HR Vision Podcast #36 – The importance of People Analytics ft. Joseph Ifiegbu

Ivo:
Hey everyone, and welcome to the HR Vision podcast. I'm your host Ivo, and every week I'm going to have a conversation that matters about HR. This week. I have a very special guest with me directly from the Big Apple, New York. I have Joseph Ifiegbu. Hey Joseph, how's it going?

Joseph:
Good, good. Thanks for having me on the podcast.

Ivo:
It's a pleasure. Joseph is the founder and CEO at eqbtl, an HR Analytics platform. He has an extensive experience in data science, HR tech and people analytics. So today we're going to talk about HR data and the importance of people analytics. I'm very much looking forward to it. Joseph, thank you so much for coming in. I'm very excited.

Joseph:
Thanks for having me. I'm excited to chat about HR analytics today.

Ivo:
Cool, cool, all right. So let's get to it. We start always with every guest by asking them to introduce them. You know, just tell us about who you are, and, a bit of your background.

Joseph:
Yeah, so so I am one of the Co-founders and CEO of eqbtl. eqbtl is an HR analytics platform. And really my background is in data science, not people analytics. So I work as a data scientist my whole career. I've worked in different sectors, I've worked in sports analytics, health analytics, ecommerce, retail, research. And about five years or so ago, I got into HR Analytics. I've worked at wework. I was a head of people analytics for three years there. We work on that also work that Snapchat, leading the HR technology team as well. So yeah, that's that's a little bit about my background.

Ivo:
That's a very interesting background indeed. I'm very interested to know, like, data was always your passion I guess? How did that happen? First of all, why did you become so interested in data? And then my follow-up question will be that transition from data to people's data; to people analytics.

Joseph:
Yeah, so yeah. I studied math and statistics in school. So I think that was it for me. Math was... I played sports. I played sports as well. I did soccer, so those are the two things. I was really good at math. In school I actually was trying - I was thinking I was going to go get a PhD at some point and math or statistics and that was because I didn't really quite know what I was going to fully do with it. I was like, I'm good at this and maybe I'll just go get a PhD and do something like that.

So after my undergrad I got this job and my boss at the time said to me like you should go and go do your maths and statistics and focus on data science and because that's going to be massive. We're seeing this trend and then yeah, he saw me and looked at it. Researchers like this actually is looking really cool. Yeah, I went and I studied that. And once I got in I said: You know studying and like seeing how what I could do with data science. I was hooked, I couldn't. I was just like wow. This is what I want to do for the rest of my career and I just enjoy it. You know, just using working with Python, R and all these other tools to solve problems getting massive amounts of data.

I just enjoyed the creativity that came with it. And yeah, that was it and I've not stopped since.

Ivo:
Yeah, I can see but. What was pulling you was that solving problems using data to solving issues within whatever that issue may be right. That was the thing that told you in?

Joseph:
Yeah, that's exactly what it was. That the look on someone's the look on someone's face when you just give them some insight that they had not known about before. Yeah, that feeling was incredible right? But it's like he just took his essential garbage, which is just data that is just numbers like I can do it. And then you do this whole magic and then you get this analysis and insights. And then you deliver that. And someone's like "oh wow!", I'm just asking more questions. That feeling was good! It's so good. I was like, I want to feel like that all the time whereby you're finding things that someone else didn't know existed. That was what was pulling me into it. That solving problems, that hidden thing in there. That was why I really went into it.

Ivo:
Awesome. Now let's go a step further which is taking that data science background into people analytics. How did that transition come about? Tell us a bit about that.

Joseph:
Yeah, I like to always say I got into people analytics by mistake...

Ivo:
By mistake, OK!

Joseph:
By mistake, right! I, outside of time, I was in the I was in Toys R Us. I was a data scientist, doing a lot of the ecommerce, retail, analytics. And one of the things actually we're doing and I did in the role was actually somebody called like a lot of the Market Basket analysis. What that that was is: you are, for example, if a mother comes to a store and buys a feeding bottle for the baby. What is the probability that they'll buy a pacifier? OK, and so we would see that usually for example, like a mom that buys a feeding bottle, has a high probability 80-85% likely to buy a pacifier. And so we're looking at these patterns of behavior of shopping and then what we will do is we'll put the pacifier right next to the feeding bottle in the store.

