A Guide To The 4 Types of HR Analytics

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A Guide To The 4 Types of HR Analytics

HR analytics helps HR professionals and their organizations to improve decision-making through data. It also offers HR practitioners the ability to contribute strategically by providing meaningful insights and contributing more effectively to the business’s bottom line.  There are 4 types of HR analytics methods that HR professionals can use, namely, descriptive, diagnostic, predictive, and prescriptive analytics. This article will discuss each of these types and their application in HR.

What is human resources analytics?
The 4 types of HR analytics explained
1. Descriptive analytics
2. Diagnostic analytics
3. Predictive analytics
4. Prescriptive analytics

What is human resources analytics?

Before we get into the different types of HR analytics, it’s important to have an overall understanding of what it is. In simple terms, HR analytics is the collection and interpretation of human resources data to support evidence-based decisions. This method is also referred to as people analytics, talent analytics, or workforce analytics.

HR analytics is a tool that correlates HR data to organizational goals and demonstrates how HR initiatives are making an impact. This insight shows what is effective and where improvement is needed. With data readily available, HR leaders can answer questions and propose solutions with concrete evidence. 

If you’d like to read some examples of HR analytics and dive deeper into all that it entails, our article on What is HR Analytics written by AIHR co-founder Erik van Vulpen provides a great starting place. If you would like to find out about the benefits of HR analytics for the business then check out the AIHR articles on 18 Benefits of HR Analytics For Your Business [With Examples].

The 4 types of HR analytics explained

There is a specific purpose for the 4 types of data in HR analytics: Each one offers unique insights and tells a part of the story. 

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Which method or combination of methods you should implement depends on what your needs are and what you have access to. These types of people analytics can be helpful on their own, but you get a more complete picture when all the analytic solutions are available. 

4 Types of HR Analytics

Now let’s get into these four levels of HR analytics and how you can use them to create business impact:

1. Descriptive analytics

The first type of HR analytics on the list is descriptive analytics. This is the most basic type that analyzes data patterns to gain insight into the past. 

It is known as decision analytics, and uses statistical analysis techniques to explain or summarize a particular set of historical, raw data. It focuses on past data to account for what happened but doesn’t make predictions for the future. 

How descriptive analytics works

Descriptive analytics can use a combination of numerical data and qualitative data. It involves performing mathematical calculations, such as central tendency, frequency, variation, ranking, range, deviation, etc. This allows HR to see patterns and inconsistencies to improve planning.

Descriptive analytics can help with:

  • Assessing behavior
  • Comparing characteristics across time
  • Spotting anomalies
  • Identifying strengths and weaknesses

Advantages and disadvantages 

Descriptive analytics advantagesDescriptive analytics disadvantages
– The simplest form of data analysis.
– Requires only basic math skills, and it allows you to present complex data in an easy-to-digest format
– Limited to a simple analysis of a few variables after the fact.
– For instance, an employee headcount summary captures a time period and reports the “what” but not the “why” or “how.” 

Descriptive analytics examples

Efficiency metrics that HR has traditionally tracked fall under the descriptive analytics category. Here are two examples:

  • PTO: Using descriptive analytics, HR can analyze the average number of paid time off days that employees use in one year.
  • Turnover:  Descriptive analytics could be used to analyze employee turnover rates to compare the annual turnover between two teams or two departments. 

2. Diagnostic analytics

Diagnostic analytics takes descriptive analytics to the next level by providing an explanation for what has been revealed. It aims to determine the underlying reasons for what the data exposes. 

Although it is based on the same historical data as descriptive analytics, there is a key difference. Diagnostic analytics goes into the next step of summarizing what happened in understandable terms. It digs for the “why” behind the data’s trends, correlations, and anomalies. 

Diagnostic analytics process

Conducting a diagnostic analysis typically involves the following steps:

  1. Identifying the patterns and anomalies within the data that raise questions and need to be studied further.
  2. Discovering what factors could be contributing to the patterns and anomalies to identify the relevant data.
  3. Determining causal connections by analyzing the data with various methods.

There are multiple diagnostic analytics techniques, including::

  • Data drilling: Taking information from a more general overview and providing a more granular view of the data.
  • Data mining: Extracting patterns from data to help predict future events
  • Probability theory: Quantifying uncertain measures of random events 
  • Regression analysis: Determining which variables will impact an outcome
  • Correlation analysis: Tests the relationships between variables
  • Statistical analysis: Collecting and interpreting data to determine underlying patterns

What is the purpose of diagnostic analytics? 

Diagnostic analytics is used to transform data into worthwhile insights. It identifies patterns, variances, and causal relationships while also considering internal and external factors that could be influencing them. This helps HR see the big picture of a situation and zero in on which factors have the potential to create problems. Then you can focus your efforts in the right place to mitigate them. 

Diagnostic analytics advantagesDiagnostic analytics disadvantages
– Shows a more comprehensive interpretation of the data for informed decision-making. – Focuses on past occurrences which makes it very reactive.
– Can’t provide actionable insights to support your planning process. 

Diagnostic analytics use cases 

Let’s look at two examples of diagnostic analytics put into action with HR:

1. Employee absenteeism

If your absenteeism rate is climbing, you can use diagnostic analytics to find out why employees are missing work more often. 

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This could involve looking at your absenteeism data to see if more unplanned absences occur on certain days of the week, when there is a long time between paid holidays, or after employees’ requests for time off have not been approved. You can also review pertinent information from employee feedback surveys and exit interviews. 

