Human resources have come a long way from the traditional focus on collecting and tracking information on employees to the modern focus of using the data to make deeper analytical connections across the business.

Beginners Guide to HR Analytics

In this guide, we’ll explain the essence of human resources analytics and the reasons your organization should take this process seriously. We’ll also explain the common pitfalls of HR analytics and present you with five steps that’ll get your organization started.


Human resources (HR) analytics refers to a process where techniques of data mining and business analytics (BA) are used for processing HR data. It is sometimes also referred to as talent analytics. Furthermore, data mining in this context refers to the practice of studying established databases in order to create new information.

There are two main purposes for HR analytics: providing insights and identifying key data.

The first purpose aims to provide the organization information on its own operations, which can help with the effective management of employees. These insights can then ensure business goals are reached efficiently within a certain timeframe.

The second key function of HR analytics helps to identify the data the organization should capture. Furthermore, it provides the models for predicting the different ways the organization is able to receive an optimal return on investment (ROI) on its human capital.

Overall, HR analytics is focused on making the most of the vast amounts of HR data most organizations have gathered. Companies often have plenty of data on things such as employee demographics, training records and so on, but it is the analysis of the data HR analytics can help with.

What’s the below panel discussion on HR analytics for more information:



HR decisions are often based on professional instinct and the gut feeling. Recruitment, for example, often relies in the personal connection recruiters make with the candidate. The problem of relying on this gut instinct is that it can normalize bad practices.

Common workplace injustices can therefore go unnoticed. The pay gap between men and women is a solid example of this. Promotions and rewards might be provided to male employees due to gut instinct, instead of relying on cold data on performance, for instance. Organizations might even consider they are paying the same, unless they study the actual data.

HR analytics can help boost the performance and predict the models, which lead to better success. It removes more of the human error from decision-making. For instance, improvements in workload management can be more effective when data is used to show which departments or teams are bearing the burden and which can afford to take on more responsibilities.

Perhaps more importantly, HR analytics has been proven to improve company growth. Training Zone reports on the findings of performance boost for one company, which used HR analytics to improve its recruitment process. Through data analysis, the company noticed the traditional key metrics of education and reference quality didn’t have a big impact on the candidate’s performance in sales productivity. In fact, it was metrics such as experience in big-ticket sales and the ability to perform in unstructured circumstanced which drove better sales performance. When the company implemented these HR analytic findings to recruitment, the company’s sales grew by $4 million the following year.

Other surveys have had similar findings in terms of the importance of HR analytics to overall company performance. A survey by MIT and IBM discovered that higher level of HR analytics use had the potential to:

  • Provide 8% higher sales growth
  • Generate 24% higher net operating income
  • Produce 58% higher sales per employee


The applications of HR analytics are vast and the important metrics to an organization depend on the industry, as well as the nature of the business.

A couple of the key HR metrics to analyze include:

  • Resignation rate
  • Time to recruit to hire
  • Turnover rate for different staff groups (first year, five year, etc.)
  • Revenue per FTE
  • Performance appraisal participation rate

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The above metrics and other such data can be used to boost business performance. The key areas where HR data can help are:

  • Recruitment – HR analytics can provide answers for finding the ideal candidates to help the business succeed. For example, as the above example of the company showed, data can be used to identify the candidate qualities that yield better results. You can compile data of candidates who end up staying with the company and find the common denominators among them.
  • Health and safety – HR analytics can better locate the problem areas when it comes to health and safety related issues. Data can point out the roles, the job locations and other such factors that have the highest rates of accidents.
  • Employee retention – You can also learn more about employee retention through data. You can use HR analytics to discover the aspects, which increase employee engagement.
  • Talent gaps – Data can reveal whether there are talent gaps within the organization. For example, certain departments might have higher skilled workers to others and this could hinder the overall performance of the company.
  • Sales performance – HR analytics can help provide details on how sales targets can be exceeded. You can notice how specific talent helps employees perform better or that certain training programs yielded immediate returns in terms of sales.


Before we look at the starting steps for implementing HR analytics, it’s worth examining some of the main challenges the application of the process creates. It’s essential to find ways to manage the below five challenges when establishing HR analytics within your organization.

Challenge 1: Data deluge

The more data your organization gathers the harder it can become to use if appropriately. Big Data doesn’t automatically generate good results. You must be able to implement the right data analytics to succeed.

If your HR department just gathers a lot of data without proper implementation of analytics, you’ll end up with bloated data. The more bloated the data, the harder it is to make valuable assumptions.

For example, all the metrics you gather should be properly defined and categorized. You must define the questions you want to solve with your data, not simply gather it for the sake of having data.

Challenge 2: Data quality

As well as focusing on gathering the right amount of data, you also need to ensure you focus on data quality. Data deluge can quickly lead to poor quality data, as you aren’t creating meaningful connections between different data sets.

It is essential to guarantee data quality by focusing on ensuring data integrity and security. The problem for many organizations is that the data used in HR analytics can come from different departments within the organization and therefore lead to issues. Certain data can be ignored, dropped, lost or the data sets cannot be joined, which can result in inadequate analysis.

Challenge 3: Low analytical skills in most HR departments

For HR analytics to succeed, the team behind it must have knowledge in both, human resources as well as data analysis. But finding HR leaders who are also competent in data analytics can be difficult.

According to Elizabeth Craig, a research fellow at Accenture Institute for High Performance, there is scarcity to finding the right talent for HR analytics. Furthermore, Craig told Data Informed certain data analytics tool require special IT skills which add extra pressure on finding the right people to take care of the process.

The problem is further expanded by findings that only 6% of global HR teams feel confident about their skills in using analytics. In addition, just 20% thought the data usage in their organization was credible and reliable enough to make decisions.

