Detta avsnitt är sponsrat av Visier, en ledande och beprövad plattform som hjälper företag att bygga och utveckla sin people analytics-strategi.
Avsnittet är inspelat direkt från HR Tech i Las Vegas och är på engelska. Tillsammans med Wayne Hoy, produktchef på Visier, dyker vi ner i hur HR-data och people analytics kan förändra sättet vi tar beslut på – och skapa verkligt affärsvärde. I avsnittet utforskar vi olika nivåer av analys och hur dessa påverkar både strategiska beslut och organisationens utveckling. Vi berör även viktiga områden som hållbarhet, mångfald och inkludering. Wayne delar med sig av sin breda erfarenhet och ger exempel från kunder som har lyckats använda dessa insikter för att driva förändring. Dessutom berättar Wayne om de unika affärsfördelarna med att använda Visiers plattform för people analytics.
Det här är avsnittet för dig som vill förstå hur HR-data kan lyfta din organisation och skapa framgång genom smarta, datadrivna beslut – och samtidigt upptäcka de unika fördelarna med att använda Visiers people analytics-plattform för att nå nya höjder!
Missa inte heller chansen att fördjupa dig i people analytics och dess affärsfördelar genom vårt gemensamma webinar med Pandora den 14 november kl. 16:00! Du kan registrera dig här: Lifting the Lid: Using Data to Drive HR Beyond Compliance at Pandora.
Transkribering av avsnittet:
Anna Carlsson: Dagens avsnitt är sponsrat av Visier och inspelat i ett konferensrum här i Las Vegas, där jag sitter ner med Wayne Hoy, Visiers produktchef. Med över ett decennium av erfarenhet inom people analytics är Wayne den mest kunniga expert jag har träffat på området. Han gör det enkelt att förstå både möjligheterna och utmaningarna inom dataanalys, och lyfter fram det verkliga affärsvärde som people analytics kan bidra med.
Vi pratar om olika nivåer av analys och hur det påverkar både strategiska beslut och organisationens utveckling, samt områden som hållbarhet - och mångfald och inkludering. Wayne delar också med sig av några kundexempel. Dessutom går han igenom de unika affärsfördelarna med att använda Visiers plattform för people analytics.
Missa inte webbinariet med Pandora den 14 november kl. 16:00, där du får chansen att fördjupa dig i ämnet people analytics och ställa dina frågor – länken till registreringen hittar du i poddavsnittstexterna
Anna Carlsson: Welcome to HR Digitaliseringspodden Wayne!
Wayne Hoy: Thank you, thank you. for inviting me. It's my pleasure to be here.
Anna Carlsson: So, we're here in Las Vegas. It's amazing that we could sit in the same room and record this session that we already discussed for some time. But as in Sweden, we are not so good at analytics and data and utilizing that in HR. And now we can talk about this with you as an expert in the field.
Wayne Hoy: I'll do my best.
Anna Carlsson: So, let's start with that you can introduce yourself.
Wayne Hoy: Sure. My name is Wayne Hoy. I'm currently a product manager at Visier. Our company does people analytics platform. I've been in the analytics space for a very long time. Almost 20 years, since my last degree. I was motivated after doing an MBA. I didn't want to do investment banking or those sorts of things. I was really fascinated with how data could be used to inform business decisions. You know, like a lot of people in undergraduate studies. I did math and statistics, and it was very academic. Doing proofs and theorems. And it wasn't until I went to do the MBA, where I learned that you could apply statistics and data to business problems. How much should we produce? How much should we charge? You know, how many people do we need to hire? And those were honestly the first time I had seen, and it really understood a practical application of data and statistics to business. And it fascinated me ever since.
Anna Carlsson: And then you got into focusing on HR and HR data?
Wayne Hoy: Yeah. Kind of random, not necessarily my intention. You know, my first Analytical job was in financial analytics and planning and corporate planning, those sorts of things. Somewhat by accident, I ended up at in an HR department at a hospital, a public hospital, about 16,000 employees, a team of seven data analysts. And I was there for seven years. Applying data to people issues, staffing issues around a public sector hospital. And that was very fascinating. And honestly where I learned most of what I learned about sort of people analytics and data management and those sorts of those sorts of things.
Anna Carlsson: So as people are not so experienced in really analyzing people data, I would say. I think a lot of people are maybe reporting on data. So, what is really the value of HR analytics and data?
Wayne Hoy: Yeah. I think I understand the value kind of in contrast with say financial or accounting analytics. Right? Accounting, double entry bookkeeping, financial analysis, public reporting has been around for hundreds of years. Like it is so professionalized, it's codified in accounting standards, and we know how to deal with it. We know what financial ratios and metrics are. In comparison though, you know who does the work that generates those financial results in a lot of places, and it doesn't have to be a financial result. Even a public sector hospital has financial results that they need to worry about. But we had 16,000 employees that did the work that delivered those results. And the level of maturity in analyzing people issues. Just do I have enough people? Should I have more? What types of people? Do I lose people? Why do I lose people? How can I keep them better? Because it's very expensive to get more. Those sorts of questions were to me as valuable or more valuable than the financial ones. But the way in which we approached how to answer it was far more crude. So that's sort of where my motivation comes from in, in making more mature the, the data analytics space. There's no reason why we can't be as mature and rigorous as the financial analysis world. Maybe not quite as ruthless because there are people at the end, but, you know, without people. How does any organization generate any kind of result, financial or otherwise?
Anna Carlsson: And I'm thinking that usually I compare those financial analytics. Then you can easily put the data into boxes. Like you said, we used to it, the metrics and so on. But isn't it hard with people data because it's also so sensitive and it's not, I don't know how to explain it. Maybe you can? But the difference in people data.
