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Understanding the need for a DEIB–PA partnership

Why focus on the partnership between DEIB and people analytics (PA) leaders?

In the summer and fall of 2020, companies made big promises to improve diversity, equity, inclusion, and belonging (DEIB) in their organizations. At the time, one of our first questions was how organizations would show the progress made on those commitments. While DEIB metrics—measurements designed to understand DEIB—are the obvious answer, how to select, collect, use, and maintain those metrics isn’t so clear.

Thus, our research initiative on DEIB metrics and analytics was born. The first article in this series, “DEIB Analytics: A Guide to Why & How to Get Started,” provides leaders with a plan for how to begin using DEIB metrics and analytics. The second article, “DEIB Metrics: An Essential Guide,” provides definitions of what DEIB metrics are and how they can be used.

This third article in the series focuses on how DEIB and people analytics leaders should partner to identify, measure, maintain, and distribute those DEIB metrics.

Why write an article on this DEIB–PA partnership? For several reasons. As shown in Figure 1, the way that DEIB and PA leaders partner (or not, as in the early days) has changed substantially. PA is increasingly at the center of DEIB metrics efforts as organizations ensure that they have consistent data sets, analysis approaches, and contexts across the board. However, PA leaders shouldn’t do this work without the support of DEIB leaders—since DEIB brings an essential level of theory, nuance, and context to the data. By DEIB and PA leaders partnering, you can clearly and consistently:

• Define terms
• Identify consistent data sources and analysis approaches
• Appropriately interpret data
• Develop targeted and appropriate action steps
• Create ongoing accountability

The combined expertise of DEIB and PA leaders enables you to make all these things happen.

“People Analytics leaders can help answer questions that DEIB leaders didn’t even know how to ask.”

—Hallie Bregman, Owner, The Bregman Group

PA is increasingly central to organizations' DEIB metrics efforts

Despite challenges, there is a known path to partnership

While you should (in theory) collaborate, it doesn’t always seem intuitive for PA and DEIB leaders to work together in practice. Why is this?

• First, many DEIB and PA leaders have historically come from different perspectives, such as social justice for the former, and math or statistics for the latter.
• Second, your individual responsibilities often have very different. Many DEIB leaders, for example, are charged with creating community and connection—and taking a data-driven approach may not be prominent on your radar. By contrast, PA leaders primarily focus on data and measurement; your only experience with DEIB may be through their participation in DEIB-related initiatives.
• Third, your reporting relationships are often very This can result in a poor alignment of priorities and little insight into each other’s work, especially if either DEIB or PA is outside the HR reporting structure.
• Finally, the data used by DEIB in the past hasn’t necessarily been the same as that used by PA—making it more difficult to create alignment on even simple reporting.

All of this culminates in the challenges outlined in Figure 2.

A look at the causes and effects of challenges to DEIB analytics work due to differences between PA and DEIB leaders

Yet, our interviews revealed that common approaches could enable a strong DEIB / PA relationship to develop and thrive.

This article is designed to help DEIB and PA leaders better understand each other—and create a partnership that will enable you to succeed. We identify 3 components of an effective partnership in this article, outline the responsibilities of DEIB and PA leaders and provide specific examples of how to make this relationship more effective. We also provide a series of exhaustive checklists in the appendix to help you get started.

As always, our research is designed to accelerate good ideas within the people management field. Some of the concepts in this article may fit your organization’s unique circumstances, while others may not. Even better, some of these ideas may inspire new insights and approaches.

Whatever your reaction to the work, we’d love to hear about it. Please feel free to reach out to us at [email protected] with any insights, questions, or feedback.

“One of the biggest challenges to a successful DEIB and people analytics partnership is that most DEIB leaders don’t have technical backgrounds. As a result, there is a disconnect between diversity leaders and people analytics leaders.”

—Sean Fay, Co-Founder, The Context Factory

What is DEIB?

Before we dive into the heart of this paper, let’s start by defining what we mean when we say diversity, equity, inclusion, and belonging (see Figure 3). Everyone in your organization should be on the same page when it comes to understanding DEIB—which starts with a single, common set of definitions.

What are DEIB metrics?

Building on the information on the previous page, defining what we mean when referring to DEIB metrics is essential. Figure 4 provides specific definitions for, as well as examples of, metrics for each DEIB area. Everyone in your organization should be on the same page when it comes to understanding DEIB. After introducing a single, common sets of definitions for DEIB, you then need to define and differentiate the metrics for each of the 4 aspects of DEIB.

Building a DEIB—PA partnership

3 practices for building a DEIB–PA partnership

Our interviews with DEIB and PA leaders revealed several things they’re doing to build a successful partnership. We grouped our findings under 3 broad practices, with specific areas that leaders should consider when developing the partnership (see Figure 5).

1. Understand your partner’s context. When starting a new relationship, you must understand how the other team operates. This involves becoming familiar with each other’s reporting and governance structures; specific concerns; priorities; and, responsibilities.\
2. Form a DEIB–analytics partnership by aligning your objectives, responsibilities, and expectations. A partnership built on clarity around who is responsible for what, along with the overall goals, allows you to achieve greater alignment. This becomes easier with both of you supporting each other in your work, as well as among your teams and throughout the organization as a whole.
3. Work together effectively within your organizational network. A successful DEIB–PA partnership is also a result of you both working together with other functions, teams, and leaders within the organization. Remember: You need to be strategic and tactical in building relationships with IT, business leaders, marketing, finance, and other functions to help ensure the success of your work and your partnership.

3 practices for DEIB and PA leaders to adopt to build a successful DEIB-PA partnership

Practice #1: Understanding your partner’s context

DEIB leader’s context: A primer

Before beginning any partnership, you must understand your partner’s circumstances. Let’s start with the DEIB leader’s context.

To start, you need to have a good understanding of the environments in which your DEIB leader works. For example, DEIB leaders typically have up to 5 different stakeholders: employees, customers, suppliers, communities, and shareholders.

Further, in many global organizations, DEIB leaders operate within a complex and demanding governance structure, an example of which is shown in Figure 6. All of these different groups require time with your DEIB leader and their teams. Note: HR—where PA teams often report—is just one of many constituents.

To help you understand your DEIB partner’s context, you should ask them the following:

• To what extent is working with DEIB your full-time or part-time responsibility?
• How big is the DEIB team?
• To whom do you report? How strong is that relationship? What other reporting relationships exist?
• What other resources (e.g., budget, indirect reports, external resources) do you have?
• What experience do you have in collaborating with PA teams on DEIB data?

DEIB leaders operate within a complex and demanding governance structure

PA leader’s context: A primer

Similar to DEIB, PA leaders also work in complicated situations.

As shown in Figure 7, PA leaders interface with various organizational stakeholders, while managing a team of technical experts. Their organizations also tend to have different governance structures (see Figure 8), impacting how they will work with DEIB.

