A practical look at data quality, data cleaning, and why “fit for purpose” matters more than perfection Most organisations say they want better data quality. What they usually mean is that they want fewer uncomfortable conversations about their data. When reports look clean but decisions still stall, the instinct is to clean more. Remove more blanks. Tighten more rules. Standardise more fields. The assumption is that if the data looks right, confidence will follow. It rarely does. When...

If Part 1 (the article before this) exposed the discomfort, this is where we name the shift. AI didn’t replace data analysts. It removed the safety net. When reporting became easy, the real work could no longer hide behind effort, tools, or volume. What remained was judgement — and that’s where the role truly begins. The Real Shift Isn’t Technical. It’s Philosophical. The evolution of the data analyst role is often described in terms of skills: More business knowledge Better storytelling...

It Exposed a Problem We’ve Ignored for Years. The meeting starts the same way it always does. A dashboard is projected onto the screen. Charts are neatly aligned. Filters work perfectly. Someone nods and says, "Looks good.” Then comes the pause. A longer one. Finally, someone asks the question no one prepared for: “So… what should we do?” The analyst looks back at the dashboard. The dashboard, predictably, looks back in silence. No one in this room is incompetent. No one failed at their job....