How data tech and analytics can improve diversity, equity and inclusion programs

Listen as notable speakers discuss the impact of IT and analytics in driving positive DE&I outcomes. Traci Shirachi, CEO of The Mark USA data analytics company, with Iveliz Crespo and John Iino, explain how data analytics broaden the definition of diversity, by drilling down into thoughts, feelings, socioeconomic backgrounds, relationships, experiences, culture, and generational perspectives. 

For more information, please visit Reed Smith's Diversity & Inclusion page.

Transcript:

Intro: Hi, I'm John Iino and I'm Iveliz Crespo. Welcome to the Reed Smith podcast Inclusivity Included: Powerful Personal Stories. In each episode of this podcast, our guests will share their personal stories, passions and challenges, past and present, all with the goal of bringing people together and learning more about others. You might be surprised by what we all have in common, inclusivity included. 

John: Hello, everyone. Welcome back to the podcast. Today, we're going in a different direction rather than some of our other guests that we've had that, that explain their personal stories. We wanted to bring a news topic here in terms of the, the role of data analytics and diversity, equity inclusion programs. So I am just thrilled to introduce our audience to Traci Shirachi, got to meet Traci more recently. Um And Traci is the president and CEO of a data analytics company called the Mark USA Inc. They're based in Irvine, California and they specialize in the evaluation and research of data. Hey, Traci, great to have you on the podcast. 

Traci: Thanks John for having me. I'm really excited to be here and talk about data. So I appreciate you inviting me. 

John: Well, I know we had such a great conversation recently just in terms of not only the role of data but also in terms of what diversity looks like and how data can really inform so much of our strategies. Also joining us on the podcast is our co-host Iveliz Crespo. Hey, how are you doing today? 

Iveliz: Hey, John, how are you doing today? Traci, thank you so much for joining us. I'm really excited to hear you talk about data analytics. It's something that as a research nerd myself, I find really fascinating. So thank you so much for joining. 

Traci: Oh, thank you for having me Iveliz. 

John: So Traci just let's start us, tell us a little bit about the mission of your company, the Mark and how data analytics can help drive some of the things that we're doing in the DE&I world. 

Traci: Sure. So what's really important to the Mark is measuring outcomes but really what I consider socio-economic outcomes and really understanding community impact and a lot of the work that we take a look at is measuring the outcomes related to either nonprofit work or academic institutions and their work um as well as what companies and the private sector are doing in terms of the impact that they want to have on their community. And really understanding not only how those resources are being used from a volunteer or resource perspective, but really understanding how systems are being transformed, how people are changing how the results of those dollars are contributed, efforts are really impacting the lives of individuals. And as part of that diversity obviously is a social topic that all of us are interested in learning more about. But really understanding how that's adopted internally within organizations and what is the impact on employees or individuals or even externally to organizations as it relates to community. So that's really something that we've embraced as part of that topic. And obviously, as a research and evaluation firm are really passionate about it and helping others to better understand how they can take action, but also better understand what kind of outcomes are achieved by the initiatives and programs that they're focused on in terms of deepening or expanding the breadth of diversity. 

John: So give us our listeners an example of, you know, one of the projects that you've worked on that really showed how you looked at the outcomes and how the data informed strategy. 

Traci: So I think one of the projects that we worked on with a number of organizations has really been around perspective and around diversity of thought and being able to triangulate that information and tie it back to really the demographic data. So it's a combination of not only acknowledging the demographics of the population that you're trying to get the information from, but also understanding perceptions, understanding how individuals feel like getting a lot more color or depth to that information. So it's acknowledging that diversity is not only just you know, age or gender or race, the things that we can see or the things that we can easily distinguish it also involves perception and perception can also be tied to experiences and it can be tied to age groups as well, aside from gender or aside from different cultures and races. So it's really making sure that the information is gathered in such a way that it's properly accounts for all these anomalies, but also accounts for similarities and differences. While at the same time being able to index that information across organizations and seeing how organizations or groups of people compare to one another. And I think when we're working with one particular organization, they're really using that information to make better management decisions to make better people and personnel decisions as well as operational and process flow decisions just by nature of how people think, how people react, how people, what resonates with people based on different cultures or based on different input. 

John: Well, that, that's so interesting, you know, when you use all that data to really inform decisions, it's just just taking the data to to another level, you know, that within our organizations and so many organizations, when it comes to diversity equity inclusion strategies, you know, they just look at typical metrics, groupings, whether it's by race, gender, sexual orientation, gender identity, disability and the like, you know. 

Iveliz: And then that's if John, that's if they're tracking it. 

John: Right. 

