Insights | Phillip Betts - Executive Analytics & Strategic Advisory

Why Dashboards Fail Leadership Teams

Most dashboards are built backward. They start with the question "what data do we have?" instead of "what decisions need to happen?" The result is a graveyard of charts that get glanced at monthly and forgotten.

The pattern is predictable: an executive asks for visibility into a business area. A dashboard gets built. It shows every metric the data team can extract. Six months later, it's gathering dust because it never answered the question the executive actually had.

Effective executive reporting works differently. It starts with a decision framework: What decisions does this leader make? How often? What information would change their course of action? Only then do you build the interface.

The best dashboards I've built contain fewer metrics than their predecessors. They surface exceptions rather than summaries. They answer "what should I pay attention to?" not "here's everything that happened."

Key insight: The goal isn't visibility. It's decision quality. Build dashboards that change behavior, not ones that accumulate clicks.

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5 min read • Analytics Strategy

Leading vs. Lagging: The Metrics That Actually Matter

Revenue is a lagging indicator. It tells you what already happened. By the time it shows a problem, you're months behind. The executives who act earliest focus on leading indicators: signals that predict outcomes before they materialize.

The shift: Stop asking "how did we do?" Start asking "what's changing that will affect how we'll do?"

5 min read • Forecasting

Forecasting Under Uncertainty: Precision vs. Usefulness

The forecast that's 80% accurate and delivered four weeks early beats the 95% accurate forecast that arrives too late to act on. Most organizations optimize for the wrong thing. They chase precision when they should chase lead time.

The question: Would you rather know something with certainty after it's too late, or with useful confidence while you can still act?

4 min read • Decision Making

When More Data Makes Decisions Worse

There's a point where additional data creates noise, not signal. Executives swimming in metrics make slower decisions and second-guess themselves more often. The discipline isn't gathering more. It's curating ruthlessly.

The discipline: Before adding a metric, ask: "What action would this change?" If the answer is nothing, don't add it.

4 min read • Reporting

The Frequency Fallacy: More Reports Do Not Mean Better Decisions

Weekly reports that nobody reads. Monthly decks that gather dust. The assumption that more frequent reporting leads to better insights costs organizations time, money, and attention. The real question isn't "how often should we report?" It's "how often do we actually make decisions?"

The fix: Match data frequency to decision frequency. Some strategic assessments need quarterly evaluation periods to be meaningful.

5 min read • Customer Analytics

The NPS Problem: When Good Metrics Go Bad

NPS was designed to measure customer loyalty. Somewhere along the way, it became a target for incentives, a metric to game, and a number that leaders optimize at the expense of actual customer experience. The score goes up while satisfaction goes down.

The warning: When a measure becomes a target, it ceases to be a good measure. Look at behavior, not just scores.

6 min read • Analytics Leadership

Signs Your Analytics Function Has Outgrown Its Infrastructure

Ad hoc requests piling up? Analysts stuck in report factories? Data definitions that change by department? These are symptoms of analytics maturity gaps. The good news: they're fixable. The bad news: they won't fix themselves.

Watch for: Metric disputes, request backlogs, and "it depends on who you ask" answers to simple questions.

Perspectives