BIO: Leena is a Diversity, Equity, Inclusion, and Belonging leader with ten years of experience focused primarily on using data and measurement to drive insights, accountability, and action. She's worked in education, research, non-profit, and finance sectors, most recently as Head of DEIB at Array. In all of her work, Leena strives to live authentically, think creatively, communicate powerfully, and connect meaningfully.

Transcript:

Leena: I'll go back to something I said in the beginning. Right? Data alone is not enough. Always question data you have. And theory alone, or sort of that narrative alone, is not enough. Question where that comes from, what it's rooted in.

You know, what are the assumptions that are baked into that? What still don't you know?

Andrea: Welcome to DEI Career Conversations. I'm your host and DEI Career Coach, Andrea G. Tatum. In this episode, I sat down and chatted with a dear friend of the DEI Career Center. Leena Kulkarni. Leena is the head of Diversity, Equity, Inclusion, and Belonging at Array, and she has more than ten years of experience using data and measurement to drive insights, accountability, and action. So without delay, let's get into it.

Welcome. I'm so glad to have Leena Kulkarni on DEI Career Conversations with us today. Leena, welcome to the show. Full disclosure. [overlapping: Leena Thanks for having me.] Yes, of course. I mean, we're very good friends and so hopefully everyone will get a chance to just enjoy this, like, behind-the-scenes conversation.

So I know your story really well, but I’d love for you to share a little bit about your journey into diversity, equity, and inclusion.

Leena: Yeah. No. So, first of all, so excited to be here, to be joining a friend and also such a, you know, well-known expert and thought leader in the space.

But my journey into this space was very… serpentine, is the word that I use. And I find that to be true for a lot of practitioners in my position. You know, when I was in undergrad or starting to think about what I wanted to do for my career, I knew I wanted to do something that generally speaking, provided some sort of positive impact. And I knew I wanted to work with people and that was about it, as much guidance as I had. And that's been you know, I've slowly been shaping and refining that, I would say, across my entire career. So I started as an educator, started as a teacher. And I think back, I think back on that now. And I'm so grateful that I started my work in DEIB, actually, from a place of educational equity, from a place of thinking about social justice and transformative justice and, you know, organizing movements.

I'm really happy that my training started there because I actually find a lot of those concepts that we talk about around DEIB in the corporate space too, have really come from, you know, organizing and educational equity more broadly.

So I started as a teacher. This was my first glimpse into sort of theories of change, thinking about the role that education, carefully constructed curriculum, and sort of outcomes-based thinking to drive certain initiatives. That's sort of where I got a glimpse of that.

I moved from education into knowing I wanted to focus more on systemic equity and inequities and sort of not only measuring that from a data perspective but also just understanding the behavioral science of institutions. So I was at, I did my master's at the Harvard School of Public Health and was also studying at the Business School how to construct and measure things like, you know, organizational effectiveness, discrimination, gender-based differences in how we put ourselves forward. And I would say that was the time in my career where I really developed very tangible sort of mathematical skills that I felt I needed in order to be effective in this space.

You know, people ask all the time, what, what actual skills do I really need to move this forward? And I will always be really grateful for that training and that grounding in data. From there, I moved to Catalyst, which is where you and I met, where I was leading some of the consultative works of taking what I knew from the research in theory and applying that to the real world, to situations that organizations found themselves in, using data to drive that. But I had this hunger for wanting to know the lasting impact of a lot of this work, which led me to Citi, where I was on the team leading the global DEI strategy and now at Array where I am the head of diversity, equity, inclusion, and belonging. And taking a lot of that previous learning data, skill set knowledge and applying this to an organization three years old that's really asking itself, how do we build this in a sustainable way? How do we build our entire sort of mission forward? Thinking about DEIB in mind, what does that look like and what could that mean for us in terms of who we want to become?

