17 May 2021

Q&A Call-Diversity, Equity, Inclusion and Belonging (DEIB) & Analytics

Stacia Garr
Co-founder & Principal Analyst
Priyanka Mehrotra
Research Lead

TL;DR

  • In this Q&A call Stacia Garr and Priyanka Mehrotra discusses DEIB & Analytics
  • The importance of partnering the concepts of DEIB & People Analytics tech together, why it’s important, and the challenges they face together
  • What the literature says
  • The types of data and analyses that orgs are using to advance DEIB goals
  • Don’t look at legal as compliance but rather as a partner

TRANSCRIPT

Introduction

Stacia Garr:
Wonderful. So thank you all so much for joining us today. For those of you whom I don't know, I'm Stacia Garr. I am co-founder of RedThread Research. And I'll tell you a little bit about us before we get started, but in the meantime, I want to give my co-host today, Priyanka Mehrotra chance to introduce herself Priyanka.

Priyanka Mehrotra:
Thank you, Stacia. Hi everybody. I'm research lead at RedThread and along with Stacia, we've been working on DEIB and people analytics for over the last two years. And we're very excited to talk about this kind of study that we have going on right now. Welcome.

Stacia Garr:
And so for those of you who haven't been to a Q&A call or haven't been in a while, here's roughly how we do it. This is very conversational. Yes, obviously we have slides, but the point is to answer your questions, you know, find out what you're most interested in with the research and the like. We'll be communicating primarily through chat or through Q&A, both of those are enabled and we can see both of those. If you want to do Q&A, so everybody doesn't know your question, that's fine. If you want to share in chat, that's great as well. Like I said, we are recording this call. And so we will be posting this to the RedThread site after today. So that folks who are RedThread members will also be able to view it.

Stacia Garr:
So in speaking of RedThread and members, we are a human capital research membership focused on a range of topics, including people, analytics, learning, and skills, performance, DEIB and employee experience in HR technology. As Priyanka mentioned, this study that we're working on, and we're going to talk about today is a really nice culmination of a number of different areas that we've been doing research on. So we've been extremely excited to get to it. It feels like the study we've been trying to get to for at least three quarters. So we're excited to do that.

Defining DEIB

Stacia Garr:
So I'm going to begin with just a little bit of level setting. So for those of you who maybe haven't been following our work. We talk about this space collectively as DEIB. So I know a lot of organizations use just DEI. Some use just DIB.

Stacia Garr:
We decided to put them all together to be inclusive. Because we think that all of these concepts are important, but you can see here on this slide, our definitions for each of these areas, and how would we see them as being a little bit distinct from each other.

Why DEIB & Analytics

Stacia Garr:
Now, I mentioned that this study is kind of the culmination of a lot of energy and enthusiasm, and I should say and clarify that this is an active study under process. That's one of the things that we do with the Q&A calls is that we get started on some research and then we will conduct a number of ways to interact with folks. Sometimes it's a roundtable, as you may have seen. We've actually got one on this topic coming up on, correct me if I'm wrong, Priyanka, May 27th, I think is the date for that, but the Q&A calls are a chance to kind of engage on a different level to understand what people are thinking about and getting initial reactions to the work that we've been doing.

Stacia Garr:
But, so why are we doing this study? One is when we launched RedThread, we started off with a focus on DNI technology. This is what we called it. Now we're calling it to DEIB technology. And then very shortly after that, we did a study on people analytics technology, which many of you who are here may be familiar with. And within DEIB tech, there was an analytics component. And we were seeing on the people analytics tech focus on DEIB, but we hadn't really kind of brought these concepts together. And then when we went out and we looked at the literature, which Priyanka is going to talk about, we found that there weren't a lot of folks who are talking about how do DEIB and analytics work together. What's that partnership look like? What are the metrics we should be looking at and how should we be making those decisions?

Stacia Garr:
So we started to think about all of these things. So, you know, those were kind of the underlying concepts of why we started this journey.

Why are we studying it now?

Stacia Garr:
But then there is I think a question about like, why now, like, why didn't we do it three quarters ago if we've been studying this topic for a few years. And I think there are a few things. First is we've seen a greater expectation from consumers to take action. And so if we look at things like Edelman's Trust Barometer particularly after the social justice movements of last summer, consumers are expecting organizations to make steps, yes, on social justice, but on DNI more broadly. They also are expecting organizations not just to do that externally, but to do that internally, to get their own DNI house in order.

Stacia Garr:
So that's one, one reason. The second is obviously the disproportionate impact of the pandemic on diverse employees combined with the social justice movements that I just mentioned. So we've done quite a lot of work particularly focused on the impact of the pandemic on women. We have also written about the impact of the pandemic on people of color. And so we know that those populations have been some of those that have borne the brunt of this the most. And so there's some of the ones that if we look to come out of the pandemic, we need to be focusing on the most as well. And then the third reason, again, back to this, why now is we're seeing these new SEC human capital reporting guidelines that went into place last November really starting to come into to be a factor for organization.

