5 Applications of Data Analytics in People Management

Last Updated August 4, 2020

When it comes to building a successful business, arguably the most valuable asset is the people within the organization. Human resource management departments are increasingly looking to data analytics to inform their key people decisions, and thanks to evolving artificial intelligence and machine learning, HR professionals now have even more data available to help inform these decisions. While tech-driven intelligence and data analytics plays a critical part in the hiring process for many organizations, a growing number are applying increasingly sophisticated HR metrics to make data-driven people decisions that will impact employees throughout their career journey within the organization. As Deloitte reported in 2017, 71% of companies said they considered people analytics a high priority for their organization with 31% rating it “very important.”

Areas that used to be determined solely through human feedback and review, including promotions, salary rates, attrition and retention, and training and development, are now increasingly data-driven decisions informed by artificial intelligence-powered analytics. A key value differentiator of these AI-derived metrics is that they can be gathered and analyzed in real time to help support in-the moment decisions.

Here are five ways HR and talent management teams are applying data analytics to cultivate employee development and create high-performing organizations.

Measuring Performance

Organizations can use analytics tools to establish employee performance benchmarks, and then coach existing and incoming employees to understand those qualities and their impact. Deloitte, along with other companies, analyzes human performance data, travel data and billing hours, to help individuals boost their professional performance as well as their wellness and energy. Organizations can even use data gathered from top-performing teams or individual employees as a means to understanding effective processes and set standard benchmarks for other groups in the organization to follow.

Informing Promotion and Salary Decisions

A major demotivator for many high-performing employees is watching under-performing peers receive promotions. There can be several factors that lead to this, but human bias and nepotism can often play a part. Taking a data-based approach can help organizational leaders watch the rate at which employees are receiving promotions and raises and what key factors drive these decisions. For example, a new employee may have just delivered an outstanding sales performance, but a longer-tenured peer may have consistently provided quality performance over time. Which performance metric carries more weight, and over what timeframe is performance measured? Should tenure be a factor at all? Gathering and using more types and sources of data and using it to train artificial intelligence algorithms can then support managers in making less-biased decisions and ensure performance-generated data is a larger part of the equation.

Understanding Attrition and Increasing Retention

Performance-based analytics can also be applied to predict which employees might be more prone to leave, while also telling a story about what factors contribute to attrition. Money may be less of a factor than the quality of managers and supervisors, according to management consulting firm McKinsey & Co. For example, McKinsey cites a case study of a major U.S. insurance company that implemented a bonus program in an effort to retain employees but saw little success. Then, the company began to apply data analytics to understand at-risk workers, and they uncovered a trend: people who were on smaller teams, went longer between promotions, and who reported to lower-performing managers were all more likely to leave. Instead of pouring money into these employees, the company began pouring resources into making stronger managers.

 Organizations can also glean data on their turnover rate (both voluntary and involuntary attrition divided by average headcount) to understand trends and address sudden spikes. For example, a surge in involuntary attrition may be an indication the recruiting and training process needs a review; an uptick in voluntary attrition may require deeper dives into specific departments or managers.

Examining Employee Engagement

A crucial metric for any HR department is employee engagement. This data is typically gathered via employee engagement surveys that are conducted by outsourced survey providers (i.e. Gallup). However, more organizations are seeing the benefit of bringing this in house to their HR departments for both faster results and to maintain the ownership of their employees’ data. Instead of the extensive surveys that many employees dread (and some don’t even fill out), in house HR departments can use brief, small surveys to regularly monitor engagement, and with the help of AI tools, gain immediate data insights.

Another tool that is both an additional source of employee data and improved engagement is gamification. GamEffective, a company that designs gamification apps for businesses, offers one version where employees can place bets about how their day will go, based off their goals for the day. This can increase not only the employee’s engagement but also motivate the employee to meet their individual and team goals, as organizations can pick specific KPIs to measure within the app.

Measuring Employee Development and Learning Outcomes

A vibrant training program can benefit organizations with a more productive workforce and improved retention. Rather than ask employees a few static questions at the completion of training, organizations can shift focus from satisfaction with the training to comprehension of the program, tracking the employee’s actual progress throughout the training. Companies can go one step further by applying predictive analytics to customize training content that better meets employee learning styles at an individual level. At an organizational level, predictive analytics can assess weak points in the training (like when employee engagement dips). Ultimately, this data can analyze patterns that make a training successful and direct companies to improve content in the right places.

Turning Data Analytics into People Analytics

While these intelligent data metrics certainly give HR professionals valuable knowledge, it’s crucial for HR to continue to maintain the human element of their role to ensure these tools truly add human value. One way is using the analytics behind these five applications to inform organizational design through predictive strategy that can help guide the specifications of future positions, help prepare workers to up their skill sets for these roles and meet the organization’s needs.

With roughly 40% of companies worldwide automating their HR departments, a data-rich HR department needs professionals who are adept in the analytical competencies to interpret and harness the power behind data-driven intelligence. Expanding skills and knowledge in data mining and management, machine learning applications and business analytics can provide HR professionals (and their organizations) a competitive advantage.

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