Part 4 of a Series: From Messy Problems to Clear Definitions When Good Work Goes Nowhere You can do good analysis… and still create no impact. By now, we have spent time getting the problem right. In the earlier articles, we moved from messy situations to clear problem definitions. We broke down KPIs to understand where issues sit, and we used Pirate Metrics to identify where in the journey things break. By this point, the problem is no longer vague. It is specific, structure

Part 3 of a Series: From Messy Problems to Clear Definitions When the Problem Isn’t Where You Think Most teams don’t struggle to find problems. They struggle to find where those problems actually happen. In the previous article, we explored how KPIs can be misleading when taken at face value. A single number, like “sales,” often hides multiple drivers, and breaking it down helps us understand where the problem sits. That already brings a level of clarity. But in many real-wor

Part 2 of a Series: From Messy Problems to Clear Definitions Why This Matters More Than You Realise In the previous article, we explored a common issue in analytics work. Most problems do not start clear. They start messy, and without proper structure, teams often move too quickly into analysis without fully understanding what they are trying to solve. That is where things begin to break down. But even when teams attempt to be more structured, another challenge appears almost

Part 1 of a Series: From Messy Problems to Clear Definitions The Problem Isn’t What You Think Most teams think they have a data problem.That’s why they keep solving the wrong thing. In many organisations, the pattern is familiar. Dashboards are built, reports are generated, and metrics are tracked with increasing sophistication. Yet when it comes to making decisions, very little seems to move. Meetings end with more questions than answers, and at some point, someone inevitabl

We say “keep charts simple” like it’s always right. It isn’t. Simplicity is one of the most common principles in data visualisation. We repeat it often, and for good reason. Simple charts are easier to read, faster to understand, and less likely to confuse. In most situations, simplicity works. But somewhere along the way, a guideline became a rule. And when that happens, we stop thinking. Because simplicity is not the goal. Clarity is. The Comfort of Simplicity There’s a rea

There’s a particular kind of organisational pain that doesn’t look like failure. It looks like activity . A business issue appears. A senior leader asks, “Can we get a dashboard for that?” The data team scrambles. A new dashboard goes live. There’s a sense of progress—something tangible, visual, and polished now exists. And then… nothing really changes. No decision. No shift in priorities. No clear action. Just the quiet addition of one more dashboard to the growing shelf of

Most people believe that better analytics comes from better tools — more sophisticated software, cleaner dashboards, or more recently, artificial intelligence. These things matter. But after working with professionals across industries, from public service teams making sense of policy data to corporate groups analysing operational performance, I have come to one consistent conclusion. The tools are rarely the reason analysis succeeds or fails. The thinking behind them is. You