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 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 work smart. I'm Johnny Campbell, CEO and co founder of Social Talent and this week we're talking about Inclusion analytics with 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 why, 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, you know, 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, 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 it 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 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, you know, 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, 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, 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 Johnny 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, having that there. But one of the challenges when we look at DI metrics and you're so right, you know 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. 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 the 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 H R I S data or the employee census data. And how good is that data? Right, that's really the question you're asking. And I think for most organizations, not very. And there are a lot of different reasons for that. One, it's very personal. And especially in, you know, sort of younger generations, people are resistant to being labeled a certain way. And a lot of those specifically, you know, the categories you mentioned around sexual orientation, around disability status, around veteran status, there's taboos that come with those things in a way that there's less that come with, let's say, gender. Unless you are, you know, maybe a non binary, gender fluid kind of person, then you might not be fully out to your organization. But most people, if they're a man or a woman and they're, and they're cis, are used to talking about their gender identity in a way that they might not be as comfortable revealing that they have a disability, especially if it's invisible, and then they have to ask for accommodations and things of that nature. So there's a few reasons why organizations struggle with this data. People are afraid to report it. Organizations are not always clear with their communication on how the data will be protected and used. So in the absence of information on knowing that the data will be kept safe, most people assume it won't. And then they don't. They just fill in the blanks with whatever disaster scenario they might imagine, right? This data will be used against me. I might, you know, they might end up using it to discriminate against me. Maybe I'm not comfortable being out. I don't want to be treated as different. And now I'll be out just by reporting this data, even if they tell me it's going to be anonymous. So the first thing is the lack of good communication around data usage, storage, protection, security, all of that. The other thing is, you know, at least in the United States, I don't, I'm not as familiar with European requirements or other countries. But in, in the United States, a lot of that data is governed for larger companies by the eeoc, the Equal Employment Opportunity Commission, and they have required mandated reporting on employee self ID data such as gender, race, ethnicity, usually veteran disability status, the protected characteristics. One thing about the EEOC that I never understood and never made any sense to me is when there are blanks because employees have not gone into the system and filled it out. Whether because they're new, they forgot, they didn't know or they intentionally didn't want to. There is some kind of tacit encouragement for the HR representative of whoever's in charge of that data to fill it out for people on their behalf to do the guessing game, right, as you say, to kind of assume in a deduce based on a name what gender somebody is or a LinkedIn picture. You might look at somebody's profile picture and decide, oh, this is their ethnicity. Right? You're just kind of looking at them. And that's highly problematic for various reasons. Nobody should be able to assign your identity to you. It's yours and it's yours to report on. But I will say that organizations that struggle with this, sometimes they're like, well, if we can't get employees to fill it out, at least as a first pass, I can get some 80% accurate data by guessing or 70% accurate data. And that's challenging because in some of those organizations really don't have the systems or the communications to get any kind of data and they have to report something. So I understand where they're coming from. I don't understand why they couldn't just write I don't know or decline to answer. Just fill in the blank with that if that's what they're going to fill out. But that's part of the reason. So lack of communication is a big one or lack of clear communication around data usage. A history of that data not being protected and used well is another one. And then the third one being the sort of tacit requirement or encouragement of other people to fill it out on a third party basis. The solution to that, of course is better communication and, you know, more ownership being given to employees and more agency to fill out their own data and complete it on a regular basis. One way to do that might be to engage your ergs, for example, or to engage other smaller groups inside the organization to speak to their own population that they represent, such as, let's say there was a black erg and you might have the leader of that group talk about filling out the race ethnicity category and how that data will be protected in an environment where it's more personal and you can have a conversation versus an email being sent out. Please fill this out. I've seen organizations also, you know, when we've worked with them and consulted to them on they're called self disclosure campaigns, like running a campaign essentially to get the self ID data to be more frequently disclosed. Actually just being vulnerable and saying, look, we Know, this data has been misused in the past, maybe even by us. Right. And here's what we're going to do to make sure that doesn't happen this time. Then this is why it's important for us to have this data. This is what you, as the employee, get out of it, right? The sort of what's in it for them. We'll have better programming, we'll have better accommodations. We need to know who we have so that we can design the DEI programs that are appropriate to that population.

