Analysing HR data

Analysing HR data: Everything HR teams need to know

Analyzing HR data is both a science and an art. Do you want to truly understand your people and make strategic, data-driven decisions? Our guide to analyzing your people data will tell you everything you need to know to improve your company’s performance and deliver more value.

What is HR Analytics?

HR analytics (or people analytics) is the process of collecting, analyzing and reporting human resources data. Effective analysis of your workforce data allows you to measure the impact of various workforce initiatives and make informed, data-driven decisions.

The purpose of HR analytics and reports is to provide companies with the key insights they need to truly understand their employees. When used correctly, they can increase company performance and make HR a true strategic partner in any company.

The importance of HR analytics in today’s world of work

Human resources is increasingly seen not only as an operational function, but also as a strategic function. In order for HR to work strategically, it needs access to reliable data and the ability to interpret and understand it.

Based on the insights gained, they can then create a business case for specific HR measures and improve company processes. The human resources department therefore occupies a key strategic position within the company.

We cannot ignore the rapid changes in the world of work that we have experienced in recent years. Today it’s more important than ever to make informed, data-driven decisions – which means analyzing workforce data is more necessary than ever.

What types of HR data are there?

Almost everything HR teams do generates data – and much of it could give you valuable insights into your workforce if you took the time to organize and analyze it.

Here are a few different types of HR data you could analyze.

Employee engagement data

A decline in employee engagement could indicate that something is going wrong – but if you don’t track it, you might not realize it until it’s too late.

Conducting regular employee engagement surveys will give you an overview of how your employees’ engagement is evolving over time. It’s also a good idea to keep an eye on survey response rates, as a lack of responses can be a sign of a lack of engagement.

Also be aware of possible clusters of dissatisfied employees on the same team, location or role, as this could indicate a local problem.

Of course, to truly measure engagement, you shouldn’t rely solely on self-reported data. Here are some other metrics you can track:

• Your turnover rate

• Your absence rate

• Your Employee Net Promoter Score (eNPS)

• Internal email engagement

• Employee productivity reviews

Performance data

Tracking key performance indicators (KPIs) in your business can help you identify dips in performance. And the more data you have, the more useful insights you can glean from it. For example, you may notice that a particular team is struggling to perform, even if your company as a whole is achieving its goals.

Performance reviews are another important source of performance data. To make it easier to track and compare this data, make sure your performance review template includes questions with quantifiable answers. For example, you might ask managers to rate certain aspects of the employee’s performance on a scale of 1-10 and then track how those ratings change over time.

Settings data

Collecting and analyzing hiring data can help you make your processes as efficient and cost-effective as possible. And the good news is that if you’re using an Applicant Tracking System (ATS) for recruiting, you’re probably already collecting a lot of data.

Examples of recruiting metrics you can track include:

• Cost per Hire (CPH)

• Time to hire

• Effectiveness of channels

• Employee turnover

• Applicant conversion rate

• Quality of attitude

• Acceptance rate of offers

Four steps to effectively analyze HR data

Would you like to start analyzing your personnel data? Below we’ve broken down the process into four steps to help you understand how it works.

1.Data collection

As mentioned, you probably already collect a lot of data. To ensure that this data is useful, you should ensure that it is accessible to everyone who needs it and can be imported into a reporting system.

There are a number of sources from which you can obtain personnel data, such as:

• Your HRIS software

• Your ATS

• All learning and development platforms you use

• Apps for well-being and wellness

• Recordings of exit interviews

• Your customer relationship management (CRM) software

• Your finance or accounting software

2.Data cleaning

If you want to gain useful insights from your people data, you need to ensure that it paints an accurate picture of your organization. However, data often contains errors and outliers that can distort your analysis and lead you to incorrect conclusions.

Some examples of “dirty” data that needs to be cleaned before analysis include:

• Missing data

• Inconsistent data

• Duplicate data

• Distorted data

• Obsolete data

The methods you use to collect the data in the first place could be contributing to some of these problems. For example, a performance appraisal should be an accurate, objective assessment of an employee’s performance. In reality, however, they are often shaped by the manager’s personal feelings towards the employee in question.

Collecting data from multiple different systems can also lead to inconsistencies and duplicate records that could cloud your analysis. Before you begin analyzing your data, you need to take the time to clean up your datasets by deleting or merging duplicates, removing obvious outliers, and ensuring that the data you’re looking at is up to date.

3.Data analysis

Next, you need to measure and analyze the collected data. A one-time snapshot of your workforce is not enough to do this effectively. Rather, it should be a continuous measurement and comparison process that provides information about how certain key figures have changed over time.

You also need a baseline against which to measure your results. For example, you can’t determine whether your absenteeism rate is acceptable if you don’t know what the standard is. Depending on the metric you track, you can also use industry benchmarks to see how you compare to other organizations.

4.Data evaluation and reporting

Analyzing your HR data is pointless if you can’t derive useful insights from it. That’s why interpreting and reporting your results is the most important step in this process.

It’s about reviewing the results and identifying trends and patterns that could impact your business. The next step is to turn the patterns you find into actionable insights. This could mean recommending the implementation of a specific HR initiative or suggesting improvements to the processes already in place.

HR reporting software can help you convert your insights into visual representations so they are easier to understand.

The role of technology in analyzing HR data

Technology plays a role in almost every phase of the data analysis process, including collecting, cleaning, sorting the data, and even extracting key insights from your results.

There are many software solutions that combine multiple functions, including HR data analysis – such as IceHrm. These tools allow you to collect HR data and then analyze it using built-in data analytics features.

A key advantage of these tools is that they can provide you with the information you need in an understandable way at a glance, e.g. in the form of a graph or chart that shows how certain metrics have changed over time.

The importance of data security

One of the main tasks of a company’s human resources department is to collect and store sensitive information about employees. To ensure the security of the data, strict checks and balances must be carried out.

For example, human resources departments should review the security credentials of any company from which they plan to license software. You should also be careful about who you allow access to certain data. The use of personal laptops and other devices should also be handled carefully to ensure there is no risk of a data breach.

Pitfalls to Avoid in HR Analytics

Here are some common pitfalls to be aware of if you’re new to workforce data analysis.

Over-complicating data analysis

If you jump straight into data analysis without a real goal, you’re just wasting time. Think carefully about what you want to find out with your analysis and then focus on those specific areas.

You ignore the crucial context

It’s important to consider the context of the data you collect. For example, many companies experienced dramatic declines in employee engagement and satisfaction during the initial COVID closures of 2020 – which was only to be expected given the circumstances. If one were to look at this data without considering the context, one might assume that these companies have major internal problems that need to be addressed.

Allow data silos to form

Most companies use a variety of different applications and platforms to manage their operations. And when these tools aren’t well integrated together, they can create data silos that are inaccessible to the rest of the company. This is especially true when companies operate in multiple countries and use different data collection systems in each country.

The only way to avoid this is to ensure that the tools you choose have a powerful suite of integration features. This way you are able to compare data even if it comes from different sources.

The HR department as a strategic partner through effective data analysis

When used correctly, data analytics gives you access to key insights that can drive decision-making across the organization. This helps companies make better decisions and ultimately increase their profits – all thanks to HR.

Additionally, HR analytics can reveal the ROI of specific HR initiatives and even the HR department itself. By tracking and analyzing the right data, HR teams can better determine the right solutions to develop and deploy, increasing the value they bring to the business.