So you increase that because like typically that's what they would buy. Someone who buys this would buy this, right. And so you do things, like we placed the toys not at the parents eye level, but at the children's eye level. And so we had all these things where you're trying to understand behaviors of shopping, and so that was that was people. That was people behaviors that was people behaviors, but in in this shopping environment. So I understand. I think about just how people make behaviors and how you use data to really change or influence that or things like that. It was really interesting projects that we worked on and so and so when I looked for a new role.

Then I went online and I ran across this people analytics. I'm like what is people analytics? I mean we are doing technically analytics about people. So I started reading about it. I'm like "oh this is a bit different than what I thought." This is interesting, right? And I'm like "OK, I will apply for this." I never thought I'd be HR, like that's never something like, yeah... And so I applied for it, and then went for the interview, the head manager was saying: Look this is what we want to build. We have all these HR systems. We want to bring everything into a place, we can tell stories of visualize, right now we don't have anything. I'm like OK this is really cool. It's a different domain and then I was like I can do it because if something it was something different. It was challenging and that was really how I got it, I got the job and then that was it. I just started learning and growing in the role, and that's really how I got into HR analytics.

Ivo:
Yeah, no, I see it's an interesting path. And a lot of things in life I think happen by accident, and sometimes they are happy accidents, right? So it seems the case, because now you work at an HR analytics platform company. So I guess you got beaten by the HR bug I guess.

Joseph:
Yeah absolutely. I mean that was what it was like. You know, once I got into once I got into the HR space. I just enjoyed like you could almost almost in real time see the value that you're providing. Like you can do an analysis and then you can say hey look, this is what think when it comes to pay. And you can actually change that immediately. You can see the changes, you're not just doing an analysis and gathering dust somewhere, in someone's drawer somewhere. No, this is something that you do in analysis and you can see that you can see that change and acting. And I just love that about it.

And then it has to do with people as well, right? Because you do this analysis, but there is this conversations that has to go on. There are some things that you have to do that involves people. And so I really enjoyed the two sides of that coin in the sense.

Ivo:
Yeah, I can totally understand that. Before we dive a bit more into people analytics I would just like to ask a general question to you. When we're talking about people analytics and data, a lot of times the idea is that it's for bigger companies and it's for more tech savvy, behind the desk kind of companies. In your view, in your expertise. Do you think a small company should also be using data analytics? And why is that?

Joseph:
Yeah, I think the idea of people analytics is just really using using numbers to help you make informed decisions, right? That's simply what it is. There's the more cool stuff. Usually ML and all this, and I mean yes, that is a part of it, but maybe only a handful of companies are really doing something that advanced. So I think at the core of it, it's just using numbers to help inform right, rather than just without numbers, just using your gut feeling.

And so I think small companies you are 50% company, you're 100% company, absolutely like. If you're trying to make any decisions, if you're trying to make things around pay. Yes, you should you should use data to inform if you are actually having some disparity in your pay or not. Like if you are looking at engagement, you 100% a company with an engagement numbers. See the trends in that. Are you trending up? Are you trending down? Are you remain the same? How is your culture being affected?

At the end of the day that's really what it's all about. People analytics, per se, is not limited to big companies. Yeah, you know, as long as you have a good amount of people, then yes you need people analytics to help you make better decisions.

Ivo:
Now I think that's a good point because I believe sometimes there's some prejudice, not prejudice, or you know small business they are focusing in other things. You know trying to grow the business and people is not the first thing that comes to mind and especially like looking into data regarding people. So that's that's why I was asking, but you just you just gave a right answer right? Even even with small things like pay you can you can look into that data. At least you don't need to do it. You know to look into the big complex things around the HR analytics. But you can start with small things and data should be integrated even with small businesses.

Joseph:
Absolutely yeah.