Andrea: Yes, I love it. And, you know, I mean, I am a huge Leena fan because of your work in data, around data, around DEI. And that's how I even kind of ended up at Catalyst because I was partnering with Catalyst, I was working with Leena and another partner, and I was just so impressed with Leena's approach and thoughtfulness to this work with that data-driven lens to all of it. And so when I think about that, right, you just talked about kind of having to go from theory to practice. And it's actually exactly what I've titled a portion of one of my newest programs, is Theory to Practice because I think we oftentimes get really into these kind of foundational ideas of the DEI. We read the articles, we read the research. But tell me a little bit about what has it looked like for you as someone who's been on this journey in your career and been behind the scenes on the research side and been into the data to move into a role at Citi and now in the role that you're in at Array. How has that progression changed for you from thinking about how you go from theory to practice?

Leena: Yeah, that's a really great question, one that I actually get all the time and it's a really challenging question because I think what's underneath it is how do we drive real progress and what does that, what does that really look like and mean? So a couple of things to that. I think, you have to have the theoretical component with the data component. I don't think either of those two things exist on their own. So you can have a bunch of data, but without the context of why it's meaningful, or maybe why the data itself isn't telling the full story, then it's not as powerful.

On the other hand, you can have all of this theory without anything to back it up, and then your theory feels really hollow. So I think the first thing I would say is. You know, definitely finding the right balance between having a theoretical framework or sort of something that's tying the broader mission of what it is that you're trying to achieve and making sure that you have the appropriate metrics or measures that are going to help support that. And I say appropriate because having a ton of data doesn't necessarily mean that that data is the right data. I also think that there's tons of room in this space to think and rethink about the types of metrics that we actually use. So, you know, historically people often think of DEIB programs and initially think, “Oh, it's all about representation,” right? It's all about the proportions of different groups that you have within your organization. And then there are different ways of breaking down those groups, depending on how you classify that information. But even then people might say, A, the types of classifications that we have don't accurately capture the employees that you have. Right? So even if you're relying on that data, if it's not a clear reflection or mirror of the people within your organization, how good is it really? Another argument there is people say, okay, we can look at representation, but is that a lagging indicator, right? Is that telling us about our progress going forward or is it telling us about some reflection of some deficiency or something we haven't done in the past? People also say, okay, I have these numbers, but now I don't have any context. Am I doing good and am I doing bad? Am I doing better than the company next door? So that's what I mean when I say the data alone isn't as powerful as the story you have to be able to tell with it. And you can be thoughtful in the construction of the data, thoughtful about the story you're telling, and thoughtful about the gaps that the data cannot answer for you. That's another misconception where people assume that data can drive every single decision, and that's simply not the case.

The last thing I'll say is data is not always sort of quantitative in nature. You can have qualitative data, too, that actually gets to the texture, the humanity underneath some of the flat numbers that you might see. And so I always tell people, do not forget the power of this qualitative data. And for a lot of organizations that might not have the infrastructure to collect some of the demographic pieces that they're looking for, a lot of times they can pivot to collect qualitative data, and that can be more meaningful because it is often an accurate, sometimes anonymized understanding of what the lived culture is today as opposed to sort of a reflection of the past. So yeah, there's a lot to be said there. You know, metrics can change. I think the extent to which you can be able to sort of look longitudinally and say, you know, what has changed over time because time is the other thing. When you think about theory and driving change, this work does not happen overnight. It takes months, if not years, to get right. And that's a really critical component. I would I would definitely say for those organizations leveraging data and theory, think about time in a thoughtful way and think about how your data can sort of how it's going to change over time and how you want to collect it over time and keep it consistent. And that's a really, really big, big component. You just said something that's really interesting. And I want to just go back to it.

Andrea: You said, you know, it takes time to get it right. And I’d love to hear, because I think so many people get really caught up in the idea of I've got to get it right. I see what X, Y, and Z company is doing. I see what this person on social media is doing. How do you, now that you're in this role as a head of, how do you decide what does right look like for you at any given point in your journey?

