HR and people ops has long been regarded by many outside the profession as a squishy part of business. The common attitude was that you can’t take a data-driven approach to people management; the hard numbers just aren’t available. 

However, this isn’t the case. HR data analytics is more important now than ever before. 

And those hard numbers? They’re readily available if you know where to look. You can get all the information you need for robust HR data analytics, and you can use this information to improve KPIs and drive business outcomes.

We show you why data analytics in HR is so important, what data you should gather, and how to leverage HR data analytics in a way that makes a tangible impact on your organization.

What is data analytics in HR?

HR data analytics is the practice of using internal people management data to build metrics that quantify critical aspects of people management. HR professionals and company leadership then analyze these HR metrics and use this analysis to make better business decisions and get better results.

The good news is that your company is probably already collecting all the information you need for HR data analytics. This comes in the form of standard HR data like employee performance information, productivity metrics, and workplace satisfaction surveys 

The issue is that most companies don’t use this data to its full potential. 

To get the full benefit of HR data analytics, you have to do more than just gather data. It’s important to stack all of that data up against key indicators and use the information to proactively address workforce issues — before they become a problem. 

This type of proactive approach to HR data analytics opens up opportunities to reduce costs, improve retention, and build a more efficient company that employees love to work for. 

In a minute, we’ll show you how to do this at your own company. But first, why is HR data analytics so important in the first place? 

Why is HR data analytics important?

Broadly speaking, HR data analytics is important because it makes the HR data you currently collect useful as a business optimization tool. 

When used correctly, HR data analytics connects your HR data to your business data, enabling HR and leadership to implement people management strategies that boost core business KPIs. It also helps prove the value of specific HR initiatives by drawing a connection between those initiatives and improved KPIs.

Integrating workforce metrics with business data

Digging a little deeper: typical HR data tracks workforce metrics, while business data tracks business outcomes. Without HR data analytics, these two data sets exist in independent silos. This makes it challenging to create people management initiatives that target specific business results.

This separation between the two data sets makes it nearly impossible to tell if your people management strategies are making an impact on core business KPIs, and to make effective adjustments to HR initiatives when needed.

Driving business results with talent management strategies 

Without HR data analytics, you have HR data and business data, and you’re left flying blind in your efforts to drive business results with talent management strategies.

With HR data analytics, you have HR data with business data. You can clearly see the relationship between the two information streams. This clarity helps you make effective changes to your people management strategies, see the impact on critical KPIs, and optimize for business success.

Reactive vs. proactive data analytics

So far, we’ve alluded to doing HR data analytics right. Simply analyzing your HR data isn’t a silver bullet.

Traditional HR data analysis is reactive. It relies heavily on data sources such as surveys to generate conclusions. However, these data sources are rearward looking, so the data analysis tends to be a forensic investigation into what went wrong after a negative event (like a sharp rise in employee attrition.)

There’s nothing wrong with looking into what caused a problem so you can avoid it in the future. You should absolutely do this sort of problem solving. Unfortunately, with this method you can only solve the problem after you’ve paid the price.

A better approach is to track and analyze as much HR data as possible in real-time and take action as early as possible to prevent workforce problems before they start impacting the top or bottom line.

What proactive HR data analytics looks like in practice

This is all well and good, but what does proactive HR data analytics look like in the real world? 

• Tracking data that predicts employee attrition or declines in productivity
• Analyzing that data in real time
• Understanding how losing certain employees or productivity benchmarks impacts KPIs
• Intervening well before those employees quit or productivity is lost

Without proactive data analytics, you essentially just have a lot of raw data without a way to use it effectively, and you expend a lot of resources putting out fires that could have been prevented.

How to be proactive with limited resources 

Knowing you need to be proactive and actually doing it are two different things. Most HR teams simply don’t have the resources to comb through mountains of data on a daily basis. 

This is where technology comes into play. 

Predictive people analytics has caught up to the needs of HR teams in a big way. A talent intelligence platform like Praisidio can now do the hard work of HR data analytics for you, keeping up-to-the-minute tabs on all of your data and proactively suggesting solutions to problems before they occur. 

Faced with an ever-more-challenging business environment, companies looking to stay ahead of the curve are quickly realizing the power of predictive people analytics. 

How to leverage HR data analytics to drive business success

When it comes to tracking data in real time and proactively addressing workforce issues, some HR metrics are better than others. The HR analytics data you need to gather in order to take a proactive approach falls into one of these five categories. 

Workload indicators 

Workload indicators predict things like burnout, workload-related productivity loss, and quality issues.

Clearly, tracking workload metrics shows you how much work each employee is responsible for. But more importantly, workload data also highlights when employees are exhibiting behavior that indicates they are burned out or overworked.

