In this episode, Jamey covers the crucial topic of analytics and tracking DEI (Diversity, Equity, and Inclusion) metrics in the workplace. He addresses common challenges organizations face when attempting to measure the effectiveness of their DEI initiatives, highlighting frequent mistakes like tracking either too many or too few data points, neglecting to set clear goals, and falling prey to confirmation bias. Jamey talks about the importance of finding a balance in data collection—enough to track progress meaningfully, but not so much that it becomes overwhelming and counterproductive.

He also talks about how having the right metrics aligned with specific DEI initiatives allows organizations to track progress over time, celebrate successes, and identify areas needing improvement. Jamey also stresses the importance of communicating data effectively within organizations, catering to varying levels of data literacy among employees, and creating both high-level overviews and deeper dashboards for more detailed exploration. Ultimately, using data thoughtfully helps organizations move beyond surface-level engagement and make DEI a central, measurable business priority, fostering stronger leadership connections and more inclusive decision-making.

To find out more or connect with Jamey or Michelle, visit: https://www.equity-at-work.com/

Key Topics Discussed:

• The importance of tracking DEI metrics to determine program success
• Common pitfalls in DEI analytics (too much/too little data, lack of goals, confirmation bias)
• The balance between data collection and actionable insights
• The process of setting clear, initiative-aligned metrics
• The role of qualitative data, such as engagement surveys
• The need for company-specific data dashboards and visualizations
• Ways to communicate data effectively to different audiences within an organization
• The benefits of becoming more data-literate and data-driven in DEI work
• How data connects leadership more closely with organizational culture and well-being

I'm Jamie Applegate, senior director of DEI at Equity at Work, and this is your DEI Minute, your go to podcast for leaders looking to navigate the ever evolving landscape of diversity, equity, and inclusion in the workplace. Whether you're just starting out with DEI or looking to sustain your long term successes, each episode will provide you with the actions you can take to move DEI forward at your organization, all in fifteen minutes or less. Join us every other week as we break through the noise and help you do DEI right. Let's get to it. Today, I wanna talk about analytics and tracking DEI metrics and all things around setting goals and determining success for DEI. Today, I want to talk about how to determine DEI success. So that means having things like tracking DEI metrics and using analytics. So a lot of clients and a lot of organizations struggle with how to monitor DEI programs and initiatives.

Maybe they do it on sort of a case by case basis and they maybe get some sort of feedback. A lot of it is more sort of how did that feel or they'll do sort of like an employee engagement style, you know, what was great about this thing? What could be improved for next time? But they're not really tracking hard metrics, around success on larger scale initiatives. So So some of the common mistakes that we see clients make are, one, doing a reverse of what I just said. So clients will try to track every possible metric. They will try to look at every possible piece of data that could ever exist, that could ever show any sort of causation or change over time. Another one, they don't set goals. They and they don't really determine metrics for DEI success because DEI sometimes to folks feels different than maybe some of their operational metrics. It's not how are we doing in our profit and loss, how are we doing in terms of hitting our output quota, how are we doing in terms of revenue.

It feels different, and so that can make some folks resistant to even tracking metrics or setting metrics or using analytics for this. We also see some clients who get blinded by preconceived notions about the root cause of an issue. So they go into this sort of confirmation bias mode and they kind of are resistant to data because it could potentially prove something different than what they think or they just wanna live in this or they cherry pick data pieces that help prove their argument either for or against DEI. We sometimes see clients get overwhelmed by the sheer volume of data that could exist or they sort of try to track, like I said earlier, they try to track every possible metric and then they get overwhelmed by the amount of data that's out there, and then they go into sort of analysis paralysis and they can't actually read it. They can't sort of figure out the difference between the signals and the noise and what's actually meaningful. And then we also see clients sometimes come face to face with data and then refute what it says, so they disagree. And that can be quantitative and qualitative data. Folks come into it with a certain sense of themselves or a sense of the project, and then they refute what the data is saying because it doesn't match their own specific perspective and experience.

