How Nigerian business owners can stop guessing, start listening to their numbers, and make smarter decisions with the data they already have
By Oluwaseun Elizabeth Orekoya
Founder, Tech for Data Services
Imagine a woman running a small fashion business in Lagos.
Let’s call her Amaka.
She has been selling ready-to-wear clothes on Instagram and WhatsApp for almost three years. She has loyal customers, thousands of followers, and a decent number of inquiries every month.
Yet, every few months, she finds herself saying the same thing: “Business is slow.”
So she does what many business owners do.
She runs a promotion. She reduces prices on some items. She posts more frequently. She spends money on Instagram advertising. Sometimes it works. Sometimes it doesn’t.
But what if Amaka’s biggest problem isn’t that she needs more customers?
What if her business is already giving her clues about what needs to change?
Suppose she takes a closer look at her sales records and discovers that two of her products consistently account for a large portion of her sales, while several other items barely move.
She checks her Instagram Insights and notices that her audience is most active in the evenings, but she usually posts in the afternoon.
Then she looks through her WhatsApp conversations and discovers something else: Many potential customers ask about delivery before they disappear. Perhaps the problem isn’t always price. Perhaps the buying process itself needs attention.
Suddenly, “business is slow” becomes a much more useful set of questions.
Which products are actually selling? When are customers most likely to buy? Where are potential customers dropping off? What is preventing people from completing a purchase?
The information was there.
Amaka simply wasn’t looking at it as data.
And that is the problem many businesses face.
Your Business is Already Producing Data
When people hear the phrase data analytics, they often imagine large corporations, complicated software and rooms filled with people staring at dashboards.
But data is much closer to everyday business than most people realise.
Every sale tells you something.
Every customer enquiry tells you something.
Every repeat purchase tells you something.
Every complaint, refund, review and abandoned order tells you something.
Even the products customers repeatedly ask about but rarely buy can tell you something.
The challenge is not necessarily getting more data.
The challenge is knowing which information matters and what questions to ask of it.
A business owner may record every sale but never compare sales across months.
A business may have thousands of customer contacts but never identify its returning customers.
A company may spend money on advertising without knowing which campaign actually produces paying customers.
Having information is not the same as understanding it.
That is where data analysis comes in.
The Problem With “I Think”
Business owners make decisions based on experience every day, and experience matters.
But experience can also create assumptions that go unchallenged.
“I think customers don’t like this product.”
“I think people won’t pay that price.”
“I think Instagram isn’t working.”
“I think December is always our best month.”
Sometimes these statements are correct.
Sometimes they’re not.
The danger is treating an assumption as a fact simply because it feels true.
Data gives you a way to test the assumption.
Perhaps customers aren’t rejecting the product; perhaps they don’t understand it.
Perhaps Instagram is generating plenty of inquiries, but those inquiries aren’t being followed up.
Perhaps December has the highest number of orders, but another month generates more profit.
Perhaps the product you believe is your bestseller is popular because it is heavily discounted, while another product quietly generates more profit.
The numbers don’t replace business experience.
They give that experience something stronger to work with.
Data Analysis is About Asking Better Questions
One reason data analytics sounds intimidating is that people often start with the technical side.
Spreadsheets. Formulas. Charts. Dashboards. Coding.
Those tools are useful, but they are not the heart of data analysis.
At its core, data analysis is about asking questions and finding evidence.
A business owner may begin with:
“How much did we sell this month?”
That’s useful.
But the next questions are more revealing:
What did we sell? Who bought it? When did they buy it? Which products made the most money? Which customers came back? Where did our sales increase or fall?
And perhaps the most important question:
What should we do differently because of what we’ve discovered?
That is where data moves from being a collection of numbers to becoming a business tool.
You May Not Need Expensive Technology to Begin
There is another misconception that stops many small businesses from taking data seriously: the belief that you need expensive software or a sophisticated data system before you can start.
You don’t.
Start with what you already have.
A simple Excel spreadsheet can reveal sales patterns.
A POS system can provide useful transaction information.
Instagram Insights can tell you about audience behaviour.
