In a country you can drive across in under an hour, why the differences between neighbourhoods still matter. Every year, the Department of Statistics Singapore quietly releases one of the richest datasets available to the public: the General Household Survey. Buried in its tables are answers to questions that shape national conversations — about wealth, about ageing, about work — questions that are usually discussed in headlines as single, national percentages. “1 in 7 househ

How good organisations slowly lose their way When a company makes headlines for the wrong reasons, our instinct is to search for the moment everything went off the rails. We look for the unethical executive, the disastrous board meeting or the reckless decision that brought the organisation down. Failure feels easier to understand when it can be traced to a single event. Howard Yu's recent article, The Science of Moral Decoupling, challenged that way of thinking. His argument

Spend a little time on Singaporean social media and a pattern emerges quickly. The cost of living is too high. Owning a car or a home feels out of reach. Work and school leave little room to breathe. There's a small, well-connected class — scholars, senior civil servants, established families — who seem to have everything, while everyone else competes hard for what's left. And, inevitably, someone points out that some other country does it better. Spend time instead with fore

For over a decade, FYT's curriculum has been built around three words: Data, Insights, Decisions. As we wrote in Bridging the Gap in the Analytics Value Chain, good analytics doesn't start with a spreadsheet — it starts with a clear question, a set of hypotheses, and a disciplined path from data to insight to action. That framework has shaped how we teach, and how our students learn to think. It has worked. But as AI becomes part of everyday work, we think it's time to make e

Why the scientific method is the right foundation for AI adoption, and what that means in practice Most conversations about AI at work start in the wrong place. They start with the tool. Which model should we use? Should we try ChatGPT or Claude? Do we need to build something custom? The questions come fast, and they feel urgent. But they are the wrong questions to be asking first — because they assume the hard part is choosing the technology. It is not. Here is a more useful

Why the rules of the job market feel broken — and what both employers and employees are missing about what actually changed. A post recently circulating on LinkedIn captured something that many graduates feel but rarely articulate so clearly: "Study hard. You will get a higher salary when you grow up." "Be realistic. Lower your salary expectations." "Just because you have a degree doesn't mean anything." The same system that told young people what to aim for is now telling th

The competitive advantage isn't in the tools you buy. It's in the system you build. Two years ago, FYT started doing something most consulting firms weren't: actually using AI in our own work. Not just experimenting with prompts. Not reading vendor white papers. Actually redesigning our workflows around AI tools, testing what broke, fixing what didn't work, and building systems that held up under real operational pressure. What we learned didn't come from the internet. It cam

We have been publishing data-driven articles on questions that Singaporeans genuinely debate — and the response has been encouraging. Some of what we have covered so far: Is HDB resale housing really less affordable today? The data gives a more nuanced answer than the headlines suggest How data literacy changes the conversation — using the HDB debate to show how better questions lead to better conclusions Singapore's religious landscape by the numbers — which groups are large

A data literacy lesson from Singapore’s housing debate Two analyses can examine the same issue and reach very different conclusions. That does not always mean one is dishonest or the other is wrong. More often, it means they are asking slightly different questions, using different definitions, or starting from different assumptions. Singapore’s housing affordability debate offers a useful example. In an earlier article, we examined whether HDB resale housing has really become

A few years ago, if you asked managers what slowed decisions down, most would point to information. The report wasn't ready yet. The analysis wasn't complete. The data still needed cleaning. Someone was still preparing the presentation. The assumption seemed perfectly reasonable at the time. If organisations could get information faster, they could make better decisions. In many ways, the last twenty years of business technology have been built around that belief. We invested