Iveliz: We've we've encountered many organizations that, you know, we asked them, you know, what does your demographic look, the demographics of your staff or your employees look like? And we're often met with silence. 

John: Yeah. No, exactly. And so those that do measure just Traci, we've talked about this before we measure things like head count of the various groups or additions or attrition, but kind of beyond these groups, talk a little bit what some of the work that you're doing just to get behind some of these groupings to help really inform decisions on a deeper level. 

Traci: So amongst the individuals in my company and as employees, as a team, a lot of my employees backgrounds are in research practices. And so it's using a multidiscipline approach, both utilizing quantitative and qualitative data to really gather information in different ways. So whether or not that's surveys or focus groups or observation, but getting input in different modes, so to speak, which also helps in terms of characterizing, I think diversity and the breadth of characteristics as well as the breadth of input and perspective. But the beauty about the data that I find too is it's non emotional. So by that, I mean, people can have a lot of more fluid and open discussions about the information that's being presented to them and critically analyze it or ask additional questions and create a safe and trusting environment to do that from which nobody can be as offended or as emotional about it because it's a data point that individuals are trying to better understand stand or think more critically about. And I think that's really important in a very highly charged environment. Whether or not it's the type of organization of people who are used to confrontation or used to a lot of emotions or it's just the nature of environment right now where people under a lot of stress and the pandemic and facing a lot of competing, I guess you could say a lot of competing things at home, right? Work, kids, spouses or whatever, it may be pets, like everything is converged into one. So I think data has really helped to open up conversations that people may not have typically been as open to have because you're just talking about what does the data say, what does the research and the information say and what is missing in that information to then layer on additional conversations or subsequent data finding activities, I guess you could say. So you start somewhere but you're able to like layer upon it and really open up bigger and wider decisions that also extend, I think more from an operation standpoint and less from a HR standpoint for lack of a distinction. 

John: So I wanted to get more granular here because I'm trying to grasp in terms of specifically what was an example of something that the data show that maybe surprised you or surprised the organization that you're doing the work for that ended up, you know, it's a critical issue, like you said, highly charged issue. But the data, what did the data show? 

Traci: So I think one example that I think of in particular is an organization where they felt that the data was going to support their lack of diversity. And they were really concerned about that. And instead what it ended up showing is that demographically, they were, were diverse but where they were lacking was perspective. So, although, you know, racially, they were diverse and they were, they were concerned about other aspects of perspective, even though from a racial standpoint, they were diverse. So it's kind of interesting because what the eye beholds is not necessarily what the data shows sometimes. And it's mainly because the data has to do with what people think or perceptions or also what they're, they're willing to share that isn't captured in the demographics, right? So it's not only a tool to validate, I think leadership, am I going in the right direction? Am I making the appropriate decisions? Am I trending in the right way that I want to be headed while at the same time, it's also able to expose and demonstrate that, hey, you know, these are other areas that you're lacking or hey, in this particular case that I was describing, even though they kind of knew that they were lacking diversity, they really couldn't put their finger on it because from a demographic standpoint they, no one would disagree that they aren't diverse from a race or nationality or a gender perspective. But what was really lack, it was a diversity of thought or perception from different generations or age groups which also lends to different perspectives and diversity of thought. 

Iveliz: So I just had a quick question. Uh I'm just curious, you know, how do you account for the human aspect? Right, of data analytics, you mentioned a lot of external factors like environmental factors or things that are going on in the world such as the pandemic or even, you know, this racial uprising that we're seeing. And I imagine that people are taking that into their workplaces with them. And so to an extent, I would imagine that that might inform how willing they are to share truthfully into some of these questions that you might be asking. So is there a methodology that you employ to, to account for that human aspect to account for those external factors that are out of your control? 

Traci: Yes. So what we do is a lot of statistical analysis and by the different modes that I mentioned earlier where you're utilizing like surveys, focus groups and observations. PhDs are triangulating that information to try to remove as much biases as possible or even personal bias. All of us have personal bias within each one of us, right? And so through their research methodologies, I always joke with them that I always defer to them because they're smarter than me as PhDs. But they're really the ones that understand like how do you conduct this to remove that bias? And oftentimes because you're using different modes of obtaining that information, individuals are also sometimes safer to talk about something knowing that that information is also being deidentified and aggregated and triangulated with other information. So oftentimes when we report anything, it's not like person a provided this information and the manager or supervisor or whoever is able to see that person a did it not at all, they see it in the aggregate. In terms of this is how the whole group as a whole based on a sample size. Is it statistically significant or not? These are the findings, this is what happened, this is how we analyze the information, this is what the results are. And so people also feel safer because even though we know exactly who provided the information because we're a third party, they're sharing that information with us. But it's not that the and you know, stakeholder who's reading the report knows what was shared. And because different people like to share information in different ways. Some people are better communicators in writing. Some people are better communicators, you know, verbally through interview and others, you know, there's non verbal communication too in terms of observation. So we try to remove as much bias, you know, bias in terms of our personal biases as well as anything that's being influenced from an external factor. 