I've met many leaders who, with great intent to make progress on inclusivity, on equity in businesses. They spent the first year just trying to get the data recorded, get it right. Is that common? And would you say that you kind of sometimes do have to wait to get the data or should wait to get the data, or can you make progress even though you haven't normalized the data, haven't got a baseline there? What is, what is the scientists in you say to organizations who are struggling to decide whether to go ahead with activities straight away or to try and get the data story correct first?

Yeah, well, I'm a scientist, a data scientist, but I'm also a business person. And so I think the business person in me is very pragmatic and I'm very much don't let the perfect be the enemy of the good. 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 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. We saw so many Organizations jump into DEI in 2020 with no data. And by the way, there were so many organizations that were doing all kinds of other things with no data without even a DEI program. Right. Like, doing nothing also has a cost and an impact. And while you are perfecting your data, like, this is funny for me to say, because I make almost all of my consulting projects are very centered around data. So we want to sell data services, but we always try to say, let's do as little of it as possible and as quickly as possible so that we can get on with the work. Because measuring it endlessly is also not the goal. And that doesn't mean we do nothing. Right. So something in between. I think, you know, a lot of the, the bigger consultancies, if you kind of traditionally look at the way they gather data, they'll bring a team of five consultants and it takes three months. And by the time you're done, you know, with the analysis and the report, it's four months old. Right. So that's not our approach. I think we, we try to be minimally invasive and quick, and at the same time we can be perfecting and improving the data as we learn more through our work with employees.

You gave the great example of the analysis that can be done in calendars to understand who attends a meeting, who engages in that meeting. Can you maybe explore some other ways that you've been able to see great data on inclusion that may not be as obvious to our listeners, they may not have thought of before that's perhaps just sitting there right in front of us, waiting to be analyzed?

Sure. I can actually show you a slide if you'd like on this.

Yeah.

Be helpful.

You might, for our audio listeners, describe what's on screen just to make sure that we cover those use cases. That's okay. Thanks, Dana.

Okay, so this is a slide for those of you who can't see that just shows a list of different metrics and different things we can track data from in order to look at inclusion where hidden bias might lie. And so on the left there's a column that says workflow. And so here these are some of the things we talked about. Access to invitations on meetings, how scene. What's the seniority of the people in the meeting, access to documents and projects. Right. Some of the, some of the ways people feel excluded from organizations is because only certain people seem to get access to the plum projects that they're staffed on. So we can look that instant messaging, inclusion in emails, response time to emails. Different people get emails responded to more quickly than others. So these are all patterns in our work process. Then we look at things around or metrics around work time. So the time you spend at work, one really interesting one that I always found fascinating, and this is a pre hybrid world or a pre Covid world, is when people were working in offices all the time and can actually look at badge data. So there's data being tracked around, badging in and out of a. Of a Company building. And you can actually see, for example, are people who are taking more coffee breaks and badging out more during the day. Are those people flight risks? Are they more disengaged? You can see if those disengaged people are more likely to be members of certain demographic groups. You can see if people who leave at 4 o' clock to pick up their children, whether that be mothers or any other parent or caregiver, but then they log on at night and get their work done anyway. What is that? How does that correlate then with getting promoted or getting access to senior seniority in meetings or projects? So that's an interesting one. The alternative to that, certainly in the virtual world, is login time. So you could look at kind of VPN data and what time people are logging in or out of their server for work requests, for PTO requests, for sick days, overtime. Is that granted when we give people discretionary kind of rights to grant that time? Just do certain people get it more easily or more readily than others, whereas other people get denied it more often and things of that nature? The column on the right is people. And so here we get into things that have to do with organizational network analysis, which is a whole rabbit hole. I don't know if we have time for. Um, but it looks at the way people interact across an organization. And the people that have the most connections inside an organization are your influencers or your information brokers. The people that have the least would be isolated individuals. So it's like, who as a person are you based on your interactions with others? What's your position inside the organization? And then you could look at things like we talked about promotions, performance reviews, departures, terminations and things of that nature.

And wow, when you look at this, there's so much information there. If someone listening to this or watching this podcast is looking to get started, do you have recommendations as to. Okay, here's the kind of minimal dashboard or recommended dashboard you think folks should be aspiring to have in place in their organization. Is it everything on that list or do you think, oh, actually, you know what, there's probably five or six big things that you. You tend to see a lot of information in that, you know, correlate with the other. The other data points.