So it's important to have a really clear sense of what you're trying to accomplish and then determining what metrics you think are going to show you whether or not you've been successful. So a problem that we run into is that we're trying to find the right balance of how much data we should have. Too much data is as big a problem as too little data. So too much data, I said earlier, causes analysis paralysis and it's overwhelming. It's also overwhelming from a logistical standpoint of we then have to track all of those metrics over and over and over and over again, and that becomes an onerous task and we then lean into, I'm really just doing compliance and tracking, and I'm not actually focused on implementation and impact of specific programs and initiatives. And then, too little data means I don't actually have any sense of what's going on. So I wanna find the right amount of data to track the right number of metrics. I wanna find the right analytics tools to be able to to sort of look into this and sort of track progress over time, but I also wanna do it in a way that really allows me to focus on the impact of the work we're doing and not get so bogged down in just analytics and just doing tracking.

And then the other thing that we see is that going off of your gut means that you're really only incorporating your own perspective and you're not actually including the perspectives and experiences of others. One reason we want to make sure that we do have metrics and we do do tracking and we do identify data points is that like the sort of common sense thing is numbers don't lie. If we determine what we're gonna track and what we're looking for over time, then we can grapple with the implications of whatever we see. So if we wildly overachieve, wonderful, we can do calls for celebration and try to figure out what led us to that. And if we don't meet our own expectations, we can figure out what we're doing there. If we just go off sort of our own feeling of the vibe, then we're not really getting a sense of what it is and we're definitely not including other people's perspectives. So when we work with clients around analytics and data and making sure that we're doing tracking of different initiatives, we do want to set clear metrics for everything. So we wanna identify what is it that we are tracking, what is our first thing that we are tracking, sort of our pre implementation of a given strategy or initiative, and then sort of monitoring, checking in on it, and then post.

So for example, one of them, this is more qualitative, but we do an employee engagement survey. So if you do an employee engagement survey, which a later episode, Michelle will probably talk about why engagement surveys are not the answer to everything, but they are a useful tool to determine sort of point in time employee sentiment. So if we check how they are right now, we can say, okay, they're at this level out of five or out of 10 or however you wanna do it, sort of, you know, net promoter score, we have, you know, oh, we're at like a 7.8 out of 10. So we're not in the detractor phase, but we're also not in the promoter phase, we're somewhere in the middle and we wanna move up. And then we can implement new strategies as we're going through it, check to see how it's going, are people feeling a little better? And then at the end, after we've implemented our strategy or our program, check-in again, and maybe we're up where we wanna be. And then we know that that was impactful. But so what we do when we work with clients is we identify the key issues that are that any company that we're working with is facing, and then we determine the specific metrics that will allow us to monitor our progress on those issues as we implement new programs and initiatives. So a key thing there is that each program initiative should be tied to one or more metrics.

Some metrics might tie to multiple programs and issues. We might be doing a sort of multifaceted approach to a given issue. So in a previous episode, I talked about recruiting diverse talent. And so if we wanna say, like, we wanna track how diverse our talent is, we wanna know sort of what the demographic data on our employees are. We should track it, and then we can implement new policies and strategies and new practices around how we are, recruiting and attracting diverse talent. And then we can look at those demographic data again. And if we are less diverse, then clearly we did something wrong. If we are more diverse, then maybe we did something right.

If we are closer to an idea of representation of the given community that we're in, then maybe we're sort of where we need to be and we feel good. Are there but maybe we're seeing that certain demographic groups are not benefiting from this or certain ones are being harmed by what we're doing. And then we need to address that and make sure that we're adjusting as we're going, but following those metrics around what makes sense. And then once we have those metrics, another thing that we do that we think is really, really important is we talk about the data constantly. So we spend a lot of time walking people through data and making space for questions and discussions so that everyone is on the same page about what the data says. We wanna make sure that when we're showing people data, we give them a high level overview so that folks that are maybe a little more hesitant around data and less inclined to data don't have to go deep into the weeds on every single thing. We can give them the headlines. So we can say, what we saw at the beginning was this, here's what we did as an implementation of a new strategy, and then here's the result that we saw on that same metric.

Did we achieve our goals? Yes. No. Where are we? What question might you have? But we also wanna make sure that we're giving people access to that underlying data so that if they are interested or want to learn more or want to go a little deeper, they can. So you really wanna make sure that you're also evaluating your audience, seeing where they are, seeing what their ability to sort of navigate and handle data is. You're gonna have people in your organization who are super data literate. They love crunching numbers. They love looking at data. They love sort of getting into the nitty gritty.

