Leaders should be curious and follow the data – here’s how to leverage analytics to measure your Diversity, Equity & Inclusion (DEI) programs.
Businesses today are increasingly recognizing the value of putting diversity, equity and inclusion (DEI) programs at the heart of their corporate culture. Not just because it’s the right thing to do, but also because companies with best-in-class DEI programs provide a better return on investment (ROI) and have greater financial performance, as McKinsey revealed in its report, “Why Diversity Matters.”
There are a host of benefits in having a diverse employee base. Race, ethnicity, gender, ability, age, religion, veteran status, work styles, and experience bring a broader array of knowledge, perspectives and experience to companies and help them reach an increasingly global customer base. Additionally, a workplace that supports a diverse set of beliefs and backgrounds enables companies to hire and retain the best employees.
Yet one of the most challenging aspects of DEI programs can be making decisions based on data rather than emotion. Leaders get stumped in determining the best methods to diagnose and measure the success of their DEI initiatives. The pressure to act, combined with a lack of comfort around making these types of decisions, increases the likelihood leaders will react without a sound, data-driven foundation. While it can be daunting to incorporate data into your DEI decisions, returning to your basic business analysis training can recenter your focus on the “why,” which should always come before the “what.”
While making decisions based on DEI data may be new for many leaders, the fundamentals of business analysis haven’t changed. In most other areas of the business, it is a natural instinct for good leaders to ask questions before making decisions. For example, if a leader is presented with data that shows the company is not meeting its quarterly sales goals, they won’t recommend action without understanding the underlying reasons: is it the sales pipeline, the sales process, volume, pricing, etc.? They also won’t deflect or just hope the issue goes away.
With DEI, the approach to diagnosing the problem might look like this: you see that X% of your employees identify as Black. How does that level of representation compare to the potential candidate population and your peer organizations? If it's low, is that because Black talent is less represented in who's coming in or more represented in who's leaving? If the former, are Black candidates less represented in who is applying or who is being made an offer? If Black applicants are disproportionately low, then where are you sourcing recruits from? If Black hires are disproportionately low, then at what point during the interview process does the drop-off occur? This type of questioning should continue until leaders understand the data.
This data should all be available from a company's HR and recruiting systems, even if it's never been measured before. HR may need to do a little digging to find the right benchmarks, but there's no reason not to invest right now in defining what that source should be.
I suspect that leaders, while tapping into their natural curiosity and determination to understand the root causes of a problem, will even find this process satisfying. To build upon the above example, imagine finally being able to pinpoint that your company has a gap in applications received from Black talent. This “why” must be discovered before the “what” can be planned.
Not Expecting the Worst
One common barrier to measuring DEI data that leaders may or may not admit to is fear of what the results will show. Expecting the worst, leaders may neglect or, worse, actively delay pulling data. While this fear is natural, it is probably counterproductive.
There is a very good chance that a holistic portfolio of DEI data will end up painting a better picture than what you have imagined. Yes, there will certainly be numbers that don’t reflect the outcomes you want. There will also be numbers that are better than expected. Taken together, you should expect that the objective data will finally put to rest false or unhelpful narratives and shine a light on gaps that you can actually close.
As an example, in the past, Point B avoided participating in third party surveys that would score us on our DEI performance out of concern we may not stack up against our peers. But when we actually looked at criteria for these surveys, we realized that we would have obtained high scores and that our gaps were blind spots that we could easily start to work on (again, the “what” coming after understanding the data, not preceding it).
Expecting the worst led us to fuel that perception. If we expect the best or allow ourselves to be confident in our journey, we can defuse a lot of emotion, including fear.
Another compounding factor for leaders can be the isolation and pressure to solve problems alone. If you’re a leader who doesn’t have access to DEI subject matter experts, the job may fall to you alone. Take the pressure off by leveraging others.
For example, one easy way of leveraging data to assess and prioritize your DEI efforts is to research certifications, rankings or awards given by various organizations. Experts have already defined methods of scoring organizations when it comes to supplier diversity, LGBTQ+ inclusion, racial equity, DEI maturity, and other categories. Knowing how the experts score companies can provide a roadmap for what to measure and what to aspire to. Leaders may even find that their companies measure pretty well in certain categories and only fair in others. This is helpful because it highlights where the most effort should go.
Take advantage of the collective operational expertise of your company. Invite leaders from other areas, including HR, communications, sales, delivery, and marketing, to participate in iterative discovery sessions. Ask for HR to come with some fundamental measures including workforce and leadership representation by race/ethnicity, gender/gender identity, veteran status, presence of disability, LGBTQ+ identity, age group, management level, and geographical location (if applicable).
Expect that each time a little bit more of the story will be revealed and a little bit more data will be needed. Other measures that may require more sophisticated or manual data analysis include:
- Completion of relevant training (anti-harassment, unconscious bias, etc.)
- Employee sentiment, including feelings of inclusion or equity at the company
- Promotion speed/representation and average tenure
Finally, don’t expect that you will have to diagnose and solve every single problem before being able to move forward. Like a doctor must triage and treat a patient’s most urgent conditions, it is ok to understand and tackle one problem really well and still need to research others. In fact, it may be a better use of your resources to do so.
Treating Data as a Bias Eliminator
My experiences as a DEI leader have shown me that data is our biggest opportunity to mitigate unconscious bias, on all sides. The high stakes and intimidation we feel often results in behaviors that actually fuel biases like confirmation bias, group think, and others. Recentering on data and our business training normalizes the search for the “why” before skipping straight to the “what.”