Identifying star performers and underachievers with people data - and what happens next

Whilst some star performers and highly engaged employees offer huge value to the company, other disruptive, disengaged, and unproductive employees come at a cost. People insights can help HR spot the difference...
HR Grapevine
HR Grapevine | Executive Grapevine International Ltd
Identifying star performers and underachievers with people data - and what happens next
People data is key to determining high productivity or underperformance

Close to a quarter of employees admit they are unproductive at work, coming at a £143 billion hit to the UK economy in lost hours.

This is just one example of the cost that can come from employees who – often unintentionally – destroy value within the organisation. Whether it’s underproductivity, distracting peers, underachieving, outright sabotage, or burnout, HR has to get a handle on when employees are not working in alignment with the company’s goals.

Determining which workers are or are not adding value using anecdotal evidence of intuition will inherently bring bias and misalignment into this process. When reviewing performance or determining output, an evidence-based approach is needed. Otherwise, managers and people leaders may misunderstand employees on their team.

A data-driven approach to determining what level of value employees are offering to the company is a crucial part of this picture. By measuring the right metrics, organisations and managers can offer each employee the necessary support, whether it’s to reward, retain, and take learnings from a star performer, or to help a disengaged employee find a path to greater productivity and performance.

Star performer or underachiever – what metrics matter and where should we be careful?

Figuring out whether each employee is creating or destroying value is a critical task for HR and people managers to understand. Research from McKinsey identifies six categories employees can fit into: the quitters, the disruptors, the mildly disengaged, the double dippers, the reliable and committed, and the thriving stars.

This type of framework is not about the level of performance. Quitters, for example, could be among the top performers in the company, but are simply no longer committed to the organisation and its goals. Disruptors, in fact, are the most dangerous to the company, owing to their ability – as their name suggests – to negatively impact and disturb other employees.

Whether it’s people analytics, people science, employee insights, or another through a similar function with another name, HR leaders must use data to understand how productive each employee is being, how engaged they are, whether they are meeting goals, how they are performing over time, and how their behaviour is impacting other stakeholders they interact with. And, if it’s helpful, this data can place them into a category using the McKinsey model or another method of grouping workers based on the level of value they create.

Once companies have defined what productivity is to them, they can start to measure and understand the conditions that high performers need to do well

Sue Lam | VP of Global People Analytics, Culture, Strategy & Planning at the Coca-Cola Company

Sue Lam, VP of Global People Analytics, Culture, Strategy & Planning, the Coca-Cola Company, argues there can be lots of contributing factors to job performance, many of which companies do not measure including the work environment, skills and capabilities, personality characteristics, motivation, coping skills, and so on.

A good place to start is with defining what it means for employees to be productive, a common measure of performance. “Many people may not work in jobs where more output is always better,” argues Lam. “The quality of the output may be just as important. Once companies have defined what productivity is to them, they can start to measure and understand the conditions that high performers need to do well.”

This data may be readily available on a company’s existing HR platforms and software, though some further data may need to be collected.

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