DEIB & Analytics: What the Literature Says

April 27th, 2021


“Black Lives Matter protests moves corporate D&I initiatives center stage”1

“CIOs double down on D&I to build stronger businesses”2

“Amazon will examine its employee review system after claims of racial bias”3

These are just a few of the headlines published in the last 12 months on diversity, equity, inclusion, and belonging (DEIB) efforts by companies. Given the growing momentum in the space, especially since the social justice movements of 2020 spread globally, we wanted to better understand how orgs are measuring and monitoring their DEIB efforts via people analytics.

We looked at more than 60 academic and business articles, reports, and books for this literature (lit) review. This article summarizes the themes from the lit:

  • Analytics for DEIB is more important than ever
  • DEIB analytics is more than diversity metrics
  • Predictive analytics for DEIB can help plan for the future
  • Don’t forget about qualitative data
  • Address issues of privacy and ethics

Much of the literature that we reviewed for this topic focuses on why analytics should be used to support DEIB efforts. However, some shining pieces did go beyond the “why” to discuss how companies can go about collecting and utilizing DEIB data. We take a closer look at these 5 themes in the following sections.

Analytics for DEIB is more important than ever

It's not surprising that we found a large portion of the lit focused on explaining why analytics is important for DEIB. As pointed out by 1 author:

“Research and data must play a role when it comes to implementing D&I strategy that actually moves the needle on equity. If you don’t collect data, it’s hard to diagnose how your company is performing. If you don’t track data, you won’t know how you’re improving.”4

Several recent articles call out the urgent need for it, especially now, as the COVID-19 pandemic and social movements have spurred companies to reexamine how they drive equity. If we look at the recent commitments to DEIB made by some of the biggest companies—such as:

  • Facebook tying improving employee diversity to executive performance reviews5
  • Target’s aim of increasing representation of Black team members by 20% by 20236
  • Starbucks’ goal of BIPOC representation of at least 30% at all corporate levels, and at least 40% at all retail and manufacturing roles by 20257

—then, we see that not one of these goals can be achieved without access to data and analytics.

DEIB analytics is more than just diversity metrics

While diversity and representation metrics remain foundational to DEIB efforts, we’re pleased to see several articles push orgs to collect and use data beyond representation metrics. One of the ways orgs can go deeper on metrics is by looking at experience data for different groups of the employee population. According to 1 author, orgs need to look at hard data:

“Are they invited to social gatherings? Included in meetings? Receiving proper mentorship? Looking at these interactions—where discrimination, microaggressions and lack of support often creep in—will reveal what’s truly derailing efforts to improve diversity.”8

These data can be helpful in measuring inclusion. Existing engagement surveys or specific pulse surveys can ask employees about concerns around inclusion. Question statements can be used by orgs to build an inclusion index and track it over time, including:

  • Employees are valued for their differences and their unique contributions
  • Employees can voice their opinions without fear of retribution or rejection
  • People are rewarded fairly according to their job performance and accomplishments
  • I have confidence in my company’s grievance procedures

These questions can be supplemented with check-ins, exit interviews, network data and other data to identify existing gaps.

Predictive analytics for DEIB can help plan for the future

Articles that focus on applying predictive analytics for DEIB talk about spotting the right patterns and identifying a potential issue before it turns into a problem. Predictive analytics can be a powerful tool to empower orgs to make smarter decisions about their DEIB efforts.

The lit contains examples of companies that are already using predictive analytics to support DEIB, including:

  • Whirlpool—to help steer the company away from a “…myopic focus on intake, while ignoring potential effects of retention risks and advancement challenges for diverse populations.”9
  • Walmart—to model and forecast techniques to understand the future through such questions as:
    • What could happen?
    • How can we arrive at the destination sooner?

Every quarter, diversity leaders and business leaders meet to review DEIB goals, as well as share insights from the data.10

  • International Paper—to analyze expatriate compatibility: Predictive analytics, based on past behavior, family dynamics, global acumen, and cultural agility, can forecast which employees would fare better with a global move. 11

Don’t forget about qualitative data

While not a recurring theme, we think the reminder about qualitative data is an important point made by some articles that deserves a mention. Very often, when we talk about data and analytics, we instantly think of hard numbers, dashboards, and spreadsheets.

DEIB and analytics leaders might find themselves trying to persuade or convince some stakeholders about the merit of qualitative data—and part of that challenge might require redefining what “data” means for the org.