Stacia Garr:
So analytics teams are being asked to provide more detail on human capital metrics and often that is including diversity data. And we expect that right now. And I was very intentional in that language. Right now it's a lot of representation data usually a bit beyond what they have to report for the EEOC, not necessarily a lot beyond that, but we expect that to change, particularly as investors start to increasingly understand the impact that we've seen in research of strong diversity and inclusion on organizations, on their financial outcomes. We think that there's going to be more investor pressure to provide more data and insights as it relates to the DEIB.

People Analytics for DEIB has arrived

Stacia Garr:
So those all get to kind of this, this why now all of this is reinforced by the study that we did on the DEIB tech that came out just at the beginning of this year, January of 2021.

Stacia Garr:
And the big finding from this study was that when we asked vendors, what problems our customers were trying to solve, that issue of DNI analytics and insights went to the top. It was number four in 2019. The last time we published that study and in 2021, it was number one, it was 19% increase in the importance of addressing this lack of DNI insights and analytics. So we know that this is been something that we've seen reflected in the data. We're seeing it in the popular press, and we as analysts have seen it as being incredibly important. So that's why we're doing this now. Priyanka.

Why it's so hard

Priyanka Mehrotra:
Interesting. So let's take a moment to understand why it's so hard to do this, and we're going to talk about what were studying in through this research, but just want you to take a moment to understand why it's been so hard and what have been some of the challenges that DEIB leaders, people analytics leaders, and organizations have been facing. And I mean, this often has to do with three things as they come to our mind, the first being that there's a Gulf between the DEIB leaders and people analytics leaders that tends to exist within organizations. And what we mean by that is that there are few things that go under this, one is that DEIB leaders and people, analytics leaders often not always, but often report to different departments or heads or senior leaders. So for example, DEIB might be reporting into CEOS a lot of the times.

Priyanka Mehrotra:
And in fact, I recently came across a research that was conducted on about 500 senior diversity leaders out of which 40% have said that they were reporting into CEOs. And what we typically tend to see with people, analytics leaders on the other hand is that they're often either reporting to the CHRO or talent acquisition leaders, or talent management leaders, or even a centralized analytics team. So one of these, the gulf I was talking about is that the reporting structure might be different for them. The other has to do a little bit about the backgrounds that these two tend to come from. So again, not all, but years of DEIB teams often came from backgrounds such as social justice or diversity focus backgrounds. Whereas people analytics leaders often tend to come from data science, computer science, math, statistics background.

Priyanka Mehrotra:
Additionally we often see the DEIB leaders, might find themselves focused on activities that may not have a lot to do with data. So for example, setting up employee resource groups or managing DEIB events or collaborating with local communities. Whereas we see analytics leaders really deeply ingrained in the data side of the organizational things that they're doing but only coming in as participants when it comes to DEIB and having little knowledge about all the curies and the approaches that go behind those initiatives as when it comes to DEIB. The second reason why we think this is so hard is that there tends to be a lack of clarity around data and how to use it. And this goes back to the point that Stacia was making, but, you know, up to now, we've been seeing a lot of use of DEIB data has been for reporting purposes.

Priyanka Mehrotra:
And while we are starting to see a shift in how leaders are starting to think about this data, these are still early stages. And there are a lot of questions about, you know, what data they should be collecting, how they should be using it. What are the types of analysis that they should be running? And I think a related reason, which is our third reason under this, why it's so hard is that there's a lack of clarity around how DEIB leaders, DEIB tech venders fit into all this. So Stacia, mentioned our DEIB tech study that we ran, that we published earlier this year, and we saw an immense growth in the number of DEIB tech vendors that are coming up in the space. But along with it, we have questions and concerns from leaders. When they're asking me questions, such as when should we bring in these tech vendors, how should they fit into the broader strategies?

Priyanka Mehrotra:
So all of these reasons kind of convoluted to making this practice of bringing DEIB and analytics together, something that's challenging for organizations in they're struggling to understand how would they get started on it and actually be successful on it. And these are the factors that actually fed into our thinking on what we should study when it comes to this topic.

What we are researching

Priyanka Mehrotra:
So if you go into the next slide, we'll just quickly talk about some of the overarching questions or teams that we're looking at through the study. So the first one that we're looking at is how should the DEIB and people analytics partner. So rethink this is sort of foundational to what organizations should be doing when it comes to this, because without a successful partnership, this work can not be done. The second area that we're looking to understand is what are the important data and metrics for DEIB?

Priyanka Mehrotra:
So, like I said, there's a lack of clarity around what is it that they should be doing? What is foundational, what is table stakes? And then as organizations mature, what are some of the more novel and non-traditional things that organizations should be looking at. And then third is about the role of vendors and techs. So looking at, you know, one of the different types of technologies that organizations are using. What are the people analytics technologies? What are the DEIB technologies? When should they come in and work as a partner and in general, what is the role that vendors should play in all this? So those are some of the overarching teams or questions, if you will, that we are looking at to understand from this study.

What the literature says

Priyanka Mehrotra:
And what we did when we launched this study was we began with a literature review and which we published last month on our website.

