Taking a Data-Driven Approach to Reduce Bias in Employee Recognition
Gender equity is not only big news in media and politics, but also it is a growing topic for socially conscious workplaces. Organizational ethics has become a key differentiator for organizations in the war for talent, and yet many organizations continue to struggle to transition from talking about equity to meaningful action.
At the recent Workhuman Live 2019 conference a panel of industry leaders took on the topic of pay equity and how organizations can make lasting change. The answer is not a comfortable one and requires major disruption to traditional organizational cultures. The good news, the panel highlighted, is that technology is available to support leaders with this change – and it all starts with data.
Source: Globoforce Workhuman at SofwareReviews.com
Socially woke organizations that are evaluating rewards and recognition vendors should be looking for features that reinforce and drive the culture they are looking to build.Workhuman’s Social Recognition and Connect platforms provide needed support to managers to identify and rectify unconscious bias. For example:
- Check-In technology that prompts managers with unbiased check-in topics
- Social recognition tools that advise users when terms tied to gender connotations are used.
Organizations should also look for recognition software with strong data analytics, which allow you to track the usage of the solutions, behaviors recognized, and demographics of both who is providing and receiving recognition in order to identify unconscious biases.
Our Take
- Unconscious bias is just that – unconscious. When selecting tools around organizational culture look for solutions, which include strong data analytics and language analysis so that you can reinforce behaviors aligned with your cultural goals.
- Using a data-driven approach takes some of the emotion out of a high-emotion topic like equity. Leverage these types of analytics to take conversations about good intentions to actionable results.