EP020 - How to use data to refine company operations with Stefan van Tulder of Talent Data Labs
About the episode
This episode focuses on data analytics and the importance of metrics for better company operations. Why should you measure how you work? What can you learn from the data? How can you use the data to refine company ops? I discuss the topic with Stefan van Tulder, founder of Talent Data Labs and co-founder of Josie App.
About the guest
Stefan is a data scientist running a variety of applications based on a core technology and data-framework developed by himself and his team. The home of his data framework is Talent Data Labs but he has many different storefronts. All storefronts are busy solving the most difficult and data-deprived HR technology problems. Currently mostly focusing on two major areas first of which being extreme workforce diversity (refugees, prisoners, people with mental disabilities) and secondly occupational health, hybrid-work, and job-related mental illnesses. He is also the co-founder of Josie App, a career scoring app.
About the host
My name is Peter Benei, founder of Anywhere Consulting. My mission is to help and inspire a community of remote leaders who can bring more autonomy, transparency, and leverage to their businesses, ultimately empowering their colleagues to be happier, more independent, and more self-conscious.
Connect with me on LinkedIn.
Want to become a guest on the show? Contact me here.
Quotes from the show
Not everyone can deal with the mental health implications of remote work. According to our data, the upper limit of possibility for remote work is 25%. So a maximum 25% of society is actually equipped to work fully remote.
Data can help us to identify errors in our system. The smaller your company is, the larger the impact is for an error. For example, imagine a toxic employee or a bad operational process. But, the bigger your company is, the larger the relevance for the same error. It has become a legacy and a trend, so fixing it from the start is important
We realize something only when we see it, which is very human. You know what you want when you see it. You know what went wrong when you see what went wrong. It's easy to point to the mistake of someone else, right? To go like, okay, obviously, right? But it's very hard to internalize it. And that's why it's so important that we keep a data-driven journey.
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Welcome everyone, another day to talk about the future of work and the future of leadership. Today we will talk about people analytics. It is a growing part of HR and people management, yet not many leaders know why it is so important and how to get the best insights. We'll discuss the topic with Stefan van Tulder, founder of Talent Data Labs, with specifically focusing on the basics and some best practices. Hi Stefan, thanks for joining.
Hi, Peter. Thanks for having me and having us. Share some of our insights here and lovely to meet you for the first time here, and then looking forward to our conversation.
Awesome. I asked this question from everyone maybe not really applies to you because you work from the office, from the Netherlands. But how did you come to work in people analytics. And what's your personal connection to remote work or asynchronous work?
So, I'll take that in two parts. Because there's a personal part of it. How's no personal connection and sort of what is my connection to it in general? I personally, so I'm a scientist, I'm a researcher. I'm a aficionado of psychology. And I really like to look at human behavior in groups and in clusters and in masses? So anything humans do fascinates me? And recently we've been seeing a lot of trends. You know, mental health crises, we've been, you know talking about the working from home pandemic and plenty of other material around that sort of category. And I monitor data closely. So our company monitors basically all human movements as closely as possible. We have millions of people in our databases stretched over B2B and B2C consumer segments. And we have personally seen sort of the mental welfare of people plummet with the insurrection of basically working from home and the hybrid work. Also coincidental of course with more health, sort of real health related issue? It's covid pandemic, but also mass layoffs and those kind of things? So but in isolation we can also say that there's a really strong remote work effect on sort of the physical and mental state of people in a today's work environment. So I mean, personally, I mean, I just, I hate working from home to be honest. So seriously, I need total sort of Transparency between sort of what everyone is doing because also make so many mistakes especially on the data science front? So if I misinterpret any terms of data that I feel that I've been personally struggling a lot of getting people towards the office, so that's why I'm officiated with it. But you know, the people in my team, they prefer working from home when they think it's going phenomenally and they're very productive and very happy. So who am I to judge?
Exactly. And how do you collect these data points? Because you mentioned that you have so much information around how people behave, shall we say? And may I use the word behave? How they feel themselves in the workplace. And just on a basic level, these kind of feelings or attitudes are kinda like harder to track a little bit, especially when you're at work. Because we have like so many issues with it? So like, how employees are able to share or relate to any kind of attitudes that they feel that's also hard to track. Do you track by trends, for example, you know, from A to B to C correlated with any other different variations? Like for example, the pandemic, you mentioned the pandemic and the working from home trends. So what kind of data that you collect from where and pretty much how.
