The Next Frontier for Human Resources? It’s People Analytics

A new way of managing talent is beginning to emerge within the human resources world called people analytics.

It attempts to remove gut instinct, intuition and human biases from talent management in order to make workforce decisions in an evidence-based and data-driven way.

In other words, we want to ensure that we make decisions around employee selection, development and separation based on criteria that we know matters—because we have correlated them statistically with outcomes—and ignore the criteria that don’t.

Transforming the World of HR

This approach was popularized by the Oakland Athletics when they used data and analytics to help scout and evaluate baseball players. But its roots run much deeper than that, as predictive analytics have effectively transformed entire industries like marketing and finance.

Whereas products were once marketed based on the gut instinct and intuition of marketing executives, data and algorithms are now used to predict consumer behavior with a high degree of accuracy. The result is a more data-driven decision-making process around how, when and where to place ads that increase their yield by an order of magnitude.

It is virtually indisputable that predictive analytics will transform the human resources world and the way that human capital decisions are made. What is somewhat surprising is the fact that we’re relatively early in this transformational process in spite of the fact that these tools and techniques have existed for a number of decades and have already been successful in disrupting a number of other industries.

Decades ago, FICO scores transformed the credit industry and moved the decision of whether someone was creditworthy out of the hands of individuals and into the hands of computer algorithms that had been validated against outcomes. Likewise, advertising used to be done in a fairly untargeted way. Now the Internet has completely transformed how we market to consumers through highly targeted, data-driven ads.

The Four Stages of People Analytics

As organizations go through their own journeys, there are typically four stages of maturity associated with people analytics:

1. Ad hoc question answering — Pulling together spreadsheets or data sources on an ad hoc basis to answer targeted questions. Analyses are typically standalone and not easily repeatable.
   Examples: Who are my best and worst managers? Is my referral program working?
2. Retrospective data analysis — Centralizing data to perform retrospective analysis using basic statistical techniques such as correlations and trend analysis, often via dashboards or BI tools.
3. Predictive analytics — Leveraging centralized data along with advanced econometric techniques (e.g., multivariate regression) to build predictive models and conduct “what if” analysis.
4. Experimental design — Running experiments and A/B tests to identify true causal drivers, evaluate interventions, and apply proven approaches at scale.

From hundreds of conversations with large organizations, the vast majority are currently between stages one and two—analyzing data retrospectively but not yet engaging in predictive modeling or experimentation.

The Next Frontier for Organizations

Predictive analytics represents the next frontier. It allows organizations to simulate changes before implementing them.

What if wages increase by $1 per hour? What if paid time off increases from two weeks to three? Retrospective data can be used to predict the likely impact on employee tenure and performance.

However, while this approach is low risk, it is not truly causal. For example, if wages are associated with longer tenure, it’s unclear whether higher pay causes better performance or better performance leads to higher pay. That’s why some organizations, like Google, run experiments to establish causality.

Google’s “Project M&M” is a well-known example. By making candy less visible and healthy snacks more visible, employees in the New York office consumed 3.1 million fewer calories over seven weeks.

This illustrates the power of data analysis and experimentation when applied thoughtfully to workforce decisions.

A Data-Driven, Evidence-Based Approach to HR

While we must be careful not to experiment recklessly with compensation or benefits, there are many opportunities to apply experimental thinking—such as referral programs, office layouts, or company events.

Designing experiments, rolling them out in a staggered fashion, collecting data, and identifying what works best mirrors the A/B testing approach that is now standard in marketing.

People analytics is slowly transforming how organizations hire, develop, promote, and separate employees. Early adopters like Google are paving the way, and over the next five years we are likely to see widespread adoption across industries.

Much like Major League Baseball’s embrace of sabermetrics, companies that ignore people analytics will increasingly fall behind competitors who leverage data-driven workforce strategies.

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