David Herrero’s Post

Clear strategy turns noise into signals. Most businesses are not lacking data. They’re lacking clarity. Dashboards are full. Reports are generated weekly. Metrics are everywhere. Yet decisions still feel uncertain. Why? Because data without direction creates confusion. Without clarity: • Every metric feels important • Every fluctuation feels urgent • Every trend feels like a signal And that’s how businesses become reactive. But strategy changes the role of data. With clarity: • Noise becomes insight • Metrics become guidance • Information becomes decision-making power That’s the difference between reacting and leading. Strong leaders don’t collect more data for the sake of it. They build the clarity required to understand what truly matters. Because data alone does not create growth. Direction does. Lead with clarity, not reaction. #BusinessIntelligence #VeriscopeDigital #StrategyMatters #LeadershipThinking #SMEGrowth #MarketingInsight #DataDriven #Clarity

Between July 2025 and April 2026, my team achieved: 📈 74% average improvement across 12 operational KPIs ✅ 11 of 12 metrics brought to standard We didn't get there with more technology. Or more people. We got there by fixing one thing first: Data Acumen. Most organizations have data. Few have the organizational capacity to turn that data into decisions. We built that capacity deliberately — through a 4-stage Analytics Development Journey: 1️⃣ Descriptive: What's actually happening? 2️⃣ Diagnostic: Why is it happening? 3️⃣ Predictive: What's about to happen? 4️⃣ Prescriptive: What should we do about it — right now? That sequence isn't optional. Skip a stage and you're recommending actions on top of misunderstood problems. What's your organization's biggest gap — data, acumen, or both? #DataAnalytics #Leadership #GovernmentInnovation #Analytics #OperationalExcellence

The best piece of analysis I've ever seen never left the slide deck. It was sharp. The logic was airtight. Everyone in the room nodded. And then nothing happened, because no one owned the unglamorous work of turning it into a decision someone actually made. I think about that a lot. In data and analytics, we're trained to chase the insight: find the pattern, build the model, prove the point. But the insight is the easy part. The hard part is everything after. Getting a team to change how they work. Getting a product to ship the change. Getting leadership to trust a number enough to bet on it. Early on, I measured myself by how good my analysis was. These days I measure myself by what actually changed because of it. It's a less flattering metric. A beautiful dashboard nobody acts on isn't a win. It's expensive trivia. The analysts I respect most aren't the ones with the cleverest models. They're the ones who can sit with operations, product, and finance and get a decision over the line. Where does most good analysis die in your organisation? #Analytics #DataScience #Leadership #BusinessStrategy

One of my biggest takeaways from this week’s Business Analytics lesson at Harvard Business School Online: Data + human judgment remains the key to making effective decisions. We explored scatter plots, normal distributions, sampling, hypothesis testing, regression models, residual plots, and more. Each tool helps us see trends, patterns, and possibilities more clearly. But data analysis is only the starting point. The Frozen forecasting case study stood out to me. It reminded me that strong analysis requires agility. Markets shift. Human behavior changes. Context evolves. The most valuable insights often come from continuously refining our understanding with the most recent and relevant data. And even with the best models in front of us, we still need diverse perspectives, industry understanding, and human judgment to guide decisions effectively. Data can guide us. Models can support us. But human judgment is what transforms information into effective action. #BusinessAnalytics #DecisionMaking #Leadership #DataAnalytics #Strategy #HumanJudgment #HarvardBusinessSchoolOnline

Some of the hardest problems in analytics are not about models; they’re about clarity. One situation that stood out to me involved a problem where: - The business objective wasn’t clearly defined - Data existed, but in fragmented forms - Different stakeholders had different expectations On the surface, it looked like an analytics problem. In reality, it was a problem definition and alignment challenge. A significant part of the effort went into: - Structuring the problem in a way that was actionable - Aligning stakeholders on what success should look like - Narrowing down from multiple possible directions Only after that did the analysis start to create meaningful value. One pattern I’ve observed since then is that strong analytics work depends heavily on how well the problem is framed. Without that, even good models tend to have limited impact. This approach also shifts the focus from outputs to decisions and outcomes. This has been a useful lens in tackling complex analytics problems. How do you typically deal with ambiguity in analytics projects ? #DecisionScience #AnalyticsLeadership #ProblemFraming #BusinessImpact #DataStrategy #Analytics

