HR Analytics: What Is It and Why Does It Matter?

Give a business enough data and you give them an inexhaustible mine. There is no end to the valuable insights that you can draw from it. The way the tech sector uses Big Data stands testament to the depths you can plumb it.

We are just waking up to this value in human resources. With the right data converted into the right metrics, HR managers can find the answers to some of the most mysterious and often persistent problems. This is HR analytics, and it matters because it offers solutions to incredibly complex business questions.

What is HR Analytics?

Called by many names – people analytics, talent analytics, and workforce analytics – HR analytics is collecting and analysing employee data to understand the workforce and its performance better.

The process of people analytics includes measuring metrics such as turnover, hiring, learning, and development using statistical methods to understand the impact of each on the business performance.

Cut the clutter, and HR analytics tells you what is working for the organisation and what is not. It gives you data-driven insights, allowing HR managers and C-suites to improve and plan more effectively.

Why are data and analytics important in HR?

Data in itself is a blank canvas. Organising, comparing, and analysing paints it into a clear picture. That picture can generate insight into how a company’s human resource contributes to producing more revenue, minimising risk, and lessening expenses. The data and metrics also provide evidence to execute strategic plans to further business goals.

It can improve hiring decisions.

With historical data in hand, HR teams can make better choices. For instance, they can pin down the traits that correspond to long-term and high performing employees. Then look for those traits in new hires. Analytics also helps understand how long it takes to hire a person and work on shaving off that time.

Another reason analytics is pivotal is hiring process improvement. With enough data and patterns, trends and stats emerge. An organisation can use them to make recruitment more efficient and swift.

It diminishes the scarcity of talent.

Talent is not scarce. It is finding the best talent that is an encumbrance. With data and analytics, HR teams can put a finger on the type of individuals they need, and what knowledge they should have. All aspects that make it easier to judge whom to target for which position become apparent with human resources analytics, something conventional acquisition methods lack grievously.

With data and analytics, HR teams can put a finger on the type of individuals they need, and what knowledge they should have. Click To Tweet

It cuts the attrition rate.

Turnover is economically prohibitive and not merely because it impacts productivity. It also lessens institutional knowledge. Metrics and analytics are the most powerful weapons in an HR’s arsenal to reduce attrition and increase retention.

Identifying problems that make people leave becomes possible with analytics solutions. It helps companies distinguish what makes employees stay. Once the reason is apparent, be it skill gap, compensation issues, or under-performance, intervention becomes possible.

It offers informed decision making.

As we have said before, HR analytics answers the most mysterious and persistent problems-

• Is there a pattern in turnover?
• Which employees will leave the company in the upcoming time?
• Are learning and development creating the required impact?
• How can the workforce be more productive?

Analysis of critical data finds an answer to each of these questions and more, giving you the power to make informed decisions.

What are the key metrics in HR analytics?

There are innumerable key metrics managers can measure within human resource analytics, spanning recruitment, performance, operations, and more. Click To Tweet

Before we dive into the depths of HR metrics, let’s unwrap what metrics are. Assume, your company had an 8% turnover in 2019. The turnover in 2020 was 10%. Both 8% and 10% are data points. The increase in turnover – 25% – is a metric. Essentially, metrics permit you to compare data and distil insights.

There are innumerable key metrics managers can measure within human resource analytics, spanning recruitment, performance, operations, and more.

Begin with the following general metrics and then move on to more complex ones:

1. Engagement rating 

A performance metric, employee engagement rating can be assessed using surveys or productivity measures. It identifies the engagement levels the workforce has with their jobs and is calculated by quantifying employee productivity and satisfaction.

2. Absenteeism

A productivity metric, absenteeism is a great tool to gain analytics insights on employee happiness. It also indicates the health of the complete workforce. It can be computed by dividing total workdays by the number of days employees miss work. Instead of using it as a standalone metric, compare it to a set absenteeism rate.

3. Revenue per employee

A performance HR metric, revenue per employee demonstrates the average revenue a person generates for the business. It’s calculated by dividing the business revenue by the total workforce. It is best applied to evaluate a company’s efficiency to use its workforce to create more revenue.

4. Turnover rate

Like absenteeism, the turnover rate is also measured in comparison to an acceptable rate. It is an operational metric and computed by dividing the number of employees who left the company in a given year by total employees. Typically, there are two types of turnover rates – voluntary and involuntary.

Voluntary turnover is the rate of employees who willingly quit their jobs. It helps spot the gaps in employee experience and reduce attrition. Involuntary turnover is the rate of employees who were terminated in a year. It helps understand the gaps in recruitment strategy and develop a plan for better quality hires.

5. Time to fill

A recruitment metric, time to fill enables HR managers to tweak strategy and reduce the time it takes to fill a vacant position. It can also pinpoint areas in the hiring process that take the most time. Time to fill is calculated as the total days starting from the job advertisement date to the date of hiring someone for it.

