5 barriers to creating data-driven HR -- and how to overcome them

In “5 Barriers to Creating Data-Driven HR — and How to Overcome Them”, the article explores why, despite widespread belief in the value of people analytics, many HR organizations still struggle to become truly data-driven. While leaders increasingly agree that data should inform workforce decisions, progress remains slow due to cultural, technical, and organizational barriers.

Industry leaders such as Alexis Fink, Intel’s general manager of talent management, argue that data enables better decisions that benefit both employees and the business. Yet, as Dan Staley of PwC notes, people analytics often remains a side project rather than a dedicated organizational capability. Most HR functions, he says, are still “crawling and walking” when it comes to analytics maturity.

The article outlines five major barriers that prevent organizations from building effective, data-driven HR functions.

First, misunderstanding the basics of data. Much HR data is transactional in nature and poorly suited for decision-making. To gain meaningful insights, organizations must be intentional about what they collect. Predicting leadership effectiveness, for example, requires robust assessments rather than relying on tenure or past performance ratings. As Fink explains, success depends on planning for the right data—much like planning a menu before hosting a dinner party.

Second, not knowing what data is missing. Insightful analytics require data that is accurate, consistent, relevant, and accessible. In practice, HR data is often siloed across systems and disconnected. Michael Housman, chief data scientist at RapportBoost.AI, emphasizes that organizations must identify data sources, assess data quality, and establish a single source of truth before meaningful analysis is possible.

Solving this problem requires cross-functional expertise. According to Housman, success comes from combining data science skills with deep HR and business understanding—not relying on one discipline alone.

Third, the urge to overreach. Experts strongly advise organizations to start small. Rather than launching large-scale data warehousing initiatives, Housman recommends answering a single, focused question using readily available data. Early wins help demonstrate value and avoid organizational fatigue caused by long, expensive projects that delay results.

Alternatively, organizations can collect new data rather than cleaning old data. Employee surveys, for instance, can quickly surface insights into performance gaps or attrition risk without complex integrations.

Fourth, being dazzled by technology. While vendors promote advanced tools powered by AI and machine learning, experts caution that technology is not the starting point. Data-driven HR is about solving real business problems, not analyzing data for its own sake. Many early analytics efforts can be accomplished with basic tools, including spreadsheets, before investing in sophisticated platforms.

Fifth, lack of trust in data. Resistance often comes from experienced professionals who distrust algorithms, particularly when data-driven insights challenge long-held intuition. Housman notes that building trust requires proving value through small, tangible examples and involving stakeholders throughout the process.

Change management is therefore essential. Leaders must show how data makes decisions easier and improves outcomes, rather than positioning analytics as a threat to expertise. As Housman explains, influencing behavior through data is just as important as the analysis itself.

The article concludes by framing data-driven HR as both a mindset and a practice. Organizations that invest heavily in technology but continue to rely on instinct are not truly data-driven. Instead, effective HR analytics uses data to guide decisions—particularly around critical issues like retention—rather than retroactively justifying decisions already made.

Ultimately, becoming data-driven in HR requires focus, patience, and cultural change. By starting small, prioritizing data quality, and building trust, organizations can move beyond experimentation and begin using analytics as a core driver of workforce strategy.