Diversity, Equity, and Inclusion in HR Analytics

Connect with Siri:

Connect with David:

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• 
• david@turetskyconsulting.com

Connect with Dwight:

• 
• dwight@turetskyconsulting.com 

Resources:

• Siri’s Work
• HR Data Labs Season 2 Episode 12
• HR Data Labs Season 2 Episode 10

• “What I care about is that the knowledge set we’re generating is actually changing the world, and in order to do that it has to get in the hands of people like HR practitioners…” – Siri Chilazi
• “Stepping back from all the things that we do automatically… we have to experiment and test and learn.”- Siri Chilazi
• “We need to approach DE&I in our organizations with the same seriousness, with the same rigor, and the same data-driven approach as we approach all other aspects of our business.” – Siri Chilazi

Announcer: 0:03

Here’s an experiment for you. Take passionate experts in human resource technology. Invite cross industry experts from inside and outside HR. Mix in what’s happening in people analytics today. Give them the technology to connect, hit record, pour their discussions into a beaker, mix thoroughly. And voila, you get the HR data labs Podcast, where we explore the impact of data and analytics to your business. We may get passionate and even irreverent, but count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here’s your host, David Turetsky.

David Turetsky: 0:49

Hello, and welcome to the HR day labs podcast. I’m your host, David Turetsky. Like always, we try and find fascinating people inside and outside the world of HR to give you an understanding about what’s happening in the world of HR, data analytics and technology. Today, we have with us Siri Chilazi series with the women and Public Policy Program. She’s a research fellow there at the Harvard Kennedy School, her life’s work has been to advance gender equality in the workplace research and research translation. Siri, what is research translation? That’s a great question, David, It’s so lovely to be with you here today. Thanks for having here. Yeah,

Siri Chilazi: 1:27

Research translation is the process of taking insights that we’ve generated through research and academia, and bringing them to the practitioners out in the quote unquote, real world who can actually do something with those insights. So it’s really great to write these papers and publish them in journals that no one ever reads. But what I care about is making sure that the knowledge that we’re generating is actually changing the world. And in order to do that, it needs to get in the hands of people, like HR practitioners, like diversity, equity, and inclusion professionals, who are really doing this work day in and day out in organizations.

David Turetsky: 1:59

That’s wonderful. And actually, so that everybody knows we are going to have a couple of your papers as attachments to the podcast, and the links will be on our web page. So if you want to read these wonderful tools that Siri has created, please go to those artifacts and read them. They’re fascinating. I also didn’t introduce and I’m sorry, but Dwight Brown is with us. Hey, Dwight. Hey, David. Good to be here serve as co host today? Yes. So one thing that you may not know about Siri, you actually have a second career. What’s your second career Siri?

Siri Chilazi: 2:36

My second career is being a fitness instructor and fitness educator. So my whole life combined has sort of the mind piece and the body piece brings it all together. It’s fun.

David Turetsky: 2:47

That’s awesome. So when we say hey, Siri, can you make me fit? That’s sorry. You’re gonna have to do the work. Sorry, sorry. I do for all of us who own iPhones? Do we you know, that’s a that’s a terrible joke. So I apologize. So our topic for today, which is a really cool topic is using data or organizations using data as an engine for progress on diversity, equity and inclusion programs. And these are very near and dear to my heart says I’ve developed DE&I analyses to try and help that. But Siri, that’s, that’s really cool.

Siri Chilazi: 3:29

Thank you. I am very passionate about this topic. Because we use data in organizations in pretty much everything else that we do. We measure our budgets, our sales targets, we have deadlines attached for product launches. But in the realm of HR and DNI, specifically, the use of data strategically is a little bit new. And to really make progress in this arena, I think we have to take the same rigorous evidence based and data driven approach as we have taken in all other areas of our business.

David Turetsky: 4:02

That’s perfect. That actually leads me to our first question of the day, which is, when we are talking about DE&I data. What are we talking about? What actually constitutes DE&I data?