Yeah, I mean, I think most organizations, when they're looking to get started, are not going to start with any of this stuff. This is very advanced for most organizations. The passive data is what we call it. It's not. There's so, okay, sorry, there's active and there's passive data. The active data is the data you gather actively from employees by soliciting their feedback. So that's your surveys, your focus groups, your interviews. Most organizations don't do those things regularly either. So when I say a total starter dashboard, I would say like, where's your engagement survey data? And are you segmenting it by demographic on a regular basis? Are you looking at differences geographically? Are you looking at differences among race identity, gender identity, sexual orientation identity and all of the above? If you are in a more advanced position and you are tracking things like engagement, turnover, you know, head count, diversity in representation, you might also look at things like career and network development. People who have development goals in progress, development goals in place. So this might be a useful dashboard to show. This is a dashboard I created for training and professional development. And I think when we get past the kind of diversity metrics which might look like this, where you have kind of each job class, this is based on in the US EEO categories, from executives to service workers, and you're looking at the diverse class versus the percentage of diverse people versus the benchmark by, you know, similar industry companies. This might be a representation dashboard. Your next level might be to look at, you know, development. And because these are more retention based metrics, the things that keep people there once they got in the door are, you know, having development goals in place, goals in progress, working on personal team development plans, participating in professional development based on various programs, promotions. So this is a kind of a career growth dashboard. You know, erg metrics are a big, a big starting point for a lot of companies because most companies start their journey in DEI by doing one of two things, erg, setting up ergs and doing holiday celebrations of various kinds, right? International Women's Day, putting up a flag for pride month or whatever it might be. So when you actually run ergs, most companies don't sit down and say, well, what's the goal of these ergs? Are we trying to create communities? Are we trying to promote career development? Those are different things and they need to be measured differently. So on this dashboard in particular for the organization I was working with here, we added in growth in network size over time because that was the main goal of those, er, to increase network sizes. So this is kind of a more, I would say, middle ground, right? It's not as, as basic as having just the diversity numbers, but it's not as advanced as having the inclusion analytics. This is an inclusion analytics dashboard. And here you see a graph on the right similar to what I was describing earlier. The vendor that created this is called Worklytics, but there's multiple other vendors you can use and I'll name some of them. This is a dashboard that just shows inclusion in the top 500 most important meetings. And it's a time sort of a longitudinal graph. So from January to December on the X axis, all the months are listed. And then you've got percentage on the y axis from 0 to 100% of various gender identities. And in this case you've got the male line going down over time, the female line going up over time, the gender queer, non binary line starting only late in the year. Because I guess either they weren't tracking or those people weren't represented in a significant way inside the organization. And that line is growing over time in a positive direction as well. What you see as the takeaway is that, you know, the gap is closing. I would, I would say technology organizations are much more likely. I've noticed tech companies much more likely to have dashboards like this set up. They're excited about it. Your typical more conservative organization in an industry like banking, financial services, insurance, academia, government, much harder to find this kind of stuff because they've got all kinds of data security concerns, understandably, which we address through our work. But this would be kind of an ideal aspirational goal, I think.

Talk to me about the future and maybe start with the present to what are you seeing today that's really exciting that is beginning to push us into new areas that we can measure are new ways we can impact behavior that's being driven by technology, new products, and feel free to mention any vendors that you'd like. Within that, when you see something, what have you seen lately that's like, wow, I want everyone to use this, or the possibilities of this are amazing. Or I love the outcome that you can achieve from this particular software.

So I have a couple things, so I'll say first of all, like, let's address the elephant in the room. The future right now, the immediate future for DEI is not looking great, right? Based on the volatility in the market, the political and legal things going on in this country. I don't know as much about what's happening in Europe, you'll tell me, Johnny, but people are pushing back against this. And what that's led for to. For us has been less of a focus on going in and measuring things like inclusion and more of a focus on working with leaders to demonstrate the business case for doing this in the first place. Because a lot of them are getting nervous pulling back and kind of regressing. So are you seeing something similar in Europe? I'm just curious.

