In today’s data-driven world, learning SQL, Python, Power BI and machine learning has never been easier. Thousands of aspiring professionals complete courses, earn certifications and build portfolios every year. Yet only a few become the trusted advisors organisations rely on when critical business decisions need to be made. Why?
For Esther Anagu, exceptional data professionals are not defined by the number of tools they know, but by the value they create.
The journey has never been about accumulating technical skills. It has always been about developing the mindset required to solve real business problems. Through her career in banking and mentoring hundreds of aspiring professionals, she has observed a pattern that continues to shape her philosophy: technical competence may open doors, but professional credibility is earned through impact.
Anyone can learn SQL, Python, or Power BI, but people trust professionals who consistently deliver value.
Esther Anagu is a data scientist at one of Nigeria’s leading financial institutions, a mentor, founder of Learned to Hired, and the author of two career development books: Landing Your First Job as a Data Professional and The Data Analyst Roadmap.
A graduate of Accounting from the University of Benin, Nigeria, she later earned an MBA from Nexford University, specialising in Advanced Automation and Artificial Intelligence. She is passionate about helping aspiring data professionals navigate the often overwhelming world of data with the clarity, confidence and practical mindset needed to build careers that create real business impact.
Reflecting on a defining moment in her career, she recalled an analysis that initially appeared routine but ultimately reshaped how she approached problem-solving. Rather than delivering only what was asked, she explored the data further, uncovered additional insights, and presented recommendations that supported better business decisions. What began as a single expected slide became a comprehensive analysis that surprised her manager, not because it was technically complex, but because it demonstrated initiative, curiosity and business thinking. That experience reinforced a lesson she now shares with every aspiring data professional:
Don’t just complete tasks. Solve problems.
This philosophy also explains why she believes there is a significant gap between how data is taught and how it is practised inside organisations. Online learning platforms do an excellent job of teaching tools. But inside organisations, success depends on much more than technical execution. Professionals must communicate effectively, manage stakeholders, navigate changing business priorities, work with incomplete and messy datasets, and explain insights in ways that influence decision-making. Real-world data is rarely clean. Business problems are rarely straightforward. And no online course can fully replicate the complexity of solving problems where ambiguity is the norm.
What she consistently observes through mentoring is that knowledge alone does not build confidence; practice does. Many learners spend years moving from one course to another, collecting certificates while postponing the work that truly develops competence. The professionals who eventually succeed follow a different path. They build projects, solve real problems, reflect on their mistakes, and continuously apply what they learn.
It’s the application of knowledge, not simply the acquisition of it, that makes the difference.
If given the opportunity to redesign a modern data bootcamp, she would remove one of the many tool-focused modules and replace it with something far more valuable: learning how to think like a data professional. Before writing a single line of SQL or Python, professionals should know how to ask better questions, understand business objectives, think critically, identify the right problem, and determine the most appropriate solution. Technical tools matter, but they are only instruments.
Knowledge, she argues, is only the beginning. Competence is built through consistent application. Confidence is earned by solving problems. Organisations are not searching for people who simply know SQL or Python. They are looking for professionals who can think critically, solve meaningful problems, communicate with clarity, and create measurable business impact.
That is the difference between building data skills and building data professionals.
The future of data careers
Artificial intelligence is reshaping the data profession at an unprecedented pace. As AI continues to automate coding, data preparation and even analytical workflows, the question is no longer whether technology will change the industry; it already has. The real question is what will distinguish tomorrow’s data professionals.
Critical thinking will become the greatest competitive advantage. As AI grows more capable of writing code, generating analyses and automating repetitive tasks, the greatest value will no longer lie in simply knowing technical tools, but in the ability to think critically, identify the right problems, and ask better questions.
AI can generate answers, but it still depends on humans to ask the right questions.
The professionals who will thrive over the next five years are those who can move beyond execution to interpretation: understanding business challenges, framing meaningful questions, and translating AI-generated outputs into actionable business decisions.
From technical experts to strategic decision partners. Technical skills will always matter; SQL, Python, Power BI and machine learning remain foundational. But as AI automates more technical processes, the differentiator will be the ability to connect analysis with business strategy.
The value of a data professional lies not just in analysing data, but in helping the business make better decisions with it.
The most underrated skills are often the most valuable
Foremost among them is the ability to ask the right questions. The quality of any analysis is ultimately determined by the quality of the questions that guide it. Without clearly defining the problem, even the most sophisticated tools and models can produce results that fail to address the real business need.
Equally important are communication and business understanding. Data professionals must be able to explain complex insights in ways stakeholders understand, collaborate effectively across teams, and connect every analysis to measurable business objectives. These skills are often overlooked because they are harder to quantify than technical proficiency, yet they are precisely the qualities that transform analysts into influential professionals.
Technical expertise may get you into the room, but critical thinking, communication, business acumen and the ability to ask better questions will determine how far you go.
