HR analytics is the process of using data to inform strategies and decisions related to people management within an organization. It helps HR teams analyze their efficiency and identify areas of improvement and development.
Its primary outcome is enabling HR professionals to measure the success of human resources initiatives and projects.
The use of HR analytics has increased within people management (partially boosted by the widespread adoption of hybrid and remote work). It relies on software tools that make gathering and analyzing people-related data more accessible and cost-efficient.
HR analytics vs. people analytics
HR analytics refers to processes that exclusively look at metrics, data, and KPIs involving the HR team.
People analytics refers to processes that analyze data related to the company staff to measure the success of people initiatives.
While there are similarities and overlaps, the two should not be equated.
Why are HR analytics important?
Measuring the effectiveness of your organization’s HR work is crucial. Moreover, with the increasing decentralization of work, designing effective people management strategies becomes more challenging.
HR analytics enables HR professionals to take data that relates specifically to human resources and use it to measure the success of their teams and projects. With the insights gained from their analysis, HR leaders can approach decision-making in a way that is data-driven and less subjective.
Optimizing human resource management in this way leads to more actionable insights and better decisions, which in turn leads to better business outcomes and higher profitability.
HR analytics can help:
- Understand employee turnover
- Optimize the hiring process
- Improve new hire onboarding
- Observe patterns in the employee lifecycle
- Identify the best talent management initiatives
- Improve employee retention
Examples of HR metrics
Typical metrics that are observed within HR analytics are:
- Time to hire
- Cost per hire
- Turnover rate
- Attrition rate
- Absenteeism
- Revenue per employee
- Employee engagement
- HR cost per employee
HR analytics is a continuous process
Ensuring that HR analytics processes within an organization are successful begins with having a clear idea of how they work.
HR analytics follows a sequence of steps that repeat for each analysis cycle.
Data collection
As mentioned in the previous section, HR analytics relies on specific metrics and data sets which are often already being collected in your company’s HRIS (human resources management system) and other data sources.
The first step is to gather the data needed to track key metrics.
As the company grows, manual data collection and organization approach quickly become unscalable. Big data sets are a treasure trove of information, but they must be tidy and well-structured for their value to be unlocked.
Important things to consider when collecting data:
- Data sources
- Data collection methodology
- Emoloyee data sets
- Data storage (Excel, shared spreadsheet, Notion)
- Data vizualization (such as dashboards)
Data analytics
This phase helps HR professionals spot patterns or trends that emerge while measuring data and use them to identify next steps, establish new benchmarks, or spot challenges the HR department must overcome.
Applying data analysis outcomes
The final step in the process involves the HR team defining and applying solutions or improvements to the findings that emerged through their use of analytics tools and analysis.
Four approaches to HR analytics
Like most other forms of data analytics, human resources analytics can be divided into four types. HR leaders must understand which type fits their workforce analytics needs best, to ensure stakeholders know what to expect and how to analyize results.
These analysis approached are not mutually exclusive. Rather, they can be seen as different complexity layers and detail processing applied to the same data sets.
Descriptive - What happened?
Descriptive analytics relies on past data to create a summary of events. It is the simplest and most common form of data analytics.
Examples of descriptive analytics findings could include:
- Variations in turnover rate
- Variations in employee engagement
- Variations in onboarding duration
To perform descriptive analysis, HR teams will look at data from sources like:
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Dashboards
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Reports
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Retrospectives
Diagnostic - Why did something happen?
Diagnostic analysis goes a step deeper than descriptive–its goal is to understand the causes behind the trends observed in descriptive analysis, using the same data sources.
HR teams will cross-reference different data to find patterns or exceptions and interpret them to draw conclusions.
Examples of diagnostic analytics findings could include:
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Determining correlations between patterns
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Determining the causes of a pattern or exception
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Identifying a list of potential causes for an exception
Predictive - What could happen?
Like social media algorithms, predictive analytics takes data from past results and patterns to try and forecast future outcomes. The higher the quality of your data, the more accurate your forecasting will be.
Predictive HR analytics could be used to determine variations in:
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Employee performance
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Cost per hire
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Attrition rate
Prescriptive - Why will something happen?
Prescriptive analytics is the most complex data analysis form and hence the least common. It involves using automation technology, artificial intelligence, and machine learning to constantly parse new data, analyze it, and suggest the best course of action.
Prescriptive analysis is supposed to interpret opportunities and risks (and how to enhance or mitigate them) rather than simply predict an outcome. This method constantly updates its conclusions to match continually changing information.
Examples of prescriptive technology most of us are familiar with, though not connected to HR, are map applications, which can determine routes and delays or calculate reroutes and alternative suggestions in real-time.