Ivo:
The next the next thing that I had for you was. You know, sometimes there's this idea, or maybe a fact that everything that is directly related, or that you assume there is directly related with growth of the company, you change it quickly. But when it comes to people, it takes some time. You know, people related issues often take some time. Why do you think that is? I believe we can agree that people are one of the most valuable assets that company has, right? Why do you take sometimes so much timeto change people related issues compared to other issues within the company. Like you know, sales, marketing, those things.

Joseph:
Yeah, I mean it could be a variety of issues, right? I mean, humans are... we are complex. When you bring just one person - there's a complexity of thoughts and their decisions. And then you bring two people that's exponentially more. And then you bring three, four, five, and then you have an ecosystem of different ideas and thoughts and philosophies. And all of that.

Then you know you bring in things like hierarchy and leadership. All of those things. Things like egos. There's so many things that like effect. So let's say you find an issue and you all you have to change it. We have seen it.

I've seen situations where changing this one thing might affect my team. As a leader, so why should we change it? So there's so there's so many reasons. It could be ego, it could be fear of the unknown, fear of change. This is what we've had. I've been in this community for 10 years. We've always done it this way, yeah it now because that's gonna affect me. So there's ego, there's fear, I mean that's what it is, right?

When it comes to people, a lot of times it might be hard, but I feel like that with companies, I think it's all about going back to the why. Like why are we building what we are building; What are our values? Trying just based on the values. So here are the values that we have. Any decision that we make being tied back to that value, then you can actually enact change. But if decisions are essentially made different from what the values of the company has, that makes things hard to change. So I think that's one of the things just really kind everything back to the value type everything back to the mission. Make sure everyone understands what we're trying to do, and I think that's kind of how you can make that easier.

Ivo:
That's an awesome insight, cool. Following up on that, how do you think data can help there? How can data able be able to enact change to help people understand that this maybe is needed, because the data is showing that? I don't know.

Joseph:
I think that data can really just show you where you are lacking, right? I mean, beyond that, like it's one thing to see the data, but I think it goes back to something you mentioned, right? You can see the data, but there's the people element of it. You have to take steps right? So I think showing data is no longer enough. There has to be actionable steps. Here's the data, but here the things that we've seen, here's the recommendations and then and then did the showmakers have not take those those insights and have actionable steps to actually change stuff.

I think what you get from data is that it shows you where you could be lacking, right? However, I think the power of data is decisions that you make off of it, not just seeing it in itself.

Ivo:
Yeah, no, that's. I think that goes also to the. To one thing that I was listening you to or another on another podcast, it's about also using the data to gain insights, but tell stories to enact change. That's the main thing, right? Yes, absolutely. I mean, that's what we're building.

Joseph:
For us, I mean, you see there's so many systems and technologies out there. People are tired of seeing, but at the same time, with the amount of HR technologies out there. There's even more gap between the data and systems and actual insights. So what we've done is like we've built this connection, we build this integrations to be able to connect to these systems, bring that into one place, create the right data structures. And then tell you a story. Leave you to tell your own stories so you can actually enact change.

That's I think that's kind of where a lot of tools should be moving towards, whereby it's like, not just showing still dashboards here. Well what am I doing with these numbers? What is it going to do for me? Can I take it a step further? Can you take it a step further and say here the things we've seen here? The places that we're doing well here, the place that we're not doing well. He has some recommendation. Here are things that we can do, XYZ and things that you should do. And then take those steps further, and I think that's kind of where data becomes useful essentially.

Ivo:
Definitely. I cannot agree more. Another question for you based on a podcast that I listen to you to, I'm paraphrasing, so take it as it is. So I believe you said something around: Start building your data foundation when you're at 100-120 employees because at 2000 you're a bit too late. You said you said something to those lines. Why is that first of all, but do you think it's just harder, because you have so many people, so many egos, so many things that it's hard to pull that off and build the system that that works with with people data.

Joseph:
Yeah, I mean at 2000, it's harder, it's exponentially harder. You can do it. Wherever you are, start wherever you are now. So if someone is listening to this and says we are at 2000, start there. If you're 100, start there. They'll be like "So I can't do that anymore". No start there, do something.