Siri Chilazi: 4:16

It’s a fabulous question. Because oftentimes, when we talk and think about this, we think of DEI data as representation as counting the people who are in our organization, the women and the men, the people of different races and ethnicities, veterans, non veterans, and so on and so forth. That’s just the D of DE&I. That’s data that relates to diversity. And that’s, of course, an important starting point. But it’s really important that we don’t make it the end point because it’s equally important to measure aspects of equity and inclusion. So let me give you a few examples. One way that you could think about measuring equity would be to look at the amount of time that different groups of people spend at a given rank or at a given level. They get promoted. And if we’re seeing systematic differences where, for example, white men get promoted faster and spend less time at various different ranks compared to, let’s say, women of color, or veterans, that might be indicative of something about the promotion process that we need to look at, that’s driving those unequal outcomes. That would be one example of measuring equity to data. The other example, of course, is pay gap analyses and looking at those by gender and race and other characteristics. And then there’s inclusion. This is where things like employee satisfaction or engagement surveys come in. This is for qualitative interviews or focus groups come in where we fill in the gaps that are left by surveys to really understand what are driving certain groups, differential perceptions of inclusion and belonging in the workplace. And so it’s only once you’ve measured all these three types of data points, whether they be quantitative or qualitative, that you can really develop a holistic picture of DE&I in your organization.

David Turetsky: 6:04

So let me go back to the question. So the question I would ask you is, is that when we think about something like overtime, and we think about time worked, I think one of the things that we tend to forget about with making decisions about people is we need to make decisions about people fairly for not just the pay that we give them, not just the opportunities we give them for advancement, but also the opportunities to actually work. How many hours have I given people? How much overtime? Have I given people? Can they work that I think a lot of times we kind of gloss over the fact that people have the lives outside of work. And while the manager may or may not know what the situation is of the person, they may either factor that in too much. And probably underestimate the need and or ability for someone to do overtime, or may make a decision in the wrong way. And not provide over time to people who desperately need it because they think their situation doesn’t allow them to do it. Which is probably the problem, isn’t it?

Siri Chilazi: 7:09

Absolutely. And this is where the data of who has worked the most overtime, might help you expose some of those patterns. And then the related aspect is working project allocation, which is not just how are people working? And when but what are they working on? Right? When there’s an opportunity to represent the company externally, you know, give a talk or speak on a panel, or work on a high visibility, stretch assignment that gets you to interact directly with the senior leadership, who are the folks that we are giving those opportunities to? These are actual tangible things that we can measure and track over time to see if there are any differences between different groups of employees.

David Turetsky: 7:49

We’ve actually seen network analysis, you know, talking about what you just described, we’ve seen and we’ve talked about on this program, network analysis, where we look at how often do you interact with senior leaders? And how invasive that is, and I think what you’re talking about is giving people the opportunity to be given those interactions or to take those opportunities to have those interactions. And part of that will definitely be you know, can people whether it’s a color or have Latino status or veteran status, can they get those opportunities as well to present in front of senior leadership or externally to clients? And one of the things I think we are going to probably talk about at length today is, at what point does that become too much and too invasive? And can we ask those questions about who someone is? And you know, should we be looking at that data?

Siri Chilazi: 8:44

It’s a deep question. I think the answer unequivocally is Yes, we can. And we should ask now, people may choose not to disclose, and that’s there, right? I always advise companies to be really open and transparent about why they’re asking for these type of quote, unquote, invasive, potentially invasive data points. And how the data is used when employees understand that this is truly being used to make the company more inclusive to spot areas where there are currently challenges. And the company has a genuine commitment to improving those areas, we find that employees in those cases are much more willing to actually disclose. And you can always do it anonymously.

David Turetsky: 9:41

And that brings up the next question, which is the how do we understand what type of data could we be collecting? And should we be collecting and I think you talked about it a little bit there. And I think we’re going to talk a little bit about anonymous versus identified and a little bit more color, but can you tell me what do we need to know or What do we not need to know? Or how far can we go?

Siri Chilazi: 10:03

Where I would always start with any organization is with all the data that you already have, as part of hiring these employees, you’ve collected a lot of data about them. And as part of their employment with you, you know, what they’re paid when they got promoted. When they switched offices or geographies with all this, you know, who they report to, if that changed at any point. So companies have more data at their fingertips than they even realize, the biggest problem seems to be that these data often live in disparate databases that are not easily accessible, maybe multiple databases that don’t talk to each other. No one’s ever taken the time to actually go in and analyze the data and pull it together and represent it visually in a way that would be easily accessible. But it’s still there, we can do all of those things. So I would say start there and first understand what you have. And then look at where are the gaps. So maybe you have some of this quantitative data about your employees advancement, but you’ve never actually asked them, you’ve never talked to them about how committed they are to staying with you sure, whether they’re happy with their career trajectory, whether they feel like they have opportunities at their fingertips, the types of opportunities that they want, or whether they’re starting to look externally, because they feel like their advancement is capped. So you might identify that as a gap that you then seek to fill through surveys to interviews through focus groups and other means.

