In the world of business, everybody can have an opinion, however, your opinion holds no value if you don’t have data to back up your points.
All fields of endeavor today rely on data to make informed decisions that will help in mitigating risks and maintaining a balanced cash flow.
Data-driven decisions hardly miss the targets set, thus the power of data in drawing inferences from the past, shaping the present, and predicting the future, can’t be underestimated.
In a chat with Olayinka Oke, Lead Data Analyst at Ingressive Capital, we learnt how data drives investors’ decisions for startup funding.
About yourself, and what you do at Ingressive?
My name is Olayinka Oke and I work as a data analyst at Ingressive. Before Ingressive, I worked as a data and business intelligence analyst at Union Bank, in the corporate banking group.
In that role, I was supporting the Corporate Banking Group in achieving its aim by helping the team to be data-driven.
I had to do lots of reports, and periodic reports that helped us to check our KPIs to see if we were achieving the goals and objectives we set out to achieve, and any other things that the executive director of corporate banking needed at the time.
Following that, I joined Ingressive Capital, as you may know, Ingressive Capital is a $10 million venture capital fund, focused on supporting startups within Sub-Saharan Africa, especially at the seed and pre-seed stage.
What I do for Ingressive typically is just like every data analyst, I help Ingressive to be data-driven. What that means is, because you cannot always make decisions based on the vibe or based on just how you feel, decisions have to be based on data so basically, I help to collect the right data, analyze them, get the right insights, and use them to make decisions.
I’ve been a data analyst for about four years now, though not spent up to a year at Ingressive.
As a data analyst, what challenges have you encountered as regards your gender?
I belong to some communities of people in tech, specifically for data analytics, or for any other kind of general technical roles, and also in communities specifically for women, where we’re focused on supporting women.
I’ve seen people say they come in contact with things like discrimination and being talked down on, but for me, I’ve not had that kind of challenge at any point, maybe because by default, I’m very outspoken.
I’ve heard people say that I occupy space, so I don’t even give you the chance to intimidate me; maybe because before I got into data analysis, I already worked in field engineering, then I got a lot of, ‘what are you doing here, you’re a woman?’ and I guess that’s when I built a lot of stamina to respond to those kinds of things.
For people that have that issue, it’s important to surround yourself with women that would support you. And I belong to one of those communities, there are quite a lot of them around.
When you are in that kind of community, and you see lots of women that have done so well in that field, it encourages you and boosts your confidence.
It is also important to be very good at your job so that no matter how people try to intimidate or ridicule you, your work will always speak for you.
Telling stories using data, please throw more light?
Like people say that every journey is a story and every idea that you’re trying to sell is a story, the job of a data analyst many times is that you’re helping stakeholders, usually senior executives to make decisions.
And because senior executives are busy people, you need to be able to tell a story that catches their attention in a short time, engages them, and makes them want to listen further.
For example, in data analysis, a lot of work goes into building a report, creating fantastic visuals, making them beautiful and things like that.
But apart from your visuals being beautiful, it is important that your visuals, your report, or your dashboard tells a story.
The flow of information in your dashboard should be systematic such that it takes people from one section to another and gives them a complete view of the situation you are trying to depict.
For instance, if you do a report that shows that startups in Africa raised $1.8 billion in Q1, out of that, $500 million went to Nigeria, ‘x’ amount went to Sudan, and ‘y’ amount went to Kenya, that is not a complete story.
You now have to then back it up with the reason why that amount went to Nigeria, usually called performance drivers; such valuable information is what makes your data story complete.
In data analytics, all those different parts of the story are called different names; descriptive analytics, diagnostic analytics, prescriptive analytics etc.
When you finish describing the situation, it is very important to also explain what the implications of your observations are for the organization you are representing.
For example, when data shows that a lot of investment is going into certain countries, it is important for us as an organization involved in investments, to start paying attention to what is happening there.
That way you have told a story, and the people using the dashboard that you have prepared can get value from how you have used visuals to tell a story from what was observed from the data collected.
“When you tell a complete story, people will be willing to listen to you.”
What do startups have to do to get funding from Ingressive?
On our website, we have a link where startups can apply for funding from us. However, as a principle, for us to fund a startup, you must at least have an MVP (Minimum Viable Product), not just that you have an idea in your head.
Even if you’re still in the beta stage, and you’re ready to proceed, or you have launched with a few customers, as long as you’re raising either pre-seed or seed and you have your MVP, you just need to go to the Ingressive Capital website and apply.
If it is something we’re interested in, we would get back to you after review.
How much data has Ingressive Capital leveraged to make its investment decisions?
We leverage two kinds of data, internal and external data. On the internal data, I’ll base it on a quote that says, “You can’t improve on what you don’t track”, what that means is that we continuously track our investment and our review processes.
How do we review the people applying for funds? And then, how successful have we been in reviewing those companies?
The companies that we thought were going to do well, are they doing well? It is just basically judging our bias in our selection processes. Thus, we track all those metrics continuously.
Another way is that we also use data from external sources, just as I said before, we are constantly looking at the news and seeing what’s happening in the ecosystem, what other new funds are coming up, and what new government regulations are coming because as an investor, it’s important for you to know the regulations that are in the country you’re investing.
We use the internal data to improve our processes and day-to-day activities, and then we track external ecosystem industry data to guide our investment focus on our investment strategy.
What is the importance of data in a startup funding journey?
From the day you start your company, you need to start collecting the right data about how your customers view you, how you are growing your customer base, how much that cost you, etc; so that after some months, you can check if your customer base improved and at what cost?
Are my operational costs going up? What is driving the movement? What can I do to reduce it, every organization needs to drive down operational costs to as low as possible.
What tracking these metrics does for you is that when asking for funding from investors, they will want to know what you have improved on since running the company for six months or whatever period you’ve been running it for, or what is something you were doing at the beginning that you think has helped you? Or what you think has been the major driver of your success.
You also need to be sure that you have the right KPIs and that you’re tracking them properly, you can use all of these to prove to an investor or even anybody to show that this is how your company has grown. As I said earlier, anybody can have an opinion but if you can’t support it with data, it’s just an opinion.
Why do you think organizations need to be data-driven?
Data is important for you to make decisions, else, you’re going to be running on sentiments and vibes.
Sometimes data can be internally generated, while other times, you need to collect external data, like many of the things that I’ve said before, data is what will help you to know how your business is doing.
For example, if you go to a financial institution or a bank to ask for a loan, they will ask you about your financial statements. The financial statements are data that you have collected over time, and that data is going to tell the bank whether your company is profitable or not, also if you are creditworthy.
Everything that we do actually has data one way or the other. Data helps you not only track your performance for yourself but also helps you to prove your performance to external stakeholders.
Your advice for young women venturing into the tech space?
This advice isn’t just for women, but for everyone; you need to be good at your craft. If you are good at your job, eventually your job will speak for you in places where you’re not present, even a person who doesn’t like you will have no hold on you when you are good at your job.
Don’t focus on whatever anybody is saying about women, once you are good and exceptional at what you do, people will look out for the value you have to offer.
The second aspect is to surround yourself with women that have the same passion and are pursuing the same goal as you.
Find mentors in women that can boost your confidence, sometimes it might be by joining a community, and that can also present you with a volunteering opportunity to support some other women who are less experienced.
Joining these communities gives you a confidence boost and in that community, you can also see women who are doing very well and it gives you the assurance that you can go that far in the future.
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