Priyanka Mehrotra:
And we did a very exhaustive, neutral journey, where we analyzed over 50 articles, business journals, academic papers, and we found a few key findings that kind of reaffirmed our thinking around this topic as well. And kind of solidified our questions that we thought we should be asking. So I just cover some of our key findings from our literature review. The first of course that we were expecting to find was, and we did find was the, the need for analytics and analytics for DEIB is more important than ever. And, you know, given all that we've experienced in 2020, COVID19, the social justice movements, it's no surprise that really starting to look at how we can use data and metrics and analysis to support this push for the DEIB that we're starting to see from organizations. And, you know, just for an example, if you look at some of the commitments and goals that all the big organizations have put out over the last year, whether it's Facebook or Target or Starbucks, they all have these lofty goals of reaching 20% to 30% increasing their representation by X percent in the next few years are tying diversity to performance reviews.

Priyanka Mehrotra:
And then you look at those goals. It's very clear that none of this can be done without data and analytics without measuring where you are and where you're going and what needs to be done. So clearly people analytics is going to play an extremely critical part of doing anything related to DEIB moving forward. The second finding that we came across was the DEIB analytics is more than diversity metrics. So we found several articles that truly try to push the thinking beyond just looking at representation data, and thinking about inclusion, thinking about the different experiences that different groups of employees are having in the organization, thinking about belonging and what that means in the organizational context, thinking about the existing processes and how they can be made more equitable and working with people analytics leaders to really understand how can they use the existing data to think about some of these processes and kind of push forward their DEIB agenda on these things.

Priyanka Mehrotra:
The third finding that we came across was around using predictive analytics for DEIB to help plan for the future. And the articles that talked about this mainly spoke about using this and harnessing this power of predictive analytics to really avoid issues from becoming into potential problems in the future and planning for planning well ahead and avoiding certain challenges that may come up in the future. So for instance two examples come to my mind that we came across during this literature review. One was of Walmart using modeling and forecasting techniques to really answer questions around like, what could happen if we keep doing this, or how can we arrive at our desired goal much faster and using those insights from that data to really review the DEIB goals and connect regularly, to understand how, what is the progress that they're making towards them.

Priyanka Mehrotra:
The other example that we found was from International Paper, which uses predictive analytics to understand their expansion rate compatibility. And what that means is using data on past behavior, family dynamics cultural agility, global accuracy, to understand and forecast which employees would fare better in a global move if they were to be placed in international settings. So these were some of our top three findings. And I just want to touch on some really interesting ones as well. And this one was my favorite, which was around using quantitative data individual stories and experiences are an important piece of the puzzle and no work on when it comes to DEIB can be compete without taking those into account? No amount of statistics can capture what it feels like to be the only ruling on a team or to be the only black member on the team.

Priyanka Mehrotra:
And so we think that qualitative data and quantitative data forms an extremely important part of doing analytics for DEIB. And finally, another key finding that we found of course, was around, you know, making sure that you're addressing issues of privacy and ethics. So aggregating data, sharing data with employees, being transparent about what is being collected and what is the purpose that that data is being used for. So, like I said, all of these findings kind of reaffirmed our thinking around what is it that we need to study in this area. And like Stacia mentioned, and our lit review confirmed it, that there's a lot written on how and why this needs to be done and very little on how organizations are actually doing it or what they should be thinking about. And that's what's was what our aim was when it came to launching the study. And that's what we've been trying to find out through our interviews. And I'll pass it on to Stacia to talk about some of our initial findings now.

Initial findings: Building a strong DEIB & People Analytics partnership

Stacia Garr:
Great, thank you, Priyanka. And I know we've had some really good questions come in through chat, keep those coming. We will try and addresses questions once we get here into the question section. So some of the initial findings and I should clarify, we've done, what is it Priyanka about 15 interviews at this point on our way to roughly 30? So we're about halfway through our interviews. So these are very initial, so we're just going to share some of the things we have been hearing. So we've been grouping the research into two areas, the first being that DEIB and people analytics partnership, and then the second one being metrics. So focused on the partnership aspect first. The first point is around the importance of the data oriented diversity leader. So we've heard a real, and this isn't surprising, but I think it's just worth underscoring. We've heard a real difference in the interviews when people said I've got a diversity leader who really gets it, who gets the importance of this work, who supports what we do, who actively helps us think through the metrics and analytics that we should be focused on, et cetera, et cetera. That's kind of been one, one story.

Stacia Garr:
The other story has been well, I'm the people analytics leader, and I know this is important. And I've, you know, done my best so far and figured out what I think is important, but I'm kind of worried, waiting on a diversity leader to get here, to help, or in some instances, this is what I've done. And we've just hired a diversity leader because as I'm sure many of you have seen, there's just been this incredible slew of hiring of DEIB leaders since last summer. And so it's actually notable how many folks are like, well, our DEIB leader just started in September or they just started in January and now we're finally starting to get traction. But the importance of that partnership in the diversity leader being data oriented was remarkable. Second, and I kind of just alluded to this a little bit, but people analytics leaders taking the lead on data. We are actually, so I think many of you may know we're doing this study, but we're also doing a study on DEIB and skills and the skills kind of side of that is the learning team.

Stacia Garr:
And what has been remarkably similar about these two studies is how the DEIB teams in the past have either been responsible for this work or they have or the work hasn't been done quite frankly. And now as DEIB has become increasingly main stream, these corporate functions. So in this instance, people analytics, but in the other study, learning these corporate functions are kind of taking back or taking over the aspects of this work that they have expertise in. So for for people analytics, it's, you know, we know how to do the data analysis. We know how to get common definitions for the data. We know how to do, you know, basic representation analysis. Like we know how to do all this stuff and because we're already doing it in all these other ways. And we have the, the source of truth dataset, ideally you know, we, we are the ones who should be doing it and then putting it into the dashboards that we're already providing to leaders.

