How to Balance Data With Human Insight

How I Leverage Metrics Without Losing the Human Element as a Program Manager at Amazon Metrics tell you what’s happening—people tell you why. Early in my career, I was obsessed with dashboards and KPIs. I thought if I could just track the right numbers, everything else would fall into place. That illusion shattered during a post-mortem when a teammate said, “We missed the signal because we were too focused on the noise.” Since then, I’ve learned that metrics should inform decisions, not dictate them. Here’s how I balance metrics and the human element: 1️⃣ Qualitative Metrics Matter In addition to standard KPIs, I collect qualitative feedback through quick pulse surveys or post-mortem interviews. One of these surveys revealed a team’s frustration with a manual process that wasn’t showing up in any dashboard but was a real bottleneck. Fixing it cut processing time by 25%. 2️⃣ Leading Indicators Over Lagging Indicators I focus on leading indicators—like open support tickets or feature usage—rather than waiting for lagging metrics like revenue impact. This proactive approach recently helped us pivot a feature before it led to churn. 3️⃣ Storytelling with Data I don’t just share numbers—I share what they mean. In a recent presentation, I framed our metrics around customer stories, making it clear why those numbers mattered. The result? Stakeholders were more aligned and bought into the plan. Metrics are tools, not targets. If you’re making decisions based solely on dashboards, you’re missing half the story. How do you balance metrics with the human element? #DataDriven #Leadership #Metrics #Amazon

Yesterday, I was able to give a talk on data storytelling. What do we do if people don't want to buy into the data or push back? We spend millions on data infrastructure, AI tools, and advanced analytics. But the biggest bottleneck to ROI is rarely the technology—it’s the psychology. The question around overcoming the doubts can come up often. Data teams presents a data story or insight, and the stakeholders don't listen, don't buy in, or listen and go back to the old way of doing things. "That number feels wrong." "My gut says otherwise." "We’ve always done it this way." When people don't "buy in" to the data, they don't may not just ignore it—they may actively push back, causing disruption and stalling transformation. How do we handle the skeptics and the resisters? We stop treating resistance like a math problem and start treating it like a people problem. Here are 4 ways to bridge the gap between data and belief: 1. Invite them into the kitchen. Don't just serve the final meal (the dashboard). Show them the ingredients (the data sources) and the recipe (the logic). When stakeholders are involved in the definition and calculation phase, they feel ownership rather than suspicion. 2. Validate the "Gut Feeling." Never dismiss intuition. Intuition is just internalized experience or maybe call it personal data. Instead of saying "You're wrong," say, "Let's see if the data supports that experience." Make the data a partner to their expertise, not a replacement for it. 3. Master the Narrative (Data Storytelling). A spreadsheet appeals to logic; a story appeals to emotion. If you want buy-in, you have to connect the data point to a business outcome they actually care about. Context creates conversion. 4. Transparency over Complexity. A "black box" AI model is a magnet for distrust. If you can't explain how the model reached the conclusion in plain English, you can't expect a non-technical leader to bet their P&L on it. Data doesn't change organizations. Trust in data and people change organizations. How do you handle it when a stakeholder flat-out rejects the data? Leaders and stakeholders matter, so, get them on board. Stay nerdy, my friends. #DataLiteracy #AI #ChangeManagement #DataStorytelling #Leadership #Culture