Leena: Yeah, that's a really good question. I think this is one I used to you know, earlier on in my career, I used to get really caught up on making sure every piece of my strategy, of my thinking, was perfect before rolling anything out. Especially when it comes to something like data and DEI, right? Like not everyone feels super confident or comfortable putting that data out there. Not everyone knows how it's being used. So I totally understand not only the very real sort of legal limitations that sometimes it can provide, but also the gaps in understanding around how this data is actually being used. And, you know, there are so many examples of data being misused. So I completely can validate that fear and that suspicion. I think in the case of DEI metrics, there is no perfect way of doing this. And I would say, at least from what I've seen, there are no companies that have it perfectly right. And, you know, even look at like how we collect data as a country. Right. Even the US Census Bureau changes these categories fairly often. And it wasn't too long ago that they acknowledge that someone could be two or more races. Right. So, you know, I don't know that we have the best models even looking at sort of other systems and institutions that collect data, not like this isn't just like a DEIB thing.

This is, you know, collecting data and doing it in the right way is a question that so many are sort of trying to tackle and get right. What I sort of stepped into, especially in a place like Array, where it's very much a culture of we don't have all the answers, but what we do have is a willingness and a motivation to try to move towards progress, knowing that we ultimately will likely need to change course. Let's just try. Let's get it out there. And when it comes to something like data, I think that can be a really helpful paradigm to have.

So, for example, a lot of companies leverage their EEOC data. Now, I could tell you all the things that are sort of imperfect with those classifications. I could tell you that they're limited. I could tell you people don't just fit into one box. Right? I could tell you all the things that could be limited with it. But I can also tell you that having even that data alone can really help you lift up and understand if there are inequities in sort of your systems and your processes. If you look at things like hiring, if you look at things like opportunity, if you look at people who are exiting your organization at higher rates than others, even that data alone can start to build in nuance around your analysis of internal systems and processes. And I would say, you know, my personal opinion is I would say that's helpful. Could it be better? Totally. Right. But is it better than having nothing at all? Personally, my opinion is yes. So, you know, don't let, what is it? Don't let perfect be the be that you know the saying, I clearly don’t. [inaudible] the good.

Andrea: Yes. Yeah. Like don't get stuck, you know, don't get, don't get stuck. Just keep moving forward. So like for those people who are feeling stuck because I think this audience is, one, a lot of people who are thinking about making the pivot into a career in diversity, equity, and inclusion, or who already may be in a role and maybe feeling a sense of, What do I do if EEOC data is all I have? What advice would you give them?

Leena: Yeah, the advice that I would give is, you know. If that's your starting point, that's your starting point, right? On the one hand, it can actually be a really powerful starting point because EEO data is one of, if not the most common form of sort of HR-related people-type data that we have.

So if you truly are asking yourself this question or rather need to ask this question of, you know, how are we doing? What does our representation look like? How might we compare that to our industry or our customer base or our peer organizations or, you know, the population at large? Well, EEOC data is maybe helpful to do that because you're going to find that there is probably the most overlap between sort of these larger databases. So on the one hand, it might actually be really helpful for you because you can build a story around tying your company data with these other databases that you find I think another way to look at it is to say, okay, we have EEOC data now. Can we ask our company whether or not they find that this allows them to accurately self-identify? You know, in a previous role, I came up against this question around, you know, having EEOC data, but knowing that it actually was not, the data didn't have all the categories that accurately reflected the community among our workforce. And so what we did was we kept the EEO data, because you are often, it's needed for a lot of sort of government related purposes.

And then we created a different categorization system with more options for folks that we felt was just more honest, more accurate, and allowed people to self-identify in a way that truly felt authentic to them. And so by having two different systems, you know, from a data collection standpoint, it was a little tricky because people had to fill this out twice. But we did a lot of communication to help people understand the rationale for why we had both. And this just, you know, I think we felt a lot more confident having that second subset of data and knowing that it was going to be a more sort of authentic depiction of our workforce.

The last piece of advice are the two other pieces of advice that I would give is if you're in a place where you're sort of constructing something new and you want to expand on EEO, it's not quite working for you. There are other countries out there that have thought a lot about this. So one example I would give is the UK. Actually, their race-ethnicity classification system is totally different than the United States. For example, if you're Black, you're not just Black, are you Black-Caribbean, Black-British, Black of African descent? Right. There's, there's so many ways to not look at a group as monolithic. And I think the U.K. classification, while not perfect, gets into some of that nuance in the way that our U.S. classification simply doesn’t.