David Turetsky: 11:27

Yeah, I think one of the problems with the data you’re talking about, though, is a lot of time, what we found at Turetsky Consulting, is that the data is absolute crap. You know, I’m not talking about the other data, I’m talking about the core data that we have in our hrms is usually poor, it’s collected over decades, it’s specious at best things change, names change, there are new rules and regulations. There’s even the situation where and I don’t know if you’ve seen this, but I’ve definitely seen this recently, where people now have a different sense of self around their gender identity. And the HRMS doesn’t have the capability of actually supporting collection if the client wanted to the collection of the LGBTQ type that they identify with, or that they don’t want to identify at all, and that they want to be, you know, kind of, you know, gender neutral.

Siri Chilazi: 12:20

Yeah, no, that’s, that’s a real issue for sure. What I would challenge companies to think about is whether they would accept this status of data in another realm of their business, let’s say their selves numbers or their financial performance, I would venture to say that most organizations would not accept not having accurate data at any moment about financial performance. So they’ve invested in systems that serve their needs in that arena. And that’s the commitment that we need to see in the era of DE&I as well. So if you’re unhappy with your current systems, upgrade them invest in a newer vendor that offers more options for gender identification for sexual orientations, that location that allows you to collect other metrics that you think are important that your current system doesn’t allow you to collect.

David Turetsky: 13:08

I think many organizations might not have that opportunity yet. But they might see it as being necessary given that things like Regulation SK, which require the disclosure of material, metrics and material human capital metrics. And you know, DE&I is one of those wonderful human capital metrics that obviously shareholders care about, and people care about customers care about. And so I’m hoping that with regulation, sk, more companies disclose things like this, that they’re there at least say publicly, whether it’s through marketing, or through their executives, that they care about them. You know, one fruit based company, or fruit labelled company, you know, really cares about the DE&I stuff. And they do a lot of things very publicly, that enable people to understand where they are in the kind of in the spectrum of, you know, being supportive or not. We hope that other companies take that example and actually do as you say, make investments where they need to, to be able to do that support that.

Dwight Brown: 14:13

Now, the thing that I would say with this, too, is Siri, just for your knowledge. I’m kind of the data governance evangelist of our group. And I think that really hits on the point that David originally brought up that you need to have solid data governance in place to be able to look at those pieces of information and say, are we collecting the right data? Are we collecting it in the right format? How are we updating that data on an ongoing basis? How are we updating that in the process reports that we have and oftentimes organizations struggle with data governance and where to focus their efforts and the the organizations who say look DE&I is a huge priority for us. That really helps them Be able to focus on the data governance side and say, okay, out of all of the things that we have in on our plate, we are going to look at this first, this is going to be a priority for us. And I think the more that we do that, the better it’s going to be. And it allows us to be able to maybe modify the current systems as opposed to having to look at other systems or it lets us know that no, we can’t, we’re not going to be able to do it with the system, we have to go somewhere else do different platforms. So I think that’s another component that that definitely goes with all of this. I

Siri Chilazi: 15:35

totally agree with you, Dwight. I think whenever something is a real genuine priority, we find a way of getting it done. Yes. In any part of our organizational life. Yes, definitely. Definitely.

Announcer: 15:48

Like what you hear so far, make sure you never miss a show by clicking the subscribe button. Now. This podcast is made possible by Turetsky Consulting and listeners like you. Thank you for your support. Now, back to the show.

David Turetsky: 16:08

So let’s move on to topic three, which is how does an organization do this successfully? What does the research tell us? What is your experience tell us? You know, give us some examples.