Data or Gut Feelings. Whenever I’ve made strategic decisions while neglecting my gut feelings, I have felt a tinge of regret. Leaders are often urged to make data-driven decisions in this age of abundant data. Data is significant; it offers valuable insights by revealing past trends and providing predictive analytics, yet I believe it has limitations. Data alone will not always account for individual circumstances, unexpected challenges, or the essential human elements crucial to effective leadership. On the other hand, intuition - rooted in experience, judgment, and the ability to recognise patterns - can be incredibly powerful, especially in uncertain or quickly changing environments. Still, we must acknowledge that biases and narrow perspectives can sway intuition. Today’s leaders face the interesting challenge of blending analytical skills with intuitive wisdom rather than choosing one over the other. For example, while data may highlight an emerging market trend, intuition empowers leaders to assess whether the timing, cultural relevance, or team readiness aligns with taking action. A potent way to bridge this gap is by asking lots of critical questions during decision-making: Cultivating a habit of evaluating choices from numerical and descriptive angles ensures a more robust approach. The essence of future leadership lies in mastering the art of merging analytics with intuition. We can achieve this by fostering critical thinking to evaluate data accuracy, employing scenario planning, evaluating multiple alternatives to juxtapose gut feelings with measurable insights, and building diverse team thinking to challenge assumptions. Practical steps, such as conducting post-mortems to reflect on decision-making processes, help bring this balance to life. When data and intuition unite, leaders can make much more impactful decisions. So, I vote for a harmonious combination.

I violated data best practices to deliver a $40K ROI. (The client renewed. Here's why.) For 4 years, I've preached data best practices: Build proper data models. Minimize tech debt. Do it right the first time. Then reality hits. A mid-sized healthcare company hires us. They need a manual report automated. Fast. Your offer as a consultant is speed-centric. Their "source of truth" is 400 stored procedures written by a DBA who left 2 years ago. Zero documentation. Spaghetti SQL everywhere. 30+ Power BI reports querying directly off the transactional database. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝘄𝗮𝗻𝘁𝗲𝗱 𝘁𝗼 𝗱𝗼: Build a clean data warehouse from scratch. Proper dimensional modeling. Governed metrics. Best practices. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗜 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗱𝗶𝗱: Replicated their messy legacy logic in the cloud. Matched their numbers exactly—even the parts I knew were questionable. Automated the manual report in 6 weeks. Delivered the $40K ROI we guaranteed. 𝗪𝗵𝘆? Because many executives don't care about best practices. They care about results. Now. You don't get 3-6 months to "do it right." You get 6 weeks to prove you're worth keeping. 𝗧𝗵𝗲 𝘁𝗿𝘂𝘀𝘁-𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗮𝗿𝗮𝗱𝗼𝘅: If you show up and tell them their legacy logic is wrong, they won't trust you. If you replicate it perfectly first, they do. Once trust is built? Then you can challenge the legacy logic. Then you can propose the proper data model. Then you can start fixing the mess. But not before. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘁𝗼 𝗯𝗮𝗹𝗮𝗻𝗰𝗲 𝘀𝗽𝗲𝗲𝗱 𝗮𝗻𝗱 𝗾𝘂𝗮𝗹𝗶𝘁𝘆: 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗾𝘂𝗶𝗰𝗸 𝘄𝗶𝗻𝘀 𝘁𝗵𝗮𝘁 𝗲𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝘁𝗿𝘂𝘀𝘁 Automate one critical report. Match legacy numbers. Show ROI fast. 𝗢𝘃𝗲𝗿𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝘁𝗵𝗲 𝘁𝗿𝗮𝗱𝗲-𝗼𝗳𝗳𝘀 "This works, but it creates tech debt. Here's the plan to fix it long-term." 𝗖𝗮𝗿𝘃𝗲 𝗼𝘂𝘁 𝘁𝗶𝗺𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗿𝗲𝗯𝘂𝗶𝗹𝗱 Once trust is established, allocate hours to build the proper foundation. 𝗞𝗲𝗲𝗽 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝗶𝗻𝗴 𝘃𝗮𝗹𝘂𝗲 𝘄𝗵𝗶𝗹𝗲 𝘆𝗼𝘂 𝗶𝗺𝗽𝗿𝗼𝘃𝗲 Don't stop showing ROI while you refactor. Balance both. 𝗧𝗟;𝗗𝗥: Best practices are the North Star. But speed to value is survival. Deliver quick wins. Build trust. Then improve the foundation. Perfection kills consulting businesses. Progress builds them. Agree or Disagree? P.S. - Full breakdown of how to balance speed vs. best practices in this week's newsletter. Link in comments. 👇 ♻️ Share this if you've ever had to choose between doing it "right" and doing it "fast." Follow me for real talk on what data consulting actually looks like in the wild.

