The last five (5) years has opened doors in the field of DEI that experts only dreamed of prior to then. Organizations all over the world now understand what it means to be DEI driven, management teams now prioritize DEI strategies in their yearly roadmap and plans. Hiring and the talent industry has also seen an improvement in DEI initiatives sparking the inclusion of all people into the workforce. 

While it is important to recognise the impeccable achievements, it is also important that we remember that more work needs to be done in the area of awareness, measurement and optimization of policies. After all, the reward for great work is more work. According to Harvard Kennedy School’s Iris Bohnet, U.S. companies spend roughly $8 billion a year on DEI training—but accomplish remarkably little. We spent a lot of time over the years talking about adoption of DEI initiatives in the workplace and in government. We must now address the sustenance of these strategies. It is one thing to implement and another to sustain and optimize for improvement. How do we sustain DEI policies, the easiest and surest way is by studying the data. One key component of implementing DEI strategies is being able to track adoption and ways to improve. 

When companies realize they are falling short in improving their operations, expanding their offerings, or connecting with customers, they typically define what they want to achieve, identify relevant metrics, and then try out various strategies until the metrics reveal progress toward their goal. It’s a practice that works, and businesses use it to address any problem they truly care about. Hence the aphorism “We measure what we treasure.” Most companies have yet to adopt evidence-based, metrics-driven practices—even though they’ve acknowledged DEI as a moral imperative and recognize how it can help their bottom line. Without metrics to measure their current status and monitor progress, DEI efforts will always amount to shooting in the dark. And that can be very costly, as CFOs are starting to realize.

It is understandable that in gathering internal DEI metrics, companies might be doing themselves a disservice in the event of a legal spill-out, and data obtained can be used to alternatively accuse companies of being impartial in their treatment of certain minority groups. However this shouldn’t stop the need for having relevant metrics. Companies today acquire data about virtually everything else, and ignore the associated risks involved, so their failure to track diversity statistics sends a message of indifference—or, worse, may be taken as evidence that the company has allowed bias to flourish.

There’s really no mystery about how to implement a metrics-based approach to diversity that gets results. Your organization probably already has protocols for handling sensitive information, whether it’s product-recall secrets or customer data, and you’ve probably developed thoughtful procedures for conducting sensitive internal investigations. You need to use those protocols and procedures to handle DEI data as well.

Choose the Right Metrics.

Many companies assume that diversity metrics are all about the “body count”—how many women, persons living with a disability, people of color, and perhaps members of other underrepresented groups they employ and in what positions. Those are outcome metrics, and they’re important. They’re a good indicator of bias; they’re vital for establishing a baseline against which progress can be measured; and they’re necessary for assessing the effectiveness of various interventions.
To do better, companies need process metrics, which can pinpoint problems in employee-management processes such as hiring, evaluation, promotion, and executive sponsorship. If your outcome metrics tell you, say, that you don’t have enough women or people of color on your staff, process metrics will tell you where exactly to focus your attention to bring about meaningful change.

Identify methods and tools to retrieve data

By utilizing data collection tools and methods organizations can easily measure the metrics they set out to achieve. Utilizing these tools will help organizations get organic data that is useful and actionable. Tools like Inclusion climate survey, DEI assessment roadmap tools and Pulse surveys. By also embracing listening sessions and reflective dialogue. 

How to protect this data

We established earlier that DEI data can be used against the company, forcing organizations to move away from collecting and hoarding related data. Some companies are committed to DEI policies but aren’t established enough to shoulder the risk if/when they come. It is important that all related DEI data are protected by company policies and processes. Examples include, trademarking all information as confidential, encrypt data, do not share to sources outside organization and if possible refer to data in verbal form to prevent liability.

Act on this data

Be ready to act on what you find. This is crucial for every organization. Before you begin collecting and analyzing data, make sure you have buy-in at the top and the budget to take persistent, reasonable measures to remedy any problems you find. Remember that you don’t need to solve every problem immediately, and your response doesn’t have to be perfect. But it does have to be prompt—don’t wait around for six months or a year.

In conclusion we must move away from thinking of DEI as something alien or a foreign concept. It’s basically people management, and once we see it as it is, then we’ll be able to adopt people analytics to achieve this. This is an important insight. We’ve been throwing money at the problem through diversity-training programs and leadership-training programs, trying to help traditionally disadvantaged groups, including women but also people of color and people with disabilities. That is not the way to go.

We are quite optimistic that big data analytics and experimentation will move the needle dramatically in the next ten years. But we are mentioning experimentation also to suggest that we don’t have all the answers yet. We have to understand what’s broken and then intervene where the issues are—really tease apart what’s broken, and then try to fix it and use data on what works to inform our decision making.