This shows you both sides of the workload coin. You can see when employees show signs of feeling overworked or burned out, and you have the data you need to adjust employee workloads before employees display the ultimate sign of burnout: quitting.

Employee connection

Employee connection is an important aspect of employee retention and productivity. People stay with an employer longer and perform better when they feel connected to their work and their peers.

Connection might seem like an emotional thing that’s difficult to track in terms of data. But there are actually several quantifiable indicators that show the strength of the relationships at a company. 

When taken as a whole, quantitative metrics like manager attrition, skip-level 1:1s, and meetings with peers help reveal the network of relationships in your organization and whether those relationships are getting stronger or deteriorating.

Recognition

Recognition is incredibly important for people in any context, but it’s especially important in professional environments. Unfortunately, many companies lack recognition programs because they believe it happens organically or that people know when they’ve done a good job. 

However, organic recognition and self-validation happen far less than you might think.

One of the best ways to measure recognition is tracking tangible rewards. Understanding who’s receiving tangible rewards and how often they get them gives you insight into how well your organization recognizes employees for their work.

Compensation

Everyone knows compensation is a big deal for attracting and retaining employees. A recent survey of over 1,200 employees revealed 50% of workers who plan to quit their jobs in the near future are doing so for better pay or benefits. 

That being said, absolute compensation numbers are not great for predicting employee attrition and other workforce issues. 

The most predictive compensation data is comparative. How well are your employees being compensated in relation to the rest of the job market? How well are your employees being compensated in relation to their peers?

Employees feel dissatisfied with their pay package because they’re being paid less than they could get from another employer or because they feel they’re being paid unfairly in relation to their coworkers.

Comparative compensation metrics such as compa ratio show discrepancies in compensation that predict dissatisfaction and employee attrition, so you can tackle the problem before these issues have a tangible impact on your organization.

Growth

People value opportunities for career advancement. This is especially true for high-performing employees. Employees will look elsewhere for career advancement opportunities if there’s no path for growth at their current company.

Growth data such as time-in-role and number of job changes gives you valuable insight into whether or not employees have opportunities to move up in the organization. 

These metrics enable you to identify employees who may be stagnating in their career, so you can find ways to offer them career advancement opportunities and help them keep growing — so they don’t change companies as a method of moving up.

Stay ahead of the curve with actionable HR data analytics 

As we said before, being proactive with your HR data analytics is key. But no HR team can do it alone; there simply aren’t enough hours in the day.

Praisidio is the platform that allows you to stay ahead of the curve by turning your raw HR data into actionable talent intelligence. See real-time analysis of all your business and HR data, and discover proactive people management strategies that target critical KPIs.

Book a demo with the team to discover how Praisidio talent intelligence makes it possible to take a proactive approach to HR data analytics.

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FAQs

Q: How does the implementation of HR data analytics impact employee privacy and data security within the organization?

A: Addressing concerns about employee privacy and data security is crucial when implementing HR data analytics. Organizations must establish clear policies and procedures for data collection, storage, and usage, adhering to relevant regulations such as GDPR or CCPA. Additionally, transparent communication with employees regarding data handling practices can help alleviate concerns and build trust.

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Q: How can organizations ensure that their HR data analytics initiatives are aligned with their specific business goals and organizational culture? Understanding the process of tailoring analytics strategies to fit organizational objectives and values would help readers navigate the implementation of data-driven approaches more effectively.

A: Organizations can ensure alignment between HR data analytics initiatives and their business goals and culture by establishing clear objectives and values-driven guidelines for data analysis. This involves engaging key stakeholders to identify strategic priorities and define metrics that align with organizational objectives. Additionally, fostering a culture of data literacy and transparency enables employees to understand the relevance of analytics to their work and encourages buy-in across the organization. By incorporating feedback loops and regularly reassessing analytics approaches in light of evolving business needs and cultural considerations, organizations can maintain alignment and drive meaningful outcomes through HR data analytics.

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Q: What are some potential challenges or barriers that organizations may encounter when adopting HR data analytics, and how can they overcome these obstacles? Exploring common implementation hurdles and offering practical solutions would provide readers with valuable guidance for successfully integrating analytics into their HR practices.

A: Adopting HR data analytics may present various challenges for organizations, including data quality issues, technology integration complexities, and resistance to change. To overcome these obstacles, organizations can implement robust data governance frameworks to ensure data accuracy, reliability, and compliance with regulatory requirements. Investing in user-friendly analytics platforms and providing comprehensive training and support to users can facilitate technology adoption and enhance data-driven decision-making capabilities across the organization. Additionally, fostering a culture of experimentation and continuous improvement encourages agile responses to challenges and promotes innovation in HR analytics practices. By addressing these challenges proactively and leveraging best practices, organizations can maximize the value of HR data analytics and drive sustainable business success. Schedule a free consultation to see how Praisidio can assist with your HR data.

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