So that's one thing, is there are other countries out there that have tried to answer this question, and so using models outside the United States can be helpful. The other thing from a sort of internal perspective, if folks are like, okay, I have this data, what do I do with it? Think about all the ways or all the systems you have within your organization, and are all those systems tied to the same data? Do you have a single source of truth? Right. If you have people filling out your survey data, is that tying back to the same source data? If you have promotion data, is it tied to the same source data? Because you essentially want to make these decisions leveraging a consistent database. You don't want a scenario where you're looking at employee data, but it's actually sourcing from totally different places. So I would really encourage people to think about the consistency in this approach and figure out right where those inconsistencies live. Because I guarantee I haven't been in a single organization or consulted with a single organization that hasn't had some kind of data inconsistency with its people. And that can cause a lot of challenges and a lot of inaccurate analysis of the data as well.

Andrea: Yeah, just, you know, like when people get really excited about going into roles, sometimes I'm like, sometimes you're just trying to like get your foundation set up. You've got to figure out like, where is all of my data housed? And hopefully, you know, your company hasn't just recently switched systems. I've seen where organizations are like, Oh no, they've lost some data because we're updating or we're upgrading and making sure that you take into consideration, one, that understanding those systems and the way they work together in and of itself is a skill set. So I really encourage people to like research, learn, understand where different data lives inside of companies. That way you can speak to it. That way you're making sure you're asking the right questions. You don't have to have all the answers upfront, but the ability to kind of know the questions to ask is really important.

And I want to circle back to something you mentioned, which I find incredibly challenging, which is you talked about the fact that data collection is different in the U.S. versus the UK.  Can you talk about how different it is just globally and the challenge that that presents and some of the ways that you might recommend dealing with data when you are in a global situation, how do you start making comparisons? Well, you know, when it's not legal to collect certain things here. And, you know, so let's talk about that a little bit. 

Leena: Oh, yeah. That is a, that it's a tough nut to crack, for sure. And one that I know so many global companies are trying to understand. Right. How do we build a global strategy, recognizing that there are all of these nuances, both legal and otherwise, that exist at sort of the local, the country, the city level. Right. Like this can get really complicated. My first the first thing I'll say is definitely go, like, follow legal parameters. Right. Don't don't try to go fishing or collecting data in places where it is not legal to do that. Not only do I think it runs a risk for your company, but also, you know, the extent to which people feel comfortable disclosing that data also might come, you might face challenges with that. So I think the first step is really understanding where are you like what countries, what, like, what is your global reach? And looking very carefully to say within those countries or maybe those regions where can we actually get this information. So starting from that is sort of the number one identifying what countries, what offices, what regions that data is available, working with those local offices to understand, okay, what is the way, what is the best, most culturally appropriate way for us to capture this information? Help me understand the historical context, the cultural context that exists around these classifications. Right? I think sometimes there is this tendency to assume that race in the way, I’ll use race and ethnicity as an example, but race and ethnicity, for example, as constructed in the United States, that these paradigms exist everywhere. And while there are many places where there's sort of a similar dynamic, there are other places where it's completely different and making sure that you understand that and approach it in a way, in accordance with sort of the cultural norms that is absolutely, absolutely critical. So, yeah, I mean, that it's not a perfect answer by any means, but I think it's really, identify where it's legal, work with those teams and use local-specific, local-specific collection criteria to make sure that the questions are being asked again in accordance with what makes sense for that locale. So for example, race and ethnicity, the categories alone that you would ask someone in the UK are completely different than you would ask in the US are completely different than what you might ask in South Africa are completely different than Brazil. So, you know, just getting very clear and very specific on what that looks like and making sure that if you do have goals that are tied to those locales, but the goals themselves are accurate. Do your goals reflect the type of data that you can actually collect and do the goals reflect what it is you're trying to advance in every place? Because driving towards equity is going to mean something different maybe in the UK, maybe in Brazil, maybe in South Africa, than it looks like in the United States. So getting clear on that piece, too, so that you truly have a global strategy, I think is key. And again, involving the people in those regions and not centralizing all of the DEI sort of thought leadership in the United States, really, really critical. 