John: I find it so fascinating because certainly we’ve said for a number of years that diversity of teams fielding diverse teams leads to better results, better processes. And traditionally, we said, ok, diversity means bring traditionally underrepresented groups, you know, focus on gender, really bringing diversity and that will lead to better results, you know, based on research and all the rest, you're really taking this to another level by saying, not only are you looking for the visual diversity or the group diversity, but you're focused on the thinking and, and thoughts. And so I'm curious can within those groups, how are you able to discern the differences within groups by focusing on thoughts, what, what's kind of the methodology to be able to discern the differences in thinking? 

Traci: So I know from, you know, reviewing some of the work of my colleagues, like they'll use different scales. So distinguishing strength of a perception is one way that they'll acknowledge the difference between something being weak or strong and based on the question asked, what was the feeling or perception behind? Right? So there's different approaches that they utilize as I'm watching them as researchers to really acknowledge the difference between to your point and just to distinguish that. And it's interesting because as a business person, I mean, I've spent the last 20 years in consulting very much was not part of the research world other than have always been passionate about it. It's always intriguing for me to see the depth of the information and the wealth of information that's in these reports because they can be as you know, deep and as extensive as 100 pages or it can be as brief as, hey, what are the top 10 takeaways that's in two pages? And what are the next steps that someone needs to acknowledge to do differently as a result of the data that's being presented? So depending on the organization or client, it's really the aptitude and the you know, data is a culture, it's how parties embrace it, but also understanding that not all data is equal. And I like to say that too because people think that hey, if I just go at it and ask all these questions, like I'll get the information I need. And it really is a more thoughtful and a more mindful practice of understanding what outcomes do you want to really achieve first and kind of working backwards from there so that you implement the proper data protocols or their proper data framework for collecting that information. And that I think is really critical because then you're able to layer on and see an evolution of how and where your data started. And by doing subsequent data collection studies or or activities, you're also able to see how that data and that the results have changed over time, which is really what people want to see is aside from adding different types of nationalities or races or gender or faith or whatever. Instead of doing those initiatives, you also want to also see what the impact of that is. And if you're not providing the right framework for that, you won't be able to truly assess whether or not your organization or people have changed as a result of that. Other than the fact that, you know, you took an action which was to add more demographic diversity. Does that make sense? I don't even know if I explained that. Right. 

John: Yeah. 

Iveliz: So I just want a quick follow up to that because I think this is fascinating and for our listeners that are  interested in maybe doing more of a deep dive into this, how long does it usually take for your company if you're hired to come in and do an assessment like this? I would imagine it differs depending on size and, and just the general overall populations that you're working with. But if you could ballpark, how much time do you typically spend with a client conducting these surveys, these focus groups and these other forms of assessments that you talked about a little bit? 

Traci: It can be anywhere from one month to a couple of months and then it's putting together what I always refer to as the evaluation strategy, like as a result of kind of doing a initial assessment. What is it that the organization should do to take the next steps to create the instruments, to proceed with the data collection itself and to then report off of that and that can also be within another couple of months. So in total, we like to say, give it roughly a three month to six month process of initially getting something implemented and started and then following up periodically, whether or not it's quarterly or bi annually to then review the data and constantly go through these data activities. So that there's layering of that data. And one thing we're specifically moving a lot towards is incorporating a lot of the data visualization tools that are out there, the technology tools are out there because one thing that I'm having a lot of conversations with with a lot of clients is that we can't get the data out fast enough before the data becomes old because we've all of a sudden entered this what I call the Amazon hit the button, accelerated time period where the clarity comes from the data, but we need that data to come in more frequently and that information to be more readily available. So we're actually incorporating data visualization tools so that the information that we're loading in and analyzing can be dispersed to a client even more quickly than that. And the hope is that it not just a quarterly or biannual type process, but it's something that can occur monthly if not for some of the more advanced clients who really really embrace, you know, the data culture weekly or daily depending on how robust of a focus they place on it. But you know, that's I think the really critical piece is that the data is the affirming validation tool. But it's also revealing for a lot of parties, what is it that I need to do differently and especially where anything and everything is kind of fair game in terms of what needs to be addressed within an organization. And there's this mad rush to try to do everything and anything simultaneously right now, I think it also helps to kind of create more clarity for a lot of organizations. 