No, actually significantly less so if I was to comment, I'd say we thought a year and a half ago we were seeing a decline in interest in D and I kind of off the back of the heightened 2020 awareness and it was waning off and then when we dug into it, we noticed it was just becoming, I don't really use the word normalized. It was becoming more ingrained in the DNA of these culture and people initiatives in lots of organizations. So it wasn't, it was going away, it just was, didn't have the spotlight because it was just becoming. This is just a required thing. And certainly, and certainly in Europe I think it's driven mainly through demographic changes. Ignore political changes because they're Perhaps they're more four to seven year cycles. When you look at the 20 year cycles of demographics, you see the generational changes and you see the Gen Z, for want of a better categorization of workers coming into the workforce globally who are led in a much more globalized world where everything looks the same on TikTok, TikTok and Instagram. So therefore they don't see as many political differences. They see, you know, I look the same on Instagram in this country as this, this country. So we're all the same. They're much more, you know, they're much more, less accepting of, of the previous conservatism around these efforts and they're much more expecting of, of change and progress and good stats and metrics. So you know, when you talk about a business case, the slow business case that I'm seeing more highlighted, particularly in Europe, is that people will not work for you in 5, 10, 15 years time. Not the people who are working for you today. It's not about them, it's about people you'll need to hire for the next 10, 15 years. You don't have a chance of hiring them. It isn't about, you know, being the best dni. If you're not at a certain level, forget about it. A lot of these folks just won't work for you and they have choice because of the nature of the skill shortages in the market as well. So to me what I'm seeing is that's driving a more long term, hopefully sustainable view of DNI that perhaps is being masked by some of the, I'm going to hopefully call it short term changes in the political system in the US that's fantastic.

It's good news to hear. Just no for sure. I mean that's really good insight. I think, you know, with my clients that I'm working with now, a lot of them are the ones who started in 2020 or maybe even earlier and they're continuing their work. Those that are like trying to get into the game now or have been failing or tried last year, those are the ones that are struggling more I think because they were late to the game, late adopters anyway. And now it's like, oh, it's extra hard, right? But what am I seeing? So I think from a technology perspective the capacity to measure inclusion through is really impressive. More than that, it's. There's a capacity to measure collaboration more generally. So it's not just for inclusion purposes, but like how do companies become more productive? There's an organization in Europe, in Poland called Network Perspective and they're doing fantastic work around helping teams come up with ways to block their calendars for group meetings versus private working time versus one on ones based on the right patterns of collaboration that will lead to the greatest productivity for an organization. And they also track, you know, meeting attendance and meeting inclusion, things of that nature. So they're doing really great work. Humanize is another organization I'll name, it's Human yze and they have fantastic research based out of MIT that they're working with. Not just measuring as many of these vendors do, the inclusion levels in collaboration software like meetings and chats and emails, but also the impact on things like engagement on employee productivity. And I think they actually have information that costs out the bottom line impact of struggling in those areas. So it's like based on research that says, you know, a disengaged employee is 8% more likely to leave. Therefore based on your level of engagement, on average this year you're, you know, you're losing 8% of your workforce in the next year. They also look at things like adaptability and they have really interesting research that it's based on. So it's not just like data for data sake, but the impact to the bottom line. The other organization I'll name is Polynode P O L I N O D E. And Polynode has I think the best in class tool for organizational network analysis that I've seen. So they're really able to map networks inside an organization to see how people work together, how much collaboration there is across silos in a variety of different metrics that really you can see not just the boxes and wires that your HR team set up for an org chart, but how people are actually interacting and they have a tool that maps it automatically. So it's not just dependent on doing a survey, although you can do a survey of asking like, okay, so here's a list of 10 people and how do you interact with how frequently, you know, how much interaction do you have with all these people? But also looking at the passive data that demonstrates those interactions through the tools we mentioned and then mapping the two. One thing Polynode has done is actually looked at the difference between perception and reality. And I think so often those two are not even measured, let alone put side by side. And I know they did some work where you could see the unconscious bias of that perception. So, for example, they compared men versus women perception of inclusion in meetings and actual inclusion in those meetings based on who was on the calendar invitations. And what they found is that men were significantly more likely to overestimate in their perception how included those women were versus where you looked at how included those women were, in reality, it was much less. And, and that was an interesting, you know, point that they. An interesting data point that you can do similar things like that in your analysis. So there's just a lot that technology can do. I think the challenges we face legally and politically shouldn't stop organizations because as you say in Europe, the demographics are not that different. In the US we're also moving towards a majority minoritized population. In this country, it's already the case for some of the younger age groups. And the organizations that don't take this seriously will not have the competitive advantage, will not be able to hire the future talent of tomorrow and have the clients in the marketplace that they in the market penetration that they'd like to have.