Individual stories and experiences are an important piece of the puzzle. Interviews with employees can help leaders supplement quantitative data to get a holistic picture. As 1 author stated:

“Statistics don’t capture what it feels like to be the only black team member.”12

Some examples of the types of qualitative data that can be used include:

  • Interviews
  • Focus groups
  • Textual analysis of written performance reviews
  • Analysis of exit interview notes
  • Analysis of hiring memos

However, the lit does offer some limitations to using this data. For example, if companies aren’t consistent or comprehensive in their qualitative analysis, then biases can creep in.

Address issues of privacy and ethics

No discussion about people analytics—especially when it involves sensitive DEIB employee data—can be complete without taking into account issues of privacy and ethics. And we’re glad to see a number of articles point out this issue. As one of the authors said:

“You want to be very careful of how you’re protecting the data and how you’re making sure that your data is being used to make fair and equitable decisions on people.”13

So, how can orgs best deal with this issue?

One way to maintain employee privacy is through data aggregation to ensure no one’s data is singled out. However, this could be challenging for small companies that may only have a few people from a particular demographic group.

Data ethics and privacy becomes even more important when collecting passive data on employees. Companies can be more responsible and ethical about collecting such data by:

  • Being transparent with employees about the data collected and who will have access to it
  • Sharing that data with employees
  • Being clear about the types of analysis being run on the data collected on employees and how those data are used

What caught our attention

Of the lit we reviewed, several pieces stood out to us. Each of the pieces below contains information we found useful and / or intriguing. We learned from their perspectives and encourage you to do the same.

The Next Generation of Diversity Metrics: Predictive and Game-Changing Analytics

Brian Baker and Michael Collins (edited by), Diversity Best Practices, 2013

Explains how predictive analytics, when used correctly, can support areas such as retention, development of a leadership pipeline, analysis of leadership and talent gaps, and creating a general talent pipeline.

"Predictive analytics will soon offer the make-or-break evidence needed to support every business case, every new project proposal. For diversity practitioners, predictive analytics offers more: A powerful tool to be smarter about inclusion efforts—which ones to ditch, which ones to double down on, which ones to invent.”


  • Link the hiring algorithm with recruitment of candidates from diverse backgrounds to revisit high-potential resumes and analyze retention data
  • Shift conversations from reactive debates to proactive consideration
  • Use predictive analytics to gain insight into what is reasonably attainable for companies in the future

Strength in Numbers: Using Data to Track Diversity and Inclusion

Marianne Bertrand, Caroline Grossman, Mekala Krishnan, Promarket, The Stigler Center at the University of Chicago Booth School of Business, October 2020

Explains how no simple solution is going to cure all DEIB woes. Change needs to be deliberate and transformational, which takes time.

"People arrived at quotas as a panacea, as the silver bullet. And it’s great that it has led to increased representation on boards, but that’s really not had the kind of spillover effects that people had hoped."


  • Quotas for executive boards, while not showing any negative effects, aren’t the transformative policy that many thought it would be
  • Culture change takes time—anywhere from 5-7 years for change to really start to trickle through the org
  • Change the norms: For example, instead of lengthening the maternity leave policy which separates women from the labor force for longer, create a parental leave policy that encourages both parents to take time off, leveling the playing field

Here’s How to Wield Empathy and Data to Build an Inclusive Team

Interview with Ciara Trinidad, Head of Diversity, Inclusion, and Belonging at Blend, First Round Review, 2018

Explains that the key to building the strongest, most diverse team is understanding and believing in why you’re doing it. Knowing the reason behind it gives momentum to the initiatives and gets people onboard.

"Discussion gives muscles to data—especially around D&I. Without it, a dashboard is a depository. A dialogue becomes a monologue, which eventually becomes silence."


  • Partner with the HR operations specialist to learn what data is stored where; use the fields needed to help create a dashboard that provides a meaningful narrative
  • Try different analyses to discover which are the most revealing. Some examples might be:
    • Analyzing hires by month, by team which can show how recruiters are faring against DEIB strategies
    • Analyzing hires by month, by race which can reveal an org’s internal biases
    • Analyzing hires by tenure which can reveal when people leave and why
  • Present the data to every person who has a stake in the company in the clearest, most digestible way
  • Keep the lines of communication open; consider using your existing talent as your DEIB professionals and pay them for that work

Actionable Diversity and Inclusion Analytics with Philips’ Toby Culshaw

Joe Macy, Gartner, 2019

Provides a case study into how companies can leverage partnerships for DEIB analytics, and making sure how data can be made comprehensive and presented in a way that’s easy to digest.

