Why good data doesn’t guarantee good decisions
October 30, 2025
It’s easy to assume that more data or cleaner dashboards will automatically lead to better decisions. But after working in product analytics at MAANG and top fintech companies, I’ve learned the hard way: the link between data and decision-making isn’t automatic. It’s something you have to design for.
Even in data-rich environments, I’ve seen brilliant teams make poor calls not because the data was wrong, but because the interpretation lacked context, the tools weren’t actionable, or the judgment wasn’t aligned. If you’re a product analyst or data professional, this is the real challenge: not generating insight, but helping others act on it.
Forecasting is only as good as its framing
Predictive analytics is a powerful tool but only if it’s focused on the right questions.
When I was at a remittance fintech, our team built forecasting models to predict cross-border currency transfer volumes. We used transaction history, seasonality, marketing campaigns, and even currency volatility to improve accuracy. The math worked. But what mattered more was how those forecasts were used.
Instead of treating the forecast as a static report, we embedded it into planning conversations like whether to ramp up hiring in customer support or adjust our marketing spend in a specific region. We also included clear confidence ranges so stakeholders could understand the level of uncertainty.
Takeaway: A great model isn’t the goal a better business decision is. Predictive analytics needs to be built with decisions, not dashboards, in mind.
Context is the missing layer in most dashboards
I’ve lost count of how many dashboards I’ve seen that are visually polished but practically useless.
At a MAANG company, one dashboard flagged a sharp drop in a key funnel metric. Panic followed. But after investigating, we realized it coincided with an A/B test introducing a new sign-up flow. The test wasn’t failing the dashboard just didn’t account for experimental context. That’s not a data problem. It’s a design problem.
Too many dashboards are optimized for access, not understanding.
Practical ways to make dashboards decision-ready:
- Lead with questions, not metrics. What decision is this helping someone make?
- Explain the "why" behind the numbers. Add annotations or context, not just charts.
- Design for your audience. Product managers don't need to see raw SQL outputs they need business impact and recommended actions.
- Use alerts sparingly, but smartly. Alert fatigue is real. But proactive nudges (e.g. "Customer churn has spiked post-release") help guide action.
Takeaway: Internal tools should act like a trusted colleague flagging issues, suggesting next steps, and filtering out noise.
Data alone isn't objective Interpretation matters
Many teams treat data like an absolute source of truth. But all data is shaped by choices what we measure, what we ignore, and how we define success.
For example, at the remittance fintech company, we once celebrated a spike in feature adoption. But when we broke down the data by user segment, we saw that the increase came mostly from users who had churned shortly after. The metric looked good until we added nuance.
We didn't have a data problem. We had a framing problem.
As analysts, it's our job to ask: What does this metric really mean? What's missing from the picture?
Three questions every analyst should regularly ask:
- Who might interpret this differently and why?
- What edge cases or outliers are we ignoring?
- If this chart disappeared tomorrow, what decisions would actually change?
Takeaway: Data doesn't replace judgment it sharpens it. Analysts need to facilitate better conversations, not just generate charts.
Final thoughts: Be a strategic enabler, not just a data provider
The most impactful analysts I've worked with and tried to emulate aren't just technically excellent. They're clear thinkers. They anticipate business needs. And they design their work with decisions in mind.
Whether you're building a dashboard, forecasting next quarter's sales, or interpreting a spike in churn, ask yourself:
- Is this helping someone take action?
- Am I providing context, not just numbers?
- Have I made this easy to understand and even easier to trust?
If not, it's time to zoom out.
Data is the input. Decision quality is the outcome. Everything in between is where analysts can make the biggest impact.