Andrea: Yeah, and I think that's exactly one of the reasons why we often have seen historically that DEI initiatives and programs and strategies often start with gender because gender theoretically has been a little bit more consistent across the board. You generally at least had, you know, male, female. And yeah, that was fairly consistent, you know, and now we are expanding on that, which I love to see with organizations getting even more intentional about their approaches to gender. But that's exactly one of the reasons why, because they're like, okay, at least here's something we have. And we know in a lot of places they're collecting data on gender. We know that race, ethnicity and all of the other things get more complicated as you go global. So it is the reason why, like we end up in so many conversations, like I love just dissecting this and loving the way that you really are thoughtful about your data because I do think it is easy to get into a role, have data at your fingertips, and have a narrative in your mind that you want the data to tell.

Leena: Totally.

Andrea: How do you avoid that?

Leena: Oh, gosh, it's hard, right? It's really, really hard because on the one hand… Well, I think, you know, let me back up. I think it comes back to letting, you know, letting the data tell the story. That said, I think you can ask certain questions of the data to make sure that that story is accurate. For example, in the global example we were talking about earlier, one of the things you can do is in addition to asking the demographic questions and seeing how many people identify as X or Y.

The other thing that you can ask is how many people answered this question, right? So. Is the representation low because the representation is truly low or is the representation low because 13 people out of a thousand actually answered the questionnaire? Right. Is this about you not… Is this a logistical and operational problem that, you know, enough people haven't opted into giving this information? Is it a security or legal issue? Is it that people don't feel comfortable providing that data? Right. Like there could be so many reasons why you're seeing those gaps. But you might look at that representation and say, wow, we need to, you know, increase our hiring or increase our retention when actually you just need to actually change the part in the process where people are answering that information or filling it out or making sure it's accurate, right? So I think maybe I'll maybe I'll retract my former answer and say, I think it can be really challenging. Right? Because do you want the narrative and then do you want to back into the data or do you want the data to tell a narrative? But you need to be able at every single turn, whichever way that happens, to ask really thoughtful, critical questions. And I'll go back to something I said in the beginning. Right. Data alone is not enough. Always question data you have. And theory alone, or sort of that narrative alone is not enough. Question where that comes from, what it's rooted in. You know, what are the assumptions that are baked into that? What still don't you know? And I think I come back to that all the time, because it can be hard. It can be hard, especially when maybe you're the only DEI person or you don't have someone else to sort of check you or push back or challenge an assumption in any given way. I think having trusted people within your organization that you feel honest can provide, shed some light on things that might be unclear, if there are gaps, can be super valuable, whether or not they're in DEI, by the way.

In my past, I've leveraged folks in operations focus in, you know, biz ops, people on the people team generally. You know, anybody really who is exceptional at understanding or unpacking data. A lot of times I'll leverage people who don't know DEI data because they can ask really thoughtful questions that maybe even I haven't thought of. So I would say leverage, leverage your, you know, the broad thought partners you have at your organization, they don't have to sit in a DEI or people function in order to provide value or even critical insight into the data that you have.

Andrea: I love that. That's such an excellent point. Just really having other people helping you to validate again when especially when you're you're in a role and you're all alone and you're just getting started and you're just figuring it out, like finding people who you trust is really important. And I think, you know, it's funny. So I said trust because I also think that in terms of data collection. It takes a significant amount of trust-building for people to say, “yes, I feel okay filling this out.” You know, it was one of the things I think surprised me when I really went into the role of DEI. I think I was just like, if I saw something that asked me who I was, I was so accustomed to checking boxes that I didn't necessarily question it. But then I started seeing other people as I started to ask questions about disabilities and about LGBTQ+ status. Yeah. Really questioning, Well, why are you doing this? Where is this going? Who's going to see it? And I was like, I love and I appreciate that. And I didn't expect the amount of thoughtful and rightful pushback from people saying, like, if you're going to ask me for this data, I need more transparency about how you're using it, why you're using it, and who's going to see it, etc..