They love reading through maybe on the engagement survey. They love reading through every single maybe open ended response at the very end, and they wanna just pick everything out and pick everything that they can and sort of understand every piece. And there are people who just wanna be told three biggest headlines to walk away with, and they're not gonna worry about the other stuff. And so you have to be able to sort of be flexible and support people where they are. And so what we do is we spend a lot of time walking people through data, also trying to understand where they are in terms of their own relationship to the ability to read and analyze data. And then also making a lot of space for questions and discussion. We don't what we don't wanna do is just send somebody a massive spreadsheet or a massive presentation or send someone, you know, a link to a Tableau visualization and say, here you go. Good luck.

Go act on it. Because through those questions and discussions, we can understand where are they still struggling, what points on the data are maybe harder for this organization to sort of grapple with or this leader is really struggling with with this specific piece that needs to be addressed. How do we sort of support them in navigating that and coming to some sort of sort of resolution on how to move forward? And then, you know, what that also entails is that we wanna build company specific datasets and visualizations and dashboards that track those key indicators across the company. So these are great because you can have kind of an a larger overview with just like, you know, here's super headline, big metrics. So for example, for tracking, you know, demographic data across different offices, here's our big page where it's just, here's our company overall. Here's how we break down by different demographic factors. We can do demographic cross, comparisons. We can compare sort of multiple demographic factors at the same time to sound again to subgroups.

And then we can also have after that. So that can be good for sort of, like, intro level connecting with folks. Here's kind of headlines where we are as a company. And then we can have underlying data that folks can explore and sort of play around with a little bit that show you more specific things that maybe pertain to them. So if you have a regional model, you can sort of break it down by region and you can compare regions to each other. You can just look at your own region and say, are we where we need to be? We can also bring in external data so we can look at, like, census data. So as we talked about, a really good thing that we think is valuable is for folks to want to be representative of the communities in which they operate. So you're never maybe gonna hit that like a % on the head, but it's just, you know, it's a goal to be as representative so that you look like the community you serve.

And so what we wanna see there is, do we look like it? And if we're way off, if let's say, you know, we are working in a town that is predominantly Latino or Hispanic, but then our company is overwhelmingly white, then we might say to ourselves, what's happening here that we are not bringing people in who are Hispanic from this community into our company. And so we can look at that and really show that, but we wanna have company specific dashboards and then more things that are specific to certain people and certain people in certain roles so that they can really engage with the data themselves. And so then when we do that, when we sort of have clear metrics, have clear data collection sources, have clear dashboards and visualizations that have spaces where we can talk about the data, so not just dumping data on people and then saying good luck, where we can talk about it. The results that we typically see are that companies become more data literate. They become more savvy around data and data collection. It impacts how they look at data in other aspects of the business too. They're not just thinking to themselves, oh, we're gonna collect every single metric possible. It also makes them reform how they pick data.

Decisions end up becoming more data driven. You're more accountable to data because we're actually tracking it. And those decisions also so they're more data driven. They're more impactful. They're also more inclusive. DEI actually becomes probably more popular and sort of people become more engaged with DEI because it's not being treated as this other thing that we're not really tracking and we just do because we think it's we say it's important. We're actually tracking it like we track any other priority in the business. We wanna make sure that we are using data to show efficacy or show where we need to make adjustments to our strategy to improve our efficacy, just like we would for any other clear priority for the organization.

And finally, what it does is it allows leadership to be more connected and tapped into the organization's well-being and culture. The leadership can actually understand where its people are, how they're feeling, what their experiences, what their needs are over time because we're giving them clear data around how people are doing both in DEI and overall culture, which is really critical because leaders can tend to get a little disconnected from their teams. And so making sure that any of our DEI initiatives are really data driven so that we can see success allows company leaders to be really tapped into what's going on. And so, again, you should have metrics for all of your DEI initiatives. You should track things over time. You should make sure that you're sharing out. You should have clear data collection methods, and then you should absolutely share data and then talk about the data to make sure that everyone is on the same page about what it says. Thanks so much.

That's a wrap. I'm Jamie Applegate, and that's your DEI Minute for today. Thank you for listening. Please be sure to follow us wherever you listen to podcasts, and don't forget to leave us a review. If you ever have questions, please visit our website or send us an email. You can also sign up for our newsletter and follow us on LinkedIn, YouTube, Twitter, and Instagram. Links to everything can be found in the episode notes. This episode was edited and produced by Pop Growth with podcast art by me, Jamie Upkade.