WhatsApp conversations can reveal recurring customer questions.
Customer reviews can expose problems that sales numbers alone won’t show.
The goal isn’t to collect information simply because someone said, “You need data.”
The goal is to organise the information you already have and use it to answer questions that matter to your business.
Sometimes the Pattern is More Important Than the Number
Imagine that a business owner notices that sales seem to drop every August.
She concludes that August is simply a bad month.
But after analysing her sales, she discovers something more specific: sales don’t fall throughout August. They fall sharply during the first two weeks and recover afterwards.
That changes everything.
Instead of asking, “Why is August always bad?”
She can ask: “What happens during the first two weeks of August?”
Perhaps customers are dealing with school expenses.
Perhaps a competitor runs a major promotion.
Perhaps the business’s own marketing becomes less consistent.
Perhaps customers are travelling.
The data may not immediately explain why something happened.
But it can show you where to investigate.
That is one of the most valuable things analysis can do.
It turns a vague feeling into a specific question.
Data Doesn’t Replace Human Understanding
Numbers are powerful, but they don’t tell the whole story.
A spreadsheet might tell you that sales dropped.
It may not tell you that customers are frustrated because deliveries are late.
A dashboard may show that one product is performing poorly.
It may not tell you that customers don’t understand how to use it.
That is why good analysis combines numbers with human understanding.
Data can tell you what is happening.
Customer conversations, experience, and context can help you understand why.
The goal isn’t to choose between people and numbers.
It’s to use both.
Why Data Skills Matter Beyond Business
The value of data isn’t limited to business owners.
As more organisations collect information about customers, operations, finances and performance, they also need people who can make sense of that information.
This is where data analytics becomes a valuable career skill.
But being a data analyst is not simply about knowing Excel, Power BI or another tool. A person can know how to use every tool available and still struggle to communicate the insight behind the numbers.
The real value of a data analyst is not simply producing a chart. It is being able to look at that chart and say: here is what is happening, here is why it may be happening, and here is what we should investigate or do next.
That requires technical skills, but it also requires curiosity, problem-solving, and communication.
You Don’t Have to Be a Mathematics Genius
This is something I have seen discourage many beginners who are interested in data analytics.
They hear “data” and immediately think, “I’m not good at mathematics.”
But becoming a data analyst is not about being the person who won every mathematics competition in school.
You need to understand numbers, of course. You need to be comfortable working with percentages, trends, and basic statistical concepts.
But much of the work is something else: thinking.
Why did this number change? What does this pattern mean? Could there be another explanation? What question should we ask next? How can I communicate this finding to someone who isn’t technical?
Those are analytical skills, and they can be developed.
We Don’t Have a Data Problem. We Have a Data Understanding Problem.
For many businesses, the information already exists.
The sales records exist.
The customer conversations exist.
The social media insights exist.
The transaction history exists.
What is missing is often the ability to turn those pieces of information into something useful.
And that is an opportunity.
For a business owner, it can mean making decisions with more confidence.
For an organisation, it can mean understanding customers and resources better.
For someone starting a career, it can mean developing a skill that is relevant across industries.
This is also part of the thinking behind Tech for Data Services: making data analytics practical, understandable and accessible, especially for beginners who may never have considered themselves “tech people.”
Data analytics should not feel like a world reserved for large corporations or people who have always been comfortable with numbers.
It should be something people can understand and apply to real problems.
Because ultimately, data is not about producing more spreadsheets.
It is about making better decisions.
The next time you look at your business and think, “I don’t know what’s going wrong,” pause before making another guess.
Look at your sales.
Look at your customers.
Look at your patterns.
Look at the questions people keep asking.
Look at what is changing.
Your business may already be telling you what to do.
Just have to learn how to listen.
About the Author
Oluwaseun Elizabeth Orekoya is the Founder of Tech for Data Services, a data-focused business and learning platform that helps individuals and organisations understand and apply data analytics to real-world problems. Through practical data analytics education and services, she is passionate about making data skills more accessible to beginners and helping people see data as a practical tool for better decisions and new opportunities.