 5 years.

I am also following initiatives like #Project774, an initiative from Google, driven by Aniedi Udo-Obong to host a developer community event in each of the 774 local government areas in Nigeria. I think that this will go a long way in making sure that all the Level 1s and 2s have a chance to succeed. If this can happen yearly, there is a potential to inspire up to 30,000 of the most motivated young people in the country every year into a career where they have the most probability of success.

It also shows the stark reality of the untapped potential that we lose every year unless something is done to give education the investment it deserves. The level 3s,4s, and 5s can only be carried along by a robust traditional educational system (in my opinion). In the absence of this, we will be missing out on talent in the order of 90,000–150,000 a year. Again, the likes of India and Eastern Europe are able to harness a greater potential of their yearly graduates for this very reason, investment in traditional education

Another question that came up in my conversations with Ola Fagbamigbe, founder of Dufuna and Bosun Tijani of the Co-Creation Hub is whether the companies of today, from the startups to the big corporates have what it takes to provide an enabling working environment for talent in levels 1 & 2. Many companies are applying human resource management methods that were built for Levels 4s and 5s to folks in Levels 1&2 (no thanks to our colonial inheritance in terms of education and work culture). I guess this is a question for my friends in talent management / HR to discuss.

Of course, there is the question that you are bound to ask yourself: what level am I? To which I would respond with two quotes.

One is the old saying attributed to Henry Ford:

“Whether you think you can or you think you can’t, you are right”

The other is a quote by Harold Klemp:

“If you can dream something, you can do it.

As long as you do not align your actions with your dreams, you are not fulfilling your destiny.”

The thoughts and questions that this framework has provoked in me are endless and I could fill a few more pages with them, but I will stop here for now to invite feedback and conversation. E.g does the framework make sense to you? Do you agree with it? Does it have any gaps? How could you apply it to how you engage talent? What other frameworks do you leverage when engaging talent? Would look forward to hearing from you.

Oh, and as always thank you for making it this far. Here is your reward