So I think that's a remarkably apt finishing statement on that point. I think you've summed up exactly what I believe should be the perception of the reason to invest in these efforts and to invest in these analytics as well. Jenna, this show is called Work Smart for a reason. At Socials out, we believe that some people are street smart, some people are book smart. But anyone, regardless of their backgrounds, beliefs, etcetera, can be work smart. Right now, Diana, we want to focus on how you make yourself work smart. You ready to take our quiz?

Sure.

All right, let's do this. So we got some work smart questions from the team at Social Talent behind some of the categories. I'm going to show you on screen. I'm going to let you pick the category you want to answer. So without further ado, the categories are the following. Desk jockey, squirrel, workspace, jam, or robot rock. Which of those categories appeals to you? And I'll whip out the question.

Desk jockey.

Desk jockey. Okay, so let's shuffle by my desk jockey question. Okay. What is your favorite desk accessory and why?

Oh, okay. I just bought this, so this is exciting for me. I have a riser on my desk that actually I can pull up and down to create a standing desk scenario.

I love it. And tell me, how much of your time do you spend standing versus sitting now that you cut this riser?

About half and half. The other thing I got is a walking pad. So it's like a treadmill without the top. So I actually can walk and work at the same time that I do for like, two hours of my day.

Wow.

And I'm getting my steps in, so that makes me very happy.

So do you find there are certain meetings that you can do that for or certain activities you're doing that suit that, and certain activities that don't suit that? And are you walking right now, Diana?

I am not walking. You would have seen me going like this. Yeah. So most. Most of the time when I'm on a meeting, certainly when I'm recording a podcast, I'm not walking. The only meeting I really. It's like, with my personal staff that we sometimes are more casual. But yeah, when I'm, you know, editing a document, reading emails, responding to things that don't require interaction with somebody on a camera, uh, I am. I'm walking a lot of the time.

Danny, you may have inspired me to make an Amazon purchase to use my desk riser and start getting my steps in as I'm concerned. And I was on vacation a couple weeks ago, and I was doubling my steps for the day, and now I'm back in work. I'm. Now it's killing me. I'm like, how do I. How do I fix this? I need a tool. Danna, it's been fantastic hearing your insights. I literally could listen to you for hours. You have so much to offer. I'm sure our listeners would love to know more if they'd like to reach out to you to find out about your services, find out more of your talks, your writings. Where can they find you? What's the best place to reach you?

So LinkedIn would be great. I'm always happy to continue the conversation with folks. Please find me on LinkedIn, should you guys have my link? You want me to share that with people?

Yes, we'll share that. That's perfect.

Okay. LinkedIn would be great. And then my email, which is Diana F. Analytics D F A N A L Y t I c s.co not.com.

And what's the perfect type of customer person that you can help? If there's, if there's a sweet spot, you think. But if you're an X working in a Y, I'm for you.

Yeah. If you have a position of authority or influence over DEI policy or DEI practices or programs of any kind, you know, whether that be in a C suite position or a director level position, wherever your organization has sat you and you lack influence and you lack abilities to measure impact and you'd like some help demonstrating that impact through data or you want to take the next step and actually create some solutions to implement the insights you have around change management, around program implementation, around training. We have a team that's very well qualified to do that. And in this market especially, even though we all know there's a business case for dei, if you're struggling to demonstrate that business case through the financial metrics that we talked about, you want to show your leaders, here is the money, the amount of millions of dollars they're losing every day by not investing in this more seriously, we have that research and we'll be able to spin those analytics up for you very shortly to help you get that buy in. So those are both, you know, actually doing the work and then making sure people give you continued engagement and opportunity and resources to get the work done. Those are, those are two of our main areas.

Well, Danny, it's been a pleasure talking to you. Our next episode you must stay tuned for and listener, you must stay tuned for as well where Raj from Sanofi is going to be talking about their DEI policies and how it is integral and part of their five year plan for growth and, and how it isn't just a business case for dei. DEI is the business case for growth and he'll be sharing that with us on our next episode of Work Smart. Dan, thanks for joining us this week. And listener, thank you for joining us. We'll be back in two weeks time. Stay tuned. Make sure to follow us on your podcast app or tune in on LinkedIn or YouTube for the next episode.

Sam.