Stacia Garr:
So this just makes sense for it to be part of this, this group. Of course though, there is a side of this, which is around selection of metrics around problem identification, hypothesis identification, and I'll get to that more on the next slide. But the big thing is just this idea that people analytics, this is firmly now in our remit, and we need to go with it. The third point, and this seems maybe obvious, but is the importance of the alignment between the two. So we've heard a lot of instances where there are either, you know, Priyanka set up the, the challenge that we see with reporting relationships. And so we're seeing when it's really effective, DEIB and people analytics reporting into the same leader is one instance if that doesn't happen, we're seeing kind of pretty formalized, dotted line relationships between people on each of the teams.

Stacia Garr:
So a DEIB team member who is, you know, sort of informally connected to the people analytics team or vice versa. The point being that there has to be a strong level of communication between the two, because DEIB is basically the, the subject matter expert when it comes to the sorts of data and analysis that let me rephrase come to the questions that should be answered. And then the people analytics team is the expert when it comes to the data and analysis that can be done. So there has to be that clear alignment. Moving, I'm sorry. Priyanka, did you have something to add there?

Priyanka Mehrotra:
I think I would just underscore the point on the alignment. I think what you said was exactly right, like having that either direct line or reporting into the same head or having that dotted line, what it does is it makes sure that both the leaders are aligned on priorities through those communications and constant check-ins, and they're aligned on priorities and goals that are connected to the overall business strategy. And I think that also gets to the point about there being trust between the two of them. And I remember you spoke about that, that the DEIB leader, as well as the people analytics leaders have to trust each other, that they know what they're doing and that this is the right data, or this is the right approach that they're going to be taking and work together as a partner on those priorities and goals.

Metrics that matter

Stacia Garr:
Yeah, great point. So if we move on to the, the metrics aspect, and I know that there, there are plenty of questions in here. And so we'll, we'll start to work our way through them now in terms of metrics, what we saw is that, and this is just consistent across pretty much every interview that we did. You need the foundation and that foundation is basic diversity representation metrics. And I say basic, but it's a little bit less than just basic because it also includes intersectionality. So meaning that you, aren't just looking at, what is the experience of black employees, or what is the experience of Hispanic employees, but you're looking at what's the experience of black women, for instance and, and that sort of basic representation data is something that everyone said you need to just get your hands on from the very beginning there was a question in here in the chat, and I'm going to go ahead and grab it now around approaches and measurement at a global scale, especially regarding ethnicity. And we actually have a really fascinating conversation yesterday with the global fortune 100 organization. And what they were saying to us is one, and this is something we've heard consistently. One that ethnicity is something that tends to primarily be measured here in the United States. There is some measurement of it in places like South Africa, in some Asia, but almost more of a country approach within Asia. And then some in Brazil, because she made the point that a lot of people in Brazil don't necessarily identify as Hispanic, though they do identify as Latino or Latinas. And so when then, but then obviously within Europe, there is no ethnicity data that's being collected. So we think, you know, the point is, is that they are, what she said was that they worked with kind of local representatives to make sure that they were getting the right information so that they could be culturally appropriate in all these different locations.

Stacia Garr:
Yeah. the other component of this is we heard a lot in discussions about doing self ID campaigns. And so, you know, that because there's obviously sensitivity in terms of what information you can collect on ethnicity particularly in the EU, it wasn't as much focused on ethnicity there, but it could be focused on things like disability or on LGBTQ status or some of these other types of information that you might want to be collecting on folks in using as part of your kind of foundational diversity representation analysis. So we've heard that quite a bit. The government collection data often is, you know, initially collected by the companies, but, you know, not necessarily in all instances but yeah, looking at what's what's externally available and then also using that potentially to help inform your benchmarking strategy so that you can be comparing apples to apples. If you're looking at what external data is out there is an important thing to consider too.

Stacia Garr:
So diversity representation, metrics being foundational. Second looking at inclusion and equity. And so the way that I have been framing, this is almost like a model. Well, you know, that's part of what we do. So in an initial model is like diversity of representation is, is kind of step one. Step two is what we're calling kind of inclusion and equity one Datto, which is basically looking at things like engagement data by representation, information. So engagement and inclusion, potentially inclusion, indices and other belonging metrics that may be being captured and looking at those by by diversity representation numbers, and also including intersectionality, like I just mentioned. That's kind of inclusion one Datto, inclusion two Datto, which is what we're seeing some of the more sophisticated companies look at is saying, okay, we've identified for instance, that we have a problem with, or we we have, you know, variances with black women in this area.

Stacia Garr:
Why might that be happening, maybe black women in finance, just to pick something, why might that be happening? And then actually, and this is where it's really important to have that strong relationship with the DEI team and pulling in hypothesis on what may be happening. So sure it could be compensation, but maybe instead it's, you know time to promotion rates, which obviously also impacts compensation, but this is a slightly different issue. It might be the, that these people are being brought in from outside, maybe because there's been a diversity effort for the last few years and these people aren't getting they're from outside and they're not getting effectively connected into the network. So it's kind of an opportunity for the people analytics leader to work with the DEI leaders and increasingly the HR business partners to understand what could be happening here and how can we actually design a study to truly understand using some more sophisticated analytical approaches.