To help you understand your PA partner’s context, you should ask them the following:

• To whom do you report? What other reporting relationships exist?
• What types of services and support does your team offer?
• How is your team structured? What are the types of expertise of the people within that team?
• How long has your team been doing their work?
• What other resources (e.g., budget, indirect reports, external resources) do you have?
• What experience do you have in collaborating with DEIB leaders on DEIB data?

“Our DEI group has great people, but I just don’t know if they are doing what we need right now and don’t know that they even have the skill set to do it. I don’t know how numbers-focused they are or how numbers-focused they should be. Right now, our DEI team is more like project management, focusing on things like benefits. They handle a lot of other stuff versus looking at data.”

—Global people analytics leader, international consumer packaged goods company

PA leaders interface with several organizational stakeholders while managing a team of external technical experts

Organizations often have one of these two types of governance structures for PA—impacting how PA and DEIB interact

“The combination of qualitative and quantitative data is ideal, but at the end of the day there is nothing that data will tell us that we don’t already know as Black people. I know what my experience was as an African-American man who worked for 16 years in roles that weren’t related to improving diversity. It’s as much heart as head in this work.”

—D&I Leader

Appreciate each other’s concerns

It’s one thing to understand each other’s contexts—but it’s another entirely to appreciate your partner’s concerns about working together.

Given the different silos DEIB and PA leaders often work within, it’s easy not to understand your partner and where they’re coming from. Based on our research, we’ve identified some common concerns each can have about the other (see Figure 9).

Be aware: This is a prime opportunity for you to lean in and address those concerns directly with your partner.

In some instances, it can require creating specific protocols or practices to address the concern, such as a standard expectation that new analysis will be reviewed by both partners. In other instances, it may need both groups to engage in certain situations together, such as when interpreting results with a senior HR business partner.

Some of these concerns will likely be addressed when you and your partner align on goals and objectives (we discuss this next). For other concerns, you both may need to acknowledge these exist, and then decide to address them on a case-by-case basis.

Some of the top concerns we heard from DEIB and PA leaders alike were regarding the work, as well as each other

Practice #2: Forming a DEIB–PA partnership by aligning on objectives, responsibilities & expectations

Identifying clear objectives

From the start, perhaps the most crucial item for DEIB and PA teams is to shift their mindsets from being 2 separate teams to being 1 integrated alliance—the DEIB–PA partnership.

While there doesn’t have to be any sort of official creation of a team (or even the naming of one!), these 2 groups must:

• Align around common objectives
• Share insights and learnings freely
• Think of themselves as each contributing equally to the DEIB metrics and analytics effort

To do this, start by ensuring everyone understands the organization’s people priorities for the year— and how those translate into DEIB and PA strategies and objectives.

From there, identify the common objectives and / or where enough overlap exists to articulate new common goals. Share this information broadly within and outside the partnership to enable a broader alignment of stakeholders.

While objectives aren’t always set or aligned at the beginning of the calendar year, it’s still essential for the PA and DEIB leaders to align—even if it means the 2 leaders discuss some hard questions about priorities.

Clarifying specific responsibilities

Another important part of the process of forming the DEIB–PA partnership is to clarify the specific responsibilities of the constituent teams. Figure 10 shows that some responsibilities belong to each group, while others are shared. Note: There may be more responsibilities than those outlined in Figure 11 and some of them could belong to different groups, depending on your organization’s structure.

After identifying the respective responsibilities, next you must put in place those practices or approaches that’ll codify how the shared responsibilities will be executed. For example, will the PA team first develop a list of data-related DEIB definitions and then run it by the DEIB team? And how will new data definitions be added?

Some of these process decisions may be determined by how the PA team supports the DEIB team (see the Uber Real-World Threads for an example). But many of these decisions won’t be, so you need a plan to make sure that all responsibilities are clearly delineated.

Breakdown of responsibilities for DEIB and PA teams individually, as well as those responsibilities shared by both teams

“But to discover the effects of bias in our organizations—and to identify complicating factors within groups, such as class and colorism among Latinos and others—we need to collect and analyze qualitative data, too. Intuition can help us find it. The diversity and HR folks described using their “spidey sense” or knowing there is “something in the water”—essentially, understanding that bias is probably a factor, even though PA doesn’t always prove causes and predict outcomes. Through conversations with employees—and sometimes through focus groups, if the resources are there and participants feel it’s safe to be honest—they reality-check what their instincts tell them, often drawing on their own experiences with bias.”

—Maxine Williams, Global Chief Diversity Officer, Meta

Creating a service-level agreement between partners

As part of identifying relevant responsibilities, the PA leader should put in place a formal service level agreement (SLA) for how PA will work with DEIB. It’s important to align expectations on what and when the PA team can deliver specific services.

Typically, SLAs should include:

• Services offerings. This describes the services provided, the conditions of the service availability (such as time window standards for different levels of service), responsibilities of each party, escalation procedures, and cost / service tradeoffs.
• Management approaches. This defines measurement standards and methods, reporting processes, and contents and frequency.

Some SLAs also include delivery metrics that are reported monthly or quarterly. While this is more common with external vendors, it can be useful for internal relationships—by creating a clear set of standards for the metrics that both parties can refer to if they’re dissatisfied with how things are going.

“When I took on the role of people analytics leader, the feedback we got from our D&I team was that the partnership was not working. The SLA was broken and there was difficulty understanding what the priorities were. The relationship was damaged on both sides, and we had to work on fixing that. Fast forward 3 years and it is one of our strongest and most collaborative relationships. Our D&I team is doing leading-edge work and has played a major role in our culture change at the company, and this has been driven by some really incredible analytics and tools from the PA team. Both our CDO and I would agree that we could not have done it without the other.”

—RJ Milnor, Global Head of People Analytics, Uber

Creating a system for ongoing alignment

DEIB and PA must have a system for ongoing alignment, particularly around requests made of the PA team.

In organizations with a centralized PA team, often a single location exists where requests are recorded before they are triaged to the relevant team or individual, according to the SLA.

“Alignment on the outcomes that you are trying to drive is one of the most critical components that make the partnership between people analytics and the Global Diversity & Inclusion team successful. Our CDO always appreciates insights the people analytics team provides.”

—Dawn Klinghoffer, Vice President of the HR Business Insights, Microsoft

In organizations for which a PA team member sits with the DEIB team, it’s still important to make requests transparent and formal. This enables broader insights into what analysis is being requested—and consistently requested analysis can be systematized. Additionally, the PA person can stay aligned to the broader goals, even if they don’t necessarily have complete visibility into them.

Whatever the exact scenario, PA and DEIB leaders should discuss requests and changes regularly. This communications flow between the partners supports the ongoing alignment of efforts to the highest priority work.