I think why I said 100; start at 100. It's because you have a lot less people. Yeah, a lot easier then. You pretty much almost know everyone's names. That's simply what that is at 2000. That's like what? 20X that. And you probably have more systems. There's more hoops to jump more like, just more people to go through in terms of decision making, more egos, more XYZ. There's so much more there that makes it.

And then frankly, if there was a problem at 100, at 2000 that problem has expanded so much more. At 100 it's so much easier to do that then. That way you create the right foundation. You have cleaner data, cleaner infrastructure, so that when you skip to 2000, it's not that problematic at that stage essentially.

Ivo:
Yeah no, that makes total sense. Looking at HR analytics, people analytics, everything that you worked on. I would like to ask you: What do you think are the main KPIs in HR that we should all be paying attention to? What do you think are the main KPIs within a company that should look at, that will drive the most growth or engagement within your teams.

Joseph:
It varies. Like beyond the core things. Everyone looks at time to hire, time to fill headcount, headcount growth, those kind of things, right? There's so many of those things people look at, I think it varies. Like they saying things that affect the that another company doesn't really; they don't really think about it. But I think conceptually, what people should be looking at is. We should be looking at things in a time series format: here's what we are now. How over time? How have we improved or how worse got things?

Then based on what we are, if we continue this way where are we gonna be? So if you look at your tradition, you saw that, we increase the attrition by 1% every month for the last 12 months. You've seen the trend, yes? And so that's a story. It's looking you in the face. Like you were at 5% attrition in January and December. You at 17%. What happened? I think seeing things over time. Time is such a such a big thing here with when it comes to people analytics. Trying to see things or any analytics trying see things over time, seeing trends. It's such an important thing so I don't like when I just get data. This number I'm like "OK? Well that's rubbish to me." and give me the right context. So you want to cut it over time. Seeing that story, seeing things in the way in a story format. It's so helpful because now gives you that, so I think that's what everyone should be looking at.

Look at your story, look at look at the timeline of things of events of promotions, of your engagement scores, of your attrition, of your growth of your burn, spend, whatever that is. Looking at that story over time helps you understand what could be going on and what you could do to change things. And that's what I would suggest to organizations.

Ivo:
All right. That's very sensible and very insightful. Yeah, because I think sometimes people just don't have the patience they want to look at the numbers. And just in a month to month basis, and you don't give it time to create that story to create that trend to understand the context. OK, because maybe you're up 5% in attrition this month. But if you look at "Oh my gosh 5%". But then next month it's 2%. So I really like that insight in like in terms of time, being patient to tell the whole story. I think that's that's very important.

Joseph:
Absolutely yeah.

Ivo:
Just looking looking a bit into what eqbtl does, I would like you to tell just a little bit about about you guys and what you're trying to do. Of course, you already explained a little bit trying to bridge the gap between data and insights, but that was just a bit a bit more so people out there can can know what what eqbtl is doing.

Joseph:
Yeah for sure. For us I think the goal for us is really speak to insights. Right? It's Speaking of stories, while we are at work, we build our systems. We built all the pipelines, the solutions, we built everything internally because there were so many systems and we had to figure out a way to bridge that gap. So we had to build all the pipelines that connect to all these different systems. Bring that into one place. Create this structure and then we had a front end. We had a visualization tool that we used right?

We spent about a year and a half. Over a year and a half, and you know a couple of million dollars. Really building out the team and the product and all that stuff. Yeah, and we learned so much from that. Right? But here's an interesting thing. From doing that we said look, a lot of companies don't have the time or the resources to spend a year and a half and $2,000,000 building this.

Ivo:
That's what I was about to ask, yeah!

Joseph:
A lot of them don't have that right? Yeah, so can we create something that helps organizations do this cost effectively and quickly? And those were eqtble came about. And so what we do and what we have built is a tool and platform that connects to pretty much all your major HR systems right, your workdays, greenhouses, your leverage, your lattices, your culture, arms of the world. Connect to those tools. We bring that into our generic data model and then we have a front end whereby we can tell you the story and we'll enable you to tell your own stories about your organization.