Stacia Garr:
So that's kind of the inclusion and equity two Datto approach that we're seeing. And then the third is the importance of understanding employee voice. And so this is, I would say it's kind of related to both inclusion, one Datto and two Datto, but it's a little bit different because it's not just employee engagement and experience, but it's, you know, what other things are employees feeling? So we've seen a rise in for instance, in harassment technology this come available particularly after me too. So are we looking at that and are we taking that seriously? And are we looking at other ways that employees might be not being heard in the organization? So this is kind of in the inclusion two Datto type of capability, but if we're looking at, for instance metadata that on who's going to what meetings are certain populations being included at the same, you know rate as others in terms of important meetings or are they being connected with others via Slack or Teams or whatever. So there's kind of all this more sophisticated analysis we can see are these people's voices literally being heard to the same extent as other groups, voices. Priyanka, did you have anything to add there?

Priyanka Mehrotra:
Yeah, I think one interesting example that comes to my mind. I think we heard this from a couple of interviewees was using wellbeing data, and I think that might fall under inclusion 2.0, as well as we're starting to understand it is looking at wellbeing data for underrepresented groups and seeing how is that different and getting to that feeling of belonging and inclusion for those groups as well. And I think also what, another thing that we heard from a couple of interviewees, what guests to employ voice is quantitative data. So we heard about focus groups and collecting stories. I believe from one of the vendors that they're doing that, and that I think was a very interesting add to the data that organizations already have and, you know, like creating environments where underrepresented groups and people are comfortable enough to speak up and collecting that data. In addition to all the surveys and pulses and metadata that they might be already collecting.

Stacia Garr:
Yeah. Great point. Great point. Okay. So that's the kind of presentation sections such as it was today. We're going to go to your questions and there've been a number of questions that have come in through chat. So I'm going to go to the chat questions first and then come back to the questions that were submitted in advance.

How do you get HR to use analytics to drive change?

Stacia Garr:
So one person asked about how other organizations are getting HR to use analytics, to drive change with DEIB strategies. And this question, I love it because it kind of hits on, on all the challenges, right? You have at least three different groups. So you mentioned we've got HR, we've got people analytics, and we've got DEIB strategies. And the magic fourth group that didn't get mentioned is legal because legal is in all of these conversations. So how are organizations actually, you know, making this happen?

Stacia Garr:
So I think we've heard a few things. One is it depends on the maturity of the organization and the maturity across all of those different groups. So does your organization, for instance, have a strong HRBP organization, which has strong connections to business leaders and does the organization have a strong DEI leader and what is their influence in the organization? How sophisticated and mature is the people analytics function in their ability to kind of imbibe and respond to requests when it comes to this. And then also, what is the risk profile of the general counsel? Are they, you know, we talked to one organization kind of more of a tech enabled organization. I would say tech enabled retail organization, where they said, we got to fix this, do what you need to do all the way to an organization where it's like, we don't want to share anything.

Stacia Garr:
No, data's going to anybody except for a very small few. And so all of that makes an impact on to your, this question, how do you get HR to use analytics to drive change? And so I think the key is figure out where your strengths are, where the maturity is. So if the maturity is for instance, with HR business partners and they have a strong, strong relationship with the business, you know, use your, hopefully you have at least a initially small people analytics team, if not kind of a more sophisticated one to start with providing that initial foundational data, you know, here's, here's where we have differences here's in the experiences of different groups. So start with that, that education and then working with HR business partners to understand what are the levers that we could pull in these different businesses to start to drive change, where is their appetite for this to do something different? Priyanka, do you have something to add?

Priyanka Mehrotra:
Yeah, I think I would just add to that education piece that you mentioned, because I remember one of the interviews that we recently spoke to a very large company. They mentioned that they're working with their vendor as a partner to broadly educate senior leadership and HR teams to not just use the data, but also understand and interpret that data. So, one, I think the role of vendor can be crucial if the vendor is willing to work with you as a partner in education and educating them. I think the other one, which might contradict my point actually, was that one of the leaders that we spoke to mentioned that they had set in place a learning requirement for people, for senior leaders before they could get access to the data. And it kind of backfired because nobody wanted to take that learning, but what it help them understand was that they needed to approach it in a different way that this was not going to work. It was clear to them that they could not force this learning course on them before giving them access to the data or getting them to use analytics, but they needed to figure out a different approach. So that, that was kind of a failing when approach that they kind of worked through. So I think those two are some of the interesting examples that come to my mind.

Stacia Garr:
Yeah. And I think that the point is experimentation, you know, to what you just said, you know, that, that organization figured out that, you know, kind of a one hour long learning on how to use DEIB data didn't work. But so they said, okay, well, how can we actually use the dashboards and the data to teach? And how do we do it in a way that maybe we don't give everybody everything at once, but we roll it out in a way that kind of through the rollout process, we're actually educating people on what it is certainly that they need to know, but also how they might use it. And this is, I think also where either vendors or people analytics teams can really come in with potential suggestions that are embedded within the dashboards and in the offerings to help people say, okay, well, given this, what, what might I do? And those suggestions obviously should be based on the data.

