The chart started with a simple observation. Today, among Singapore residents aged 25–29, about 65% of women hold university qualifications, compared to only 51% of men. Source: SingStat What makes this particularly striking is that just 25 years ago, university attainment rates between men and women were almost identical at around 28%. Source: SingStat Why the divergence? And perhaps more importantly, what happened along the education journey? To make the comparison fairer,

Why Understanding Cardinality Matters Before Joining Tables One of the most common mistakes in data analysis happens quietly. The query runs without errors. The dashboard loads. The numbers even look reasonable at first glance. Yet somewhere in the process, the analysis has already gone wrong. This often happens when two tables are joined simply because they share a column with the same name. The fields look compatible, the join executes successfully, and everything appears t

The Moment Data Finally Starts to Matter I once presented an analysis that I knew was solid. The data had been carefully cleaned, the charts were clear, and the conclusions were supported by evidence. When the presentation ended, people in the room nodded politely. A few even complimented the work. And yet when the meeting ended, nothing really happened. No disagreement. No follow-up discussion. No decision. If you work with data long enough, you will eventually recognise thi

What Anthropic’s research reveals about the real impact of AI at work AI is supposed to replace millions of jobs. Yet the evidence so far suggests something very different. One of the most interesting findings from Anthropic’s research is this: AI is currently used more to support workers than to replace them. According to the analysis, about 57 percent of AI usage involves augmentation , where AI helps people perform their tasks more effectively. Around 43 percent involves a

Singapore recently announced that its Total Fertility Rate (TFR) has fallen again, from 0.97 to 0.87 , the lowest in the nation’s history. The news quickly triggered a wave of commentary. Fertility experts appeared overnight offering explanations and policy prescriptions — from housing costs to cultural attitudes. Some opinions were thoughtful. Others clearly reflected personal agendas. Rather than adding another opinion to the debate, it may be more useful to start with the

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