Enhancing HR Insights: Navigating the Levels of People Analytics Capabilities

People analytics is the practice of using data to make better decisions about human resources and organizational performance. People analytics can help business and HR leaders understand their workforce, optimize their talent management, and drive their strategic goals. However, not all people analytics are created equal.

There are different types of data analytics categories, methodologies or capabilities which range across a spectrum, from relatively simple from descriptive analytics (ex: reporting) to sophisticated adaptive, autonomous analytics (ex: recommendation engines). In this article, I will explain the differences between these types of analytics capabilities, provide some examples of how they can be applied in practice, and show how they vary in terms of data needed, sophistication, and business value.

Descriptive analytics

Descriptive Analytics focusses on describing or summarizing the existing data using existing business intelligence tools to better understand what is going on or what has happened. For example, standard reports that show the number of employees across demographic, geography, department, etc. This capability is within reach for all companies regardless of size and can be generated using common software such as Excel. Descriptive analytics are the most basic and common type of people analytics, but they have limited value for decision making. They require low levels of data and sophistication, and they provide low levels of business value.

Diagnostic analytics

Diagnostic Analytics focuses on past performance to determine what happened and why. The result of the analysis is often an analytic dashboard that visualizes the data and identifies patterns, correlations, or outliers. For example, employee engagement survey result analysis with the aim of understanding where engagement is healthy or unhealthy, and what factors influence it. Diagnostic analytics are more insightful than descriptive analytics, but they still rely on historical data and do not offer solutions or predictions. They require moderate levels of data and sophistication, and they provide moderate levels of business value.

Predictive analytics

Predictive Analytics focuses on predicting future trends based on historical data and statistical models. For example, turnover prediction that estimates the likelihood of employees leaving the organization based on their characteristics, behaviors, and feedback. Predictive analytics are more valuable than diagnostic analytics because they enable proactive actions and interventions to prevent or mitigate undesired outcomes. They require high levels of data and sophistication, and they provide high levels of business value.

Prescriptive analytics

Prescriptive Analytics provides suggestions based on predictive analytics and optimization techniques. For example, a prescriptive model could offer managers suggestions on how to strengthen employee engagement based on survey results and available actions. Prescriptive analytics are more actionable than predictive analytics because they offer specific guidance and recommendations to improve performance or satisfaction. They require very high levels of data and sophistication, and they provide very high levels of business value.

Autonomous analytics

Autonomous Analytics encompasses self-learning algorithms with the ability to automatically detect relevant anomalies, patterns, and trends, and to adjust or improve their own models and outputs over time. For example, recommending connections between datasets to further refine or scale insights, or creating new hypotheses based on emerging signals. Adaptive/autonomous analytics are the most advanced and innovative type of people analytics, but they are also the most challenging and complex to implement and interpret. They require extremely high levels of data and sophistication, and they provide potentially extremely high levels of business value.

As you can see from this framework, people analytics is not a one-size-fits-all approach. Depending on your objectives, resources, and capabilities, you may choose to use different types of analytics for different purposes. However, it is important to note that these types of analytics are not mutually exclusive or sequential. They can be used in combination or in parallel to complement each other and provide a more holistic and comprehensive view of your people and organization.