So when you think about data transparency, what are some tips that you would give to people for finding kind of the right approach to saying like, this is what you keep, this is what you share? How do you think about data transparency?

Leena: Yeah, I think the first thing is coming into an organization and asking the question. Have harms been caused from data not being sort of handled appropriately in the past? So are you walking into a place where there's an existing suspicion that surrounds voluntary data or data of any kind? Data inaccuracies, right? You could even be in an, in a type of organization where data privacy is what you do right, or right like, there… I think understanding the culture that surrounds it can be beneficial. I think it's also just providing education. To your point on how the data is used. Right. Is the data being used to understand workforce demographics in an attempt to drive towards equity? Well, that can be great. And some people might say, oh, absolutely, I'll put my data forward.

Other people who might be sort of risk-averse might say, well, if you're using it towards equity. And I, you know, I identify as part of the majority group, does that actively mean that this data is now going to be used against me? Does that mean that I'm not going to get opportunity? Right. So it's being really clear about having an honest understanding of your workforce. If you are driving towards greater equity, it's making sure that you use this data to lift up, to see whether there are sort of outcomes tied to… Right. Are you looking at promotion and do you see inequities in who gets opportunity and who doesn't? So explaining sort of that process can be really, really helpful. 

The other thing I would say is most of the time, at least I've seen, data is very heavily protected on sort of who can see it at any given organization. Usually there are very few people who have access to all data, even in companies that might have really sophisticated dashboards and things. There's usually an entire universe, an ecosystem of provisioning. But what I'll say is, a lot of times when data is shared, there are two things that happen. One is it's either shared completely at the aggregate level, so there's no ability for it to be personally identifiable. And then sort of a sub-point from that is there's usually a max number of people, a minimum number of people for certain data to be revealed, meaning, you know, maybe your threshold is like this category has to at least have 15 people, 20 people, three people, whatever it may be, before we can show you, because if it were any less than that, you'd be able to identify the people and we want to protect that. So helping people understand what protective mechanisms exist as well.

I would say—.

Andrea: Oh, Leena, Leena, Leena.

Leena: It's so real. It's so real and it's so complicated. And I think there's just a lot of people just want to understand how it's being used and giving people the option. Look, we are trying to use this to drive towards equitable outcomes for all people. The way we do that is to have an honest understanding, an accurate understanding of who's here. We want that to be voluntary, and we want that selection to be representative of who you are. Right. Self-ID. But you always have the answer, the option rather, to prefer not to specify should you choose to. And that is completely your right. 

Andrea: Absolutely. I love it. So I'm going to pivot off because I mean, I know the two of us will talk about data all day, every day with no problem.

But I really want to kind of think about, for you as you've moved into this head-of role and kind of shifted your career, what is one piece of advice? Because tell me, how long have you been in role at Array now?

Leena: It's been six months at Array.

Andrea: Yes. Awesome. So even in your shift from Citi to Array and looking back, what is one piece of advice that you would give yourself? Either young, young Leena in preparation for this, or like career transition Leena, what advice would you have given yourself knowing the work that you're doing now?

Leena: Yeah, I don't know that this is specific to my transition into this role or just sort of general principles that I follow, because I have made a lot of sort of pivots in my career. I would say, understand what doesn't… It is almost as important to know what does not serve you or give you purpose as it is to know what does. And I figured out pretty early on what didn't sort of ignite my fire, my purpose.And by the way, they were things that had I not tried them, I would have told you 100% I’ll love that. And then I did it and I was like, no, that just wasn't for me. 

So functionally, what is the thing? What are what are you doing on a day-to-day basis that you love or something that you really don't enjoy, it doesn't give you purpose. So that is one thing, knowing what doesn't do that and being honest about that. 

