How to trust data over public sentiment

What "I don't trust the data" actually means: - "The data doesn't match my expectations" - "The data contradicts my experience" - "I don't understand how the data was collected" - "I've been burned by incorrect data before" - "I don't know the limitations of this analysis" - "I have information the data doesn't capture" - "The data threatens my position or authority" - "I'm not ready to change based on what I'm seeing" Data trust isn't just about accuracy. It's about: - Psychological safety - Transparent processes - Consistent definitions - Acknowledged limitations - Aligned incentives - Respected expertise Data quality matters, but even perfect data will be rejected if these human factors aren't addressed. Building trust requires more than validation. It requires vulnerability, empathy, and patience.

As a leader have you ever truly believed in being certain about your action, only for the data to tell a different story? What should you do when your conviction and the numbers don’t align? These are moments every professional faces, especially in leadership roles. You made a call based on instinct, experience, or consensus. You felt confident, and then comes the report, the dashboard, or the unexpected metric, and it challenges everything. Here’s what to do when that happens: 1️⃣ Before you jump to justify, think. Step out of the story you wanted to tell and listen to the one the data is revealing. It might hurt your ego, but it’s not personal. It’s a sign. 2️⃣ Instead of seeing it as a contradiction, approach it with curiosity. The real growth happens when we dig into the ‘why’ behind the change in perspective. 3️⃣ Don’t assume the numbers are unreal instead you should audit your sources and methods: - Compare with historical trends or parallel datasets. - Cross validate against external benchmarks or qualitative feedback. - Look for outliers, missing values, or sudden spikes in the data. - Ensure tools and processes (surveys, tracking code, APIs) captured what they were meant to. - Involve a neutral analyst or domain expert to review your approach. 4️⃣ Bring in someone who is not impacted by the decision. Data without context can mislead, and diverse views can surface factors you may have overlooked. 5️⃣ If the insight holds up, don’t hesitate to make changes. Owning the pivot demonstrates maturity, agility, and leadership, more than hanging on to a story that no longer serves the business. 6️⃣ Data doesn’t invalidate your instincts, it refines them. Use it to enhance your decision making resources, not suppress your feeling. Impactful leaders always balance both. 7️⃣ When teams see leaders embracing truth over ego, they learn that course correction is a strength, not a weakness. That creates a culture of trust, continuous learning, and better outcomes. So next time your data tells a different story than you do, understand it and not ignore it. Remind yourself the goal is not to be right all the time, it’s to get it right in the end. Photo via unsplash.

Some data headaches are really just trust issues in disguise. Let me explain: I once met with a medical device company’s President who complained endlessly about their on-prem ETL failures and nightly data fires. On the surface, it was a purely technical problem - broken scripts, crashing servers, and no backup plan. But as I asked more questions, I realized the true pain was deeper... Nobody trusted the numbers. Reports conflicted, definitions varied, and decisions were stalled or based on gut feel. In short, they had no data management strategy. Every stakeholder boiled their frustrations down to “broken servers,” when the real issue was the foundation. Helping the president of the company see this issue helped us close the deal. So, here’s the takeaway... Before you dive into code fixes, pause and ask: Do people actually trust these numbers? If they don’t, no amount of faster queries will solve the real problem. Build trust by: 1. Defining consistent metrics and ownership 2. Establishing lightweight data governance (even a small team can make a big difference) 3. Validating data end-to-end to ensure accuracy Fix the foundation first, and the rest will follow. ♻️ Share if you know a data leader who needs to address the trust gap. Follow me for more on building data strategies that drive real business impact.