What the data says when we move beyond rhetoric Few topics in Singapore generate as much emotion as housing. For good reason. A home is likely to be the largest purchase most Singaporean households will ever make. When resale flat prices rise, people feel it. When headlines highlight million-dollar flats, people notice. When younger households compare today’s prices with what their parents paid decades ago, the conclusion can feel obvious: “Public housing is no longer as affo

Singapore’s fiscal position is often discussed using a single headline number: surplus, deficit, balanced budget, or fiscal position. But when we look deeper into the data from Singapore’s Ministry of Finance (MOF), a far more interesting story begins to emerge. Recently, I visualized Singapore’s fiscal position across four different measures: Primary Balance Basic Balance Overall Budget Balance Overall Fiscal Position The resulting charts revealed something important: Data a

Why More Data Still Doesn’t Guarantee Better Decisions The Quiet Frustration Inside Many Organisations Over the past decade, organisations have invested heavily in becoming more data-driven. Dashboards became more sophisticated, reporting became increasingly automated, and teams gained access to more information than ever before. Entire functions were built around analytics, business intelligence, reporting, and digital transformation. On paper, this should have made decision

We live in a time where technology is advancing faster than most organizations can absorb. Dashboards can now be generated instantly. AI can summarize reports, create visualizations, automate workflows, and even recommend actions. Analytical tools are becoming more accessible, more powerful, and increasingly embedded into everyday business operations. Yet despite all this progress, many organizations continue to face the same underlying challenge: They still struggle to turn

In one of the sessions I was facilitating recently, a team was reviewing a dashboard that had clearly taken a considerable amount of effort to build, and you could sense a quiet confidence in the room even before the discussion started. The data had been cleaned carefully, the charts were organised neatly, and the layout itself looked polished enough to reassure everyone that the work had been done properly. Revenue trends were broken down across multiple channels, conversion

Part 1 of a Series. AI Adoption Is Not the Problem. Thinking Is. Last week, I sat in on a discussion that, at first glance, looked like things were working exactly as they should. A team had just reviewed a summary of customer feedback generated using AI, and the output was everything you would expect from a well-trained professional. It was structured, balanced, and written in a tone that felt measured and appropriate, the kind of summary that makes meetings move faster beca

What the numbers show us Look at the chart and the story seems straightforward: Singaporeans eat a lot of chicken — far more than pork, and dramatically more than beef. Consumption has also been trending upward over the decades. Did you know that Singapore consumes an average of 40kg of chicken per person each year? 23 kg of pork per person each year 7 kg of beef per person each year Key insight #1 - Data tells us the "What" and "How much" The chart gives us a clear picture o

I started this on Friday. It’s now Tuesday, 4pm. All I needed to do was extract 849 records from a 53-page PDF. No analysis. No modelling. Just getting structured data into Excel. It sounded like the perfect use case for AI, so naturally I used it. My report was about claims so accuracy is of upmost importance. At the beginning, it actually felt promising. The outputs looked structured, almost usable. It felt like I was just one step away from getting this fully automated. If

A lot of the loudest commentary about AI and work starts with the same assumption: AI will replace jobs. A more useful starting point is simpler (and less dramatic): AI replaces (or reshapes) tasks — not whole jobs. Most roles are bundles of tasks, stitched together into a workflow. When technology changes, the bundle changes. This is exactly why task-based research often finds that “automation risk” is frequently overstated when we treat occupations as all-or-nothing. So the

Recently, a study on overqualification in Singapore made its way across headlines, reports, and social media. At first glance, it seemed straightforward: Singapore has a highly educated workforce Some workers are “overqualified” for their jobs And depending on who you read, this is either a concern… or not But here’s where it gets interesting. Three different sources — the Ministry of Manpower (MOM) , NTUC , and Channel News Asia — all reported on the same topic. Yet, they t