They Had the Data, but Still Got It Wrong 📊 The team had everything they needed. Reports were ready. Dashboards were full. The numbers looked impressive. On paper, it appeared to be the perfect setup. But when it was time to make decisions, things went wrong. They focused on the wrong metrics. ❌ They overlooked critical signals. ⚠️ They made decisions based on what looked good, not on what truly mattered. Weeks later, the results were clear. 📉 The issue was not the data. It was how it was used. 💡 Data delivers value only when the right questions are asked. Insight comes from focusing on what truly matters. Results follow when decisions are driven by the right information. Having data is not enough. Understanding it is what makes the difference. Have you encountered situations where data was available but misused? #DataDriven #Leadership #DecisionMaking #Analytics #TechLeadership #FEDTC

Most dashboards look impressive. Very few drive action. A KPI that doesn’t influence a decision is just noise dressed as insight. The real test of data isn’t accuracy, it’s impact. If it doesn’t change what you do next, it’s not analytics it’s decoration. The "So What?" Test: If a metric moves 10%, do you have a specific play to run? • Signal vs. Noise: If you have 20 "Top Priority" KPIs, you have zero. • Context is King: Totals are for ego trends and ratios are for execution. The Challenge What is one "Vanity Metric" you’re ready to delete, and what’s the one "Action Metric" you can’t live without? 👇 Let’s talk in the comments. #DataStrategy #Analytics #Leadership #KPIs

What is measured is monitored. It’s a common phrase meant to reflect the need for a quantitative definition of progress. It’s also a driving component of SMART goals and initiatives. Once we choose the metric, data dashboards can develop quickly. I am a proponent of data dashboards and, admittedly, a bit of a data nerd. But I have seen the slippery slope. As we lean into the data, we learn new capabilities in our tools, and the dashboard evolves. Its growth has an impact, and not always in the way we intend. #Leadership #GoalSetting #Priorities #Data #PerformanceManagement We have to ask ourselves, just because we can measure something, should we? Measurement is not neutral, it nudges priorities. If we track something regularly, it starts to feel urgent, even when it is not the most important aspect of our work. What is one metric or notification you have had to push back on lately?

A meeting can end with complete agreement… and still produce no decision. The dashboard is clear. The numbers are trusted. The analysis is solid. And yet the discussion keeps expanding: pricing, operations, customer behaviour, market conditions. Everyone sounds reasonable. Nothing moves. I’m starting to think the real challenge in analytics today is no longer access to data. It’s alignment. I wrote a deeper reflection on why good dashboards still fail to create action, and why “decision-first” thinking matters more than ever. Link in the comments. #DataAnalytics #DecisionMaking #Leadership #BusinessIntelligence #ArtificialIntelligence FYT Consulting Derrick Yuen Jeremy Poon Pei Ning Kwok

The Team Kept Asking for More Data. What They Really Needed Was a Decision. Every week, the same conversation happened. “Can we get more numbers?” “Can we pull another report?” “Maybe we need more analysis before deciding.” So the team waited. More spreadsheets. More dashboards. More meetings. But while everyone searched for perfect certainty… opportunities quietly passed by. Because one of the biggest misconceptions in business is this: More data automatically leads to better decisions. Not always. At some point, the problem is no longer lack of information. It becomes fear of commitment. Good Data Analytics should reduce uncertainty enough to support action — not create endless hesitation. Because in business, delayed decisions also have costs. And sometimes the most expensive mistake is waiting too long to act. Do you think businesses today sometimes overanalyze instead of making timely decisions? #DataAnalytics #DecisionMaking #Leadership #BusinessGrowth #AnalyticsMindset #BusinessIntelligence #DataDriven #GlobalBusiness #LinkedInDaily