The last decade has seen huge advances in the field of people analytics. Collecting data on employees can help answer a range of questions – from predicting who is most likely to hand in their notice, to classifying employees by personality traits, or measuring the strength of connections between teams. It seems we are only beginning to scratch the surface of applying data to the challenge of management, but have we stopped to consider what we might be missing? What if certain dynamics and situations cannot accurately be captured in the data and metrics we are collecting? Are we propagating issues that arise in the workplace, exacerbating biases in promotions and hiring, or even missing the factors that make employees the most productive?
Some examples of issues that arise when relying solely on predetermined metrics include, but are not limited to ;
- Time Management: The most productive employees spend comparatively less time on tasks, but achieve more in a given time frame. Measuring time spent at work for employees who “clock in” and out of work for certain roles such as stacking shelves may lead to inaccurate assumptions about who is working hardest.
- Motivations: Using metrics to predict whether an employee is disengaged and at risk of leaving the company is more of a “lagging” indicator than a “leading” one. In order to understand what truly causes employees to find meaning in their work, we must seek to understand their underlying motivations. This may only be possible through qualitative research – talking to people and gathering feedback, rather than seeking data points related to their performance – when it may already be too late to intervene and prevent their resignation.
- Innovation: Employees who seek creative and innovative solutions to challenges, rather than finding safety and comfort in the status quo, may be reflective of a deeper attachment to the organization and alignment with the organization’s success. Measuring innovation and creativity with data is an almost insurmountable task. If we fail to allow for qualitative and subjective assessments of employees’ work outputs, we may rely too strongly on categorizing them into predetermined levels, rather than allowing for the unexpected.
Aside from the risks associated with relying too heavily on data for management decisions, there are additional concerns related to the use of employee data. For example – employees’ rights are called into question if they are not aware of data that is being collected .
However, employees may find some solace in the fact that data can more efficiently back them up in claims over pay equity and other litigation .
Overall, data can be a helpful resource for managers who want to better understand their employees, particularly as they cannot be present for every minute of the day. It can help them to be more objective when comparing team members, and help them recognize when they may be unfairly favoring one employee over another. However, managers need to remain cognizant of the factors that cannot be measured by the data and metrics they are collecting, and think about how to incorporate the qualitative factors that more accurately reflect the true nature of their employees’ performance.