“You cannot get to decision-making without aligning on the basics first. It took 18 months to get us aligned on the definition of ‘headcount’. Unless there’s a single definition, we could not move to the next level of conversation.”

—Product Manager, a large technology company

Supporting each other publicly

Another critical component of the DEIB–PA partnership: The 2 groups publicly support each other.

DEIB metrics are sensitive and subject to scrutiny. So, if DEIB and PA teams don’t present a united front with the data and insights, then it’ll be even harder to get other leaders to act on those metrics.

Some of the ways in which the DEIB–PA partners can communicate their cohesiveness to the organization at-large include:

• Presenting together at large-scale events (e.g., town halls and / or business unit-specific meetings)
• Writing internal articles, blogs, and / or participating in videos on DEIB metrics and progress on them
• Writing external articles and / or presenting together at conferences
• Collaborating on discussions with senior HRBPs and business leaders

Remember: DEIB and PA leaders should defer to their partner when questions are outside their expertise and / or responsibilities. In other words, both partners must have a clear understanding of each other’s strengths in order to be (and appear) collaborative.

Practice #3: Working together effectively within your organizational network

A complicated web of relationships

While it’s incredibly important for DEIB and PA leaders to be aligned and work together effectively, they must also work well with the rest of the organization.

As shown in Figure 12 at least 8 different types of organizational stakeholders could be involved in the work of the DEIB and PA teams. Given this significant number of stakeholders, DEIB and PA leaders must be aligned to reduce confusion and conflict.

From our interviews, we have prioritized these relationships based on the impact and frequency with which DEIB and PA leaders need to work with these groups. Roughly speaking, we have put them into the following categories:

• Top priority. HR, and legal & privacy teams
• Critical priority. Business leaders, IT / centralized data teams, finance, and compensation
• Important priority. Corporate social responsibility, external communications and marketing, and external vendors and consultants

For the rest of this section, we discuss these different stakeholders and why they’re critical. We also outline how DEIB and PA teams can work with each group.

There are various stakeholders for DEIB and PA at differing levels of priority as represented by each stakeholder group

Top-priority relationship: HR

The most crucial relationship that PA and DEIB partners must navigate is with the broader HR function (see Figures 13 and 14).

When PA and DEIB both report to HR, the partnership is much easier—because both functions are aligned with HR’s overall goals.

Further, leaders of both functions have an opportunity to align themselves, so they can achieve those common objectives.

Conversely, it’s far more difficult when PA and DEIB report into different functions—because it becomes much easier to lack upward and horizontal alignment (between DEIB and PA).

In this situation, there’s a significant onus on the PA and DEIB leaders to understand the overall business and talent strategies—and then align their teams’ work to those broader strategies as well as to each other.

Regardless of the reporting relationship, DEIB and PA leaders must understand:

• The overall business and talent strategies
• The CHRO’s responsibilities when it comes to DEIB
• The needs of business units and functions that result from working with individual HRBPs
• How to collaborate with COE leads to integrate DEIB metrics into their work on talent practices
• What support is needed from HRIT to access critical people data
• How DEIB metrics and analytics can help HR operations teams be more effective

When it comes to driving the relationship with HR, both PA and DEIB teams should take equal responsibility.

PA teams should be responsible for identifying the metrics and processing requests, while DEIB should take the lead in influencing talent strategy and practices.

More than 80% of chief diversity officers (CDOs) report either directly to the CEO or CHRO

More than 75% of PA leaders report one level below the organization’s CHRO

Top-priority relationship: Legal & privacy teams

Another critical relationship for the DEIB–PA partners is with the legal and privacy teams, a relationship for which they have equal responsibility in building and maintaining. It’s essential to collaborate early and often with these groups, especially given the sensitivity of DEIB data and variances of privacy laws among different countries.

Based on our interviews, we identified 3 different archetypes of legal and privacy teams (see Figure 15):

1. “The less said, the better.” These leaders see their job as eliminating—as much as possible—any potential risk to the business. An effective way to get them to move on DEIB metrics and analytics is to work with senior business leaders to help these leaders see why this analytical work must be done.
2. “I say no until you convince me to say yes.” These leaders see their job as shutting down any bad or poorly thought- through However, once DEIB and PA leaders develop a clear data collection, analysis, and distribution plan—with a clear business objective, these leaders can be swayed to support DEIB metrics and analytics plans.
3. “I want to help you do this work safely.” These leaders are the ones that all DEIB and PA leaders hope they have. They understand the need for this work, may already have some experience doing it, and collaborate and brainstorm on safe, legal, appropriate solutions.

For example, while PA takes the lead in ensuring appropriate data are collected and shared, DEIB might educate and provide broader context around why it’s important to share the information

We’ve discovered 3 common types of legal and privacy teams that DEIB and PA teams find themselves working with in their organization

Critical relationship: Business leaders

While the DEIB and PA team may be responsible for driving the quantification and interpretation of DEIB metrics and analysis—business leaders must be involved in this work since they (see Figure 16):

• Influence the practices that impact DEIB
• Need to understand the underlying logic of how DEIB is measured so they can encourage it
• May be held accountable for meeting DEIB metrics
• May be fearful of being held responsible for meeting DEIB metrics

Given this situation, DEIB and PA leaders must work with business leaders to understand their perspectives on the current state of DEIB in their part of the business.

Some business leaders are very interested in making progress on DEIB metrics, while it can be a bit of a “check-the-box” activity for others.

Understanding business leaders’ levels of interest in DEIB metrics is essential, so that the analysis and insights can be tailored to what they care about most. Some of our interviewees worked with those more passionate business leaders and developed truly insightful metrics that business leaders used to drive significant changes.

The point is: The person with the strongest relationship, who is most likely to get the business leader's attention, should lead the conversation.

When determining which team (DEIB or PA) will take the lead with business leaders, the decision will be very organization- and leader-specific. In some organizations, PA has the strong data-focused relationship with the business leaders, so bringing DEIB data into the conversation will be very natural. In others, the DEIB leader has a stronger relationship, so it might make sense for the DEIB leader to begin the conversation and then pull in the PA leader when necessary.

In other situations, it may be that the DEIB and PA leaders work with the senior HRBP, as that person has the relationship with the business leader regarding all “people topics.”

“It’s unlikely that a C-suite leader will come to people analytics with a tactical question. Most often, it’s a strategic question based on a business need. It is up to us to translate that into a research question, find out the answer through data, and translate it back into a data-driven story that provides clarity on the larger strategic issue.”

—Jeremy Shapiro, Executive Director, Workforce Analytics, Merck

Several reasons exist as to why business leaders are integral to DEIB analytics

“There is often a hesitation or reluctance on the leader’s part to open their doors to analytics due to fear that it might reveal some unpleasant findings about their business, which makes it hard to establish a good working relationship.”