John: So Traci we, you know, we talked earlier, you focusing on, on thought and other things. But are you also in data just looking at it hard facts, whether it's head count tenure attrition and those kind of things recently, I was speaking with another company and they said some of the data that were veal through some of the analytics is they're asking people that are, you know, identified as diverse on what percentage of the the matters you work on, are you only the only person of color or the only woman on the team and extracting out that data then correlating to the amount of tenure, how long those folks stay with, with the company? And it was really interesting because the the data showed that the folks that were put on these teams that were really only the only person that looked like them on the team were really being tokenized. And as a result of that, those people were actually staying with the organization in a short amount of time. So that kind of data analytics was really fascinating to me and say, OK, if this is what we can learn, let's start looking at who's on these teams that's really on an island and they're, they're isolated. So that's not really getting into thought. It's more thinking just around some of the factual data that you'd gather. I know your thoughts on that in terms of this is it that some of the projects that you work on as well along those lines. 

Traci: Yes, I mean, we'll look at the factual data, but we'll also look at kind of the gaps between the factual data because of the acknowledgment that what we see and the, and the actions that we take may not necessarily result in the intended outcomes that we wanted to achieve, if that makes sense? So it's making sure that somebody's feelings and perceptions are also incorporated into that because that way, you know that it's a definitive action that's resulting in what you wanted to achieve. So for example, um we maybe because there's only, let's just say one female that's part of a particular group, um Someone may think that's enough diversity or that and that no other females are necessary. I'm just using a hypothetical example. But what you may find is that as you're pulling everybody or as you're taking in their input and their information, individuals may feel that, you know, that end of adding another female or two more females might be helpful because that's when somebody will actually share how they truly feel. Whereas if there is one female out of a group of, let's say five or 10 people, the individual may be honest and say, I don't feel comfortable necessary, always voicing my true opinions about things because I don't want to be, you know, ostracized or I don't want to be left out or think that I'm not being a team player, whatever it may be, right? So the feelings and perceptions are helpful in terms of understanding. Do you add more female individuals or not? Rather than only just looking at as, hey, we're lacking females. Therefore, that's why we're going to try to add more. And I don't know if I explain that perfectly, but the difference between the two is really understanding how people are feeling what they're thinking and that perception and inside thought, so to speak, that's being articulated and triangulating that data aside from the fact that you're describing John, and then seeing what the relationship is between those two, aside from just looking at the hard data, which is, you can see how many females or males or nationalities, those things we can easily identify. 

John: That's amazing to me because that just takes it to another level. We come up with these hypotheses based on these factual data, but you're actually getting into people's heads, right? And so that's really instructive. And so what we might hypothesize based on some external factual data may not even be true, true. 

Traci: And I think the other piece of that is when we think about diversity initiative, what is it that we're ultimately trying to achieve? And I think ultimately we want everyone to have an equal opportunity to advance. But what are those tools that are necessary for different type groups and people and individuals? What is it that they need to feel comfortable that they have the support and necessary resources to do that? And isn't that the ultimate objective of why we talk about diversity rather than only adding and making sure that individuals associate with and learn from individuals who are different from them? Right? So I think it's being really clear for a lot of organizations, what they're trying to achieve as a number of different outcomes and diversity conversations, that's really important and then working backwards to ensure that the data actually demonstrates how far or how close an organization or group is from those outcomes. And that typically isn't necessarily always done. I think, especially right now, today, we see a lot of organizations, they're at least aware that they want to focus on diversity because individuals are open to talking about it. But that instruction in terms of hey what are the outcomes that I should be thinking about before I invest resources? What are those things that I want to ultimately accomplish both in the short term and long term and then work backwards from there. 

John: That's so interesting. You know, we at Reed Smith, we uh last year hired Russell Reynolds Associates to do an inclusivity survey for all of our employees got back about 1600 responses. And the idea being, understanding what the culture of the organization trying to get a pulse of the culture of the organization, you know, anticipating, refreshing that data, whether it's annually by annually and the like. But I was with another Chief Diversity Officer who said, OK, now the key is with all this data can kind of come up with KPIs. What are you really gonna focus on over the next year or so to see we want if between now and wherever we do the measurement again, what are the key areas that we think are really going to make a difference, whether it's people feel that they can bring their authentic self or, you know, people feel that they're supported and just again, going to those feelings. And I thought it was a really brilliant concept to say there's so much data here that we gather from these surveys, but boiling it down to some key KPIs for management, really to focus on going forward. That to me was was kind of connecting the data with strategy. 