Yeah. It's a very broad question that I have to answer with a very broad answer, unfortunately. So when I say we track people movements across the board, we have especially the company that sort of is hosted behind me, we have a lot of B2B clients on board and B2B clients, they're very good at Monitoring their employees. And that sounds a bit creepy because it totally is. They really want to know, so how many sick days do people have, right? So how engaged engagement is such a fascinating thing. It's the most simple survey out there, but companies insist on continuously pushing it outwards without trying to affect it very much, right? So it's such a passive tool that companies don't really use, but as a benefit, the data is widely available? People like me can access it easily? I API myself towards most of the suppliers out there. I also facilitate engagement service myself. And those surveys, they include a couple of interesting data points? We ourselves have one that also includes a burnout, sort of burnout, mental health related issue index. But plenty of them, they include a degree of satisfaction, you know, organizational commitment. Things that relate to people interacting with their organization that all that data is available sort of widely? So anyone working for a corporation will have a data set like that and build upon them? So we take all anonymous data and we interact with that in a certain sense? But that's not just it. Because I want to see, you know, how happy people really are. So luckily we've built a couple, we've built ourselves a couple of B2C platforms? Business to consumers. One of them is fully owned by us, which is called Josie, Josie app. And that platform really tries to help people be less unhappy with the situation they're currently in. And to make people less unhappy, you need to understand how happy they are and those kind of things. So we're getting a lot of that sensing from the market directly. And then there's a last sort of hybrid way we do things which is B2C to B. Now that sounds stupid cause it is, but it basically means there's platforms where there's lots of users, lots of talent, lots of individuals trying to do something, and lots of companies trying to interact with those talents in one way or another? I can't specify the names cuz sometimes it's a bit hush hush whether our algorithms are inside or not. But these are platforms that, you know, use data and our technologies and our data models to figure out whether people fit together in one way or another and build recommendations and whatnot. That's how we get all of our data?
This sounds a little bit so not, I mean usually not promoted, but I'm original sociologist and so I studied a lot of like data related stuff, like statistics and demographics and I don't want to go like super deep into that. But still I think some of the points that you mentioned are mostly like passive data collection? So for example, you mentioned sick leaves which I wouldn't say that it's a good indicator on burnout, but it's an element where you can measure or kinda like, correlate to the level of burnout for within the company or within the organization. The more sick leaves they take, the more likely that the people are burning out or whatever. Just one relation, obviously not, I don't want to oversimplify the whole thing into that, just, you know, marking the correlation.
Sure, sure.
And these are like passive stuff, so obviously you have access to the I dunno, HR platform that the company is using, whatever it is, you collect the data from there, you put it on charts and oh, by the way you have this and this and this, and this kind of data charts here, which are declining those like five to 10 different data sets or data points. We assume from that actually it leads to this like, which is a burnout, for example, but that was just, just your example. But these are all passive gatherings, right? Meanwhile creating a survey and asking proactively the employees, it's a, you know, it's an active or a proactive approach. Do you do that as well or is it part of the whole data collect.
Yeah, actually we've developed two surveys. That one is sort of the remote health index which is really interesting, where people just figure out how, how healthy of an environment they've created to work remotely and how that's affecting them mentally. And parts of that is also kind of like related to self check-ins around. How are you doing? How do you feel? How's the home situation and everything? It's really about wellbeing and mental sort of officiated mental effectiveness at sort of your mental state in short term.
Do you, so I guess again, Sorry to be a little bit basic here for your taste maybe, but I'm trying to make sure that that people understand why this is so super important. Do you also interpret the data? Or you just provide the insights you know, you see the trends, you see the data. And from that, obviously gathering the insights is one of your tasks, but also one step further is to actually interpreting, or I wouldn't shy way to say that suggesting things to the company that, you know, you need to improve this and that in order to get that or.
And again, so that's something so we discussed this a little bit before we started this podcast? So you're really, you're really good at the qualitative part, right? The interpreting, driving that home and doing that properly and implementing solutions and those where we are much stronger on the data side at identifying, hey, this specific department is showing a significantly worse dip, or significantly better rise than another department. And we can isolate that based on certain effects. So look into that but usually we really rely on people like yourself. We have a very strong network of these kind of coaches that really drives that message home. But I would say, 75% of our clients do not sit on that level, on a qualitative implementation level, just on the analytics level? Now on the implementation level, it's, we do do some activities, but nothing as in depth or as good as you do it?