Data as business strategy
What does genuine data-driven leadership look like? Becoming truly data-driven is not about adopting sophisticated technologies; it’s about cultivating a culture where data consistently informs decisions at every level of the organisation. Effective leaders empower their teams to think analytically, encourage people to ask better questions, and measure outcomes objectively. More importantly, they invest in data literacy across the organisation so that evidence-based thinking becomes everyone’s responsibility, not just the job of the data team.
A truly data-driven leader creates a culture where data becomes part of everyday decision-making, not something that’s only consulted after a decision has already been made.
What’s one misconception executives have about machine learning? That machine learning can solve every business problem. Executives sometimes expect it to do things that aren’t technically feasible, or assume it can produce answers regardless of the quality of the data or the nature of the problem. As data professionals, we have a responsibility to set realistic expectations: explaining what machine learning can and cannot do, and when another approach may be more appropriate. Managing expectations is just as important as building the model itself.
Have you ever recommended against AI because a simpler solution was better? Yes. In one familiar example, the business question was straightforward and could be answered through data analysis alone, without a complex AI or machine learning model. The decision was guided by the business need rather than the technology. If a simple analytical approach gives stakeholders the insights they need, there is no reason to introduce unnecessary complexity.
Industry insight: Africa’s overlooked opportunity
Africa’s digital economy is expanding rapidly, and organisations across banking, healthcare, agriculture, education, logistics and e-commerce are generating more data than ever before.
Africa doesn’t need more professionals replicating projects from online tutorials. It needs more professionals solving the real challenges facing African businesses and communities. Too often, aspiring analysts spend countless hours recreating popular datasets and portfolio projects. While these exercises build technical skills, they rarely address the challenges businesses encounter every day.
We don’t need more copied projects. We need more people using data to solve the problems around them.
Rather than asking, “What project should I build?”, professionals should ask a more important question: “What problem around me needs solving?”
Before AI, build a strong data foundation. Reliable decision-making begins with reliable data. Organisations must establish the right processes, governance practices and quality controls to ensure their data is accurate, consistent, complete and trustworthy. Artificial intelligence is only as effective as the information it learns from, so building a strong data foundation is not simply a technical requirement; it is a strategic business investment.
Without trustworthy data, it’s difficult to make good business decisions, and even the most advanced AI won’t produce reliable results.
Leadership and influence
Teaching deepens understanding. Explaining technical concepts to beginners has taught her to strip away unnecessary complexity and focus on what truly matters. If you cannot explain a concept simply, you may not fully understand it yourself. That mindset changed how she approaches problems at work: rather than jumping straight into SQL, Python or machine learning, she spends more time understanding the business challenge, asking thoughtful questions, and defining the problem before considering the technical solution.
Stop collecting tools. Start solving problems. One piece of advice she thinks is overrated is simply telling aspiring analysts to learn Excel, SQL, Power BI and Python, then move on to the next tool. These tools are essential, but learning them alone isn’t enough. Stop collecting courses and start using what you already know. Build projects, solve real problems, share your work, and teach others. That’s where real competence, confidence and growth come from.
Building people is the greatest legacy. While she is proud of the models she has built and the problems they have helped solve, she hopes her greatest legacy will be the people whose lives she has impacted. Technology will continue to evolve, but investing in people creates a lasting ripple effect.
Reflection
One belief about data she has completely changed her mind about: that every problem requires a technical solution. Earlier in her career, she was excited about building models and using advanced techniques. Today, she recognises that sometimes a simple analysis is all that is needed.
What matters most is solving the right problem, not using the most advanced approach.
Advice people resist but later need: sharing your work publicly. Many people resist because they say they’re introverts or are afraid of being judged. But visibility creates opportunities. You can be highly skilled, but if no one sees your work, they’ll never know the value you can bring.
The signal that someone is thinking like a data professional: they start asking better questions instead of jumping straight into writing code. They want to understand the business problem first, clarify what’s being asked, and think about the best way to solve it before choosing a tool or writing a query.
Her books
Writing was never about adding another title to the growing library of data books. It was about answering questions she encountered repeatedly from aspiring professionals, questions that went far beyond learning SQL, Python or Power BI. The first, Landing Your First Job as a Data Professional, helps people who have been learning data land their first role. The second, The Data Analyst Roadmap, focuses on building a sustainable career, because many people teach the tools, but very few teach you how to think like a data analyst.
Don’t make learning tools the end goal. The end goal should be solving problems.
One lesson for every aspiring data professional
Think. Practice. Share. Don’t just learn; apply what you’re learning. Build projects, solve problems, and keep improving through practice. And don’t keep your work to yourself. Visibility matters, because opportunities often come from people seeing what you’re capable of.
Asked what she hopes a reader in 2035 will say about her contribution to the data ecosystem, she answered: “Through Esther’s resources, I implemented the strategies she shared, and they helped me land my first opportunity. I went on to use those same principles to mentor others, many of whom are now working in great companies. Today, I’m a senior consultant, and I’m paying forward what I learned.” If that’s the story people tell, she says, she’ll know her work created an impact far beyond her.