Traci: Exactly. And I think it's under like you said to have, understand what the what are the key outcomes you want to achieve first and then incorporating the data framework to measure those outcomes. So you know how far, how close you are and what decisions need to be made or changed or thought processes or any of that, that sort need to be changed in order to bring you closer to those outcomes is really important because oftentimes many organizations get what I call data overload where you're collecting data layering on more data, collecting more data. And then all of a sudden you get lost in what you were trying to measure in the first place. So and it's easy to do when you get into like the devil in the details, right? I think all of us can acknowledge it's really easy to do. But how do you avoid those pitfalls? 

John: We have so many scorecards and things like that. And it's really been our challenge to try to boil it all down to, you know, dashboards and things like that that are really easily digested as opposed to just mounds and mounds of data. 

Traci: Exactly. So you guys, you guys are applying it and understand and that's really where we really enjoyed like working with different clients to tackle that, so to speak. 

Iveliz: Traci. What are some of the benefits of using data analytics to support DEI programs? And on the flip side of that what are some of the pitfalls? 

Traci: I think a lot of the benefits is providing greater objective clarity to decision making. That's one thing that a lot of our clients have found useful. But I also find useful in looking at data is that objectivity knowing, having insights into something that maybe I thought was XYC as a result. And instead it's ABC, right? It's affirming and also changing one's thought processes and decision making and just having insight, more insight and clarity. And that's been, I think really helpful, especially when leaders of organizations feel like things are coming at them in, you know, 20 different directions. What do I start first with and what do I prioritize? So I think clarity is really important and I think the other piece is being able to create process and systems around an organization is also helpful people to a certain extent, like to automate how they do things. Whether or not subconscious we actually kind of like doing it. Like when we drive, sometimes we don't like to think about how we drive, you know, what order do we turn the car on then, you know, you know, release the brake, we do those things subconsciously. And there's a part of us that takes comfort in that. And so I think it helps to give people more security and confidence. I think the pitfalls of using data is that if it's not applied in the right way going, you know, kind of adding to our conversation earlier about thinking about the outcome in mind and then working backwards to understand how you're going to measure it and consider the different ways in which you can measure that information. A lot of organizations get end up spending more money and end up spending more resources in time than is really necessary for managing data and really incorporating it as part of the organization and the culture of the organization. So I think those are kind of, you know, in just the benefits but also the pitfalls and it's been mindful of both. So I appreciate the question. 

John: So Traci, I know we're running out of time but the close kind of generally, what do you see the future as to diversity equity inclusion programs as our technology, data analytics, digital transformation continues to accelerate? 

Traci: My hope is that we consider diversity more broadly and more deeply in terms of the conversation we're having here about thought,perception, feelings, experiences, things that we can't necessarily or haven't necessarily measured beyond demographics or beyond the facts and being able to better understand individual stories and individuals needs and rather than grouping and individuals of the same type or the same group together. I think all of us like to be acknowledged for the uniqueness of what each person brings. And so how can we better understand that? But I really think, you know, moving forward, we've opened up the door for those type of discussions and, and created an environment to hopefully have safe and open discussions that we weren't as open to consider before. I mean, the idea that we're talking about diversity in general, I think is a, is a great thing and I can only hope and I think the direction that we're moving in as a society is to continue that conversation, but also acknowledge where we're lacking. And some of that is the critical thinking and the relationships more broadly that we need to have with different people who challenge our thought, who may not think the same as ourselves. So it's seeking out those differences that I think will only create a better social, economic culture and community, but more thriving organizations too. So I think that's a really great thing and I'm glad that you guys embrace that and hope everyone else does too. 

John: That's why I always say it's all about the inclusivity and all about inclusion because then you're really getting into the individuals as opposed to their groups. So it's fantastic, Traci, thank you for coming. And this has just been a really, really fascinating discussion, a little glimpse into the future, but also insightful in terms of what we're doing right now. 

Iveliz: Absolutely. Thank you so much, Traci. 

Traci: Thank you for having me. 

Outro: Inclusivity Included is a Reed Smith production. Our producer is Ali McCardell. This podcast is available on Apple Podcasts, Spotify, Google Play, Stitcher, PodBean, and reedsmith.com. 

Disclaimer: This podcast is provided for educational purposes. It does not constitute legal advice and is not intended to establish an attorney-client relationship, nor is it intended to suggest or establish standards of care applicable to particular lawyers in any given situation. Prior results do not guarantee a similar outcome. 

All rights reserved. 

Transcript is auto-generated.