—People analytics practitioner

Critical relationship: Compensation & finance

Let’s talk first about the compensation team, as this relationship especially matters when looking at pay equity issues.

Pay equity is one of the more approachable DEIB topics to address—meaning that it’s something the centralized organization can influence directly—and different geographies are enacting laws that require pay equity transparency.

Therefore, many organizations address it relatively early in their DEIB journey.

If DEIB or PA already has a working relationship with the compensation team, then it makes sense for that team to lead.

In terms of the DEIB and PA teams’ relationship with the compensation team, a few critical items of focus include:

• Ensuring the compensation data and other DEIB data are consistent
• Being able to put the results of the pay equity analysis into the context of other DEIB metrics and priorities provided to business leaders
• Collaborating with business leaders, compensation, and finance to determine the highest priority compensation adjustment actions
• Working with compensation and finance to identify and align any metrics that’ll be used in compensation decisions

Again, which group (DEIB or PA) leads the relationship with the compensation team will depend on the existing relationships in place.

Turning to the relationship with finance, it’s almost a certainty that the PA team will have experience working with that team due to their other responsibilities.

However, if neither has such a relationship in place, then it makes sense for PA to lead the discussions—as PA leaders are often already well-versed in compensation technicalities and will probably be able to get to the appropriate analysis faster.

“ We were reporting to the C-suite when the CFO made it clear that they did not see any value in our report. Once we walked them through it, the CFO was amazed at the insights and told us that it was a rich discussion. Once we were able to show the value, they became highly engaged in our work.”

—People analytics practitioner

In this vein, the PA team should (with input from the DEIB team) focus on actions, such as:

• Aligning on critical data sources and ensuring data / analysis consistency
• Showing the financial impact of DEIB when needed
• Putting a monetary value on DEIB events / initiatives

“For our CFO, is gold. He is using people analytics to understand what’s on the horizon and if they can forecast better, based on the human capital data. We actually built a CFO dashboard for him. It’s a great relationship to have because he does not see people analytics as a service function. It’s very much a partnership of equals.”

—Head of global people analytics, multinational consumer products company

Critical relationship: Centralized IT & data operations

Centralized IT and data operations teams are important to the DEIB–PA partnership because they can help:

• Access centrally managed technologies and data
• Identify and provide access to novel data sources
• Troubleshoot technical or data-related difficulties
• Share novel data collection, analysis, and distribution practices

From our interviewees, we heard 2 common challenges when it comes to working with centralized IT and data operations (see Figure 17):

• Trust. Many organizations haven’t historically had a data-led PA or DEIB function, so they’re not accustomed to accommodating requests for access to centralized systems or data. Therefore, requests for support are usually met with suspicion.
  Typically, organizations get around this by leveraging senior leaders who can provide a rationale, or by showing the type of work done in the past and how it was used. In addition, providing information on data privacy and security often help.

• Time. Many IT projects were put on hold during the pandemic, and centralized IT and data teams were overwhelmed. Teams have been managing this by reducing project scopes to the most necessary items, finding other resources who could do some of the work, and / or leveraging senior leaders to help with the reprioritization of work.

PA leaders should take the lead—with DEIB’s input—when it comes to driving the relationship with IT and data operations.

The PA team should:

• Share information on how data and technologies will be used to reduce hesitations and fear about data sharing
• Showcase the broader impact of the work on the business overall
• Find additional resources when IT lacks the bandwidth

Time and trust are 2 common challenges experienced by DEIB and PA teams when working with IT and data operations

Important relationship: Corporate social responsibility

The next set of relationships concerns how DEIB metrics and analytics are shared outside the organization. Many companies include DEIB efforts within their corporate social responsibility (CSR) efforts. As a result, communication with that team on appropriate DEIB metrics—some of which may end up in an annual CSR report—is essential.

But there’s more to it than that. As noted in this article by Dr. Rohini Anand, the well-known former Chief Diversity Officer of Sodexo, DEIB and CSR are often working toward similar goals but from different perspectives:

“… We sometimes approach the same goal from different starting points. Take gender equality. We both want it, but D&I might focus more on recruitment and leadership, whereas CSR might focus more on community empowerment. These can seem like different goals, but they are, in fact, merely different approaches to the same end: improving quality of life.”

To that end, it’s essential for DEIB to take the lead in driving this relationship to understand CSR’s goals, and to align the DEIB metrics and analytics approach to support those goals as much as possible. For many organizations, this relationship will likely build over time, with some initial, relatively “easy” shared metrics at the beginning and more complex metrics with time.

The point is to work across organizational silos and to align on a standard set of measurements and approaches. Without this, CSR and DEIB will look poorly in front of business leaders.

The PA team can serve as a subject matter expert (SME) on the appropriate metrics that’ll serve the purpose of both CSR and DEIB. Also, as the relationship between DEIB and CSR matures, PA can be brought in to identify the next-level or more complex metrics, and how they should be shared.

Important relationship: External communications & marketing

While your organization’s CSR team may share some DEIB metrics externally, it’s fairly certain that your external communications and marketing teams will be very involved in any external communication efforts (see Figure 18).

Some organizations release a dedicated DEIB report that needs to be developed in conjunction with the external communications and marketing teams. Others involve the external communications and / or marketing teams when communicating about DEIB metrics in financial reports or other situations.

While these are very pragmatic ways the DEIB–PA partnership may well engage with the external communications and marketing teams, much more possibility exists in this relationship. These teams:

• Are experts in understanding how to tell stories effectively—meaning they could be beneficial to the DEIB and PA teams as they work to creditably tell stories based on data
• Can share resources that might be useful in communicating about DEIB, such as this Diversity Style Guide
• Can also give some larger context to other critical messaging, both within and outside the organization

The relationships with external communications and marketing can provide DEIB and PA with opportunities to tie some of their own messaging to these more prominent topics. This insight can also serve as a starting point for identifying new data types and analyses that the DEIB and PA teams might want to use in the future.

DEIB should take the lead in working with external communications and marketing teams.

As the “natural” owner of DEIB work, DEIB leaders are responsible for crafting the story and messaging around the insights and how they tie to the broader business goals. On their end, PA leaders should help by:

• Providing DEIB leaders with the insights that shed light on the progress of the organizational goals
• Ensuring that the metrics are interpreted and reported accurately
• Answering questions that arise from the external communications and marketing teams

5 ways external communications and marketing teams can help DEIB and PA teams in their work

Important relationship: External vendors & consultants

Many organizations turn to external vendors and consultants when they need:

• An external review / perspective on DEIB metrics
• A separate party to do data collection and analysis so as to maintain privacy / independence
• More bandwidth to complete data collection / analysis / recommendations
• Specific expertise / insights

It’s essential to consider whether the external vendor / consultant relationship will be short- or long-term in nature. Due to the sensitivity of the data, this is especially important to consider with DEIB metrics and analytics.

