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Inclusion and Belonging – An Untapped Potential for People Analytics

In the wake of George Floyd’s murder and thousands of hate incidents against Asian Americans, companies across the globe pledged to improve and support ethnic and racial diversity. Many organizations have formed new Diversity and Inclusion (D&I) or Diversity, Equity, Inclusion and Belonging (DEIB) teams. Some organizations have started reporting out on diversity metrics for the first time in company history. I mentioned during a recent Keynote this was an area with untapped potential for People Analytics. While the people analytics team might have been tasked to create data visualization, dashboards and reporting on diversity metrics, I argued this was simply not enough. In fact, there is much more people analytics teams can do in this space. Here are a few ideas to get you started.

First, measure inclusion and belonging in addition to diversity. You can find vendors with an inclusion index and directly leverage if it serves your organization’s needs. Gartner and Korn Ferry, among other organizations offer ways to measure inclusion via surveys using validated instruments. Many employee listening vendors such as Glint and Perceptyx, also offer validated questions to ask about inclusion and belonging. It is important to find a balanced approach that works for your organization. Depending on how important benchmarking is for these measures, you may decide to select one vendor over another. For those of you operating in a global company or plan to provide the survey in multiple languages, I’d highly recommend vetting the translated survey questions with employees to ensure there is not too much “lost in translation” on these complex topics. One question I often get is “should I write my own inclusion and belonging survey questions if my organization is unique?” and I’d recommend it only if you have survey design expertise internally. Survey questions are complex, especially around concepts such as inclusion and belonging. You are better off having experts validate the design to ensure the questions are truly going to measure what you need to measure. One you are able to collect such data, you can identify areas of strengths and opportunities along with trending data to see your progress.

Second, identify inclusive behaviors to improve a sense of belonging. What does an inclusive leader look like in your workplace? Do you know what they do to be inclusive? You could use employee survey data to better understand what leaders who receive higher inclusion scores do differently than those with lower scores. As I mentioned on a recent podcast interview, one mistake I see companies make is not collecting data. It can be daunting to collect data, and with the proper expertise, it doesn’t have to be. One simple change you can make is to allow survey respondents to enter comments following each question, and this would give you contextualized feedback, which is particularly valuable for complex ideas such as inclusion and belonging. In addition to identifying areas of the organization doing well and areas of opportunities on inclusion and belonging measures, you can look within the comments to further identify keywords and specific behaviors that are inclusive and improves employees’ sense of belonging. Once you have applied analytics to validate these drivers of inclusion and belonging in your workplace, you can deploy nudges to further encourage employees across the organization to demonstrate these behaviors.

Third, quantify the ROI of DEIB programs. Have you rolled out a program to improve inclusive leadership or unconscious bias recently? Have you been able to quantify the ROI of such programs? Do you have data to show how inclusion has improved after individuals go through certain trianings and programs? I know from experience often the people analytics teams are tasked with creating dashboards to track progress on training completion. I’d challenge both people analytics and DEIB teams to take it a step further. Program evaluation and training effectiveness are opportunity areas where analytics can make a greater impact. I can appreciate the difficulty in quantifying effectiveness as it takes more than a few participants having “aha” moments during the training. Ultimately, you need to quantify behavioral changes to understand the impact of your programs and training.

Fourth, let advanced analytics be your friend. Organizational network analysis (ONA) has recently gained significant attention as a way to better understand DEIB within organizations. There are active and passive ways to gather data to perform network analysis. In either case, I’ve found it helpful from experience to visually map out the network of different demographic groups and used it as an input to understand other talent outcomes, including retention and internal mobility. Imagine if you are able to identify the gap in networks internally, which further explains the gaps in promotion to senior leadership in your organization. As an example, the graph below from TrustSphere shows the difference between male and female employees in connecting with employees in higher grades.

Fifth, leverage technology to point out your own blind spots and reduce unconscious bias. At PayPal, I have the joy of looking after HR technology as part of my role so tech is very much on my mind. Years ago, when I was part of the Corporate D&I Council at one of my former employers, I had piloted a solution that could remove names from resumes and another vendor that checked how inclusive job descriptions were. Fast forward to 2021, there are now abundant new technologies in this market. I also recently discussed in an article the do's and don't of tech-enabled, skills-based strategic workforce planning, which is another way to help reduce bias in talent processes and shift the focus towards identifying those who have the skills to succeed.

What will YOU do to move the needle on this important topic?

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