Don't Overdo Your Analytics

The Risks of Overdoing Analytics

There’s no doubt that analytics play a crucial role in modern business. Davenport and Harris (2017) argue that analytics provide a competitive edge. Kaplan and Norton (1996) have long emphasized the importance of data-driven insights in translating business strategies into action, and authors like Gomes and Ramano (2025) continue to point its importance in maintaining strategic alignment. However, despite its value, analytics can be taken too far. While some may disagree, my answer to the question of whether analytics can be overdone is an unequivocal yes.

Before diving into this discussion, I should admit that I’m a numbers guy. I genuinely enjoy working with data and statistics (as odd as that may sound). The idea that adjusting one aspect of a business process can lead to measurable outcomes fascinates me. I still remember how excited I was when I first learned that employee satisfaction could be directly linked to financial performance (Rucci, Kim, & Quinn, 1998). But over time, I’ve learned an important lesson: the effort we put into analytics must always be weighed against its practical value. Striking this balance is essential.

A Lesson in Analytics Overload

Years ago, I worked for a consulting firm specializing in outsourced analytics. One of my colleagues, a senior statistician, was known for his deep expertise in analyzing consumer behavior. He was highly respected, largely because he used complex statistical terminology that few fully understood. Many assumed that his analyses were valuable simply because they were intricate. The company appreciated him because his in-depth work translated into billable hours, and some clients liked having someone so knowledgeable on their team—even if they didn’t always grasp what he was doing. However, not all clients felt the same way. Some grew frustrated with missed deadlines and overly complicated analyses that provided little actionable insight.

When I was put in charge of the analytics team, I set out to understand what each statistician was working on. Most discussions were straightforward, but my conversation with this particular statistician raised concerns. He was consistently delivering analyses that far exceeded what clients needed—like someone ordering a Honda Accord and receiving a gold-plated Mercedes SUV instead. Worse, his approach was causing delays, leading to complaints and even lost business.

Avoiding the Analytics Trap

As I worked with him to refine his approach, we established three key principles to prevent analytics from becoming excessive and inefficient:

1. Define the Business Question Clearly Always ask, “What business question are we trying to answer?” If this isn’t clearly defined, work with stakeholders until it is. A lack of clarity often leads to unnecessary analyses that don’t provide meaningful insights.
2. Choose the Most Efficient Method Ask, “Is this the simplest and most effective way to answer the question?” Analysts often favor complex techniques because they’re trained to appreciate their depth. However, sometimes a straightforward method—like a simple correlation instead of a complex statistical model—delivers the same value with less effort.
3. Ensure Clear Communication Ask, “Can I explain this analysis to a non-statistician?” Analytics should serve business leaders who may not have deep statistical knowledge. If an explanation requires a deep dive into multivariate regression theory, it’s probably too complex for practical use.

The Outcome: Simpler, Faster, and More Effective Analytics

Applying these principles didn’t just benefit this one statistician—it improved the entire team’s work. Clients received their results more quickly, understood them better, and were able to take action with confidence. The true value of analytics isn’t in complexity for its own sake, but in providing clear, useful insights that drive better decisions.

References

Davenport, T.H. & Harris, J.G. (2017). Competing on analytics: The new science of winning. Boston: Harvard Business School Press.

Gomes, J. V., & Rom?o, M. J. B. (2025). Balanced Scorecard Promotes the Strategic Alignment. In?Entrepreneurial Ecosystems Driving Economic Transformation and Job Creation?(pp. 281-312). IGI Global Scientific Publishing.

Kaplan, R.S. & Norton, D.P. (1996). The balanced scorecard: Translating strategy into action. Boston: Harvard Business School Press.

Rucci, A.J., Kirn, S.P., & Quinn, R.T. (1998). The employee-customer-profit chain at Sears. Harvard Business Review, 76(1), 82-97.

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