Priyanka Mehrotra:
Exactly.

Stacia Garr:
Okay. We're getting some more questions in here. That's great.

Which groups or identities to prioritize as they're all important

Stacia Garr:
So there was a question about, and we've kind of addressed this, but I want to come back to it, but there's its about understanding which groups or identities to prioritize as they're all important. I think that's, that's absolutely true. What we have seen organizations do though, is just kind of just similar to what we do with all people, analytics data, or really ideally, you know, our HR efforts is to say, okay, where's the business need here? Where's the need the greatest. And you know, that you can do once you have that representation data and you can kind of overlay what's important to the business in terms of business goals and strategy. And then where are the biggest gaps in that data? But using those two as initial ways to make a decision about what to prioritize, and then the overlay on that is who is going to be open to trying something new.

Stacia Garr:
So we've, you know, we heard, for instance in one of these organizations, they were talking about how most of their metrics are, you know, externally facing, and that's what leaders care about and any of the internal stuff that can actually maybe help you make decisions about actions to take, they were less interested. And so we asked that leader, we said, well, how do you find the interested leader? Like you've got great insights. How do you find the interested leader? And you know, some of it had to do with finding people who felt personally connected to DEIB and felt, you know, whether that was through their own experience or through someone that they loved. We can't tell you how many people, how many to be Frank, how many white men have said, I care about this because of the experience my wife has had, or I care about this because I'm a dad of two girls. Like, it's almost, it's remarkable how many times we've heard that. So find those people who have that connection. And then secondly hopefully people who have that connection to DEIB, but then also have influence over their peers. They're respected by their peers and using them giving them an opportunity to kind of shine and be the exemplar of the changes that are possible. Then that's the other way that I think about prioritizing.

Impact and accelerating the integration of DEIB & People Analytics

Stacia Garr:
Okay. another question here, does architect, the alignment of career planning, pathing and skills, capabilities, and experience have a role in this arena and impact on accelerating the integration of the DEIB and people analytics more broadly. So yes, yes. So I mentioned that we're doing a study on DEIB and skills. These two studies are running in parallel. That study is really trying to understand what are the skills that contribute to a culture of DEIB. So that's one component, but the other angle on skills and DEIB is using skills to potentially address any biases that may be happening. So under understanding of people's skill sets and what they want to achieve and using that to help us with people, better understanding career path opportunities, better understanding things like availability of opportunities to internal talent marketplace and that kind of thing. So I think that there is very clearly an overlap between particularly understanding skills, data, and leveling the playing field for diverse populations. So I think this a really important thing. We're seeing people just beginning to talk about this. But it's not I think its something that's going to have to be driven from the learning side of the house, because we're not, we're not really hearing anything on the people analytics side of the house on this, but we think it's an area of opportunity.

What are some of the challenges to building a partnership between DEIB & People Analytics?

Stacia Garr:
Okay. I'm going to turn to some of these questions that we received. We're going to go with this one first Priyanka about the challenges to building a partnership between DEIB and people analytics. Do you want to talk about that one?

Priyanka Mehrotra:
Yeah, sure. So I think we already touched upon some of these things when we spoke about our initial findings. So I think one of the biggest challenges that we've heard, especially as it pertains to people, analytics leaders is when DEIB leaders don't believe in data or don't come from that data background and are not open to receiving that data or looking beyond data for reporting purposes. So I think that's one of the main challenges that we heard coming in from people analytics leaders. The other one has been about lack of our missing a data culture in your organization and resistance to changing that mindset of really going with the data and being open to experimenting on middle and trying to find out what is, what is it that they can do and what is it that can be done with this data?

Priyanka Mehrotra:
And just a general lack of data literacy and awareness. And there are ways that we can, that organizations can work work on this. As we've talked about, the people analytic leaders tech can take a lead, the vendors can come into play as a partner in spreading that education broadly across the organization. But in general, I think so CDO is not believing in data and a lack of data culture in the organizations would be, I think the top two ones that we've heard. And I think connected one to that is lack of support from the leadership in general. And you know exactly to your point, what you said earlier, we've seen a lot of push come from people who are personally impacted by it, or see it around them have experienced it. But if that is missing at the top then there's a general lack of support for this kind of work that, that, that can be challenging in building this kind of partnership between DEIB and people analytics. What else would you add to this?

Stacia Garr:
We mentioned it a little bit earlier, but the issue of trust, I think in general is comes through. So maybe a little bit less with the relationship between DEIB and people analytics, but certainly with HR in the broader organization. Somebody we interviewed recently talked about how the HR organization didn't want DEIB and people analytics to release data broadly because they were afraid of getting called out or others knowing something that HR didn't and this idea of we have kind of an adversarial relationship. We own the data, we should know everything, and then we can control and communicate it. That is problematic. And you know, the mindset needs to shift to more of a more eyes on the data are better than fewer we're in this together. We're gonna figure out solutions together. We're going to distribute decision-making to make things better at scale, et cetera. And that mindset shift is very hard. And so that's not necessarily something just between DEIB and people analytics, but it requires a strong perspective between those leaders to then go, wow, and kind of push this broader agenda of, we need to share data so we can make change so we can measure what's happening. And people will know if we're making progress and if we're not, then we can make changes that will drive that progress.