Most AI implementations can be technically flawless—but fundamentally broken. Here's why: Consider this scenario: A company implemented a fully automated AI customer service system, and reduced ticket solution time by 40%. What happens to the satisfaction scores? If they drop by 35%, is the reduction in response times worth celebrating? This exemplifies the trap many leaders fall into - optimizing for efficiency while forgetting that business, at its core, is fundamentally human. Customers don't always just want fast answers; they want to feel heard and understood. The jar metaphor I often use with leadership teams: Ever tried opening a jar with the lid screwed on too tight? No matter how hard you twist, it won't budge. That's exactly what happens when businesses pour resources into technology but forget about the people who need to use it. The real key to progress isn't choosing between technology OR humanity. It's creating systems where both work together, responsibly. So, here are 3 practical steps for leaders and businesses: 1. Keep customer interactions personal: Automation is great, but ensure people can reach humans when it matters. 2. Let technology do the heavy lifting: AI should handle repetitive tasks so your team can focus on strategy, complex problems, and relationships. 3. Lead with heart, not just data (and I’m a data person saying this 🤣) Technology streamlines processes, but can't build trust or inspire people. So, your action step this week: Identify one process where technology and human judgment intersect. Ask yourself: - Is it clear where AI assistance ends and human decision-making begins? - Do your knowledge workers feel empowered or threatened by technology? - Is there clear human accountability for final decisions? The magic happens at the intersection. Because a strong culture and genuine human connection will always be the foundation of a great organization. What's your experience balancing tech and humanity in your organization?

“Data-driven” has become the corporate equivalent of kale smoothies. Everyone swears by it, few actually consume it. Too many companies confuse “collecting data” with “making data-driven decisions.” Spoiler: they’re not the same. Executives today are drowning in dashboards, reports, and AI outputs. The problem isn’t lack of data. The problem is the illusion of certainty. Real data-driven leadership isn’t about blindly following the numbers. It’s about: 1) Asking the right questions before looking at the data. 2) Understanding the limits of what the data can and can’t tell you. 3) Using judgment, context, and experience to interpret the signal from the noise. 4) Building a culture where decisions are transparent and the reasoning is shared, not hidden behind “the model says so.” When I was leading a product strategy review last year, we had a dataset that “proved” a feature wasn’t worth pursuing. Yet one odd anomaly kept showing up. Instead of ignoring it, we dug deeper. That anomaly turned out to be a new customer segment that eventually drove double-digit revenue growth. The lesson? Data is the compass, not the map. Leaders must still choose the route, adjust to the terrain, and sometimes take calculated risks where the compass can’t see. I’m curious how do you balance intuition and data in your decision-making? Do you lean heavily on one side, or is it always a blend? #Leadership #DecisionMaking #DataDriven #Strategy #Innovation

People data has its biggest impact when you use it to tell a story. 📖 The role of data in the HR/People profession has changed. It used to be primarily about payroll accuracy, compliance reporting, and headcount tracking. But it has evolved into a powerful tool to shape business strategy. Its power comes not from just reporting the data but from pulling insights and bringing different data points together to tell the story about what is happening in the organization. In a recent interview, I shared two essential points that can help HR leaders break into storytelling with data: 1️⃣ Don’t be intimidated by data analysis. Much of the analysis that is needed is not complex math, and insights come from having strong definitions of what you are reporting on (like attrition or regretted attrition). While being able to link multiple data points together – like regretted attrition, primary exit reason, or high performers' top issues in the engagement survey. 2️⃣ Storytelling often includes qualitative data that we, as HR leaders, gather from our unique perspective on the people's sentiment in the organization (the “why they really feel that way). These nuggets provide context for the story and insights that bring the data to life and inform what actions really need to be taken. Being able to tell that story makes it easier to sit at the strategic table, providing vital insights into employee sentiment or organizational health that the numbers alone don’t reveal, and driving change on the things that really make for lasting improvements in employee experience and business performance. Having the curiosity to ask yourself “What information do I need to really know what is going on here?” is a good starting point. This mindset not only makes our insights stronger, but empowers us to lead with the confidence that comes from truly knowing our people. ❓But I want to hear from you, too. What do you think are your biggest challenges in using data within the HR function? Is it the data itself, or how to showcase it? As a way to support you, I’m excited to share two new resources: 1️⃣ Our content hub, designed to empower you with the tools and knowledge you need to harness data effectively. https://lnkd.in/gnJS3MDX 2️⃣ Our step-by-step guide on using data to get buy-in and budget for your people initiatives. I’m excited about this one, because it shows you how to really get your point across to your executive audience. https://lnkd.in/gxXKhMQF Happy storytelling!