So, how large of the data that we are talking about. So that's also I think part of the key questions. Before we jump into the why you actually need to do this data gathering anyway in terms of people analytics. But let's discuss the data set that you're working on because why I'm asking this is pretty simple. Obviously you need at least some amount of data to drive to any kind of insights? That also means that you need at least some level staff or sizable team or sizable company where you actually have that kind of data. Now what if the company has only a hundred people or less, let's say do you think that you can still make any kind of data collection that presents any kind of insights on smaller sizable data chunk or maybe that's 100 company not hundred strong company is within an industry and you're gathering numbers anyway from that industry. So you can correlate that to the industry related big data chunk. So how big you should be as a company to actually use properly any kind of people analytics. That's the main question.
Yeah. And, and I love that question and we get it a lot. Actually contrary to most of us are actually most people that work with us and also, so our clients, they've taken a course of statistics or two or three, and we're always trying to focus on these things of p values or significance and error margins and whatever Now. You do have to realize that indeed, as you said, we have a huge database of people doing things right? So similarity is key here? So whether you're working in an Excel spreadsheet in the office of a company like Nike or Adidas, you know, it's different in terms of culture, but your day-to-day tasks are fairly similar, right? Obviously inference is a big thing. So sometimes an organization, whereas one person doing a very similar task as they would be doing as the 10,000 people at Nike, or 10,000 people at Adidas would be doing, you know, would create similar properties but yet we do a research study on that one person in that one environment. No statistical model will say this makes sense. So, Partially, it depends in one way everything that's the same as everywhere else is easy to track, right? So our system can detect fairly well whether people work within the boundaries of normal for certain behavior. Now, when I say that is your, basically are the day-to-day effects on this person or expected of a person doing these kind of tasks and these kind of jobs and these kind of behavior and that helps a lot, right? So without going into too many details, we can infer pretty well on very small organizations. Now, whether we can be sure it's a completely different topic, right? Of course, a group of hundred people can still have a low confidence interval depending on the results? But we have had groups of seven people having such consistent indicators of things going very well or very wrong. We could still say, okay, you know, these are three traits that are severely, positively, or negatively affecting this, these seven users with statistical confidence? So yeah, the answer really is it starts with any amount of numbers, as long as the behaviors within the norms. And then, you know, if any strong effects happen in your organization and really wide sort of ranges of effects such as, you know, high performance or the sick leave I mentioned before, or people showing up late. Or people referring other people to the company. Like really strong indicator of behavior, if they strongly appear in very few people with certain traits and never appear in people with different traits. That says a lot. At a very small number. You could already, with a certain certainty, with 90% certainty say something not, I mean, not enough to write a scientific article about, but again, we have to get our minds a little bit out of the scientific community. Cause especially in the corporate world, we're trying to be just a little bit better than our closest competitor.? That 1% better makes all the difference? Yes. And if we stop all these process, To be able to be a hundred percent sure. You know, we're not gonna win realistically. Of course. We're going to be so conservative that we're just gonna be stuck. Of course, to answer your question, any number is the right number? It just depends if we get enough metadata and enough environmental data to isolate effects and, and understand whether things behave.
That's a perfect answer. Why people should use it. So why, again, I'm like a mid-size business for enterprises, I guess it's a kinda like a no-brainer to do any kind of people analytics. But what we see right now at least in the remote work environment as well, where everything shifts from human resources to people first mentality. People usually spend more resources on for example, analytics wellbeing or anything around that can help them to understand how people behave within the organization. Now, Again I'm a mid-size business, why should I care about people analytics? What are the best use cases that I can use people analytics to do anything that's, you know, can be better for my company?