But the thing about that is: It's quick. It's not a year and a half is not six months. It's not all three months. You can do it like within less than a month, few weeks in some cases, and with some of our systems within hours. So all of a sudden you sign up with us. Within like a couple weeks. Boom, you have all your analytics about different systems and you bring that into one place. Now you can tell your story. Connect your performance data to your attrition, to your diversity and you tell in one place and tell a cohesive story. And that's what we do with organizations: speak to insights.

Ivo:
Awesome, awesome stuff. last question for you, a bit of a look into the future. What do you foresee in people analytics? Do you think there's still a lot of companies out there that need to work with data? What do you foresee? Do you think data will be more and more and then the undeniable assets for HR teams?

Joseph:
Yeah, I mean it's not going anywhere, right. We're not going away. I think people analytics is not a "nice to have" anymore. It's a must have. And I think companies that refuse to embrace it. I don't know. I think you're pretty much in the stone ages at this point, right? Or the dark ages I would say. But I think it's gonna be involved. I think there's gonna be more more the push towards actionable, more actionable insights.

The push towards data for change. The push towards just yeah really just using data to inform decisions and then even just like not even being, you know like and it not being reactive but even more proactive you. Make sure that you have the right data in place from early around to be able to kind of remove problems and even come up right. So yeah, I think more and more teams are gonna embrace. I think you're going to see more and more tools, but simply I think going to see more consolidation of tools as well.

So you start to see some of that. Like to see some of the big companies say, OK, we want to get this tool and buy this tool. So we going to see more consolidation. Again, I think that's what it is. Because the nature of innovation is it expands before it contracts, right? It always happens like that. So you go to tools and then you can start seeing local consolidations here and there. Some tools will not make hits, some tools will become bigger. Purchase others. You're going to see a lot of that and then over time you're going to have setting tools that are at the staples for doing certain things, right? You're inside to see that right already in the industry like you see like pretty much like work, they greenhous, lever, lattice. These are things that people like. Already are going towards for setting things right?

Yeah, I think just what it is. It's just the nature of innovation. But I think it's an exciting time in the people space. I think we're still in in the beginning stages. And I think there's so much more we can do in this space to help companies make better decisions about their employees.

Ivo:
That looks that looks great. Just one more thing, before I let you go. I'm sorry. I just wanted to have just wanted to have like a very kind of down to Earth advice for people. Someone that is listening they never did any people analytics just one one insight or one advice like "How do I start?".

Joseph:
Yeah so, that's a good question. So this is some of the people asked me this quite a few times. When I started with people analytics, in the initial stages. Again, I am trained as a data scientist, but when I first met, before I ever said to code or do anything, I did basic stuff because at the time people didn't have access to basic analytics. I used excel. I just set up averages, I would just download data, start with Excel, insert the data and say "Here's what we see." and people start to ask more questions. They wanted things to be quicker and more automated. And then that's kind of when announced that. OK, well we need to build the systems into XYZ.

So if you want to start with where you are. Like what are the issues? Start with the questions. OK, what are the issues we're having right now right? Where are the gaps? OK, these are the gaps. OK cool. With the tools that I currently have in place. What can I do to at least help solve a little bit of that, some of the problems. I have excel, I can download this data set. All right cool, let me just start from there. So I think starting from there and then starting to tell the stories. Helps build trust helps build equity in terms in their minds and wow, this is really, really good. And then you can actually request for more, more money, more resources later.

So that's that's what I really did for the first six months. It was just me building things in Excel. And then, like I downloaded the free Tableau reader. I'd do the stuff and I'll share with people and tell them "download Tableau reader" so you can see it your laptop as well. That's how I started and then and then three years later I had an 18 person team.

Honestly, the truth is that. Just start where you are with whatever tools you have and then grow from there. That's what I would say.

Ivo:
Awesome advice. Joseph, thank you so much. Thank you for your time. This was super insightful. I really enjoyed this conversation. I hope you did too.

Joseph:
This was awesome. This was fun. Thank you so much for having me appreciate. I appreciate you bringing me on the on the platform.

Ivo:
No problem at all. We appreciate you coming on, so thank you so much. People listening out there. We'll see you next time.

Joseph:
Thank you so much.