If Part 1 (the article before this) exposed the discomfort, this is where we name the shift. AI didn’t replace data analysts. It removed the safety net. When reporting became easy, the real work could no longer hide behind effort, tools, or volume. What remained was judgement — and that’s where the role truly begins. The Real Shift Isn’t Technical. It’s Philosophical. The evolution of the data analyst role is often described in terms of skills: More business knowledge Better

It Exposed a Problem We’ve Ignored for Years. The meeting starts the same way it always does. A dashboard is projected onto the screen. Charts are neatly aligned. Filters work perfectly. Someone nods and says, "Looks good.” Then comes the pause. A longer one. Finally, someone asks the question no one prepared for: “So… what should we do?” The analyst looks back at the dashboard. The dashboard, predictably, looks back in silence. No one in this room is incompetent. No one fail

“Let the data decide.” It’s a phrase that sounds sensible, rational, and modern. It’s also deeply misleading — especially in large organisations. In practice, data rarely delivers the answer. More often, it delivers multiple, valid, and conflicting answers , depending on where you sit in the organisation. And this is where many data-driven initiatives quietly break down. When the Same Data Tells Three Different Stories Consider a familiar scenario in a large organisation rev

(It’s Confusing Because of How We See) I’ve sat through many presentations where the explanation starts like this: “Let me walk you through this dashboard.” Ten minutes later, people are still lost. Not because the data is wrong. Not because the metrics are unfamiliar. But because the dashboard doesn’t organise itself in a way the brain expects. Before anyone reads numbers, we all ask the same silent questions: What belongs together? What should I look at first? What matters

If you work in analytics long enough, you’ll recognise this scene. You’re halfway through presenting a dashboard to stakeholders. The story is flowing well. People look engaged. Someone even leans forward — always a good sign. Then a hand goes up. “Just a quick question…”(Of course it’s never just a quick question.) You begin answering. You talk about the data. Then the filter logic. Then the campaign period. Then the customer segment. Something about seasonality. A side not

How Hidden Forces Derail Good Presentations — and What To Do About It Before you even reach the recommendation slide, you can usually sense it — that subtle shift in the room. The story is flowing, the audience is following… until the first objection surfaces. Then another. And suddenly, the narrative you crafted so carefully begins to wobble. Every data storyteller has lived this moment, even when the story was strong. 1. You Thought the Story Was Landing. Then It Didn’t. Yo

Productivity is one of the most hotly debated topics in Singapore.Whether we’re discussing competitiveness, wages, or the future of work, productivity becomes the centre of every conversation. Yet beneath these discussions lies a subtle but dangerous trap: Different datasets use different definitions — and if we don’t understand what the numbers truly represent, we risk drawing the wrong conclusions. In data analytics, small misunderstandings can lead to: pointless debates mi

When I finished the video, the first feeling wasn’t fear. It wasn’t excitement, either. It was heaviness . Not because Tristan Harris painted a doomsday scenario, but because for the first time, I could feel—viscerally—how fast everything is moving, and how slow society is reacting. AI is no longer a tool sitting quietly in the background. It’s accelerating, reshaping jobs, rewriting incentives, and slipping into parts of our lives we rarely think about: childhood, therapy,

(My reflections after watching Tristan Harris on Diary of a CEO) Humanoid robots used to feel like a distant science-fiction idea—something we’d joke about, not something we’d have to plan our lives around. But after watching Tristan Harris explain how fast robotics and AI are converging, I realised this isn’t a “one day” conversation anymore. It’s a “we should probably understand this now” conversation. And frankly, I’m not an expert in AI safety or robotics. I’m just a data

A personal reflection inspired by Tristan Harris on The Diary of a CEO Recently, I clicked on a video I assumed I’d watch for five minutes.Three hours later, I was still glued to my screen. The conversation was between Stephen Bartlett and Tristan Harris , one of the most influential voices in tech ethics. I’m not an engineer, researcher, or policymaker. I’m not an expert in Artificial Intelligence (AI) — systems designed to perform tasks that normally require human intellig