Representation data is one indicator of the state of DEI at your company, but it won’t give you the full context you need to make an informed action plan.
Jessica Marcus | Director, Research & Evaluation | LinkedIn
As companies across the country are becoming more vocal about their DEI ambitions and accompanying tactics, our team at Grads of Life has noticed an interesting trend take shape: Representation data has become a proxy for DEI data. The conflation of the two is not a surprising development. Representation data — that is, workforce composition by race, gender and other demographic characteristics — is often the data that companies can most easily track within their current systems and usually the only data that companies choose to disclose publicly year over year, if at all. But equating representation data with DEI data broadly comes at a major cost: Measurement of a company’s DEI success is limited to only one type of DEI data point.
This is asking a lot of representation data! Imagine trying to evaluate the success of your business by only looking at profit margins. Without also reviewing all the inputs on a balance sheet, you’d be unable to spot red flags or understand levers for change. The same is true here: Relying exclusively on representation data reduces visibility into cultural challenges and opportunities. If we reframe representation data as part of the calculus, instead of the whole, we create space to:
- Explore a wider range of corresponding DEI-related data floating just beneath the surface.
- Implement more nuanced strategies and goals based on a wider range of critical DEI data.
Strengthening DEI Data Collection
Optimizing your approach to DEI data collection starts with understanding the variety of data sources at your disposal. Your company’s human resources information system (HRIS) is often the go-to system of record because it stores representation data. But it is not the only place to find useful insights. An applicant tracking system (ATS) will contain crucial information about your talent pipeline and hiring process, like average time to hire or the percentage of underrepresented candidates who advance from one stage in the application process to another. Additionally, employee sentiment surveys and interviews or focus groups can capture a wealth of qualitative data about lived experience, a key driver of overall DEI success that should not be ignored. Beyond these, many companies frequently have access to other less traditional but equally valid DEI data sources. For instance, an internal budget may reveal pay equity disparities. Similarly, a list of your company’s most common recruiting sources can help assess whether you are over-indexing on predominately white institutions and have opportunities to expand to historically Black colleges and universities (HBCUs), community colleges, and workforce training programs.
Once you’ve identified your data sources, consider who you might rally to help inform and advance data collection efforts. While team names and individual titles may differ company to company, at a minimum you’ll want to engage the following stakeholders: the chief DEI officer, chief HR or people officer, HRIS administrators, talent acquisition staff, and members of the staff development team. Together you can align on the company’s overarching DEI ambition, identify metrics to help benchmark your progress, and tailor your data collection process with those metrics in mind. These team members can also provide critical support in managing the new process going forward.
With a clear understanding of the metrics you need, it’s time to take a closer look at the details available to you. To track a wide range of DEI data metrics beyond representation, companies should draw on a core set of data elements, many of which are already commonly stored in HRIS and ATS databases. These include but are not limited to: employee representation data (i.e., race, ethnicity, gender, sexual orientation, disability status, etc.), employee educational attainment data, employee wage data (including companion living wage data from MIT’s living wage calculator), job posting data, hiring data, promotion data, and retention/turnover data.
It’s perfectly normal for your company to be tracking only a portion of these data elements right now. For example, fields like educational attainment status or disability status may be absent from your systems because they haven’t been data collection priorities in the past. But if your current or future DEI goals rely on your ability to identify groups of people by their education or disability status, then you’ll want to begin building processes that enable the collection and storage of those items. We’ve seen companies approach this in several ways, including incorporating the data collection request into their new hire onboarding process and launching campaigns to encourage incumbent employees to self-report.
Setting up your company for long-term DEI success requires treating DEI data collection as a dynamic activity, one that will evolve over time in response to your workforce needs. Embrace revisiting and retooling practices that no longer serve you, and remember that your organization is not alone in this work. Many companies are mulling their approach to DEI data collection and working together to determine what does and does not work. Consider joining a community of practice like OneTen or Markle’s Rework America Alliance to reap the benefits of peer collaboration. Ultimately, learning from other companies’ experiences can shape your understanding of your own company’s needs, helping to bring your data collection vision into clearer focus.