Inclusion analytics | with Diana File

So I definitely think people should not stop everything in an attempt to get their data perfect. The data will never be perfect. And that's part of what we learn in behavioral economics or in psychology research in general. You're studying humans. Humans are ever changing, right? So they're going to change by the time you do anything with their data. So the better we can optimize our tools. That's why I'm such a proponent of technology that can do this quickly and at scale. You know, in terms of tracking the the more we can improve our tools to get in, get out, the better off we'll be. And we can, you know, kind of iterate and revise our strategy and our initiatives over time as we learn more.

Hello and welcome to worksmart, a podcast from Social Talent. It's all about becoming more WorkSmart. I'm Johnny Campbell, CEO and co founder of Social Talent and this week we're talking about Inclusion analytics. The Diana Fire. Let's do this. Diana, I believe you're joining us from New York City this morning. You're very welcome to the podcast. I was wondering if you could give our audience a little introduction to yourself and explain what, why this topic, how you got in this area, what makes you the expert on inclusion analytics and I'm the person saying you're the expert.

Thanks, Johnny. Well, it's great to be here. I am right outside New York City in New Jersey, really excited to be here. So I am the CEO and founder of DF analytics and Consulting and we are a data analytics strategy and training firm with a tech driven and neuroscience based approach to measuring human behavior. Diversity, equity and inclusion is a big specialty of ours. We also do more general people and culture, HR solutions, we do change management, we do some M and A. And the way I started this business is my personal background is as a psychology researcher and data scientist. Got my master's in that. And I also lived in the Middle east and tried to solve peace there in my twenties. That was my pretentious twenty something self. And as I was doing that, I developed a lot of assessment tools as part of my research to kind of pulse people living there who are experts in policy building, interfaith work and various other diplomatic relations kind of stuff. And on how, what are the factors psychologically that motivate peace and motivate resolution of conflict. And I really loved the idea that we could measure human behavior and then change different conditions to see if human behavior changes on those conditions. I really loved experimental and research psychology. And then I moved back to the States where I grew up began my consulting firm. And at the beginning I was doing just more general people analytics type stuff, measuring the impact of change on human behavior, on various technology changes, you know, business changes, various things like that, project management type stuff where you really need to measure things in a different way, different work stream involved in the change. And 2020 came along and a colleague of mine approached me and asked if I could assist him with the analytics and assessment work stream of a DEI transformation he was working on for a client of his. I was very happy to assist and I did that project successfully changing the conversation because they really began to understand how employees feel and behave. And so it wasn't just the CEO said inclusion was important or diversity and equity were important, but it was where are the sort of inflection points inside the organization where things go wrong because of misguided beliefs, biased beliefs, and in their case it was middle management. And I had a lot to do with recruiting beliefs that we couldn't find qualified candidates who were members of diverse categories, Things like that we unpacked because we were able to measure their behavior through surveys, through focus groups, through interviews of various kinds. So that work really brought together a lot of my passion around the conflict resolution piece, the sort of, you know, building ways for people to bridge gaps across difference and work together better with the measurement piece. And I did that and then I did. Somehow one thing led to another 43 more of those assessments in about two years. And that really became my expertise and we started branching out a lot beyond just your standard kind of way that people assess DEI things, which is your diversity numbers, your representation of your workforce. Sometimes people go and look at the representation of the applicant pool, but representation in terms of heads and counts of heads is pretty much where most organizations leave it. Some organizations also go the extra step and look at engagement and that often, or inclusion, and that often is measured through people's perception of how included they are or how much belonging they feel in in a work environment, usually measured through surveys. So what you're getting is biased data because you're going through the, the filter of employee self perception. And that can can be biased. Right. So the way that we found in different organizations I work with, different vendors I work with in the technology space. It's why we're a tech driven organization. We really have found ways to bring the most cutting edge tools that harness the power of data already existing in an objective way inside the organization to measure some of these things in a more objective and less subjective manner. And then you can actually compare how people perceive themselves to feel and be included to how they actually are. And that looks very simple. Like, you know, when you ask people what happens when you're excluded. Well, I'm not in the room where key decisions get made, right? I don't, I don't attend the key meetings or the key meetings happening in this company where business decisions get made. All the constituents and participants of that meeting, those meetings look the same. You know, they have similar backgrounds, went to college in the same places, whatever it might be. And so being in the room, being in those meetings is a big factor. So you can actually look at calendar invitations and you can track the data, we call that metadata because it's not the data of what is happening or the subject of that meeting necessarily. Like you're not looking at the minutes of the meeting or who says what. You're looking at the attendees meta data about the meeting itself. And there are lots of automated tools I can name some vendors happy to do that, that do various different things like that. And they can actually go in to the calendar software, whether it's Google or Microsoft, and track your Percentage of men vs women vs non binary folks in a meeting in, in your average, you know, 500 meetings that happened in the calendar year and you could look at a dashboard with those lines. So over time, if things are going well, you're going to see gaps shrink in terms of the percentage of men versus women, for example. Similarly, you could look at people of color versus white people or any other way to parcel out ethnic and racial differences. If you wanted to go by category, you could do that for disability status, veteran status, LGBTQ + community, any, any number of different demographic variables. So that's how I got into this stuff because I was really looking for ways to measure things objectively and because I was privileged to partner with a lot of different cutting edge technology vendors that are doing these analytics and selling them as kind of SaaS solutions that we can then design some consulting around so that you're not measuring for measurement's sake, but you're really customizing based on the organization's comfort level with sharing that data, reporting out on that data and taking action based on that data.