Challenge 4: Often executive support for HR analytics is lacking

HR analytics has not yet become the mainstream process for many companies and there is often a lack of executive support. But for the process to work, HR departments must be able to convince the executive leaders on the benefits of using analytics.

Executive support is important as it provides access to better resources, as implementation of a proper HR analytics system is not cheap. It can also provide better access to data across different departments. In order to convince the executives, HR departments must focus on highlighting the possibilities of a strong ROI with the initial investment.

Challenge 5: HR analytics costs a lot and ROI is often not visible

Finally, organizations must be aware of the cost challenge. The price range of HR analytics tools is as varied as the availability of tools. According to the Data Informed article, the platform costs can range from “$400,000 to $1,5 million for a company with 5,000 full-time employees”.

Furthermore, the cost estimation doesn’t include the increase costs organizations might face in terms of hiring new staff to implement the programs or training existing staff in the use of analytics.

In addition, the ROI for HR analytics isn’t the most visible. This is because the benefits can be shown across different departments and over a longer period. For example, improvements in employee retention won’t become evident until later.

The challenge becomes from the realization that aiming for cheaper HR analytics platform doesn’t necessarily yield bigger savings. Insufficient software and tools can lead to inefficient and incomplete results, which in result won’t create high enough ROI to justify the investment.

Learn from Google’s VP of People Analytics and Compensation how to make betteer people decisions using human resource analytics.



If your organization wants to implement HR analytics, what is the right route to go about it? Below are five starting steps, which can help your organization to setup the process.

Step 1: Define the business questions you want to solve

The first and the most important thing to do is to define the business questions you are looking to solve. You can’t start gathering data and then blindly looking at it to find correlations.

Define the issues you’d like to improve in the HR sector. For example, these could be issues to do with workplace diversity, improving employee retention rates, measuring the amount of money spent on training or understanding the workplace absence levels better. These are some simple issues you should start with and later start looking into the wider issues.

For instance, you might want to understand how the HR effort impacts things such as profit margins.

Once you are aware of some of the HR related issues you’d like to examine more closely, you need to start outlining the required metrics for solving these problems.

Some of the HR metrics that highlight the HR department’s performance include:

  • The resignation rate – How many employees resign within any given period in terms of the overall workforce?
  • The recruitment times – How long does it take to fill a job position, as well as the time it takes for a candidate to accept the offer and become an employee?
  • Staff turnover rate – How many recruits leave after the first year, five years and so on?
  • Workforce diversity – What are the percentages when it comes to women, men, religious groups, and ethnicities?
  • The revenue from full-time employees – What is the revenue generated per full-time worker?
  • Amount of overtime pay – How high is overtime pay and how often is it implemented?
  • The ratio of permanent to temporary workers – How many of the employees are part-time compared to full-time?

Step 2: Identify the data that answers the above questions

Once you have the questions and the problems defined, you can start identifying the data required to solve and answer them.

First, your attention should focus on HR-related data, which your department already has stored. This includes information on recruitment, performance and succession. Your HR department should already be monitoring these common datasets.

Second, you want to start gathering data on things such as employment engagement, surveys and exit interviews. Depending on the level of data gathering within your organization, you might already be creating these datasets.

Finally, you need to extend your data gathering to other business systems and departments. You should start gathering important metrics from finance and market research. These include things such as turnover, sales performance, money spent on market research and training.

Step 3: Implement ETL: extraction, transformation and loading

As already explained above, the HR department must work in close connection with the IT department, as certain software and data extraction might require specialized data analytic skills. It’s therefore a good idea to start implementing closer connections between these two departments.

Part of this process is the implementation of ETL: extraction, transformation and loading. There are tools, which you can use to implement this process automatically. For example, IMB’s WebsphereDataStage and Cognos Data Manager, or Microsoft’s SQL Server Integration Services are among the most popular options. While non-technical employees can use these platforms, it can be beneficial to ask the IT department for assistance.

This process essentially allows you to extract the necessary data from a source you define, transform it to the correct clean and consistent format, and load it to your analytical platform to be used in the analysis.

Step 4: Incorporate the findings to business operations

When your HR data analytics start generating results, you need to start implementing changes. For example, if you focused on looking at workforce diversity and your data shows you’re not receiving enough applications from ethnic minorities, you can start changing your recruitment strategy.

This could include targeting recruitment agencies that focus on ethnic minority candidates, conducting interviews within ethnic minority groups to see whether the community views your organization negatively, and creating more tutoring opportunities with ethnic minority employees.

Furthermore, you need to draw a connection between the HR data and other business measures. For instance, reduction in staff overtime can directly correlate to productivity and profitability. A KPMG report People are the Real Numbers, talked about the importance of these solid connections in an example of workplace absence and cost-efficiency.

While it’s helpful to track absences by location or versus prior years, if HR could also show that improvements in absenteeism positively correlate with manufacturing cost efficiency, then line leaders would be more likely to see the value of HR,” the report stated.

Step 5: Perform regular analysis

Finally, HR analytics shouldn’t be a done irregularly and remain irrelevant in the majority of times. In order to enjoy the benefits of the process, it’s essential to implement a regular schedule.

For example, as you’ve defined an issue you want to look at with HR data, you’ll perform data analysis to find an answer to the issue. Once you implement the solutions to your problem, you need to return to the issue regularly to see whether the changes are sticking and whether new issues might have risen.


HR analytics is an essential part of data management and its implementation can yield positive returns for any organization. But as the above has shown, the management, analysis and interpretation of data isn’t always straightforward and organizations need to approach HR analytics one step at a time.

The key to successful HR analytics relies on the understanding that the size of the measured data isn’t the key to success, but rather, the impact the data can have on decision-making in the organization. HR analytics shouldn’t be seen as influential only in the HR department, but rather as something, that has the potential to create value throughout the organization.

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