The second thing is I always try to chase the question. So, you know, my pivot from education into being at Harvard, the question was how do you actually focus on systems instead of sort of just one-to-one interpersonal relationships? What does it look like to change equity or drive impact at a systems level? And what are the skills you need from a tactical standpoint to do that, and from a data standpoint to do that? That was the question. Moving from that to Catalyst, the question was, I have all this theory, what does it look like in practice? So that was what moved me into a consultative place. Moving from that to Citi was, okay I consult for a short time, but what does it look like to actually do this work with longevity and to see how hard it is to actually work within an organization to drive change? And what drove me to Array, the question was what does it look like to do this work at the beginning stages of an organization? Being proactive and forward-looking and forward-thinking, instead of looking back and saying, Oh, we should have done X, Y, Z differently. What does it look to actually build and move forward through the lens of DEIB with these principles in mind? So follow, follow that question and see where it takes you. I think those are the two sort of big pieces of advice I would give or that I sort of, that inform how I move through this space.

Andrea: I mean, that's so spot on. And the way I try to think about it in terms of when I work with my coaching clients, I have so many people who are like, I'm really passionate about diversity, equity, and inclusion.

And the first thing I often ask is like, well, what does that mean? I love the idea of passion and I get it. I, if you'd ask me the same thing, I say it all the time, it is a starting point. But you have to ask yourself a lot of deep questions about, like you said, what serves you, what brings you joy, what don't you like to do? And that's why I love working as a career coach, because I really want to help people set them up for success and not just get into a job, but get into a role that they're actually going to enjoy the work that they do. It's hard. It's work. We're honest about that. But that you're not ending up in a job where you're like, oh my gosh, I hate data. I hate data so much. And I spend 98% of my day doing data. I don't think that role is for you. Or if you're like, I really, really want, like I just heard you say what excited you about Array was the ability to be a builder. And I ask that question a lot, like, who are you in a company? Do you like being a builder? Do you like starting from next to nothing and being okay with the kind of trial and error of getting it right? Or do you want to go somewhere where you're established and you're having mentorship and you're learning on the job or part of a big team? Like if you don't know the answers to those questions, it is a surefire way to set yourself up for just stress and being like, I would like to get out of this. And I think it's one of the reasons why the tenure of people in DEI is so short, because there's just that lack of information about like, what am I actually getting myself into? And it's not such a broad strokes job. Like what are the specific skills and things that I'm doing on the job day in and day out? So, so I love that advice that you gave yourself and using that as a way, as a path forward and in defining how you navigate your career.

Leena: Yeah. No, I think what you're saying is absolutely right. The build I mean, the reality is a lot of people in this role have to be builders have to move from a place of, you know, zero and go forward, have to build from the ground up or you're inheriting something and you need to be okay with what you've been dealt and knowing that sort of changing a system or structure that's been in place for ten, 15 years, that comes with its own set of challenges. So I think you're absolutely right. It's knowing what type of environment you're walking into and the skills that you'll need and the type of discomfort you'll have to endure in any role. But I think in DEI particularly, it's what are the challenges you're okay with and what are the challenges that you're not okay with and being just really honest, honest about that. You know, I think in a startup environment I was like, I know that really rigid frameworks and systems and like ways of doing things may not be established. And I love that. Like, I love that it’s easier. I love that freedom, I love that flexibility. I love to try and iterate and, you know, obviously try not to cause harm, but I love to try things and learn from them and then figure it out from there. That really works for me and I think Array is a place and an environment where I've really been able to to do that, but that's not for everyone. So it's just figuring out and just being honest, right? Just being really honest and introspective with sort of who you are, the environment in which you thrive and the type of challenges that you're willing to endure that might even motivate you. And the ones that are really going to sort of dim your light, I think that that can be a helpful framework.

Andrea: I heard you call out some specific things and you've said them at different times, but I just really want to highlight this for the listeners that I think is really important, that I think about this even from the approach of, if you are going into a DEI interview, if you're thinking about going into a role, come prepared with questions. And you've listed several things about like understanding the culture around data in a company, you know, ask that upfront. Those are really solid questions to understand before you accept a job. Understanding what they already do and don't have. Ask them questions about like, what's their risk tolerance in the organization? Are they a company that's like, oh no, we've done it one way. We just want you to come in and keep doing it that way. That tells you about who they are. And asking those questions to try to understand if the role is right for you is really important. And so you've dropped so many great gems. I love love, love this conversation. So I want to end by asking you one last thing. What is one book, podcast, other resources, classes that you would recommend to someone who is saying, hey, Leena I'm really thinking about diving into DEI as a career. What would you recommend for them to check out?