I will translate that question is, should we use people analytics in general to figure things out. Kinda so, I mean, I'm actually leading the question to we obviously, yes. Like obviously you should analyze things to see where opportunities arise, but I'll give you a very concrete sample. We're a small company. We're only 24 people? We're 24 people. My three best data scientists. At the beginning of this conversation, I said, I'm not a big fan of remote. First one said, I want to move to the island of Tenerife. And which is in a different time zone or another big, not a big difference? And I'm gonna work on a beach so in the mornings I can do glide surfing,? Another one said I wanna move to Greece. And the last one basically just left the Barcelona? So to chase love by the way. But I can't stop love. So I have two options. I have the option one. I say, okay I prefer people working from the office, so let's cut our working arrangement and I'm without three of my best people. Or I say, let's figure out whether it affects your productivity? Or your commitment and you know, all the things that matter to us? Your error rates. So, Should I use people analytics to do this or should I go with my guts feelings right now? Obviously I use people analytics and the conclusion is very, you can actually Google all my employees and see where they're currently log. I don't sure if they're putting that on their LinkedIn, but yeah, none of them are in Amsterdam. I can tell you of these three. Which has enough. One of them currently is in the Philippines. I mean that's really difficult cuz that person has to wake up very early to do very, wait, sorry. He doesn't wake up. I wake up very early to speak with him.
He has to stay up very late.
It's me suffering amongst this. Yeah, true. I have a very young child, so I mean, but yeah, so in the end, you know, measuring effects even on one person and one individual is key to, you know, making good decisions. And if something doesn't work, especially in a small company? And a small company is the best mechanism to understand what's going wrong on a bigger size company? If you see that, you know that one missing piece is creating a lot of slow down a lot of effects? You have to fix that really fast at scale. If you're a larger skill, you won't see that anymore? But that's one employee. You know, frustrating all the meeting schedules because they happen to be in the Philippines, right so that one thing, right, has a massive impact on a small organization. So there you can really measure it. You know, you need to measure extremely carefully and extremely diligently if you're a medium organization already. Even more so in a larger organization. So the impact is larger when you're a smaller organization, but the relevance is higher when you're a larger organization. Does that make sense, what I just said?
It totally makes sense.
The smaller you are, the more noticeable it is, but the larger you are because it's less noticeable. It's even more important cause you're creating a sort of legacy problem that you're not going to be able to fix.
Again this is the perfect answer. How there are some like or can be some missteps, I guess, or misinterpretations of data. I think especially when you're a, you know, small chunk of dataset for a smaller company. Have you seen or do you have any examples where you collected some insights but they interpreted the insights wrongly because, you know, there were only, I dunno, 80 people in the organization. Of course the, you mentioned this before, that if you know there is any kind of deviation from up or down from any kind of activity that can, you know, say a lot But have you seen anything around when there was a deviation up or down and, and you, they misinterpreted that deviation just to make sure that, you know, people can learn from this show
Yeah. And, and I think what you're asking here is a very valuable thing. And I think it's something that we enforce in all our commercial processes. It's key. It's the key to success, but misinterpretation of data is incredibly common? So yes, if you look at ChatGPT, which everyone has looked at by now.
Yes.
It's a very advanced copy paste machine. So when you're copy pasting, things might be wrong, right? And that's the thing is if there's source data problems or there is, there's always gonna be something that could cause a problem somewhere. So I think a third of our project ends up with a recommendation saying, okay, listen, your organization will be in a healthier, better shape if you focus more on rewarding merit. And rewarding performance and focusing a little bit less on you know, constantly team building and you know, the family based thing. Cuz a lot of people just don't have the time to focus on that. And they get frustrated by it and they get anxious. So that's a result that come out, can come out right now you could barrel down in that direction and go like heavy numbers focused, heavy meritocracy focused. Heavy performance based focus immediately based on that results, because we see that those traits affect positively all the things we're trying to affect positively. But if you're wrong, right, you might chase away your three, four key employees that are actually much more family oriented or apparently the support layer. So, There are interaction effects that make this really difficult to implement sort of from scratch. What do we do? And again, this is where people like you come in to a large extent, but we also definitely apply this. We do a thing called a roadshow, right? A roadshow is we really present the data internally, do a lot of stakeholder management where you go like, okay, so listen, how would it affect your day-to-day life and your work if we're going to, you know, promote our work, people based their actual output and performance. Type of the effect they have on a certain kpi. And that part can sometimes flip the data-driven result from sort of, Hey, we should be performance driven to, no, no, wait, wait, wait, wait, wait. We won't get anything done anymore. So we should really focus on sort of micros segmenting this and, and only do some more performance based management on individuals, but really have an organization focus on this family values and on, you know, group facilitation and social interactions and sort of, this is a very literal result we had from a study that we did less than a year ago. I think it's so important that you do that roadshow because you involve the people that will be affected most incidentally with doing that. And how they think and how they interact with it. And it's not a long thing. And that alone, so that roadshow alone, if we measure that in data alone, so just doing that increases sort of the engagement and the short-term performance of the people involved in that. So being asked, what do they think? How will it affect them? And actually the tenure, the long-term tenure, how long they stay goes up, paying for, involving themselves. It's really, it's, it's a phenomenal thing to do. It itself drives better data, right? Drives better results. So yeah, you need to standardize that qualitative touch as a process. Now that being said, ideally you leave someone in that organization that takes that home for the next three to four years. Now that we don't do right, because then you really drive those results consistently.
