Eyitayo Ogunmola is the chief executive officer and founder of Utiva, a company which uses technology to help people learn skills for the future of work. In this interview with Stephen Onyekwelu, Ogunmola shows where career advancement opportunities in data science and analytics are; and how businesses can leverage big data to improve performance conditions. Excerpts:
Demand for data analysts and data scientists seem to be rising, and every now and then you find online learning platforms creating and selling courses to satisfy this demand. What is driving this?
Well, the interesting thing is that the demand and supply of talent and human capital across the globe is changing so fast and this change is predicated on three things.
If you want to think about them in such a very simple way, we would say the future of work, the advent of more technology solutions, and the convergence. The world is becoming a smaller place and because we are all connected on our devices, the volume of information that we generate is becoming so much of an asset for those that want to use it.
So companies and businesses are on their feet. Everyone wants to use this asset to make more intelligent and strategic decisions. That is why data and its analytics is super powerful. Another way to think about it is this, Nigeria is quite traditional and what I mean is that most of our companies are still in the traditional sector, and these companies are thinking of how to use technology to outcompete. So as companies try to outcompete, they are building more data-centric solutions. I can certainly predict that the next 15 years will be about Data in Africa.
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What are the differences between data analytics and data science?
So let me try not to complicate this because that is one of the many questions we get from our students too.
Data Science, like every other science, is an umbrella term that describes every area of knowledge that covers Data, Artificial Intelligence, Machine Learning, Robotics, et cetera. That is why it is called Science. Science because it’s a study of all those multiple areas of knowledge.
Data Analytics is another subset of that science. Maybe I should say that analytics is the first layer of these other areas of study and that is why analytics is the easiest component. So you’d typically see everyone starts from that first layer, which provides the basics of data science.
Big Data is on the lips of many people today. What is it and why is it so important?
Yes, this is another level that needs to be unveiled. Data actually exists in different forms. But to summarise this, data can exist at its minimal and that type of data is most often not dynamic. For instance, when you collect rat blood samples for laboratory tests, that is an example of a small data-type with a minimalist model.
But there are instances where you are generating tons of data and the data sample keeps changing every minute. Think about the number of people that tweet per minute and the size of that data and its types. Text, numbers, emojis, pictures, likes, comments et cetera. That is big data.
So in the study of analytics, we think of Big Data from these 5 perspectives: volume, velocity, veracity, variety and value of the data. So back to why it is important- the fact that many organisations are plugged into the technology ecosystem and there are tons of data out there. And the business reality is that strategy today is predicated on the value of data.
Where are the opportunities for a data analyst or scientist?
This is really a multi-million dollar question. Though reports and researches actually affirm that data analytics is one of the top three job opportunities out there. I see about 5 different and new vacancies per day. The biggest career value is in data science though because it solves some complex data problems. With the development of computers and an ever-increasing move toward technological intertwinement, data analytics has equally evolved.
Most data analysts work with Information Technology teams or strategic teams where they have to use analytics or models to help improve business operations. Let me also mention that data analytics is not a tech skill only. I always say this when I have the opportunity. It’s a strategy skill because the focus is to improve business conditions and achieve strategic objectives.
What are the chances of someone without a technology or science background excelling in this space?
Yeah. I think this is coming from the earlier question. I can tell you this for free; we have trained over 3000 people in our Data School and about 70 percent of them do not have a science background. These are folks from Management, Banking, Commerce et cetera.
In fact, the largest part of this percentage for the Utiva School is from the banking sector. So, to answer the question, while Data Science sounds very scientific, anyone can learn it and it has less to do with having a science background and more to do with a commitment to learn and study. Although there are a few calculations here and there, and most times you have to write some codes, the fact still remains that anyone can learn it.
You set out to become a medical doctor, how did you end up where you are today?
For me, I got lucky! I failed quite early and that failure shaped my later career. I wanted to become a doctor and in fact, I was admitted to the University to study Medicine but the school dropped me and I had to figure out a new career for myself.
One critical lesson that I got out of that early failure is to always take charge of my life and focus on me when the world puts me off balance.
How did you start Utiva and what are your short, medium, and long term goals?
I actually started Utiva at a very interesting time of my life. Although, I had played in the education space as someone that wanted to develop young folks. I also created a programme called Human Capital Development Centre in those days. But I started Utiva when I was in the United States.
I was simply not comfortable with the state of unemployment and of course, poverty in Nigeria and I thought that education is a major driver of poverty alleviation. And since it was 2016 and 2017, I figured out that quality digital education could help Africans navigate the unemployment barrier.
At the time, I just wanted to do one simple thing, which was to help Africans learn technology skills and then we grew from having the biggest Data School to launching a Product School and then, Design, AI, Digital Marketing, Programming and now we are a one-stop-shop for everything technology education.
In the future, we want to be a single infrastructure that helps Educators build their digital school. We are not there yet, but we are working hard towards that goal.
How much social or emotional skills do you require to excel in the Data Science or Analytics space, or is it simply about crunching data alone?
No, it is 30 percent of crunching and analysing data. Being a data analyst is a strategic role so it requires so much business and leadership skills. Although what usually happens is that most folks have been so relegated to focus on the technical side of their career, there is a large part of this that is so much around understanding the business side of the data, communicating value to stakeholders, interpreting problems, analysing outcomes of analytics and being able to tell a complete story of the work you do.
Who are your mentors and how important is this?
I have had so many mentors on this journey and I would have loved to mention them but to be honest with you, mentoring was my strategy for emergence. Top of mind for me is my friend and mentor, Olufunbi Falayi. He is someone I have listened to, watched, learned from and above all studied.
What is the future of Big Data applications in Nigeria’s economy?
This is the part that excites me and I think we need to look critically into this. So thanks for this brilliant question.
One of the best thought leadership and research work out there by Mckinsey says that globally, Big Data could generate an additional $3 trillion in value every year in just seven industries.
The total global gross domestic product (GDP) now is $142 trillion and that means about $3 trillion could be added to this year-on-year. And come to think of it, Nigeria represents 0.37 percent of the world economy.
So that means we could add about $11 billion to our economy. And that is across seven industries. If everyone gets so crazy about big data, we would be producing new goods and services that are customer-centric, optimising business processes, running more-targeted marketing that injects customer feedback into product design, have better organisational management, and a whole lot more. While Big Data application in our economy is currently modest, as compared to other countries such as the United States of America, China and Japan, I foresee a steady increase in adoption across several sectors and industries.
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