Once you understand the most common reasons for absences, you can develop strategies to change these factors.

2. Employee engagement

Diagnostic analytics can be used to improve your employees’ engagement and your company culture. Digging into the data from internal surveys and exit interviews should uncover the areas that make employees feel connected and satisfied in their work and those that don’t. 

Since highly engaged employees tend to be the most productive, linking engagement scores to performance measures will show the impact. A 2019 report noted how shoe retailer Clarks discovered that for every 1% improvement in employee engagement there was a 0.4% increase in the company’s performance. 

3. Predictive analytics

Estimates what might happen in the future; it forecasts future outcomes. The process involves categorizing past and present data to isolate patterns, correlations, and irregularities followed by estimating a model to predict what will occur in the future. Then the model’s accuracy is evaluated by applying it to new data. 

Predictive analytics in HR

Predictive HR analytics support better HR decisions. It translates historical data gathered from areas, such as job skills, employee engagement, productivity, resumes, etc. into forecasts about what to expect in the future. These predictions furnish HR leaders with information that will improve decision-making in areas such as hiring the right candidates, bridging the skills gap, and retaining top talent.

Predictive analytics advantagesPredictive analytics disadvantages
– It can reduce human error, help you avoid risks, improve operational efficiencies, and refine the forecasting for your organization.  – It requires substantial and relevant data (big data sets).
– It’s also challenging to ensure that all of the variables are considered, and the model must be updated as data changes. 

Predictive analytics is a valuable tool in many HR functions. Here are two predictive analytics use cases:

1. Recruitment

Predictive analytics can analyze data from the hiring process (resumes, job descriptions, etc.) to narrow in on the desired skill sets. Certain elements of social media profiles and answers to automated application questions can also reveal the key attributes that indicate a candidate is a right fit for long-term success with the organization. Then you can tailor your recruitment strategy to attract and engage this type of applicant. 

Furthermore, you can implement predictive analytics to estimate what your future demand for certain roles will be. This allows you to start recruiting at the appropriate time and target suitable candidates.

2. Retention

With predictive analytics, you can forecast various talent management outcomes, such as who will quit. The HR department at consumer credit institution Experian developed a predictive model that can identify who’s at risk of leaving and what the surrounding factors are.

Gathering and analyzing data for employees’ projected flight risk can expose who may leave and what their reasons are. These indicators point out what needs to be evaluated. For instance, the growth opportunities or compensation and incentive packages your organization offers. With this knowledge, you can make changes that will keep more staff with the company and lower the turnover rate. 

4. Prescriptive analytics

Prescriptive analytics is the final and most complex stage of the analytics journey that transfers predictive analytics into ideas for what to do next.

A general prescriptive analytics definition would be the targeted recommendation for decision options and actions based on the findings of predictive analytics. It offers options for where and how to act to achieve success.

Prescriptive analytics relies on big data and uses an assortment of technical tools, including:

  • Machine learning
  • Algorithms
  • Artificial intelligence
  • Pattern recognition

How prescriptive analytics works

You can think of prescriptive analytics like Netflix for business. It works in the same way that Netflix suggests films based on viewing behaviors. Prescriptive analytics goes beyond predictive analytics with a more pre-emptive approach to looking at the future. 

Predictive analytics simply predicts a decision or action’s most likely outcomes. With prescriptive analytics, you can forecast what will happen next, why, and what you can do next. It anticipates the most likely scenarios and which interventions have the potential to bring optimal results. 

Predictive analytics advantagesPredictive analytics disadvantages
– Equips HR leaders to make informed, real-time decisions to improve performance, solve complicated problems, and take advantage of opportunities.
– For example, it can recommend strategies for training that will boost
– An iterative process that requires time. Also, the quality of recommendations is dependent on the quality of the data, so it won’t be effective if your data is incomplete or unreliable.
– You must also be careful about weighing the options presented and ensure that taking the recommended action is reasonable from an HR perspective.
– Algorithms can’t always reflect the diverse intricacies of dealing with human beings.

Because of its complexity, prescriptive analytics is also known as the ‘final frontier of analytic capabilities’. It requires more advanced analytics skills that you can develop by participating in an AIHR People Analytics Certificate Program.

Prescriptive analytics examples

Here are two prescriptive analytics use cases related to HR. 

1. Staffing

Prescriptive analytics can help you prepare for upcoming staffing needs. Data surrounding employees’ interactions with digital benefit options can reflect potential openings. An uptick in activity surrounding retirement planning or medical and family leave policies can lead to staffing recommendations that will address departures and long-term absences.  

2. Attrition

As mentioned above, Experian is using AI to predict high-flight-risk employees. However, they are also taking the next step with prescriptive analytics to prevent the contributing factors to flight risk from happening. 

According to Experian HR executive Olly Britnell, “We’re using machine learning to track interventions such as changing the team structure offering more training, and then tracking which ones are having an impact.” 

To conclude

HR analytics is the driving force behind effective planning and decision-making in HR. The insights analytics reveals provide a more complete perception of what’s really going on in the company. This opens up possibilities and opportunities you wouldn’t otherwise be aware of. 

The better you understand the different types of metrics and analytics in HR, the more relevant information you’ll be able to gather from data to help meet business goals. Your ability to leverage analytics puts you in the position to serve your organization in a more strategic capacity.

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