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8 Steps to Build Meaningful Diversity and Inclusion Analytics

October 07, 2021 Talent Management
Stacia Sherman Garr co-founder and principal analyst RedThread Research
By Stacia Sherman Garr

Diversity, equity, inclusion and belonging, or DEIB, are on the minds of many business leaders today. Yet, to make meaningful change in DEIB, organizations need to measure, analyze and report on it with much greater sophistication and effectiveness than ever before. For many organizations, this is where things get sticky.

We recently completed a research study, DEIB Analytics: A Guide to Why and How to Get Started, for which we interviewed more than 25 people analytics and DEIB leaders. One of the primary learnings was that many organizations have a solid foundation in diversity metrics due to legal requirements, but do not know how to get started on inclusion metrics and analytics.

Based on that research, here's a guide for organizations looking to get started on building meaningful diversity, equity, inclusion and belonging analytics.

The 8 Steps of DEIB Analytics

Through conversations with executives, we identified an 8-step model to get started on the DEIB analytics journey (see Figure 1). While this model is presented in a somewhat linear fashion, it’s anything but. There is significant iteration and refinement of the problem, data and analysis needed throughout all these steps alongside organizational partners.

The 8 Steps of Diversity, Equity, Inclusion and Belonging Analytics
Figure 1: The 8 Steps of Diversity, Equity, Inclusion and Belonging AnalyticsPHOTO: RedThread Research, 2021

Step 1: Identify Partners

The most important first step is to identify analytics partners. Who is essential to your ability to understand priorities, access data and drive change? The most critical ongoing relationships for DEIB and analytics leaders to maintain include:

  • Chief human resources officer or head of HR
  • Senior HR business partners (HRBPs) of large parts of the business
  • Legal, privacy and security teams
  • IT and centralized data teams

Step 2: Get Demographic Data in Order

In conjunction with identifying and building a partner ecosystem, it’s critical to identify and refine demographic data. Some of the most common data sources include:

  • HR information system (HRIS)
  • Applicant tracking system (ATS)
  • Office of DEIB for data tracked through other means

Figure 2 outlines the typical and novel demographic data that organizations are collecting. (Note: italicized items are US-only). It is important to remember that legal limitations exist in different jurisdictions, so it’s critical to work with the appropriate legal and privacy professionals to comply with those laws.

Types of Demographic Data Collected by Organizations
Figure 2: Types of Demographic Data Collected by OrganizationsPHOTO: RedThread Research, 2021

Step 3: Understand the Problem and Ideate Stories

Our research revealed there’s a set of standard DEIB analyses organizations do to gain a base level of understanding. These include:

  • Internal talent analysis (e.g., level, geography, function)
  • Leadership (e.g., succession lists and promotions)
  • Basic talent analysis (e.g., turnover and retention)
  • Engagement
  • Talent acquisition (e.g., pipeline)

Companies analyze this information by demographic data and will increasingly conduct intersectional analyses (e.g., Black women, Asian men). The intention is to understand if and to what extent diverse populations are having an employee experience different from majority populations. From there, leaders can start to ideate the reasons for those differences and what the overall story could be.

Step 4: Identify Additional Inclusion Data

Once the initial problem is understood, it's time to hypothesize what's driving the problem. This hypothesis phase will clarify the additional data, usually inclusion data such as employee engagement data, that may be necessary to understand what's driving the problem.

In this phase, leaders need to determine the difficulty of using the data — such as difficulty in access to the data, data structure or data mapping — and use that to help determine the overall scope for the analysis.

Step 5: Prioritize Problems

When it is roughly clear the overall scope of the problem, it is important to prioritize which DEIB questions to pursue given the limited time and resources available. Here are examples of the questions that analytics and DEIB leaders are thinking about when it comes to prioritizing the DEIB problems to solve:

  • To what extent is this problem aligned to the big-picture DEIB strategy and priorities?
  • What will be the top-line or bottom-line impact?
  • How many employees will it affect?
  • Who is asking the question? (CEO, CFO, CHRO)

At the beginning of your organization’s DEIB journey, it is likely that reporting activities, such as creating dashboards and quarterly reports, will dominate the team’s priorities. With those foundations in place, though, the team can then begin to pursue more strategic and challenging questions.

Step 6: Analyze and Refine Data Stories

There are a wide range of analysis approaches, and detailing them all here isn’t the point. That said, consider what approaches will most directly answer the questions your organization needs to answer. It is easy to get caught up in fancy analysis techniques, but you may lose your audience, who doesn’t understand the analysis or why it was used.

Always remember that DEIB analysis is some of the most sensitive analysis the organization will do, and as such stakeholder engagement throughout is critical. To that end, it is important to:

  • Apply an iterative approach: Consider why something might be the case or what you might be missing in your analysis approach. Don’t stop on the first iteration.
  • Avoid doing analysis in secret: Engage a variety of people in the analysis process to gut-check results, and see if they have a different perspective on what could be happening.
  • Design check-ins as part of the process: Engage interested stakeholders to the extent they truly are interested in the analysis process so they can go along the journey. This avoids surprises at the end.

Step 7: Share and Explore

Once analysis is done, it's important to share it. Primary stakeholders should have been involved in the analysis process, but it is important to share it more broadly, too. The following points are important to consider when communicating the story the data tells:

  • Clearly define the terms, data and the different contexts used.
  • Restate the DEIB question you're trying to answer.
  • Tell a story using data to make your points. Don't get lost in the details of the metrics, analyses or data.
  • Make clear the “what” and the “so what.” Some folks need to know that a clear decision has been made for every key point.
  • Identify areas where it may be appropriate to dig more and structure conversations with stakeholders on those areas.

As with all the other steps, the conversation and the insights remain the most important thing. Simply delivering a presentation or a canned speech doesn’t fit the need.

Step 8: Take Action and Practice Accountability

While it may feel great to share insights with stakeholders, that's just the beginning. If the question was important enough to spend time understanding the answer, then keep leaders up to date on how they're progressing on solving the problem.

To that end, it's important to think about delivering DEIB insights as a product, and not a project. This means:

  • Defining metrics that will capture progress on the DEIB objective in consultation with stakeholders.
  • Making metrics as transparent and accessible as possible to as many people and as often as possible.
  • Ensuring data is continuously updated. No Excel spreadsheets with dead data.
  • Leveraging technology to create scale and agnostic authority. Tech can help identify insights that either were missed or are too politically sensitive to surface.
  • Building DEIB metrics into existing dashboards so they're seen as part of how the business is managed, not something special or different.

This is a quick guide to getting started on the DEIB analytics journey. We hope you will take these insights to begin, or accelerate, your work on measuring DEIB. By focusing on integrating DEIB practices and measurement, organizations will begin to see progress in this all-important area.

About the Author

Stacia Sherman Garr is co-founder and principal analyst at RedThread Research and a thought leader on talent management, leadership, diversity and inclusion, people analytics and HR technology.

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