Siri Chilazi: 16:19

I’d love to share with you a story that actually crystallizes a lot of what research would suggest are the best evidence based practices in this area. And this story comes to us from the BBC, the British Broadcasting Corporation headquartered in the UK, they have a very ambitious effort underway to equalize the representation of women and men in all of their journalistic content. So the idea is that if you watch an hour of TV, you should see 50% women and men represented on screen in that time, or if you read an article on their website, it should quote by name 50%, women and men and so forth. And by the way, they’re extending these monitoring efforts now to ethnic representation, as well as disability representation. But that’s too new to talk about quite yet. But what they did is they basically had all the journalists and content creators themselves record the data of who they were featuring in their programs. And I think this is the first lesson is oftentimes in organizations, we have some people sitting in HR in people analytics, who are the collectors and holders of the data. And then we have our leaders and managers in a totally different part of the organization, who are actually making the day to day decisions about hiring, firing promotion, sure, that drive what the data look like, in being able to bridge that gap between who’s generating the data and who’s monitoring and tracking the data is very powerful. Because what we kept hearing from the journalists and the content creators was that as they were looking at their own data on a daily basis, and then tallied up weekly and monthly, they were able to course correct immediately, once they saw that there was a huge gap in merging. Whereas if you just get a an annual report from HR, real by the time you see the data, it’s too late to do anything about it. Right. So that was one lesson. Another big thing that the 5050 project at the BBC has done is they share all participating teams data every month. So the data is generated every day. But once a month, it gets shared among all participating teams on an internal dashboard. And again, behavioral science shows us that that peer comparison, and seeing how you’re doing step stacked up against others is an enormous motivator of behavior change, nobody wants to be at the bottom. And when you see how you’re doing compared to others, that’s really a motivator to kick things into gear. And this is another aspect of DEI data collection and sharing that is missing in most organizations. It’s really strategically using it as a driver of behavior change.

David Turetsky: 18:57

But Siri, what kind of training has to go along with those kinds of visualizations orthat kind of data? Because you could imagine that it might go too far in one direction. And kind of you were talking about the competing aspect of it. All too often, we see that there’s collateral damage from, you know, going too far or not going far enough. And how do they get to that right balance and does training come into it?

Siri Chilazi: 19:22

That’s a real potential pitfall? Well, there’s a couple of pitfalls, actually. One is the myopia effect, which is you only start to focus on the things that are being measured. But of course, that’s not all that matters. And so then you start excluding or ignoring a lot of other important things. So that’s one real pitfall. And then the other real pitfall, too Is it becomes a check the box exercise, right? Instead of data being a tool to make the real progress in the organization that you’re looking for. So the numbers might look good, right in the short term, but if people are having a horrible experience, if you’ve hired a bunch of people of color onto your team, But you’re never giving them an opportunity to actually contribute to decision making their voices are ignored. They’re not getting equal opportunities to advance sooner or later, they’ll leave. And so that is part of the argument for measuring dei holistically, right? We can’t stop just with those representational numbers. But we also have to look at what those people’s experiences of the organization are once they’re in house, and how their career trajectories evolve over time.

David Turetsky: 20:28

But and I think that drives another question that I have, which is that it doesn’t have to be just the DE&I data that they’re utilizing in order to be able to make better business decisions about their people and about what’s going on. Because that’s just one aspect of the manager making better business decisions, right? She or he or they really have to take into consideration the kind of and I hate to use this word, because it sounds so psychological, the Gestalt, you know that everything that makes the business decision better. So yeah, there is p&l, metrics to go in. There are HR metrics that go in, there’s competitive metrics, like the competitive market for hiring people. There are other probably aspects of the jobs that they’re looking at, too, whether it’s experience, whether it’s customer feedback that you’re getting, to be able to put the right people in place to be able to do the right job. So how is it working in that DNI fits into the overall analytic strategy, and the overall data strategy of an organization,

Siri Chilazi: 21:29

The hiring example that you brought up is a really good one. So one often use traditional metric for hiring teams is time to fill a role. Exactly. The idea being that the faster you can open roles, the better you’re doing it, that’s kind of what you get incentivized on and evaluated on. We also know that that often goes against goals to diversify the workforce, because diverse candidates, people who don’t look exactly like all the people that you already have in your organization, it just might take longer to find them. Because you have to build relationships, you have relationships, you have to fish in pools that you haven’t fished in before. And so you have to start with your true goals and the true strategy and say what is our priority is our priority to just find someone quickly and put butts in seats, or is our priority to find the best person. And in fact, ideally, someone who brings new and complimentary strengths to a team, which by definition means that they don’t think exactly the same way as everyone who we already have. And if that’s a real priority, then we recognize that it might take a little bit longer. And we might need to shift how we incentivize and evaluate our recruiters, we might have to change those metrics, as you were saying,

David Turetsky: 22:40

And I think it goes back to also changing the process to not just those metrics, which I love your example, because it drives another great, I think what would drives another great example of how sometimes we have automated things that probably shouldn’t be. And I’m thinking more so about those questions that get asked on the front end of the recruiting process that actually will filter out candidates almost immediately, then they may actually be more diverse candidates that get filtered out. And I think we have to kind of change the recruiting process, to look at those filters, and make sure they’re not, you know, violating some of these new prospective rules or cultural things that we’re trying to bring in thinking about more a more diverse world. Because, you know, when you try and go for a job, and you go past those filters, and then you get 10 minutes later, you get the rejection email, you know, that’s not a person doing that, that’s AI, or that’s a filter on the roll. So, Hey, have you thought about the process, the transactional process that recruitment has gotten ourselves into