Do you measure any kind of outcomes later? For example, so for example, you, if you do a, oh yeah. That's also super important I think, because, you know, you measure something, whatever it is, you present the data in a roadshow. But again companies and, you know, especially leaders have different alignments on the interpretation of the data, and also they approach the interpretation and the facts totally differently based on their values and their preferences. So suddenly the interpretation and the results of the data from a fact point suddenly becomes an operational problem. And they need to implement or change or whatever they want to do with the, with the data set. They do the change within one or two, three, whatever years. And do you measure the outcome after that or or do you have any kind of, you.
Absolutely. In three layers actually. So layer one is autonomously, where we just monitor sort of the performance of the people in that organization that would benefit the most. Are they being promoted? Are they leaving, are they staying? So then we do without the company telling us anything. So we do that for our own database and our own data sake. Again, anonymized. We just figure out, so what is the impact of this policy on a couple of important indicators but then also actively. We're trying to, a client is only as happy with us as the outcome they achieve with us. Because in the end, our kind is not the board, and it's not sort of the people that I would say are affected positively. So they are part of our client, but they're not the one paying. So the ones that are paying, in the end, they want to show these results to the rest of the organization as well. So, really important and really easy to do, and really a no-brainer. You asked subtly. I think a really important question that I'm just gonna ask right back at you is how often when you implement these kind of things, do you feel that people are nodding a lot, saying a lot of Yes. Agreeing with you a lot, especially.
Actually it's quite the opposite. So it's so interesting to see how people view basic operational setups so differently just because they value different things personally as a leader or manager. And they have a belief system that was presenting their careers for, I don't know, for a decade. And it's so hard to change. Even if all the freaking facts, all the studies, everything shows that you need to change this and that in order to get result A or B or C, and yeah, but. Usually that's the answer. And the other other thing is like which again, not sure that you show the life of Brian, the movie, which one of my favorites. That everyone is an individual. Everyone is different. Everyone is unique. Everyone thinks that their company is super unique. Let me tell you, most of the problems, most of the challenges that they face is, well, it's not unique. It's especially if they are operating with the same size or like, sort of like the same size within the same industry or, you know, relative industries usually they have the very, very, very same problems. People are not that different. So that's companies. So again for you, I guess I wouldn't say it's a convenient seat, because you get the data, you gather everything, you evaluate stuff you present insights and yeah, that's it. Here is Joe who will help you to implement if you want or change your operations to listen that. But the main responsibility will sit on those who are actually the buyers of yours. I guess because they have the skin in the game, they have to interpret everything within the organization. They have to change and drive the change within the organization, even if they have like an external help like me or someone. But you, you are pretty much sit in the scientist seat, which is you. This is what I have. This is what I can present to you and you can deal with, and you can deal with it.
I mean, they call it the ivory tower every now and then for a reason, right? Arguably, everything you said there is reasonable. But that being said, I still get the data right. So personally, I'm already happy so, I mean, I'm already, I'm at the level as, as for personal gratification, you know, like I've discovered something new or something old, or discovered something interesting, my knowledge level grows, whatever. From a sort of investor perspective. No, they won't. You know, they worked on company to flourish and to be fantastic. So obviously we prefer implementation, but here's the catch, and this is a really important man sized, but when we're talking about impact, when we're talking about results, when we're talking about moving needles. In society I'm gonna propose two options for you. Where do you think the individual listens and collaborates and, you know, absorbs this kind of advice or things better at your boss telling you B2B or me telling sort of and random individual coming to my B2C platform at an advisory note? B2c.
Obviously the latter. Usually people believe the external advice. That's why we have the industry that it's called consulting.
