The Beginners Guide to HR Analytics
Human resources has come a long way from its traditional focus on collecting and tracking information on employees to the modern focus on using the data to make deeper analytical connections across the business.
This guide explains the essence of human resources (HR) analytics and the reasons your organization should take this process seriously. It also explains the common pitfalls of HR analytics and offers five steps that will get your organization started.
What are human resources analytics?
HR analytics — also known as talent analytics — uses the techniques of data mining and business analytics (BA) for processing HR data. Data mining, in this context, is the practice of studying established databases to create new information. The two main purposes for HR analytics are providing insights and identifying key data.
The first purpose aims to provide the organization with information on its own operations to help with the effective management of employees. These insights can then ensure business goals are reached efficiently within a certain timeframe.
The second purpose is to help identify the data the organization should capture, as well as provide the models for predicting the different ways the organization can 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 an organization has gathered, such as employee demographics and training records; but it is in the analysis of the data that HR analytics can help you.
Why should your organization focus on HR analytics?
HR decisions are often based on professional instinct and gut feeling. Recruitment, for example, often relies on the personal connection recruiters make with the candidate. The problem with 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 which models 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 are used to show which departments or teams are bearing the burden and which can afford to take on more responsibilities.
HR analytics has been proven to improve company growth. Training Zone, a UK learning and development publication, reported on the findings of performance boost for one company that 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 such metrics as experience in big-ticket sales and the ability to perform in unstructured circumstances that drove better sales performance. When the company implemented these HR analytic findings to recruitment, its sales grew substantially 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 a higher level of HR analytics use had the potential to: provide 8% higher sales growth; generate 24% higher net operating income; and produce 58% higher sales per employee.
Key ways to use HR analytics
The applications of HR analytics are many, and the important metrics to an organization depend on the industry as well as the nature of the business. Key HR metrics to analyze include:
- Resignation rate
- Time to recruit to hire
- Turnover rate for different staff groups (e.g., first year, five years)
- Revenue per full-time employee
- Performance appraisal participation rate
These metrics and other such data can be used to boost business performance. HR data can help in these key areas:
- Recruitment: HR analytics can provide answers for finding the ideal candidates to help the business succeed. 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 related to health and safety issues. Data can point out the roles, job locations, and other factors with 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 that increase employee engagement.
- Talent gaps: Data can reveal any talent gaps in the organization. For example, certain departments might have higher-skilled workers than others.
- Sales performance: HR analytics help provide details on how sales targets can be exceeded. You can discover how specific talent helps employees perform better or which training programs yielded immediate returns in terms of sales.
Five challenges to HR analytics
Before we look at the starting steps for implementing HR analytics, it’s worth examining the five main challenges the application of the process creates. It is essential to find ways to manage these when establishing HR analytics in your organization.
1. Data deluge
The more data your organization gathers, the harder it is to use 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 use of them. 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 them for their own sake.
2. Data quality
As well as gathering the right amount of data, you also need to focus on data quality. Data deluge can quickly lead to poor-quality data, as you aren’t creating meaningful connections between different data sets.
To guarantee data quality, you must focus 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. Data can be ignored, dropped, or lost, or the data sets cannot be joined, which can result in inadequate analysis.
3. Low analytical skills in most HR departments
For HR analytics to succeed, the team behind it must be knowledgeable in both human resources and 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, the right talent for HR analytics is scarce. Furthermore, she added that some data analytics tools require special IT skills, which makes it even more difficult to find 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
4. Executive support for HR analytics often lacking
HR analytics has not yet become the mainstream process for many companies and often lacks 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. To convince the executives, HR departments must focus on highlighting the possibilities of a strong ROI with the initial investment.
5. HR analytics expensive and ROI often not visible
Finally, organizations must be aware of the cost challenge. The price of HR analytics tools is as varied as the availability of tools. According to a Data Informed article, platform costs can range from “$400,000 to $1.5 million for a company with 5,000 full-time employees”. And this doesn’t include the increased 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, because the benefits will show across different departments and over a long period. For example, improvements in employee retention won’t become evident until years later.
The challenge comes from the realization that aiming for a cheaper HR analytics platform doesn’t necessarily yield bigger savings. Insufficient software and tools can lead to inefficient and incomplete results, which won’t create a high enough ROI to justify the investment.
Five steps to implementing HR analytics
How can your organization implement HR analytics? These five steps can help you set up the process.
1. Define the business questions you want to solve
The first and most important thing is to define the business questions you are looking to solve. You can’t start gathering data and then blindly look at it to find correlations.
Define the issues you’d like to improve in the HR sector, such as workplace diversity, improving employee retention rates, measuring the amount of money spent on training, or understanding the workplace absence levels better. You should start with simple issues and later look into the wider issues. For instance, you might want to understand how the HR effort impacts profit margins.
Once you are aware of which HR-related issues you’d like to examine more closely, start outlining the required metrics for solving these problems. HR metrics that highlight the HR department’s performance include:
- Resignation rate: How many employees resign within any given period in terms of the overall workforce?
- Recruitment time: How long does it take to fill a job position, and how long 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?
- Revenue from full-time employees: What revenue is generated per full-time worker?
- Amount of overtime pay: How high is overtime pay, and how often is it implemented?
- Ratio of permanent to temporary workers: How many of the employees are part time compared to full time?
2. Identify the data that answer those questions
With the questions and problems defined, you can start identifying the data required to solve and answer them.
First, focus on HR-related data that your department already has stored, including information on recruitment, performance, and succession. Your HR department should already be monitoring these common datasets.
Second, start gathering data on employment engagement, surveys, and exit interviews. Depending on the level of data gathering within your organization, you might already be creating these datasets.
Finally, extend your data gathering to other business systems and departments. Start gathering important metrics from finance and market research, including turnover, sales performance, money spent on market research, and training.
3. Implement ETL
The HR department must work in close connection with the IT department as certain software and data extraction might require specialized data analytic skills. Therefore, start making closer connections between these two departments.
Part of this process is the implementation of ETL — extraction, transformation, and loading. This process allows you to extract the necessary data from a source you define, transform them to the correct clean and consistent format, and load them onto your analytical platform to be used in the analysis. There are tools you can use to implement this process automatically. While non-technical employees can use these platforms, it can be beneficial to ask the IT department for assistance.
4. Incorporate the findings to business operations
When your HR data analytics starts generating results, you need to start implementing changes.
For example, if you focused on looking at workforce diversity and your data show you’re not receiving enough applications from ethnic minorities, start changing your recruitment strategy, perhaps by targeting recruitment agencies that focus on minority candidates, conducting interviews within minority groups to see whether the community views your organization negatively, and creating more tutoring opportunities with minority employees.
You also need to draw a connection between the HR data and other business measures. For instance, reduction in staff overtime can directly correlate with productivity and profitability. The KPMG report People Are the Real Numbers discussed 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.”
5. Perform regular analyses
Finally, HR analytics should not be done irregularly and remain irrelevant the majority of the time. 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, 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 the management, analysis, and interpretation of data aren’t always straightforward, and organizations need to approach HR analytics one step at a time.
The key to successful HR analytics is understanding that it’s not the size of the measured data that’s 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 as something with the potential to create value throughout the organization.
Reprinted with the permission of Martin Luenendonk. Visit him at www.cleverism.com. The complete article can be found at https://www.cleverism.com/beginners-guide-hr-analytics/
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