• Shorter-term contracts may mean the DEIB and PA teams limit the data shared to reduce some of the risks to the
• Longer-term relationships may require some additional data privacy and security

In addition, it’s crucial to get the most relevant internal teams aligned on the reason for bringing in the external vendor / consultant and the scope of that party’s work. We’ve heard of way too many instances in which DEIB practitioners brought in external consultants to do DEIB metrics and analytics work without any consultation with the PA team. As a result, the work effort was wasted because the external team lacked adequate data or nuance to interpret the data in a meaningful way.

While DEIB leaders might often find themselves leading the work when it comes to working with external vendors and consultants, DEIB and PA leaders must work together and take equal responsibility.

Given the long-term nature of identifying, measuring, and distributing DEIB metrics, you must have everyone aligned on the work being done and who’s doing it. This is one of the areas in which it makes sense to go slow in moving forward.

“The DEI team hired a vendor for reporting. The team would send the vendor our SAP data and the vendor would send it back to us in some kind of a format that would be used for reporting. Looking back we can see that the numbers and reporting were very inconsistent.”

—Global People Analytics Director, a multinational consumer products company

You should partner closely to make sure:

• The services and technology offered are truly needed, and aren’t already being done in-house or by previously purchased tech
• A plan is in place as to how the data and findings from the consultant will be used
• Clarity exists around who is ultimately responsible for driving and implementing the recommendations and actions that come out of the review and analysis

“I see people analytics departments outsourcing DEIB analysis to external consultants, who will run multivariate regression models and provide independent assurance. The outcome often shows no significant bias. Well, of course, because there is no policy to pay people differently! I wish DEIB leaders would analyze potential bias in all the moments that matter. Then they will really see what’s happening.”

—Dirk Jonker, CEO and Founder, Crunchr

Next steps

Getting started

Now that you’re familiar with the practices required for a successful DEIB–PA partnership, what should you do next?

The 3 practices should be embedded as part of the broader strategy for both PA and DEIB teams. Here, we offer a few steps that you—as a DEIB or PA leader—can start taking immediately.

Step 1: Do a quick audit of where things currently stand between DEIB and PA with the organization. Some of the questions to help you do this include:

• Is there a formal partnership between DEIB and PA within the organization?
• Does leadership support the relationship if it exists?
• How often do DEIB and PA leaders interact or meet?
• Does senior leadership understand and value PA's role in DEIB?

Step 2: Identify the gaps and build on the practices. Determine the areas in the partnership that need attention and which of the 3 practices discussed earlier should be leveraged. Think through the following questions:

• Do DEIB and PA leaders have a clear line of sight into each other's work?
• Is there clarity between the 2 teams regarding responsibilities and expectations?
• Do you have in place an intake process for DEIB requests, data sharing, and communication of insights?
• What other relationships do DEIB and PA teams need to leverage within the organization?

Step 3: Communicate and share broadly the work you are doing together. Sharing your successes and lessons learned within the partnership is essential to supporting each other. Think through the following questions to understand how you can get started:

• How are we currently sharing our work with others in the organization?
• Is our senior leadership aware of our work's impact on the organization?
• Are there external opportunities we can leverage to showcase our work?
• Are we leveraging resources (e.g., community groups and other industry leaders) to get feedback and insights on what else we should be doing?

We also provide a series of exhaustive checklists for each of the 3 practices in the appendix. Use them as a "grab-and-go" list of actions to start building your own successful DEIB analytics partnership.

“There are people who partner with people analytics reluctantly because they need data, and then there are others who want to work with them and use their brain. Diversity leaders should strive to be in the latter group because only then will they gain the insights needed to be successful in their work.”
Chief Diversity Officer, a global retail company

Final thoughts

Over the last 2+ years, many events, trends, and changes have thrust DEIB and PA into the spotlight. While they may be on the stage together, these 2 teams have very different backgrounds and don’t always have the “chemistry” they need. Yet, they must play their respective parts to help organizations through these unprecedented times.

We discovered from our research 3 critical practices which DEIB and PA teams should do to build the kind of partnership that will allow them both to flourish:

• Understand each other’s contexts
• Build a plan for partnering
• Manage the complex organizational network together

By doing these things, PA and DEIB leaders can partner effectively to deliver high-quality DEIB metrics and analysis to their organizations. This partnership will enable organizations to make higher-quality, data-backed decisions that ultimately impact millions of lives.

We strongly encourage you to complement this study with our previous study on DEIB analytics: DEIB Analytics: A Guide to Why & How to Get Started and DEIB Metrics: An Essential Guide. If you still have questions, please reach out. We love to learn from you.

Note: for Appendices, including research methodology, and checklists for getting started please download the PDF report.

9 common and systemic obstacles make it harder for some employees to find, access, and participate in development opportunities.

L&D functions can reduce or remove these obstacles to make employee development more equitable and inclusive–ensuring more employees have the skills they (and their organizations) will need in the future.

This infographic summarizes key findings from our research report, Less DEIB Training, More Learning Equity. Click on the image below for an expanded view.

As always, we'd love your feedback at [email protected].

 

Download This Report[/button]

Introduction

At this point, the business case for diversity, equity, inclusion, and belonging (DEIB) is clear. Our own research (see Figure 1) shows the relationship between having a strong DEIB culture, and critical individual and performance outcomes.¹

Yet, for years, the representation of diverse populations in organizations improved almost imperceptibly.

Then we had a global pandemic and the rise of a social justice movement, sparked by the murder of George Floyd. Along with that came the heightened awareness that the pandemic was impacting diverse populations much more—particularly for women and people of color who were dropping out of the workforce at higher rates than other populations. As a result of this confluence of events, organizations began making big promises on DEIB in the summer of 2020.

When this happened, one of our first questions was how organizations would show that they’d made good—or at least made progress—on those commitments. While DEIB metrics measurements designed to understand DEIB—are the obvious answer, how to select, collect, use, and maintain those metrics is not so clear.

Thus, this research initiative on DEIB metrics and analytics was born. The first article in this series, “DEIB Analytics: A Guide to Why & How to Get Started,” provides leaders with a plan on how to begin using DEIB metrics and analytics. We’ve shared an 8-step guide with details on the actions and considerations that organizations need to take to effectively implement DEIB metrics.

