Williams-Sonoma, Inc.

Data That Drives Better Decisions

My approach is simple: data matters only when it changes a decision. Early in my career, I saw teams produce impressive dashboards that generated admiration but not action. That shaped how I work to this day.

Leading both people analytics and HR technology reinforced something equally important: trustworthy insight starts long before reporting. It comes from disciplined systems design, governance and ownership. When that foundation is weak, even sophisticated analytics lose credibility quickly.

At the same time, I never view data as separate from the employee experience. Metrics reveal patterns, but people live the outcomes. The goal is not just to measure the workforce more precisely. It is to create insight leaders can trust and use in ways that make work better for employees.

Building Trustworthy HR Technology Foundations

The biggest challenge is that people analytics and HR technology often mature on different tracks. Analytics teams focus on insight. Technology teams focus on implementation. When those strategies are not designed together, organizations end up with data that exists, but cannot be trusted, scaled or translated into action.

System fragmentation makes that worse. Most companies operate patchwork systems across HRIS, ATS, learning, engagement and performance platforms, each with different structures and definitions. Even core concepts like headcount, job role or turnover can vary by system, quietly weakening every analysis built on top of the inconsistencies.

Underneath the technical complexity, though, the real challenge is organizational. Sustainable integration requires shared ownership across HR, IT and analytics, with alignment on data standards, governance and longterm priorities. The strongest organizations treat this as an operating model challenge, not a vendor problem.

Keeping the Human Side in Analytics

One of the best examples came from exit survey data I analyzed recently. Compensation was consistently cited as the top reason employees were leaving, so the logical response was to examine pay structures. But when we looked deeper, compa-ratio did not explain turnover. In fact, some of the highest-paid employees were leaving at the highest rates, while compensation satisfaction was also high on engagement and onboarding surveys.

What the data revealed was not a compensation problem alone, but a human one. Salary was the safest answer to give. It avoided harder conversations about culture, leadership or team experience. The real drivers could not be derived solely from the data.

"Too many organizations produce impressive dashboards that generate admiration but not action. Data only matters when it actually changes a decision."

That taught me an important lesson: data should sharpen judgment, not replace it. The value was not in taking the survey at face value, but in using the contradiction to ask better questions. That ultimately led us toward a more effective retention strategy focused on management quality, culture and targeted pay actions where real patterns existed. The human side isn’t separate from the analytics. It’s what makes the analytics honest.

Moving Toward Decision Support

We are moving beyond the era of dashboards. For many HR teams, the current model is still to build reports, share links and hope someone acts. That is where too much value gets lost.

AI will change that by making insight more immediate, contextual and embedded in the flow of work. In the near term, it will augment dashboards rather than replace them, because most organizations still need cleaner data foundations and stronger semantic structure. But the long-term direction is clear: leaders will expect direct, immediate and self-serve answers and recommendations, not static reporting.

That shift also changes the role of analytics teams. The work moves away from producing recurring reports and toward building the conditions for trustworthy intelligence: governed data, clear definitions, auditable logic and intuitive experiences. The real opportunity is not better reporting. It is decision support that reaches leaders at the exact moment action matters.

I began in HR, first in recruiting and later as an HRBP, and that foundation has been invaluable. Understanding the business and people challenges behind the data is a major advantage. I moved toward analytics by stepping into problems beyond my formal role and learning what I needed along the way. Curiosity and a willingness to operate at the edges of my role were truly key drivers.

That’s the first thing I’d say: get comfortable beyond the boundaries of your job description. The pace of change in this space is now measured in weeks, not years. Staying relevant requires continuous learning, not just in tools, but in how those tools apply to real business problems.

Invest early in fluency across disciplines. You will constantly translate between HR stakeholders who think in terms of people and risk, and technology stakeholders who think in terms of systems and scale. You don’t need to be an expert in everything, but you need to earn trust on both sides. That fluency compounds over a career in ways that technical skill alone doesn’t.

Finally: get close to the decisions before you worry about the data. The analysts who become indispensable aren’t the ones with the most sophisticated models. They’re the ones who understand why a problem matters and show up having already thought three steps ahead.