10 to one. Basically 10 to one. It's b2c. So and that's sort of you coming in telling sort of the manager to tell their employees is even less efficient than you coming in to tell the employees of the manager sort of the same conclusion. So, yeah, but, so we've built all these B2C or we're inside all these B2C platforms to make sure we can drive these things home with or without sort of B2B players. Now you also have to look at it like this. So one of our biggest companies that are in our client list has 80,000 employees, right? 80,000 employees. Sounds like a lot.
Very. Yes.
That is one week of sort of users on one of our B2B cloud B2C platforms, right wow. So, There is no amount of companies I can onboard already given sort of the internal conversion rates and internal sort of people absorbing information, doing things, taking actions they will ever be able to compete on the B2C site. Now, there's one company that I would love to get in the list client of Mind that is the one exception there, and that's basically at Walmart. Which I think has thousands employers or something ridiculous like that. But besides Walmart. Most B2B interactions are a colossal waste of my time in terms of creating an impact. Now. That doesn't mean that it's a waste of sort of everyone time, cuz it's still, it's, it's a sub-segment of where we create insights on, and this is exactly what you just said. Most people in similar size companies behave the same. And I said it earlier as well. Most people within similar industries behave the same. So the norms on how things work and how things interact, yeah, we definitely the things they interact with, right, which are the organization. So missing the B2B site is not an option, but the biggest impact will always have is on the B2C side. And that's really, I think also why we're having this conversation. Because how do we get more movement into the market for specialists, of course. But remote work, you know, and, and as I said in the beginning of this conversation is that we see these tremendous mental health nose dives and those kind of things. So clearly there's something going wrong. I mean, what was the number of I mentioned this I think before, but I think 65 plus percent united States employees want to work remotely. So they say actively I'm looking for a full remote work. And...
But the mental health is actually nose diving.
Exactly. So there's like a I dunno. It's weird. It's like, I wanna pick up smoking, but I will die of lung cancer. So Yeah.
Exactly. Exactly. Exactly. And that's and I think there's such a big educational, we're at the precipice, right? So we're at the beginning of us trying to do remote work well, and we are driven by other people on Instagram sitting on a beach, you know, taking selfies and going, look at me working with my laptop there. I'm clearly working because, you know, there is a laptop, there is, you know, there's a screen with probably an Excel sheet. So obviously I'm definitely not on Instagram. That's the society we live in as well, right? So, We want a lot. We can see a lot. The market is so transparent on what's possible, right? That everyone wants something. And it's very difficult to understand whether they should something right. And that whether you should. If you answer, should I work remotely? You'll find 2025 blog posts by bloggers that say, you know, oh, oh, benefits Beach Cheap food you know, learn a new language. You can surf in the morning, whatever. Whereas, you know nobody goes like, downsides, massive mental health pandemic. Sand in your laptop. And I think that that's sort of, that's the world we live in and to deal with that. Right. So it requires a whole new set of tools. Then your manager saying, You know, I approve you working the next two months from a beach in Indonesia. I don't approve of you working the next two months from a, that the, the amount of conditions, the edge cases, the data, the everything we need to bring into that play. And I guess that's why you and I exist. So because both on a macro and a microenvironmental, Impact. Yes, we need to help those poor souls that have seen one of their very hot and, and probably sexy friends tipping on a beach somewhere. You know getting away with it.
I love the example by the way, that, because that's one of the suggestions that I had for, for someone that he wanted to work remotely so much. And, but always went to the office and whatever, and. And my suggestion was not just for him, but for most of the people said, just try to convince your manager, that you are going away for a month or two still working from a beach whatever you want. I don't care. And I'm a hundred percent sure that like, I don't know, 80, 90% of the people who come back from that two months of vacation, vacation, whatever we call it, they will be eager to go back to the office. Because Okay. I tried. I don't like the sand and the laptop and what I, whatever. Okay 10 or 20% of those people will won't go back and whatever, because I mean, that's finally the freedom that I wanted. But most of the people, they usually just, they just don't know what they want. They want that because they saw it on TV instead, blogs, whatever. And never experienced it. You know, fear. Fear of missing out. Finally when they are not missing out and doing it, they just realized that, hmm, I don't know. I dunno. I don't know. It's not really a work for me. Which is totally fine. You address like five times that survey, remote health stuff that you had to study. Tell me like, Because we are short on time for you, I guess tell me like the three most important insights from that study, because that's super important to see.