This article: An essential guide to DEIB metrics

This report focuses more narrowly on the appropriate metrics and analytics for DEIB. We aim to provide DEIB leaders, people analytics practitioners, HR business partners, workforce planning and talent management leaders with:

1. A foundational understanding of the different metrics that can be used to measure and track their DEIB performance
2. Insights on how those different metrics might vary, depending on their org’s sophistication with DEIB and analytics

This article is based on a wide range of information, including our research on:

• People analytics technology²
• DEIB analytics³
• DEIB strategies⁴
• DEIB technology⁵
• A literature review of DEIB and analytics⁶
• Interviews with ~20 people analytics and DEIB practitioners

Our research focuses specifically on the people within an organization’s existing workforce. We know a number of other DEIB metrics exist that orgs should also consider, such as those which apply to their supply chain, community efforts, ESG (environmental, social, and governmental) requirements, etc. While critical, those areas are outside the scope of this report.

We would also like to mention that this report is the first of its kind, in that it attempts to provide a holistic look at all talent-related DEIB metrics. Any first try will miss some critical elements and we acknowledge this report may be incomplete. We invite you to share any suggestions, feedback, or additions you think appropriate by emailing us at [email protected].

The DEIB space is evolving quickly, and we will only make progress by putting out our best ideas and amending them quickly as new information becomes available. Thank you for being part of that process and pushing forward toward greater opportunities for all.

Defining DEIB

Let’s start our essential guide by defining our terms (see Figure 2).

Why are DEIB metrics & analytics important?

Some of the common reasons why leaders start to focus on DEIB metrics and analytics include:

• Creating a clear business case for DEIB
• Measuring the return on investment (ROI) of DEIB expenditures
• Tracking the impact of critical DEIB initiatives

In addition to these, a few more reasons why orgs should use DEIB metrics and analytics include:

• Busting myths or addressing anecdotes that may or may not be true
• Checking assumptions about DEIB
• Meeting consumer, investor, and employee expectations when it comes to progress on DEIB

While these are all good reasons to use DEIB data, one of the most compelling motivations for why DEIB is critical was articulated by one of our interviewees:

“Companies have been setting diversity goals for decades but have struggled with “goal-getting”—meaning the clear accomplishment of those goals—because of a lack of feedback and data to help them get after those goals every day. Without any feedback on progress, companies lose sight of the goals.”

—Phil Willburn, Head of People Analytics & Insights, Workday⁷

Why do orgs find DEIB data difficult to use?

Many leaders struggle to use DEIB data for reasons such as the following (see Figure 3):

• Challenges in identifying and using appropriate metrics. Historically, very few orgs have attempted to track metrics for DEIB and even fewer have ventured beyond collecting diversity data. Often, leaders are unsure which metrics can and should be measured for DEIB. Even if they’re able to identify them, leaders then often face challenges around tracking and integrating the data.
• Legal, security, and privacy issues. DEIB data involves sensitive information—and this comes with legal and security challenges around data collection, storage, and usage. As a result, some orgs hesitate to collect and use it. Additionally, employees may be hesitant to provide it, due to data privacy and access concerns.
• Poor alignment with goals. Orgs find it challenging to use the data if there’s no or poor alignment between the data collected and the overall DEIB goals that the company wants to achieve. As result, there can be a sense of helplessness, which can render the data not as helpful.
• Data responsibility issues. Because DEIB data can reside in multiple systems under several functions (e.g., HR, D&I, IT, sales), there can be a lack of clarity around who is primarily responsible for the data and how / when it can be shared.
• Data interoperability issues. Related to the previous point, orgs often find it challenging to use data collected in one system on another due to integration issues and capabilities of the tech solutions in place.

For this article, we focus on the first bullet to help orgs identify the range of metrics they can use.

“When you have members of a minority group who are leaving at a higher rate, that’s telling you something is wrong, and it helps steer you to where the problems are. It needs to be measured at quite a low level in the company because that’s the way you find where your hot spots are.”

—Fiona Vines, Head of Inclusion and Diversity and Workforce Transition, BHP⁸

Clarifying diversity metrics

As we highlight in our report “DEIB Analytics: Getting Started,” the essential first step to creating diversity metrics is collecting appropriate demographic data. Essentially, the data collected should allow orgs to answer 3 questions:

1. What does our current workforce look like across different levels (hierarchy) and functions / business units?
2. Who are we hiring (internally and externally) across different levels?
3. Who is leaving the org and at which level(s)?

It’s important that leaders not only look at simplistic diversity numbers, such as gender or race / ethnicity—they also need to consider multilevel diversity, known as intersectionality, such as Black women or gay Asian men. This additional analysis helps leaders understand their workforce at a more nuanced level, and make better recommendations and changes.

Many orgs track basic diversity numbers: 96% of U.S. companies report the gender representation of their employees at all levels and 90% report gender representation at senior levels.⁹ However, far fewer orgs look at intersectionality: Only 54% of companies track gender and race / ethnicity—such as Black or Latina women in senior leadership.¹⁰

Figure 4 is a list of common demographic data that we’ve seen orgs collect (for a more comprehensive list of data that could be collected, please see our definition in the earlier section). It’s important to note the significant legal limitations in different countries as to which of the following can be collected and stored. Your org’s legal counsel should always be involved in determining which data to collect.

While comparatively easy to collect and analyze, orgs should be wary of trying to do everything at once when it comes to diversity metrics. Leaders should first figure out the immediate challenges or business issues they want to solve for and identify the appropriate metrics accordingly.

Examples of diversity metrics

Figure 5 offers a list of the metrics that orgs can use to measure diversity. Many orgs already collect most of these metrics through their human resource information system (HRIS) or applicant tracking systems (ATS). By adding a demographic lens to these metrics, orgs can quickly understand the state of diversity within the org.

Understanding equity metrics

Equity metrics can help orgs understand the effectiveness of their processes, and identify unfair or biased systems, practices, and policies. Research conducted in 2021 revealed that when employees are treated fairly, they’re:¹²

• 8 times more likely to look forward to going to work
• 3 times more likely to have pride in their work
• 4 times more likely to want to stay a long time at their company

Equity metrics can be measured from data collected via several sources, such as:

• Learning and development data
• Performance management data
• Payroll
• Employee engagement / experience data

Ensuring fairness in the distribution of resources, opportunities, and access can help leaders address existing systemic inequities within the orgs. The point to note here is that the distribution needs to be fair, not equal. The difference between these two concepts is shown in Figure 6.

Thus, the goals of measuring and tracking these metrics should not be to ensure equality or sameness for everyone, but rather to:

1. Detect areas in which systemic inequities exist
2. Identify differences in capabilities, resources, and needs
3. Implement systems and process that take these into account

While orgs have a strong case for creating a fair and equitable environment, many struggle to do so. For example, our 2021 study on performance management trends revealed that only 48% of employees believe their performance evaluation process is fair and consistent.¹³ As orgs continue to manage unique needs and challenges for different employees, leaders will increasingly need to address issues around managing fairness and equity across varied employee experiences.

