March 19, 2019

Want to Keep Your Best People? Learn How Data Improves Reviews

Imagine asking your company’s employees the following questions right now:

  • “Do you know what you need to do today in order to advance your current work goals?”
  • “Does your supervisor actively help you work toward those goals?”

How many people would answer “Yes” to both questions?

Currently, only 29 percent of employees say they always know whether their performance measures up, and about 50 percent of employees say their bosses rarely or never take an active role in their growth and development.

Employees who know their strengths and use them daily are eight percent more productive, six times more likely to be engaged and three times more likely to report an excellent quality of life on the job, say Vibhas Ratanjee and Andrew Green at Gallup.

Annual performance reviews make it difficult for employees to focus on building their strengths from day to day since this once-a-year event doesn’t provide the regular feedback and progress tracking employees need to grow.

Today, however, data analytics tools are making it easier than ever to record performance data, provide feedback in real time, and tailor training and goals to each individual employee’s specific needs.

Why Performance Reviews Aren’t Pulling Their Weight

While many executives still believe in the power of annual performance reviews, workers don’t reap growth benefits from a once-a-year conversation. According to the Growth Divide Study by Wakefield Research, 94 percent of executives believe their workers are satisfied with the company’s performance review process, but 61 percent of workers consider those processes outdated.

“The data provides a rare, quantifiable look into the drain traditional annual performance reviews put on managers’ time and energy, and consequently, the impact that has on employees’ ability to consistently be aware of areas they need to maintain or improve upon,” says Nathan Richter, senior partner at Wakefield Research.

Additional research supports the notion that the performance review has passed its prime. “95 percent of employees are dissatisfied with their company’s appraisal process,” says Dori Meinert at the Society for Human Resources Management (SHRM). “What’s more, 90 percent don’t believe the process provides accurate information.”

If Not the Performance Review, Then What?

Instead of a once-a-year meeting, employees are increasingly looking for current, individualized feedback that aligns with their specific strengths, weaknesses and goals. One way to provide this feedback is through a real-time, 360-degree assessment, says Thomas Koulopoulos, founder of the Delphi Group.

Taking a personalized, data-driven approach allows companies to pursue multiple goals. General Electric, for example, solved two problems with one digital solution. The company’s PD@GE app replaced annual performance reviews, allowing staff to share feedback throughout the year in real time. The app also allows the company to gather data seamlessly throughout the year

Not everyone agrees that the annual performance review is dead, though. For instance, Brian Westfall at Capterra predicts that 85 percent of companies will still be using annual performance reviews in 2025. However, Westfall says, incorporating a data-driven approach to performance reviews is essential, whether they happen once a year or throughout each day.

improves reviews

Using Data to Pinpoint and Predict Worker Performance

Human resources departments are embracing the power of data analytics to improve retention, engagement and productivity. These tools can also be used to pinpoint exactly what specific employees need in order to strengthen their current skills and develop the abilities they’ll need to advance on a chosen career path.

One pitfall of performance reviews is that they don’t address damage until it has already occurred, says Zachary Chertok at Aberdeen. By focusing on real-time data analysis, companies gain the chance to spot problems as they develop.

Getting Ahead of Problems: Case Studies From Academia

Research on using intelligent systems to improve student outcomes abounds in the field of education. Many of these algorithms and platforms can be adapted to help employees learn and grow, as well.

For example, a Worcester Polytechnic Institute research team led by Anthony F. Botelho compared three different methods of predicting students’ aptitude at various goals by analyzing a set of 21 observable skills. The researchers found that in some instances, intelligent tutoring software could accurately predict when students would struggle with a concept or skill before the student was required to apply that concept or skill. That can allow teachers (or bosses) to intervene and provide support before failure occurs.

Another study, in Artificial Intelligence Review, focused on using machine learning to predict where students would struggle based on students’ performance during each learning session. “The student’s performance prediction is an important research topic because it can help teachers prevent students from dropping out before final exams and identify students that need additional assistance,” says Mushtaq Hussain of Shanghai University, one of the five researchers on the team.

Some students (or employees) may simply quit when faced with excessive adversity; others will move to new schools (or employers) that offer development opportunities more in line with their skills.

Fighting Bias With Algorithms

A study by Astha Soni and fellow researchers at the SRM Institute of Science & Technology in Tamil Nadu, India, leveraged the massive amount of student data already present in school databases to develop models for predicting student performance and recommending interventions for specific pupils.

The research team’s approach discovered that leveraging the correct data can help reduce bias in allocating assistance to students. “Experimental answers show that suggested procedure significantly outperforms prevailing procedure due to the misuse of family incomes and students’ personal data component sets,” the researchers noted.

Likewise, a data-driven approach may help to eliminate bias in performance evaluations, say Lori Mackenzie, JoAnne Wehner and Shelley Correll in the Harvard Business Review. When individualized data is necessary to provide specific training, development and goal-setting, it becomes essential to collect data in standardized, quantifiable ways. Thoughtfully developed criteria can guide supervisors in rating performances and in comparing employees’ results.

improves reviews

Tailoring Professional Development to Individual Needs

“Rather than an orderly, sequential progression from job to job, 21st-century careers can be viewed as a series of developmental experiences, each offering the opportunity to acquire new skills, perspectives, and judgment,” say Dimple Agarwal and fellow researchers at Deloitte.

When data is leveraged to tailor developmental experiences to individual employees, these workers — and their skill sets — are more likely to stay with their current companies than leave in pursuit of growth opportunities elsewhere.

From Performance Reviews to Goal-Setting

One way to focus on individual development is to turn performance reviews into goal-setting sessions, in which employees and their supervisors work together to set both short- and long-term goals, say McKinsey researchers Sabrin Chowdhury and Elizabeth Hioe.

These goals should be treated as dynamic, evolving targets throughout the year. “One common mistake is setting goals at the beginning of the year and forgetting about them until review season,” Chowdhury and Hioe write. “As realities fluctuate throughout the year, failing to revisit goals can be demotivating.”

By regularly reevaluating goals, both employees and leadership can help workers stay connected to the organization’s overall achievements while building each employee’s specific skill set.

As a case study from Jeffrey T. Polzer in the Harvard Business Review demonstrates, data alone won’t form an adequate basis for hiring, development or promotion decisions. Guiding employees’ development will remain a human-centered process, in which data can help illuminate individualized approaches but cannot dictate the single best path for any employee or supervisor.

Targeted analyses can, however, provide both employees and their supervisors with the insights they need to understand an employee’s particular learning challenges and potential. With that insight, workers and leadership can combine forces to maximize development, engagement and overall success.

Images by: Edhar Yuralaits/©, Dmitriy Shirosonov/©, Dmitriy Shirosonov/©