6. Time to hire

While time to fill focuses on a position, time to hire focuses on a candidate. The metric is useful in gauging how to improve the onboarding experience and reduce the time taken to do so. It is calculated as the total days taken by a candidate to accept a job starting from the date the candidate was approached.

7. Training efficiency and expense

Training efficiency and training expenditure per employee are intertwined HR metrics. When efficiency is low, the company needs to reassess its training expense. Training efficiency enables businesses to evaluate a program’s effectiveness. It is estimated using a mix of data points, including test scores and upward movement of employees succeeding in a training program. Training expense is calculated by dividing the overall cost of training by the total workforce in training.

How to implement HR analytics?

Collect, measure, analyse and apply. Those are the four steps to implementing HR analytics. That said, the rule of thumb is, to begin with, small projects that offer tangible results quickly. These quick wins will convince C-levels to invest further in data analytics.

Step 1: Collect

To analyse, you need data. Gather internal data as well as external data. The prior is available within the core HR system and includes employee profiles, high and low performers, onboarding, remuneration, demographics, training, promotion history, and more.

External data comes from other departments of the company. This can be necessary when you want a hawk-eyed view of the workforce. For instance, financial data is a prerequisite to computing revenue per employee. This will be available from the finance team. In a few cases, data from beyond the business may also be required.

Step 2: Measure

Data can quickly pile on because the gathering process is not a one-time task, and there are vast amounts of data sources. Because data collection is a continuous process, it requires monitoring. Also, remember to measure metrics against a baseline of acceptable rates, industry norms, or historical averages.

Step 3: Analyse

The third step is to discern patterns in the data by practising predictive analytics and other analytics tools. When you analyse collected and measured employee data, it brings forth trends that become insights.

Step 4: Apply

Insight into candidates is pointless if they are not applied. So, the final stage of implementing HR analytics is using the insights to improve mission-critical decision making.

A caveat:

A lot of data extracted for human resource analytics may fall within the purview of compliance laws. Consider every legal factor before implementing HR analytics. These will include employee privacy. Ensure that you have consent on the type of data you gather and the amount of it. This will require informing the employees of the goals of the data collection.

Practical Examples of HR Analytics

HR analytics can be leveraged for various aspects of the business – from talent management to training, from compensation to rewards, from employee performance review to employee retention. Here is one practical example.

Assume a C-level executive feels that too many employees are taking sick leaves in the given month. A dive into the data shows that absenteeism is at 15%. While the data is useful, it doesn’t provide much information. To make a judgement call, you need a baseline to deduce if the number is too high, too low, or as it should be.

Let’s say the industry norm is 8%. With that metric in hand, you can establish that the number of people taking sick leaves is far more than expected. Then using analytics, you can identify the cause of the leaves and how it affects the company.

Reducing time to hire.

Because identification is a problem, it takes a long time to hire someone. Combing through the limited information given in a resume hinders the process. More often than not, it causes HR teams to overlook fitting candidates.

With data analysis, you can filter which qualities are crucial for a position. For instance, it could be creativity that matters more than years of experience. You can sift through the applicants to look for that particular quality. This is possible because analytics offer more in-depth information into wide-ranging variables oscillating from cultural fit to development opportunities.

Another way HR analytics helps reduce time to hire is by streamlining the application process. Through it, you can discover the barriers in the procedure, be more informed, and are prepared to take corrective action.

Cutting the legs of turnover.

There are plenty of reasons employees leave an organisation, from dissatisfaction to low compensation. HR analytics gives you a better understanding of why people quit. Using metrics, you can spot the contributing factor. Then take steps to avert it.

There is no end to the application of HR analytics. It empowers businesses to collect employee data automatically and quickly. It can identify the correlation between high-performing employees and attributes.

It delivers historical data such as periods where hiring is stagnant or excessive. It removes human bias during the recruitment and offers equal opportunity because it’s data-driven. The only limitation is how far you are ready to take the digital transformation.

Reinventing the HR wheel with Analytics

Google began using Big Data to predict turnover over eight years ago. Credit Suisse started using an algorithm to forecast which person is likely to quit soon six years back. These are just two of the many examples. The likes of Deloitte, Cognizant, HCL, and many others have reinvented the wheel with people analytics.

When your most valuable and often the most expensive asset is human capital, you do all it takes to insure them. HR analytics becomes the safeguard for this investment when you put it into daily practice.

It takes away guesswork and predictions and makes people’s operations scientific and data-driven. It blows wide open the door to finding out what is going on with your human resource. Nonetheless, HR analytics should only be a priority, not the sole deciding factor. Keep in mind it is a tool to be leveraged, not the complete answer.