Source: Robert Wood Johnson Foundation, 2017¹⁴

Examples of equity metrics

Below is a list of metrics that orgs can use to understand, measure, and track equity. All metrics should be analyzed by the different demographics collected by the org to understand the differences in opportunities, access, and renumeration for various groups.

Identifying inclusion metrics

After diversity, inclusion is the most common area that organizations tend to measure. According to a 2018 study, a little more than 50% of orgs measured inclusion.¹⁷ While the focus and urgency around this area has increased over the years, few orgs are doing anything beyond tick-the-box exercises.¹⁸

“Let's say that the engagement score for our company is high at 80%, and that makes us happy. And then you realize that 80% of your employees are White—which means that you’re not really hearing the voice of those under-represented groups. Inclusion analytics is about pulling that out, and making sure you have a good sense of where everybody's falling on all of your core metrics.”

—Hallie Bregman, PhD, Global Talent Strategy and Analytics Leader¹⁹

There are a few reasons why orgs should focus on understanding and measuring inclusion. Orgs with an inclusive culture:²⁰

• Are twice as likely to indicate they met business goals from last 3 years
• Are 81% more likely to indicate high customer satisfaction
• Have employees that are 45% more likely to stay
• Have employees that are 2 times more likely to give a positive Net Promoter Score® (NPS)

If these reasons weren’t enough, the volatility of 2020 and 2021 has resulted in many companies facing tough questions around their efforts in this area. According to a recent analysis of S&P 500 earnings calls, the frequency with which CEOs talk about issues of equity, fairness, and inclusion on these calls has increased by 658% since 2018.²¹

Inclusion metrics can help orgs understand whether employees feel:

• Accepted by others in the workplace
• Integrated into and a part of the wider organization
• Respected for their work by others

As alluded to above, orgs can typically approach inclusion metrics in 2 ways—employee perception data and object data. We explain the differences between the 2 in Figure 8.

Examples of inclusion metrics

Figure 9 offers a list of metrics that orgs can use to understand, measure, and track inclusion. These include metrics that directly impact an employee’s sense of inclusion (e.g., mentor relationships and strength of connections with others), as well as some not-so-obvious metrics that can drive inclusion (such as the average distance between office and home, which can adversely affect employee experience).

Defining belonging metrics

While closely related to inclusion conceptually, it’s important that orgs pay equal attention to measuring and understanding belonging. We explain how belonging is different from inclusion in Figure 10. A high sense of belonging among employees can result in:

• An increase in employee happiness and employee engagement, which in turn impacts employee retention²²
• A significant increase in job performance²³
• A reduced turnover risk and a decrease in employee sick days²⁴

Analytics based on belonging metrics can serve as a leading indicator of critical diversity outcomes as well. Specifically, belonging metrics can help orgs to:

• Gain a deeper understanding of the sense of security experienced by employees
• Find out if employees feel connected with the org’s values and purpose
• Bolster their ongoing efforts around inclusion and equity

“When someone is experiencing a sense of Belonging, they feel freer, they feel more creative and their opportunity to potentially have an impact at work is significantly increased.”

—Kate Shaw, Director of Learning, Airbnb²⁵

Examples of belonging metrics

Figure 11 offers a list of metrics that orgs can use to understand, measure, and track belonging. While some metrics speak to belonging directly (e.g., a belonging index as part of an engagement survey), others should be used in combination with one or more additional metrics to gain a better understanding. For example, by looking at metrics around the number of resources groups offer and the participation rates for them, orgs can try to understand if employees feel supported. Employee feedback comments specific to these topics can provide even more context of the underlying issues.

DEIB metrics: Strengths & limitations

DEIB metrics are most effective when multiple types of metrics are combined to gain a clearer picture of DEIB holistically. (See Figure 12.) For example, by combining inclusion metrics with equity metrics, orgs can understand not only that different groups may be feeling less included, but also the specific reasons (e.g., unequal development opportunities or biased performance reviews) for it.

Using data sources for DEIB

Now that we’ve covered the specific metrics, let’s look at the data sources orgs can use for them. Orgs should keep a few things in mind when using such data:

1. All data should be looked at with a demographic lens. For example, the number of trainings accessed by the workforce would mean little unless analyzed to see if white women access training more often than Black women.
2. Data are more powerful when combined with other data. For example, data from the HRIS that shows exit rates should be combined with data from exit interviews, surveys, and employee comments on external review websites.
3. Connectivity between data sources is essential to being able to use the data effectively. Data interoperability, or the ability for different data between systems to work together, is a necessity in order for orgs to drive DEIB. As such, they should look for tech and tools that enable them to do that.
4. The partnership between DEIB and people analytics functions is critical. As we mention in our report “DEIB Analytics: Getting Started,” DEIB and PA leaders often come from different backgrounds and parts of the org, which mean partnership challenges may exist that must be addressed. The insights and expertise of both groups are necessary to use and interpret DEIB metrics effectively.

Common data sources for DEIB

Figure 13 shows that most of the data sources can be used for more than one DEIB area.

Beginning the DEIB metrics journey

Orgs at the beginning of their DEIB journey should try to answer the question: What’s the current state of DEIB within the org? As such they should focus on 2 things:

1. Understanding the state of diversity
2. Identifying “low-hanging” challenges—areas that need attention and are easy to quickly start working on

When it comes to selecting metrics, orgs should start with the basics, like:

• Getting their basic demographic data in order
• Measuring metrics around headcount, retention, and turnover to understand diversity
• Leveraging employee perception data—such as engagement surveys, feedback, and focus groups—to understand how different groups perceive DEIB at the org

Orgs should ensure that the selected metrics are clearly tied to overall strategy and that processes exist to track their progress.

A people analytics leader we spoke to mentioned creating a Python script to pull different metrics that they’re already collecting around talent acquisition, internal mobility, performance, engagement, and exit rate to understand where the biggest gaps are between different employee groups. This allowed them to quickly identify areas with the biggest gaps, start working on them, and track progress over time.

“The DIB world is so enormous, and you could do a thousand things. It's hard to understand where to start and where to focus your efforts. We should be intentional about identifying our biggest gaps. Every company has some problems around DEIB, but we should work on finding where our biggest internal gap is and focusing on that first.”

—Head of People Analytics, a large technology company

Moving up to an intermediate level with DEIB metrics

Once the orgs have a clear sense of where they stand or the “what,” they need to understand the “why,” such as:

• Why do certain groups experience a low level of inclusion and belonging?
• Why are certain groups being promoted at lower rates than others?

Orgs can begin to supplement existing data to gain a deeper understanding of the systemic issues that impact DEIB. When it comes to metrics, orgs should look at data from existing systems:

• Learning & development data
• Performance management data
• Payroll data
• Wellbeing data
• Data from employee feedback comments

A technology provider shared an example of a customer project that conducted text analysis on data from employee feedback to understand why promotion rates for women were low in a company. The analysis revealed that the existing initiatives to drive promotions favored men and received positive feedback from them, as compared with women. Some of the concerns that surfaced included difficulties faced by women around childcare and the inflexibility around work schedules. The analysis of the data allowed the company to identify the systemic issues that were negatively impacting promotion rates for women and their overall DEIB efforts.

