Many of us grew up with a fairly tidy storyline: Study hard → get a credential → land a stable job → work your way up → buy the things adults buy → and you’ll be “set”. Sir Ken Robinson often challenged the idea that education (and by extension life) is neat and linear — that you move from one stage to the next and arrive at a guaranteed outcome. And here’s the thing: that storyline does  work out for many  people. Just not for everyone . That gap—between “this is how it work

When one chart tries to do everything Opening: The Familiar Confusion Have you ever sat in a meeting, looked at a chart, and thought: “This looks impressive.” “This must be important.” “…I have no idea what it’s telling me.” Chances are, that was a combo chart . Bars and lines. Two axes. Multiple measures fighting for attention. A combo chart is a visualization that combines two chart types (commonly bars and lines) to show magnitude alongside trend or rate. Combo charts are.

A data lens on the fresh graduate job search — using FYT’s 6-step analytics approach A clip circulates: a fresh graduate describes doing “everything right” — stacked internships, strong grades, certifications, leadership roles, a polished CV — yet still no job offer. The emotion is familiar: frustration, anxiety, and the quiet fear that the system is no longer working. One interpretation is that the market has turned against graduates. Another interpretation is more actionabl

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

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

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 fail

“Let the data decide.” It’s a phrase that sounds sensible, rational, and modern. It’s also deeply misleading — especially in large organisations. In practice, data rarely delivers the  answer. More often, it delivers multiple, valid, and conflicting answers , depending on where you sit in the organisation. And this is where many data-driven initiatives quietly break down. When the Same Data Tells Three Different Stories Consider a familiar scenario in a large organisation rev

(It’s Confusing Because of How We See) I’ve sat through many presentations where the explanation starts like this: “Let me walk you through this dashboard.” Ten minutes later, people are still lost. Not because the data is wrong. Not because the metrics are unfamiliar. But because the dashboard doesn’t organise itself in a way the brain expects. Before anyone reads numbers, we all ask the same silent questions: What belongs together? What should I look at first? What matters