"Because of the partnerships with internally facing HR analytics and reporting teams, the Talent Intelligence team could access information already available at Philips and avoid starting from scratch to find the needed information. The partnerships also ensured the different teams were on the same page and understood how to impact D&I at Philips."


  • Break the project down into 3 smaller phases:
    • Gather internal data to understand the current state by partnering with the HR analytics team
    • Gather external data to understand the feasibility of changing the current state by looking at the markets in which Philips operates
    • Synthesize the internal and external data in a segmented way to drive action
  • Present data by creating easy-to-consume materials designed to drive action
  • Thoroughly understand the competitive landscape worldwide to find the right talent and understand the feasibility of diversity goals

Delivering On Diversity and Inclusion: How Employers Can Achieve Measurable Results

White Paper, Visier

Encourages orgs to move away from the traditional top-down approach to D&I practices and, instead, empower frontline workers to initiate change. This approach must include data that’s readily understood by all and looks to the future instead of criticizing the present.

“When data can be accessed in a way that facilitates exploration (without the need for a data science degree!), it can help organizations understand where to focus their talent efforts to achieve broader goals.”


  • Avoid common data pitfalls, such as measuring diversity as a blanket number and prioritizing reports over insights
  • D&I taskforces are more effective than top-down approaches to change
  • Unify data from multiple sources, so that users can dig deeper into the data
  • Utilize D&I analytics to:
  • Compare the org to the most recent EEOC benchmarks
  • Clearly communicate changes and diversity through dynamic, real-time visual storytelling
  • Demonstrate how D&I initiatives have an impact on business performance metrics
  • Understand engagement among diverse employees, and monitor the impact engagement has on turnover and exit patterns

Additional Reading Recommendations

  1. “Better People Analytics: Measure Who They Know, Not Just Who They Are,” Harvard Business Review / Paul Leonardi & Noshir Contractor, November-December 2018,
  2. “15 Ways People Analytics Can Improve Workforce Diversity,” / Rosie Harman, August 2020,
  3. “Why you should apply analytics to your people strategy,” McKinsey & Co., The McKinsey Podcast / Simon London, Bryan Hancock & Bill Schaninger, April 2019,
  4. “Support Diversity, Equity, and Inclusion with People Analytics,” Human Capital Institute, Nine to Thrive / Phil Willburn,
  5. “4 Things Walmart Learned About Using Data to Drive Diversity,” The APQC Blog / Elissa Tucker, September 2019,
  6. "How CEOs and CHROs Can Connect People to Business Strategy", Harvard Business Review Analytics Services, 2017,
  7. “Data And Diversity: How Numbers Could Ensure There’s A Genuine Change For The Better,” / HV MacArthur, August 2020,
Priyanka photo
Priyanka Mehrotra
Research Lead at RedThread Research


  1. “Black lives matter protests moves corporate D&I initiatives center stage,” Forbes / Geri Stengel, June 2020,
  2. “CIOs double down on D&I to build stronger businesses,” CIO / Clint Boulton, March 2021,
  3. “Amazon will examine its employee review system after claims of racial bias,” CNBC / Annie Palmer, April 2021,
  4. “Strength in Numbers: Using Data to Track Diversity and Inclusion,” Promarket, The Stigler Center at the University of Chicago Booth School of Business / Marianne Bertrand, Caroline Grossman & Mekala Krishnan, October 2020,
  5. Facebook ties improving employee diversity to executive performance reviews,” CNBC / Salvador Rodriguez, July 2020,
  6. “Target pledges to increase number of Black employees by 20% as companies are pressured to take action,” CNBC / Melissa Repko, September 2020,
  7. “Starbucks to tie executive compensation to meeting its diversity goals,” ABC News / Catherine Thorbecke, October 2020,
  8. “Is data analytics the answer to improving diversity at work?” / Lisa Ardill, October 2020,
  9. “The Next Generation of Diversity Metrics: Predictive and Game-Changing Analytics,” Diversity Best Practices / Brian Baker and Michael Collins (edited by), 2013,
  10. “4 Things Walmart Learned About Using Data to Drive Diversity,” The APQC Blog / Elissa Tucker, September 2019,
  11. “The Next Generation of Diversity Metrics: Predictive and Game-Changing Analytics,” Diversity Best Practices / Brian Baker and Michael Collins (edited by), 2013,
  12. “Numbers Take Us Only So Far,” Harvard Business Review / Maxine Williams, November-December 2017,
  13. “Why you should apply analytics to your people strategy,” McKinsey & Co., The McKinsey Podcast / Simon London, Bryan Hancock & Bill Schaninger, April 2019,