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

Over the past three decades, a subtle but important shift has been unfolding in Singapore’s education statistics. Among residents aged 25 to 29 , women are now significantly more likely than men to hold a university degree. Recent data shows that about 65% of women in this age group hold degrees , compared with 51% of men  — a gap of roughly 14 percentage points . What makes this trend particularly interesting is when the divergence began . In the early 1990s, men actually ha

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