“Metrics are a way to communicate what’s important. Orgs should limit themselves to how many metrics they push. It’s like the weather, I don’t want a million different metrics to know if the weather is good of not. Orgs should figure out the goal (what is ‘good’ weather) and the metrics should help achieve that.”

—Dirk Jonker, Chief Executive Officer, Crunchr

Using a mature approach to DEIB metrics

The questions orgs should look to answer at this stage are:

• How can we address existing issues and drive our DEIB efforts effectively?
• How can we measure progress longitudinally?
• What creative analyses or approaches might help us answer questions we haven’t yet been able to answer?

When it comes to metrics and data, orgs should consider complementing existing data with:

• Network data
• Communication data from sources such as emails, calendars, meetings, etc.
• Workplace tech data from tools used by employees to get work done such as Zoom, SharePoint, Slack, Teams, and Asana
• Employee reviews and comments on external websites

Orgs should consider using advanced approaches to people analytics such as connecting text analytics with social network data. Text analysis can help orgs identify existing gaps in inclusion. Network analysis can help identify influencers. Orgs can relay feedback to influencers and leverage them to fill those gaps and drive greater efforts.

DEIB is a continuous effort rather than a “once-and-done” approach. Orgs should look externally to compare their performance to avoid becoming complacent in their efforts and update their goals regularly. Specifically, orgs should look at how other high-performing orgs that rank high on DEIB are performing, instead of industry or national averages.

“When it comes to selecting metrics, don’t go with the flow, and get something off the internet or another company. How you define metrics really matters, and orgs need to be intentional about what and how they measure them.”

—Lydia Wu, Head of Talent Analytics and Transformation, Panasonic North America

Conclusion

When it comes to DEIB, orgs need to do more than provide training and courses to employees. They need to think about and approach it in a holistic manner so that it’s built into the way the business is managed, instead of something that’s an afterthought or special.

To that end, orgs need to:

• Understand where they currently stand and how are they perceived by their employees. They should know what issues currently exist.
• Understand why those issues exist. Orgs need to find out the reasons why they are falling short in those areas.
• Identify what can they do to fix them. Orgs should plan their targeted initiatives and interventions in order to get the maximum value and results from their efforts.

In order to achieve that, companies need to apply a greater focus, and put more emphasize on using metrics and data than they currently do. As we’ve mentioned before, the growing demands from customers, investors, and employees around more action on DEIB is likely to keep increasing. Orgs stand to lose a lot more if they do nothing, not just in terms of lagging performance, engagement, and innovation—but also in future talent that’s going to place a lot more importance on these issues going forward.

It's time companies take their DEIB data seriously. Moving forward, we hope to see a greater acceptance of and creative thinking around how these data and metrics can be used to enable all people and do their best work.

Appendix

Below we share our own as well as indices used by other organizations to help understand their DEIB culture.

Figure 18: Gartner inclusion index | Source: Gartner.²⁷

Figure 19: University of California San Francisco’s Belonging Index | Source: University of California San Francisco.²⁸

In this webinar, Stacia Garr from RedThread Research, along with Janice Burns and Susie Lee from Degreed, discuss the biggest findings from the latest study on DEIB and Skills.

2020 and 2021 saw a significant increase in focus on DEIB that stemmed from change in expectations from investors, consumers, and employees. As a result, we can see orgs making marked investments in DEIB.

Skills can form an important part of the efforts to drive DEIB. This presentation covers important questions such as:

• Why do we need skills for DEIB?
• Which skills matter most?
• What should you do now?

This is followed by a panel discussion and a Q&A.

Agendas for boards and CEOs have never been so crowded with talent-related topics—workforce strategies and wellbeing; diversity, equity, inclusion, and belonging (DEIB); culture; and, corporate purpose.

Traditionally, many leaders made people decisions based on anecdotal conversations with employees and their guts. The COVID-19 pandemic has shown that this kind of approach can no longer work.

People data-based insights will help C-suite leaders manage their companies more effectively. The question, though, is: What is the role of C-suite leaders in enabling and using those insights? And what can CHROs and PA leaders do to help C-suite leaders leverage people analytics to make better decisions?

With this report, we explore:

• What types of behaviors, approaches, and questions should C-suite leaders use to get better people-related insights?
• What types of behaviors, approaches, and insights should CHROs and people analytics use to support C-suite leaders?
• What types of results might leaders expect of effectively integrating people insights into critical business decisions?

The report includes a wealth of examples, useful insights from practitioners, and lists of do's and don'ts for C-suite, CHRO, and people analytics leaders.

This study is a culmination of 6 months of qualitative research which involved a literature review of more than 60 articles, interviews and conversations with more than 30 people.

Click image below for download.

 

People data-based insights can help C-suite leaders manage their companies more effectively. The question, though, is: What is the role of C-suite leaders in enabling and using those insights? And what can CHROs and PA leaders do to help C-suite leaders leverage people analytics to make better decisions? We have tried to answer these questions, and more, through our recent study on people analytics and the C-suite.

This infographic (click on the image below to get the full version) highlights key insights from our report Unlocking the Hidden C-Suite Superpower: People Analytics.

As always, we’d love your feedback at [email protected]!

Our team has recently spent a lot of time trying to understand novel opportunities on which orgs can focus their DEIB efforts. Enter skills.

This infographic (click on the image below to get the full version) highlights key insights from our report, Creating A DEIB Culture: The Skills Every Employee Needs, through which we have tried to answer 3 questions as they relate to skills for DEIB:

• What skills contribute to DEIB, specifically in fostering diversity, enabling people to feel included, and building a culture of belonging in the workplace?
• How those skills might vary, depending on factors such as an employee’s level, role, diversity characteristics, etc.?
• What can orgs do to develop and leverage these skills, including specific approaches and modalities?

As always, we’d love your feedback at [email protected]!

The COVID-19 pandemic, the social justice pandemic, and now, the uneven, uncertain return to the office have all contributed in shifting our perspective on diversity, equity, inclusion, and belonging (DEIB). We all know that what gets measured is what gets done. As a result, people analytics (PA) is increasingly more involved in DEIB efforts than ever. Yet, many leaders struggle to bring together the 2 disciplines of people analytics and DEIB.

This infographic (click on the image below to get the full version) highlights key insights from our report, DEIB Analytics: A Guide to Why & How to Get Started, which includes an iterative, 8-step model that leaders can use to map out their DEIB analytics approach.

As always, we’d love your feedback at [email protected]!