I was conducting interviews for a manager position this week, and one candidate's response completely reframed how I think about leadership decisions. The question: "How do you balance departmental needs with library-wide priorities?" Most answers focus on either the data needed to make informed decisions or focuses on the relationships and working with the people. But this candidate said something that stopped me in my tracks: "You need data AND dialog." 🤯 Notice what happened there: ✅ They rejected the false choice between data-driven vs people-centered ✅ They recognized that sustainable decisions require both elements ✅ They understood that data without dialog lacks context ✅ They knew that dialog without data lacks foundation This two-word framework is a perfect example of how we should be leading: 📊 Data gives us the "what" and "how much" 💬 Dialog gives us the "why" and "so what" 📊 Data shows us patterns and trends 💬 Dialog reveals the human impact behind those numbers 📊 Data provides the foundation for decisions 💬 Dialog ensures those decisions actually work for people When we default to "either/or" thinking, we miss the power of "both/and." The best leaders I know don't choose between being analytical or empathetic—they master the art of weaving both together. Remember: 🩵 Your data tells a story, but people live that story 🩵 Numbers inform decisions, but relationships determine success 🩵 Evidence guides the direction, but dialog lights the path As a lifelong learner, I appreciate when candidates teach me something new. What's your approach? Are you more naturally data-driven or dialog-driven, and how do you strengthen the other side? #Leadership #DataDriven #PeopleCentered #DecisionMaking #LibraryLeadership #Interview #LifelongLearning #BothAnd #LeadershipDevelopment

Two years ago, our self-serve activation was dying. Engineers built a 9-step onboarding flow because the data said users wanted "full customization." Completion rates were abysmal. We were bleeding potential customers. I kept staring at our analytics dashboard like it would magically fix itself. Heat maps, drop-off points, A/B tests on button colors. Nothing moved the needle. Then we did something stupid simple: We watched actual humans try to sign up. This one guy opened 4 different browser tabs trying to figure out our pricing as it compared to contracted boards. Another woman literally said out loud: "Why do you need all this info before I've even seen the product?" That's when it hit me. The data was showing us what people DID. Not what they WISHED they could do. See, we'd been walking the pure data path. And data only shows you extensions of what already happened. It would've given us a better Nokia brick, not an iPhone. Because no survey ever said, "let me touch the screen." No focus group asked to eliminate keyboards. But watch someone juggle a camera, iPod, and phone? The human need screams at you. That's the data plus human path: where real innovation lives. We brought in a product growth expert who'd found $1M in net new revenue at his last company – in one quarter. First thing he did? Threw out our 9-step flow. "Your users don't want control. They want confidence that they picked the right tool." 2 steps. That's it. Everything else AFTER they're already inside. Results after 6 months: – 5x more users active after 30 days – Support tickets plummeted This is why we only hire people who can dive deep into data but keep their EQ intact. Because data gives you the "what." Human intelligence gives you the "why does this matter? And to whom?" Most teams pick one path: 1. Pure data = incremental improvements 2. Pure intuition = instinct and lucky guesses The magic is walking both simultaneously. Your dashboard is your rearview mirror. Your EQ is your windshield. Drive with both … or crash into walls you never saw coming.