Data tells your what, you Brain finds out why - illustrated by Singapore's meat consumption habits
- 6 hours ago
- 3 min read
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 of what is happening — chicken leads, pork follows, beef trails. But notice what the chart cannot tell us: why this is the case. That's where the human analyst steps in.
Numbers without context are just numbers
To truly understand these consumption patterns, we need to bring in what we know about Singapore as a society. This is a skill no algorithm can fully replicate — it requires a human analyst who understands the world the data lives in.
Race, religion, tradition, affordability — none of this lives in the dataset. It takes a human analyst, with knowledge of Singapore's social fabric, to bridge the gap between the numbers and the real world.
When data surprises you - that's the moment to ask better questions
A skilled analyst doesn't just read charts — they notice when something looks out of the ordinary and treats it as a signal to dig deeper. These two dips are a perfect illustration of that habit.
1999–2002
Pork plunges nearly 40%. The Nipah Virus outbreak in Malaysian pig farms — Singapore's primary pork supplier — triggered mass culls of over one million animals. Supply collapsed almost overnight, and consumer confidence in pork safety took years to recover. The data shows the drop clearly; the reason required human investigation.
2006
Chicken consumption falls 23% in a single year. The H5N1 Avian Influenza (Bird Flu) outbreak swept across Asia. Import restrictions, mass poultry culls, and widespread public fear drove consumption down sharply. By 2007, it bounced back 47% as the crisis eased — a recovery pattern the chart shows, but cannot explain on its own.
Key Insight #2 - Anomalies are invitations to ask better questions
When you spot an unexpected spike or drop in data, that is not a problem - it is a prompt. The dips in 1999 and 2006 didn't explain themselves. A curious, critical thinking analyst noticed the pattern and went looking for context. That investigative instinct is a skill worth building deliberately.
What this means for you as a data professional
This case study illustrates three principles that sit at the heart of everything we do at FYT:
Data is the starting point, not the finish line. Charts and numbers surface patterns — but meaning comes from context, curiosity, and human judgement. No tool can replicate that.
Asking better questions is a learnable skill. The analysts who spotted these dips and investigated their causes didn't have magic abilities — they had trained habits of critical observation. That's exactly what we build in our workshops.
In the age of AI, human thinking matters more, not less. Tools can surface the pattern. Only a thinking human can decide what it means — and what to do about it.
Want to build these skills in your teams?
FYTs practivcal workshops help professionals at every level develop the critical thinking and data interpretation habits that turns numbers into decisions. Contact us to see if we can help.
Comments