Siri Chilazi: 23:43

So much. This is the main focus of my work, extra research was so much to say about how in practice, we can do by some of these hiring practices, processes, how to make them more equitable. So one, like you said, is pre select the criteria, and be really thoughtful about are these truly requirements. Another example of how we often unwittingly exclude candidates is by requiring a four year college degree. And if we actually stopped and thought carefully about what makes someone successful in this role, we might realize that a four year college degree has nothing to do with it, what I generally advise companies to move away from check the box type qualifications like such and such degree or x years of experience in y field, and focus instead on the skills and the capabilities. Because you know, you might have an undergraduate degree in finance, and that’s giving you some finance experience. You might have also worked a job at age 18 for two years that has given you equivalent finance experience. And in fact, you’re probably much more well equipped to succeed in the workplace because you’ve actually already spent two years in the workplace as a college graduate hasn’t Right, right. So stepping back from all the things that we now do Automatically just replicating old patterns and saying, Wait a minute, let’s review this job description, let’s think about which criteria absolute must and which are actually nice to haves that we could just let go of, let’s rethink how we’re attracting candidates. And we’re we’re talking to them, if we’ve only been posted on LinkedIn, and we get a certain profile of candidates, let’s find other job boards to post on and see if that helps to find different people. You know, we have to experiment and test and learn.

David Turetsky: 25:33

And I think one of the things that we will find, as we look at the metrics over time, is first year performance is a much better indicator of whether or not we’ve hired the right person, then, you know, time to feel like you brought up Sure, and hopefully, if they are looking in different pools, they may actually be much more successful, and then hopefully replicate that process over and over again.

Siri Chilazi: 25:54

Yeah, in speaking of first year performance, and that’s great for once you’ve hired them, but before you have, research shows that the most predictive evaluation method, it’s not an interview, it’s not a resume review. It’s actually a work sample test, which is quite simply an exercise or scenario that’s designed to mimic the actual job as closely as possible. So let’s say you’re hiring someone into a role where they’re going to have to write a lot of things, instead of talking to them about how they would write have them just write something, evaluate the quality of the writing, right? And that’s actually the most predictive way to identify who will be successful in the actual tasks that you’ll want them to do. And it’s a very underutilized tool in organizations at the moment, these work sample tests,

David Turetsky: 26:45

Isn’t that much more of a resource hungry methodology as well. I mean, I don’t know there may might be AI that can go and actually evaluate the writing sample. But that also might need human interaction.

Siri Chilazi: 26:57

I think it depends entirely on how you execute this. So think about the time that it takes to review a resume? What if you took that time, and instead had someone read a two paragraph writing sample from a candidate? Right, that’s not necessarily increased the amount of time taken in the hiring process, but you would be looking at much more diagnostic, high quality information?

David Turetsky: 27:18

Oh, no, I think it’s a really great suggestion and a really great example of how we can you know, pick the process fair? Absolutely.

Siri Chilazi: 27:26

Or another example could be, you know, let’s say, now you conduct a 30 minute interview, and the interview goes along the lines of So tell me about yourself? Why should we hire you? Right? very vague questions that really do nothing to suss out the candidates, capabilities or skills, what have you instead spent that same 30 minutes role playing an actual scenario from the job with them?

David Turetsky: 27:48

Totally agree Siri. But I think that goes back to the training, there needs to be a retraining because managers aren’t born managers are trained. And I think I can count on my one hand, how many hours of manager training I’ve received over my lifetime in interviewing skills, and we have an employee here, so you can vouch for it. But I think it goes back to what can we do to make the managers experience better, and then the candidates experience better and even the recruiters experienced better by giving the manager or the hiring team actually, the right questions and skills in order to be able to find that right person, not the way in which we used to do it, but the way in which we want them to do it?

