Bias is the number one enemy of performance management systems. HR data analytics is the key to removing bias and conjecture from performance management.
According to Gartner, HR data analytics (also known as people analytics) is the practice of collecting and applying talent data to improve critical talent and business outcomes.
HR leaders use the data to guide their talent decisions, improve processes, and foster a positive employee experience.
In a performance management context, HR analytics show how successful employees are in their position. For example:
- How many of their sales calls generated sales?
- What percentage of team objectives did they meet?
- How many of their initiatives did they abandon?
This article discusses why HR data analytics is so critical to performance management. You’ll learn how to build metrics that matter and the role of HR analytics in real-world examples.
Why would you use HR data analytics?
HR professionals use data analytics to identify top performers and employees who need support or training.
Data analytics will highlight gaps where a team or department needs additional resources. For example, the marketing team could benefit from a freelance graphic designer to pump out content faster.
Your engineering team may want a new release communication tool to improve product velocity. Use these insights to compel action.
In HR, data analytics can also capture the effectiveness of your performance management process. The data will help you tell whether your performance management process is helping employees, having no impact, or making things worse.
For example, an employee may have increased their project output, but the quality isn’t as high.
The monthly professional development chats would be more effective as biweekly sessions. Focusing solely on the short-term quarterly sales targets means teams overlook the forest for the trees.
Finally, you can also use data analytics to decide on promotions and compensation. Use determinants like:
- The number of promotions the employee received in their past
- The number of deadlines met
- Team contributions
- How well they met or exceeded individual, team, and company-wide goals
For a holistic evaluation, weigh the data in tandem with intangible factors like leadership potential or helpfulness.
What kind of metrics should you analyze?
Before you decide on metrics, think about the current state of the organization. Are you in the middle of significant transitions and restructuring? Would your time be better spent improving current processes?
Your resulting performance management metrics must align with the strategic objectives of your specific organization. A new startup’s priorities will be much smaller-scale than those of an enterprise organization.
You can take different approaches to determine the metrics that will be most impactful for your specific organization.
One is to manage performance at an overall level. Some organizations give employees an E for Exceptional Performance, S for Successful, or NI for Needs Improvement. The obvious downside is that this classification is too broad and doesn’t give context.
Another way is basing metrics on goals, like:
- The percentage of goals completed—quarterly, departmental, or organization-wide
- A self-review rating
- 360-Peer Review ratings
- A manager’s performance ratings
Goals-based metrics reflect an employee’s contributions. They can also be customized to each individual. The downside is that they may result in underachievement. If the goals are low, an employee could get away with doing the bare minimum.
What tools help with HR data analytics?
You’ll need a software tool like PerformYard that lets you track, analyze and report on performance review data. You should be able to see all the goals in one place alongside review form questions and answers.
Organize reviews from solutions by department and employee, analyze rating scales, and make decisions based on the data. It’s much easier to do this with software than with an informal, paper-based, or email-based performance review process.
What are some examples of HR data analytics in the real world?
Here are three real-life HR data analytics examples. The organizations used HR analytics to transition from general processes and metrics to quantifiable, data-driven goals and insights.
Dominion Due Diligence Group
Dominion Due Diligence Group (D3G) is a full-service environmental, engineering, and energy due diligence firm. The firm’s HR department wanted to shift from general performance management metrics and processes to specific, practical measures.
D3G transitioned from using paper-based performance processes and evaluations to digitizing them with HR data analytics tools like PerformYard. From there, they drill down into the elements that determine job success.
“People were setting goals, but they weren’t measurable. They would say, ‘improve sales.’ But we didn’t say how long it would take, by what percentage,” said Beth Petersen, HR Director.
D3G was able to set specific, quantifiable objectives like a professional goal, an operational goal, and a personal goal. Managers could track them, and employees could use them for development. They can now tie performance to measurable metrics.
Cline Family Cellars
Cline Family Cellars is a family-owned winery business. The business owns multiple vineyards, tasting rooms, and olive oil subsidiaries.
Cline Cellars went through a restructuring to better enable long-term growth. Previously, the team conducted employee performance reviews on an ad-hoc basis, and employees weren’t clear about how higher-ups evaluated them.
Thanks to a new HR director and partnership with PerformYard, Cline Cellars created a standardized performance management process that tied employee goals to organizational objectives.
“Performance reviews themselves are now based on more objective, transparent metrics. This has helped employees in every group better understand how their performance is being graded. It’s also helped to reduce subjectivity and bias in the review process.” Nicole Winslow, Director of HR.
VCC Construction is a general contracting/construction management firm licensed in 50 states. The firm wanted more robust performance management processes than the simplistic annual review process they already had.
With PerformYard, they evolved their processes to an annual review and a mid-year check-in. The yearly review gives a 360 review, a manager review, and a self-review. The reviews could now include factors like technical skills, interpersonal skills, and areas for improvement (among others).
Managers can use the data to track employee goal progress and see who has potential for advancement. Beyond individual goals, the team has visibility and insight into how well the company accomplishes organizational goals.
“Being able to track these goals makes it more real. It helps us make sure that we're always looking at how we can improve; we can now better measure our successes.” Candice Offonry, Talent Acquisition Specialist.
How do you get started with HR data analytics?
Plan out your performance review cycles so that everyone fills out the review simultaneously and is on the same page.
For objective, measurable results, collect your data with quantifiable review form questions. Some example question types are rating scales, binary, or rankings. Use a mix of these questions for a well-rounded review.
Set quantifiable goals and make sure you have a place to track and monitor them. Cascade the goals to different teams and individual members. Don’t forget: keep the goals tied to your organization’s objectives.
It’s not easy to make sure each goal connects to those of the company. With PerformYard, you can track each goal and ensure its alignment with the company’s strategy and mission. Contact us to learn how.
What is Data Analytics in HR?
Collecting and applying data to improve employee and business outcomes.
Why is Data analytics important in HR?
HR people use data to measure employee progress, identify strong and weak spots, and inform people decisions.