Nice. And so, and I think that the most important one is based on the numbers we were just talking about, right? So people going there and, you know, the vast majority will be away for a month or two months and come back and you named 20% will possibly stay 10, 20%. So our estimates, so we're trying to isolate this. So based on other effects as well. So try into purely isolated on remote work. We say the upper limit of people not depreciating their mental health. So no negative mental health side effect. The upper limit of possibility there is 25%. So maximum 25% of society is actually equipped and eligible to fully work from remotely.
I have, I haven't read the study. I'm a good guesser, so.
Yes, you're an incredible guesser. So, but maybe you are spot on. So I'm just saying the upper limit, so the in with confidence say it's definitely not higher than that. So it could even be 1% there that's possible, but it's definitely not gonna be more than 25%. So that's one of the major findings. And then we found that one of the biggest reasons actually, that people find out, Is that the sort of raw energy, confidence and sort of overall feeling of satisfaction? It's mostly those kind of things like commitment to the work they're doing to the organization gets a really big boost from just sitting around the table with some colleagues and just bantering a little bit. So having a much more human More casual, sort of more a sideway type of conversation. And a lot of people that also see, they read the reports. So the biggest chunk of that is people actually realizing that in indeed the thing they miss the most is just depth. Just being able to face it on a table, and interact. And, and we also find that the depth of interaction that people perceive from these kind of conversations digitally versus like sitting next to someone, even smelling someone's maybe a little bit of armpit, sweat or whatever are sort of so much more high quality that being the in-person interaction than yeah than, than these pitch. The ones that they feel like their societal needs sort of, they're there evolutionary needs to be part of a society are not fulfilled, which is obvious. So, and then the third one and I think this. Also obvious so I don't understand how we got this wrong. So people say, yeah, working remotely makes me more efficient. I'm better. So I focus, I don't have to commute. I start my day, I start my tasks, I finish my tasks. I get some time to do, you know, my remote stuff. But that being said, we all make this mistake constantly cyclically. That efficiency over in isolation is exactly what we need in every organization. But inefficient organizations create, you know, random encounters, chance encounters at the water cooler, as a lot of people say it I haven't seen a water cooler in over a decade, but whatever. It creates sort office gossip, it creates creativity, it creates an understanding where other people are working up. It eliminates redundancy, weirdly enough, because people say, Hey, well Peter is already working on that. So stop it. Forgetting about that effectively only creates short-term efficiency and then creates long-term inefficency. So we found that sort of all the managers sort of interacting with that reported that their long-term efficiency went down, where short-term efficiency went off. Especially, that's so interesting. Software development teams, so software development teams, they, you know, cleared a lot more tasks. They cleared a lot more tickets. They, you know, they were doing great. They were doing great. They were doing great. Sort of legacy eventually builds up and interactivity goes down, and then they have to spend months refactoring everything to put it simply right. So I know this is a bit of a shortcut of the real intricacies there, but it's just that.
No, actually people lose touch lose the connection on the long term. They become greater in productivity. Of course, they don't have to travel they can, you know free of choice in terms of their time, where they allocate it, when and how, and how much. So it makes sense that most of the level are going up. But on the long term, if you lose the personal touch As you said by the way, that's why remote first companies are investing a lot in quarterly retreats and, you know, just to connect the people together, at least for a limited time because that's what keeps them moving forward.
Mandatory. Awesome. Venting. Only venting, right? So only complaining about stuff, you know? Yes. It's an important human thing. And now where we have a rule that you have to sort of, if you complain about something, you have to also be positive about something else in our organization. But people complain, they love complaining.
And it's so interesting to see that these kind of like mental health issues declining of course for remote work. Correlating to that, you know, personal relationships amount of divorces. I mean, it was flying up. During, absolutely during the pandemic. So people just, I mean I need to see you now every, every hour, every day because we are both working remotely. I just realized how much I hate you. So that actually, that happened like, like, like, I don't know, from 10 to five at least at least within my circle and just, this is just my circle.