Priyanka Mehrotra:
Yeah. Yeah. I think that also speaks to something that we heard about fear of data being released without the context. And we heard a lot of people analytics leaders talk about how the other ones who take the lead when it comes to framing the data in the right context and putting that communication in that right frame before it's published externally or internally. And it's been interesting to see that it's the people analytics leaders who are taking the lead on this when it comes to communicating the data and putting that right context of DEIB to work.

What is the role of legal?

Stacia Garr:
Yeah, definitely. Cool. Let's move on to the next question. What is the role of legal? All of our folks, whether they're people analytics leaders or DEIB leaders sort of chuckle when we get to this question, because they're like, Oh, legal. So, you know, obviously the role of legal is to keep all of us out of trouble. You know, this is sensitive data, it's important to treat it with the due respect, et cetera. So I don't want to underscore that or, or undermine that, excuse me. That said what we also have heard is that there is great variance in what you can do based on the risk profile of your general counsel. And a lot of times what happens is the general counsel needs just education. You know, their job is to find the problems and there are always going to be concerns when it comes to DEIB data.

Stacia Garr:
And so the question is how can we work with general counsel to reduce the risk to a level that makes it acceptable and, or to make it clear that this level of risk is acceptable versus the risk of us not doing anything? So, and I think part of that is also helping them understand how others might get to this data. If the organization isn't controlling the message to some extent. So for instance, we had one interviewee who's general counsel said, I don't want you to publish anything, not nothing out there. And the people analytics leader went back and said, look with this set of data, we are, that we provide to the government. Employees can legally request the right to this data to have access to this data. So all it's going to take is a smart employee asking this question to get this information out, by contrast, we could share it and we could put some context around it. We could put clarity around what we're trying to do, and we could head that off. So there's this risk that already exists out there. And actually by releasing the data in this way, we are reducing that risk. The general council eventually agreed, right? So it's about thinking through sometimes very creatively. How do we work with legal to help them understand the appropriate level of risk Priyanka? What else did we hear?

Priyanka Mehrotra:
I think one of the best advice that we heard come out of our interviewees was don't look at legal as compliance. You get them as a partner. So like the way you partner with the DEIB, or if you're a DEIP leader the way you partner with people analytic. Work with legal as a partner, because they are the ones who are going to help you put the data, the right context, made sure that you're being able to continue sharing that data. And just in general, they're going to be helpful along the way. So I don't see them as putting barriers to the work that you do, but actually supporting you just by pushing you to be more clear about it, by being more intentional about it. And by thinking about it from all perspectives.

What is the role of vendors?

Stacia Garr:
Yeah. That was a good, that was a great point. Yeah. Cool. I'm gonna keep us moving so we can get through a few more of these. So what's the role of vendors? So there were, I think there are a few, one is vendors can broadly educate folks about data. We've already talked about that. Second, depending on the vendor they can certainly enable self-service for the access to the data, which is, which is a powerful one. Third vendors can help get up to speed quickly for small teams. So particularly if it's a vendor that the people analytics team is already using and they have a DEIB offering. So think like what Visier offers or what Workday offers in the context or cruncher in the context of their overall offering. Those are, those are ways that they can that they can, they can support.

Stacia Garr:
That said, we have heard from a number of people, analytics leaders, deep frustration with some of these vendors, because they're like the DEIB leader just went to the vendor. Like they didn't even talk to us about what data we could offer or the capabilities we have. Like we were just completely cut out of the loop. And then when the data that they had was different than the data that we have, senior executives came and were frustrated and said, get it right, et cetera, et cetera, you can kind of see where that whole train goes. And so, you know, there's an opportunity that vendors can offer some really good things, but it's really important to make sure that you have that alignment and clarity on first the data set itself and what's going to be used. But then two, how it's going to be leveraged back in the organization is, are the insights, the vendors producing, going to be integrated into existing dashboards or reports that leaders are already getting, what's going to happen. You can't have the vendor out here as an island is the point. They can really help you, but they can't be an island over here when all your other data stuff is over here.

Priyanka Mehrotra:
I think the only thing I would add too, is that they can also help share data broadly where it's appropriate. So one of the questions that we had asked our venders in our people analytics tech survey last year was, do you share insights collected on employees for themselves to help them take actions on them. And majority of the vendors said that they do. So I think that's another place, another area where vendors can enable organizations to help employees gain value out of the data that is being collected on them. And I think more and more organizations are starting to do that, especially when it comes to things like their sense of belonging and inclusion to better understand, okay, where is it that they are lacking in what is it that they, maybe the kind of behaviors that they should be working on to enable that culture of belonging and help people feel like they're included part of the teams. So I think that is another rule that vendors can play in helping just sharing that data and providing that access to those insights that that organizations are collecting on employees.

What analytics are being used for DEIB?