I think we could have a, probably have a conversation about behavioral economics all day long. I imagine you're equally inspired by folks like Tony Kahneman, Amos Tursky and others in the behavioral economics world. And I share that passion. And it's partly why I created Social Talent with my co founder Vincent, was to try and bring an Objective science based approach to how we do recruiting initially and how we help others work with other people. It reminds me of a tool we built years ago, we since retired. We created this tool to objectively analyze what recruiters are actually doing on LinkedIn as opposed to just listening again to your point feedback saying we can't find candidates who have, you know, or female candidates in that area, say, well actually let's look at it, let's look at your searches, let's look at the profiles you view. And just making people aware of it and having it tracked began to change their behavior. They were then conscious of the fact that for example, one recruiter is only looking at 19% female profiles in her or his searches. And therefore can I get that 25 to 30 to 35% just having that there. But one of the challenges when we look at DI metrics and you're so right, that so many people just tend to look at counting, so they count the amount of people that are in an organization, count amount of people perhaps that are in a recruiting funnel and maybe further down in the counter pool. Some of those metrics are easier to capture. Easier know gender is generally easy to capture. You can even infer gender with 98% accuracy from someone's first name. But how, how do you basically build really rich analytics if you are struggling with data on, for example LGBTQ community data disabilities? How, how, how accurate is the data that most enterprises have on their employees? And what are the categories that we still struggle with so that you know, no matter how you analyze the metadata on meetings, for example, and who's in there, you need to know who those people are as in what their representation is amongst these categories. So how do you go about solving for that first or how difficult is it? Diana?

It's a great question. So your data is only as good as the inputs and the people, you know, kind of the accuracy of the people inputting that data. So that's an excellent point, Johnny. And I love that you guys were tracking what the recruiters were actually as a sidebar were actually doing in their searches. Because of course that's another way to bring some objectivity to the scenario of people reporting on how hard their, their challenges are. And like, well, let's look at what, what you actually did. Right. So similarly with the way that data gets tracked about self id. Right. So a lot of these, the vendors I talk about where they look at metadata and not just from calendar invitations, they look at emails, they look at meeting, sorry meeting notes and then documents that people are collaborating on so you could see who's commenting on those documents, chat threads, all of that stuff. But then you have to marry it to the HRIS data or the send.