Yeah. Which is a, a funny tangent we'll say for a completely different time. But we had some research on that as well. We found like the couples that are at home that are more, that more convergent versus divergent personalities. So are more the same. Right. They do fine with that. With both being on top of each other, are living each other's life basically. Yes. But people that are divergent. The opposite attract type of phenomenal. They're doing horrible, horrible and we have this, so we make this algorithm now to should you both work from home? We have not shoot, you work from home, but if your partner is also there, should you both do it? And that's like no surprise. I mean, sort of when you're divergent. So when you're so radical opposites. You know, it's not, it's not a good idea. It's so not a good idea. The expectations on each other versus, you know, seeing each other and then not communicating like different angle. All obvious, strangely enough, but 90% of the time when we do these reports and we create these individual studies for the individual, they always respond the same. I said, well, I haven't thought about it this way, but you're definitely right. I think you realize something only when you see it, and that's a very human thing. You know what you want when you see what you. You know what yes went wrong. When you see what went wrong, it's easy to point to the mistake of someone else. To go like, okay, obviously. But it's very hard to internalize it. And that's why it's so important that, you know, we keep making this a data-driven journey. And, you know, we keep investigating, examining our own biases all the time. So I hate the term examine your own bias because it just means that you're just not forgiving people for making mistakes anymore.
Well, it's about self-awareness, I guess. So like, in a way yeah. Yeah, but most people aren't. So, I mean, seriously, it's hard.
It's very hard. It's so hard to, to look at yourself and you know how punishing it is to blame yourself all the time. You know how depressing that is. Yes. To be great. And overall great human being is, it's just difficult. Yes. It's just not something you, you wake up in the morning, I go like, okay, this is my to-do list, to be a great human being. You just tried. You just go out there and you try and you'll make mistakes. You know, to learn from this space is hard because there's no direct feedback on anything you do. So they get embedded into habit and then changing a habit. Well, I mean, Google book changing habits and you'll have, you know, 3.6 million pages of Google search results probably.
And you can read all the books and not change anything.
That's exactly, other than more reading.
Right. It's like mental masturbation. Yeah. Sorry. Just what was my ran about self-help books and whatever. Anyway one last question, which is a personal one a little bit. You mentioned like again a few times that when you got presented by some sort of like data insight. Oh, it's not really surprising. We knew this. Right? But I think you're not right. Actually, I think what this is a scientist mindset. I think that you think that data was always there. Well, actually you saw the insight and Yeah, that makes sense. That was the reaction that you had and that was not surprising to you. Again, the question is, have you ever got really surprised by any kind of findings that you had? Like, oh fuck, I didn't think about that. That was mind blending?
Well I was gonna say all the time first. But then it's always things that, the things that are always the most mind bending now make the most sense to me. Everything that falls to my mind are the kind of things where I go like, yeah, but that totally makes sense. Yeah. But for example, when we just talked about variance within industries. So I think there was a study with it maybe eight years ago, like a long time ago, about sort of which industries showed to figure out so where people working in the different industries sort of in different jobs, different companies, whether they're more similar to the people in the same industry or more similar to people in other industries and what sort of the spread and all of that and. We had, we argued, so, okay, let's guess which industries are completely the same and very different. And we thought, okay, the financial sector is so broad and so wide that anyone there should be radically different. And when the consultancy is such a narrow, narrow thing where you just, you have to be like have to be that model and yet it needs to fit in a square and that's it. Right? So whether you make the PowerPoint, the Excel, or you know, yeah, you look at the Excels, doesn't matter. That's all the same. So, so, and by a large everything around it in something. And then we found that the opposite was true. So the banking sector was the exactly the same, but everywhere, like front desk to, I don't know, controllers, to auditors to what do we find the other day? It was the insurance underwriters, right? So we went, okay, those people must be, you know, those must be specific type of people that basically go, yeah, you can't get your claim because because you did this, this, this wrong. So there must be a specific, but no, all exactly the same. Never looked at the consultancy and like anyone is a consultant, anyone. So, and now we say, yeah, obviously, and, but then that was what went wrong here.
That's a great example. Thanks for sharing. Again, thanks for coming here. Where can people find you?
Yeah. So that's a really good question, and that's sort of the B2C side. So if you as an individual listen to this and you feel inspired and you wanna do something new with your career or life, Go to josie app.com and let sort of our knowledge guide you into how to do things best or how to avoid doing things worse. From a B2B perspective, which is actually the logo that I pointed at twice during this conversation, realizing we don't do video, so that's talent data labs.com. We're also, if you Google talent data, we're the first result because, you know, we are the company with possibly the most, but definitely the best data on people.
Again, this was a truly inspiring conversation. Thank you. Thank you for sharing. Thank you for coming for the show. Appreciate.
Thank you very much for inviting me, Peter. It was a lovely conversation. Looking forward to you know, speaking more to you in the future about how our businesses evolve around this topic.
