Why Most Data Analyses Go Wrong (And How to Do It Right)

Over the years, I’ve seen a recurring pattern in how people approach data—and it’s flawed in subtle yet significant ways.

Most of us dive into analysis with either a confirmation bias—seeking patterns that support what we already believe—or we jump into solution mode far too quickly. The result? Shiny dashboards and long decks that look impressive but miss the point.

Here’s what I’ve learned:

➔ Don’t let your past wins cloud your lens

Just because a certain insight worked once doesn’t mean it’s the answer every time. Clinging to past frameworks or outcomes can blind us from uncovering what’s actually new or different in the current context.

➔ Define the problem, not just the data

Good analysis doesn’t start with numbers—it starts with a sharp understanding of the problem. If you haven’t spent enough time framing the right question, even the cleanest dataset won’t help.

➔ Focus on the 20% that drives 80%
We often get lost in granularity, chasing metrics that sound fancy but don’t move decisions. Instead, prioritize clarity. Clients and teams value actionable insights, not just elegant analytics.

➔ Lastly, be sincerely curious

Tools and techniques matter, but what really sets apart great analysts is curiosity. Stay humble, question everything, and be willing to let go of a hypothesis if the data doesn’t back it.

In the end, data is only as good as the mindset of the person analyzing it. So don’t just crunch numbers—think, probe, and solve with intent.