Siri Chilazi: 28:33

Yeah, I totally, totally agree with you, David, about the importance of training, I would say the magic formula is training plus process. So an example would be interviews and again, you know, the traditional hiring interview is completely unstructured. Every interviewer asked the candidates, different questions, whatever questions they want, the questions don’t necessarily have anything to do with competencies. So we can’t even really compare candidates apples to apples. So a great evidence based research based process change there would be to move to structured interviews for all the candidates get asked the same questions in the same order. And we have an evaluation rubric that says, okay, a good answer to question one looks like this. So the scale is one to five, and so on and so forth. So that would be the process change. But then to make it really successful, you want to train the managers, right? You want to explain, here’s the science behind why we’re making this process change. Here’s why structured interviews are superior to unstructured interviews. Here’s how you really evaluate candidates in the moment. Here’s what to be on the lookout for. And I think it’s when you combine those two components, you both give candidates the best experience absolutely, you enable the interviewers to feel successful and to feel well equipped, but you also increase the chances that the process change will stick.

David Turetsky: 29:53

But I think you have to then marry that with collection of that data at the end and then carry Fully keep that data, I don’t mean keep it on the shelf, I mean, actually collected inside of a system, and then be able to judge, we hired this person based on those answers, and create the right algorithm to say, what would have been the better combination of answers, that would have got us a successful employee, because over time, we now have modeled the pattern. We’ve seen the results. And now we can create the right algorithm that says, this is the right person hired based on the collection of data in a better process.

Siri Chilazi: 30:31

Exactly. 100% agree. And it’s a never ending learning loop to make a process change. Now you collect a couple years worth of data to evaluate its impact. And then you tweak again, exactly. And then you keep monitoring the data. Again, just like in business, you’re never done, never sold enough. Right? All right, you always have to create in your rush, and you need to sell more, and you need to develop a better product.

David Turetsky: 30:58

But can we please do one thing as a process change? I beg of you, can we please get rid of the spirit animal question. You want to see the data? I want to see the data of who thought asking about spirit animal was the right question during?

Siri Chilazi: 31:15

Well, and that’s that’s the question that I put to all the organizations that I work with, when they asked me to review their interview questions from a research standpoint. I will I will flag those little comments in Microsoft word in the sentence say, which competencies and capabilities is this testing? What the answer to this question tell you that helps you to determine the candidates competence?

David Turetsky: 31:39

Yeah, yeah. Well, I was asked that recently, when we were hiring someone, and I just said, Yeah, okay. That’s great. Thank you. We hired the person anyways, and they’re actually turning out to be phenomenal. So like, No,

Siri Chilazi: 31:51

I’m dying to know, what is your spirit animal?

David Turetsky: 31:55

That’s a great question. I have to go back and remember it. I don’t remember it. I think it might have been a turtle? No, no, it was a raccoon. It was a raccoon. Anyway, stored in an HR database. So you know, we’re gonna see that in a metric. Right? That’s right, that has my data governance. So Siri, we talked a little bit about what DE&I data is, what’s the Diversity, Equity and Inclusion data, what it consists of, and how you collect it. We’ve talked a little bit about how do you drive process change? And how do you actually collect this data? And actually make sure it’s good data. And then we talked about process change necessary through a really cool example. And we’ve talked about how does that actually work in practice? And I think I think, as I said that, that example will be great for people to take away. What else would you like to kind of cover and anything else to support the the DE&I strategy?

Siri Chilazi: 33:12

If there’s one thing I hope that you take away from this conversation, it’s that we need to approach diversity, equity and inclusion in our organizations with the same seriousness with the same rigor and with the same data driven approach as we approach all other aspects of our business. So if we’re serious about changing cultures, about creating environments that are more inclusive about attracting and retaining different types of people than we have before, we need to use data to our advantage. And we need to make whatever changes and investments are necessary in our data infrastructure in our data governance to help us do that, because that’s precisely what we would do if we needed to launch a new marketing campaign, or if we needed to pivot our sales strategy. So that’s what it comes down to. It’s using the muscles that we already have, just in a different way, applying them to different tasks.

David Turetsky: 34:05

Outstanding. Siri, thank you so much.

Siri Chilazi: 34:07

Thanks for having me. It’s been a real pleasure,

David Turetsky: 34:09

Dwight. Thank you. Thank you. This has been great. Really appreciate your time, Siri. Yeah. Siri, we might ask you to come back on and talk a little bit more about DNI.

Announcer: 34:18

I’d love to, there’s always more to talk about when it

David Turetsky: 34:24

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Announcer: 34:48

That was HR data labs. Please visit Turetsky consulting.com forward slash podcast to review the show. add comments about this episode, or add new ideas about upcoming shows you’d like to hear Feel free to be creative. But please be nice. Thank you for joining us this week on the HR data labs podcast and stay tuned for our next episode. Stay safe