Stacia Garr:
Yep. Great. Okay. Next question. We received, what are some of the types of analytics being used for DEIB? So we've, we talked about some of these particularly kind of the, the basic representation data the representation data applied to engagement or inclusion and belonging, indices, that's some of the more kind of common analysis that we're seeing we're increasingly seeing in terms of more novel approaches, we're increasingly seeing the use of ONA. So particularly to understand the strength of networks of diverse groups and how those might differ. So for instance, looking at maybe looking at the networks of women and how these differ from men, particularly by seniority and organizations, we actually wrote a study on that a couple of years ago on women networks and technology. We also see them using ONA to understand if there are kind of hidden stars in the organization.

Stacia Garr:
So people who senior leaders may not know could be high potentials or be making an outsize impact on the organization, but who are highly connected within their network kind of indicating that, that outsize impact and then using that to help with potential hypo identification practices and in putting people into leadership development programs and the like so there's, those are a couple of ways we've seen ONA. We're also seeing more use of natural language processing and used in this kind of gets at that qualitative data aspect that Priyanka mentioned at the very beginning from the lit review. So using that to identify themes within certainly within engagement or belonging in our inclusion indices but also using that when we are looking at performance reviews looking at to what extent are certain groups may be having certain types of themes or texts being written about them that others are not. So for an example of this might be again, kind of going back to some of the research we've seen in women versus men. Women's feedback often tends to be more about their behaviors. Whereas men's feedback often tends to be more about their actual outcomes for business impact. So those are the types of differences that you might be able to use NLPM. Priyanka, what else have we seen?

Priyanka Mehrotra:
Something that was very interesting was tying wellbeing data DIN data. So seeing that, cutting it across, slicing it to see how different groups underrepresented groups, different cohorts might be fairing when it comes to wellbeing. I think the other thing that stuck with me that was pretty interesting and you've just heard that from one company was, they were, they were doing was counting high-fives on a watch on the watch with internal communications back from that they have to understand allyship and sponsorship amongst employees and managers and senior leaders. That was something interesting. That'd be hard as well.

Stacia Garr:
Yeah, so we we've actually seen that also. We saw it with high fives in this research, but also I've seen it with recognition platforms. So like a work human or an achievers Work Human themselves have actually done some analysis to see if there are differences by demographic background in terms of who recognizes whom and at what amount, cause I do like points or, you know, dollar amounts associated with recognition. And the theory there being that those recognitions are much less you put less thought into them than you do a performance review. So they may reveal biases that exist a bit more. And they do show differences by all the demographic groups that you might expect. So anyway, I see we've just got two minutes. So I just wanna see here. I want to go to the question that is in the chat, cause I think this is, this is a really good one.

Evidence of accountability via reward, accelerating progress or being effective in general

Stacia Garr:
And this is about, have we seen evidence of accountability via rewards, accelerating progress or being effective in general? So this is such a hot topic right now because we see all these organizations now coming out and saying, you've got to tie DEIB numbers to some sort of accountability metrics in order to get people's attention. There was when I first started doing research in this space and like 2013, that was like the thing, the thing that everyone was trying to get to and the 2013 version of myself would probably be cheering this hugely. The 2021 version of myself is not so sure. And particularly given some of the things we've heard in these interviews. The the reason for that is well, while tying metrics to accountability can be really powerful and it absolutely can.

Stacia Garr:
What it can also do is get people to focus on the wrong thing. And right now people are really worried as they should be that as they proliferate the DEIB data, that people will see it as a quota or a target, and that is illegal. And so there is a real concern about people misinterpreting what is trying to happen and kind of going after the wrong things. And the accountability makes that even more, more public. I think that if done well, accountability is a good thing. So if, for instance, you're tying to behaviors that we know drive certain types of outcomes. I think that the accountability can be a good thing. The devil is in the details on the measurement, of course. But I guess I would say my perspective is that it can be good, but use it with caution.

Stacia Garr:
I have not seen any holistic research studies that look at this. And even if we did, I would be concerned about like what correlation and causation researchy things. So that's it, if you want to talk more about it, I'd love to talk more about it. I think it's an important topic, but that's kind of my off the cuff.

Conclusion

Stacia Garr:
We're at time. So I'm just gonna real quick flip through to our last thing, which is next Q&A call. Maybe not relevant for folks here, but for anybody who maybe is watching the video, it is on learning content. So we did a study to understand how do we deliver the right content at the right place, right time, right person right modality, et cetera. And we're going to be discussing some of our early findings from that. That study will be coming out itself in mid-June. So that one will be further along than this study. So if you're interested, I'm sure it'll be really great. It'll be with Dani Johnson and Heather Gilmartin Adams. All right. Thank you to everybody so much for the time today. Thank you, Priyanka for your co-host on this session. And we look forward to seeing everybody again soon. Have a good rest of your day.

Written by

Stacia Garr Redthread Research
Stacia Garr
Co-Founder & Principal Analyst

Stacia is a Co-founder and Principal Analyst for RedThread Research and focuses on employee engagement/experience, leadership, DE&I, people analytics, and HR technology. A frequent speaker and writer, her work has been featured in Fortune, Forbes, The New York Times, and The Wall Street Journal as well as in numerous HR trade publications. She has been listed as a Top 100 influencer in HR Technology and in D&I. Stacia has an MBA from the University of California, Berkeley, and a master’s degree from the London School of Economics.

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