Leena: Yeah, that is a great question. I love that question because I am someone who is constantly learning and absorbing information and theories and data and insight. My personal opinion is, to do this job and to do it well, you have to be a constant and hungry consumer of new data that's coming out all the time, because this is a space that's moving and evolving really, really fast. There are incredible books out there that detail how to do this work. Like from end to end. They have strategies, they have questions, they frameworks, but like, real books can be super beneficial.

Lily Zheng.Daisy Auger-Dominguez . Cynthia Owyoung. Michelle Mijung Kim. Adrienne Maree Brown. Miriame Kaba. Liz Kleinrock. Like, there's so many people that like super prolific, brilliant minds that have thought a lot about this. They have put it in a book. So I would say. Build up your repertoire, your library. I have books that have like little tabs and I annotate and I rip them up like, that I think is fantastic. So not everything is going to be an HBR article. Not everything is going to be like a research study. I think books, a lot of these books written by exceptional practitioners are a great place to start. The other thing is, when thinking industry-specific, I would try to see who among your industry is actually putting out their own research because there tends to be, especially now as people are trying to answer and are hungry for these broader questions around DEIB in their specific industry. There are a lot of reports out there, either from DEI sort of consultancy type organizations or research organizations or even industry-specific organizations. That data can be often a lot more recent than stuff you might find more in like academic circles, more than books, just because, you know, they can sort of churn it out on an annual basis, for example. So really look to see what reports exist out there.

For example, in the fintech space, the company Plaid puts out a fintech fact report and it has great DEIB related insights on sort of driving progress for historically underrepresented or systematically left behind communities. And, you know, that's going to speak to our stakeholders. So finding anything, any thought leadership within your own industry, often in the form of online downloadable reports, is something I would encourage everyone to look for. And then the third one is podcasts. There are a bunch out there. It's just a really nice way to consume great tidbits in a really digestible fashion. I would say the thing I honestly read the least are articles not because I don't think there's great articles out there. But I. I think you should you know, people should be really careful about how things sometimes can be really distilled and oversimplified in articles, especially if it's like a really clickbait-y type title. So I would just encourage a little more rigor on the article front.

But books, reports, podcasts are really where I drive most of my learning in this space as I grow and move forward.

Andrea: I love it. Leena Thank you. You are such a huge friend of the DEI Career Center. I mean, I appreciate you on so many levels.

Also, I mean, shout out to Leena for also being a part of the DEI Career Center's Pivot Your Passion program, where she really goes into detail about if you want to be in this role, like, what do you do with all of this data? Like how can you set yourself up for success? So she's one of the instructors. I'll make sure that we have more information about this new program. All the details will be below. But thank you for sharing your time, your energy, and your knowledge with us today.

Leena: I know I loved it. Sending you so much love. I thank you for the resources you put out, both for our broader DEIB community and for people looking to pivot in this space. I know you've been a tremendous resource of just yeah, just exceptional knowledge, truth. It's great to see and something that our community so needs. So just thank you for all that you do.

Andrea: Thanks, Leena.
I hope you enjoyed this episode of the DEI Career Conversations. Don't forget to, like, subsc,ribe and hit the bell so that, you know, as soon as new episodes are live.

Also, if you'd like to learn more about our brand new program called Pivot Your Passion into a DEI career, be sure to check out the links below or visit DEICareer.com and look at our courses. This new course is so exciting because not only are we going to help support you as a job seeker thinking about pivoting your passion into a career into DEI, but we're also going to make sure that you have the tools and resources you need in order to learn how to create data-informed strategies for the DEI, how to create metrics, how to make sure that you know how to do the work of the DEI by getting unprecedented insights from DEI professionals who are doing this work day in and day out. So I'm so excited about it. I hope that you'll join us. We have monthly community connection calls for anyone who's a part of the program and you can learn more about it all at DEICareer.com.