Data Doesn't Change Economies. Decisions Do.
Patrick Anekwe
7/17/20264 min read
Every government (federal and state) wants better policies.
Every business wants stronger performance.
Every development organisation seeks greater impact.
Consequently, enormous resources are invested every year in collecting information. Government Budget, Planning & Statistics agencies within Nigeria conduct surveys, businesses invest in analytics platforms, researchers publish reports and international organisations produce increasingly sophisticated datasets.
Across every sector within the country, the expectation remains the same. Better information should produce better outcomes.
Yet reality tells a more complicated story.
Nigeria now possess more information than at any other point in her history, but persistent challenges continue to dominate public discourse. Agricultural productivity fluctuates despite decades of research. Infrastructure projects remain unfinished despite extensive feasibility studies. Small businesses struggle to scale despite countless enterprise development programmes. Organisations invest heavily in digital transformation while productivity often improves only marginally. The availability of information has clearly increased, yet better outcomes have not always followed.
This apparent contradiction suggests that the relationship between data and development is frequently misunderstood.
More than a century ago, the economist Frank Knight argued that economic decisions are made under conditions of uncertainty rather than certainty. His distinction between measurable risk and genuine uncertainty remains relevant in Nigeria today because organisations rarely possess complete knowledge about future events. Research therefore exists not to eliminate uncertainty, which is impossible, but to reduce it sufficiently for better decisions to become possible.
This observation leads to an important conclusion.
Data does not create value when it is collected. It creates value when it reduces uncertainty enough for someone to make a better decision.
Understanding this distinction changes how research itself should be viewed. The purpose of collecting information is not to produce thicker reports, larger databases, or more sophisticated dashboards. Its purpose is to improve judgement. Data becomes valuable when it enables decision-makers to distinguish between assumptions and evidence, identify opportunities that were previously hidden, and recognise risks before they become crises.
The management scholar Herbert Simon described decision-making as a process constrained by what he called bounded rationality. Individuals and organisations rarely possess complete information, unlimited time, or perfect analytical capacity. Instead, they make decisions using the best evidence available at a particular moment. Improving decisions therefore depends less on pursuing perfect information than on ensuring that relevant evidence reaches decision-makers in time to influence their choices.
This distinction explains why many organisations within Nigeria remain information-rich but decision-poor.
Over several decades, governments, universities, consulting firms, development partners, and research institutions have generated extensive evidence covering agriculture, healthcare, education, infrastructure, enterprise development and demographics. We possess considerable knowledge about post-harvest losses, logistics bottlenecks, access to finance, infrastructure deficits, and regional economic disparities. These challenges are well documented, yet many continue to reappear in policy discussions because evidence frequently ends with publication and talk shows rather than implementation.
The same pattern can be observed within the private sector.
Many organisations invest heavily in collecting customer information, monitoring sales, analysing financial performance and tracking operational metrics. However, information only becomes an asset when it influences strategic choices. Sales records should determine inventory decisions. Customer data should shape product development. Financial information should guide investment priorities. When information is collected without influencing management decisions, it gradually becomes an archive rather than a source of competitive advantage.
Agriculture demonstrates this relationship particularly well.
Decades of research have shown that agricultural productivity depends on much more than cultivated land or annual rainfall. Storage infrastructure, transport networks, security, irrigation, mechanisation, market access, processing capacity and access to finance all influence the performance of agricultural value chains. These relationships are no longer speculative. They are supported by extensive empirical evidence. Nevertheless, policy interventions often continue to focus on increasing production while giving comparatively less attention to the wider systems that determine whether production ultimately translates into affordable food, competitive industries and sustainable farmer incomes. The evidence identifies these relationships. The challenge lies in allowing that evidence to influence investment priorities.
Digital transformation reflects a similar principle.
Writing in the Harvard Business Review, Michael Hammer argued that organisations should avoid simply automating existing processes because technology alone cannot improve fundamentally inefficient ways of working. Decades after his publication, his observation remains remarkably relevant. Many organisations continue to digitise workflows without questioning whether those workflows should exist in their current form. Consequently, technology often accelerates existing inefficiencies rather than eliminating them. Once again, the limiting factor is not insufficient information but decisions that fail to respond to what the evidence already demonstrates.
These examples point towards a broader principle that extends well beyond individual sectors.
Economic transformation rarely begins with additional resources. More often, it begins with better decisions about the resources that already exist. Better decisions improve the allocation of capital, labour, technology and public investment. Improved allocation produces stronger organisational performance, greater productivity, and more effective public services. As these improvements accumulate across businesses, institutions, and governments, they gradually reshape the performance of an economy.
The relationship is therefore cumulative rather than immediate.
Data reduces uncertainty.
Reduced uncertainty improves decisions.
Better decisions allocate resources more effectively.
Better resource allocation produces better outcomes.
Better outcomes, sustained over time, contribute to economic transformation.
Seen in this way, data occupies an important but carefully defined role within Nigeria's development. It informs decisions, but it does not replace judgement. It supports leadership, but it cannot substitute for leadership. Information creates possibilities, yet people and institutions determine whether those possibilities become reality.
This understanding also changes how organisations should evaluate their investments in research and analytics. Success should not be measured by the number of surveys conducted, reports published, or dashboards produced. Those activities represent important outputs, but they are not the ultimate outcome.
A more meaningful question is whether the research altered a significant decision. Did it influence investment priorities? Did it improve operational performance? Did it reduce uncertainty sufficiently to change the course of action?
If the answer is yes, the research has created value.
If the answer is no, the organisation has collected information without fully converting it into intelligence.
Perhaps that is the more useful way of thinking about data.
Its value has never been determined by the quantity collected or the sophistication of the technology used to analyse it. Its value is determined by whether it consistently improves the quality of decisions made by individuals and institutions.
Because data does not change economies.
Decisions do.
Further Reading:
Hammer, M. (1990). Re-engineering Work: Don't Automate, Obliterate. Harvard Business Review.
Knight, F. H. (1921). Risk, Uncertainty and Profit. Boston: Houghton Mifflin.
Pfeffer, J., & Sutton, R. I. (2006). Hard Facts, Dangerous Half-Truths, and Total Nonsense.* Harvard Business School Press.
Simon, H. A. (1947). Administrative Behaviour. New York: Macmillan.