Why Overvaluing Industry Experience Limits Innovation in Data Leadership

A senior exec once asked me whether my experience with streaming vs. batch processing and customer data platforms “really translates” across industries. On the surface, it’s a fair question. But it reveals a hiring mindset that quietly constrains many companies: the belief that industry experience is the primary predictor of leadership success. The Illusion of Uniqueness Every leadership team thinks their industry is uniquely complex. Sometimes that’s true. Regulatory nuance in healthcare, trading rules in …

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Data Teams Are Not Service Teams

Over time, a lot of data teams drift into a pattern that’s hard to break out of. They become ticket takers, constantly responding to requests, pulling data for one-off questions, fixing dashboards, and explaining why numbers don’t match. It feels helpful at first. But eventually, the work starts to pile up, context starts to disappear, and the team is mostly reacting instead of building anything meaningful. The service model becomes the default, and that’s a …

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A Daily Checklist for Staying Close to the Business (as a Technologist)

Over the years, I’ve come to believe that the most effective technologists are the ones who stay deeply connected to the business. Not just during planning sessions or leadership reviews, but in the day-to-day rhythm of their work. This wasn’t always intuitive for me. Early in my career, I focused heavily on technical architecture, coding, and project delivery. But I eventually realized that without business context, even excellent technical execution can miss the mark. Here’s …

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Doing Great Work Isn’t Enough: Why Alignment, Visibility, and Sponsorship Matter

You’re grinding. You’re pushing through. You’re delivering. But is anyone noticing? In corporate world, execution and independence are table stakes. But real impact, and real career growth, come from something more: alignment, visibility, and sponsorship. Pushing Through Is Necessary, But Not Sufficient As I wrote in Pushing Through, the ability to keep going through ambiguity and resistance is critical. It builds resilience. It earns trust. But grit alone doesn’t guarantee your work will land. You might deliver a technically …

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Structuring Data and Analytics Teams for Impact

As more companies invest heavily in data as they grow, the question of how to structure the data and analytics function becomes increasingly important. It is not just about reporting lines or team charters. Structure influences how effectively data is used, how talent is retained, and how aligned the team is to business priorities. In my experience, I have seen a range of models in action: centralized teams, embedded analysts, and hub-and-spoke hybrids. I have …

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Leading Analytics Without Losing the Plot

In today’s data-saturated world, analytics leaders are under constant pressure to deliver more. More dashboards. More tools. More data. And yet, despite all that output, many teams struggle to drive outcomes that matter. Somewhere along the way, they lose the plot. This post is about staying grounded. It’s about leading analytics with clarity, avoiding common traps, and making sure your efforts connect back to what truly matters: progress, not just process. The Story Is the …

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Throwing the Baby Out with the Bathwater? Maybe You Should.

We’ve all heard it: “Don’t throw the baby out with the bathwater.” In business, this phrase is often deployed as a caution against overcorrection. It urges us to preserve the core while cleaning up the mess around it. Fair enough, except when the core itself is the problem. The phrase assumes there’s a precious “baby” worth saving. But what if there isn’t? Or what if the effort to preserve it is what’s anchoring your organization to outdated …

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40 Retail Analytics Use Cases Every Data Leader Should Know

Retail analytics is one of the most dynamic and mature domains in data. It sits at the crossroads of customer behavior, real-time operations, and constant decision-making. But here’s the truth: while the retail context is unique, the analytics patterns it relies on are highly transferable. Whether you’re in fintech, education, manufacturing, or SaaS, the tactics used in retail analytics—segmentation, forecasting, personalization, optimization—are universally applicable. Retail’s dynamic environment—marked by real-time customer behavior, high transaction volumes, and …

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