Wayne Hoy: Yeah, I think the word that comes to mind is fungible. Fungible meaning one thing is as the same as another. So, a dollar is the same as another dollar. A krona is the same as another krona. It doesn't really matter. They're not intrinsically different. You know, you stick €1 in one account, and it moves to another account. It doesn't matter that it's literally the same euro, it's just the euro. People are not like that. People are individual human beings. And you might have a team of seven data analysts like we did. But if you change half of them, all of a sudden you have a different team that performs differently. It's maybe a different level of diverse, has less experience, has different skills, and therefore their performance will be different. So, people are not exchangeable in that way. That's one big difference. I think the other huge difference with people data in particular, still on that topic of being humans, is that humans exist over a period of time, right? They're not instantaneous transactions. A buying of additional office supplies in your GL isn't, in a way, an instantaneous transaction. It hits the account and then you reported on your balance sheet or your income statement, and then you're done. When you hire a person, they will hopefully be with you for three, five, seven, ten years. They will grow, they will add new skills, they will perform different things. And people analytics and HR analytics. I always like to use the term person and analytics. Person, as in singular a single person. Because it's that person that carries through over their career with you. And they may be one thing at one time and a different thing at another time. And there, hate to use this word, but their value to your organization depends on when you're looking and what they're surrounded with. So, I think those are the two big differences in terms of the nature of the data. People are not exchangeable, and people have stayed. They exist over time. Which means, you know, when you're writing queries and setting up your databases and making your analyses, you need to consider these sorts of things.
Anna Carlsson: And maybe that's also why HR can be a bit reluctant? Or I don't know why we are not further ahead with utilizing data and analytics? If you feel like you don't want to put like a dollar label on them or you know, you can't compare them as such. So, it's a different story with all the people data. And maybe you feel if you work in HR that you don't want to do this calculation? I don't know.
Wayne Hoy: I think that's there's a lot of truth in that. It is strange to represent a person, a human being, as a number or a sequence of numbers to characterize someone. And even in my work before and now we're not, you know, digitizing a human and turning them into a 256-digit number. That's not what we do. But we do use numbers to represent teams. We use numbers, I think, to represent people. More so teams than individuals. But that's changing as well with things like skills data. I don't think that there's anything inherently wrong with that. You know, the other big difference with financial data that you mentioned before is there is, of course, a lot of privacy and security concerns, and especially in Europe. You know, there are there are burdens of responsibility for the organization to be a good curator of that data to make sure that and it's not used for bad purposes, but also that it doesn't get out. So, between sort of the risk of it and the I'm not really sure how to analyze it. I don't have 200 years of history and accounting textbooks to tell me how to analyze it, that yeah, there can be some reluctance to get started.
Anna Carlsson: Mhm. So, I just want to come back and touch on the thing that, there was a report just released two weeks ago from the center of Global HRM in Sweden, which is based in Gothenburg. And in there we can see that only 60% of Swedish organizations do not use data and analytics at all. That's at least the answer in this report. When you compare it to the rest of the Nordics, the rest of the Nordics are about 30% not using data. And in the US, it says 25% are not using data. I can understand this is a kind of a maturity level discussion. But 60% in Sweden. And I think it was like 8 or 10% really utilizing data and analytics. So, do you know how it looks like? I mean, you've been in this field for long. You have been quite lonely then. Or how does it look like in other countries, do you know? What do you meet? Who do you see?
Wayne Hoy: Yeah. So, I can speak to some of Visiers customers. We're of course stronger in North America than we are in Europe and in Asia. But of our customers and there's over 200 large enterprises. So, we define large enterprises, several thousand employees or more. All of our customers will have a people analytics team of some size. I remember from my prior life as a practicing analyst in healthcare, we had 16,000 employees and a team of seven. So, for ease of math will say it was 14,000 employees, so about one analyst for every 2000 employees. And I think that sort of indicates like that there is a level of scale before maybe an organization is willing to invest in people analytics in particular, maybe analytics in general. Maybe they view it as an overhead function, a back-office function. I could hire a person, you know, if I work in software and my industry is software, I can hire a programmer who will help me make more software to sell. I can hire salesperson to help me sell, actually sell more and produce revenue, or I can hire a back-office person. I think control over that back office or SG&A type of expense, that overhead expense is very tight. You know, I feel like at least the monar Visiers customers, even my old company with one analyst per 2000 employees is already a fairly low ratio. I think our customers have used potentially higher ratios than that. Maybe because, you know, the tool helps them be a little bit more efficient. But I don't know that very many companies want to build out, you know, one analyst per 1000, one analyst per 500, to get to that size scale. It's expensive.
Anna Carlsson: Yeah. And we saw that in in the report as well. That if you have a good ratio between HR team and number of employees, can be an indication that you have a focus on people in your organization. Then they also do analytics. So, it's like changing. We need to change the world to utilize the data but also understand the value of people.
Wayne Hoy: Of course. Yeah. And even, you know, I think it's more than just investing in HR. There is a, I don't know what the right word is regressive way to invest in HR. You know, traditionally HR I've heard, is often regarded as a back office. But like transaction processing. They're there just to put out requisitions and process hires, and process payroll process leaves and terminations and those sorts of things. And, you know, we see a lot of HR influencers, yourself, Josh Bersin, always talking about elevating HR to be a more strategic function. Right? This is how you compete. And I think that ties back to what we talked about earlier about, you know, we spend so much time analyzing money. But the way that you achieve those results, money or otherwise, is through people. There should be that same discipline, that same strategic, targeted intentional discipline and how you manage people to generate those results. So that it's not an accident. It's not through force of will or luck that you achieve great results. It's by design, right? And if you're going to design your workforce, the way to do it is with data. There's no way to do it without.
Anna Carlsson: And otherwise, we have a lot of bias into it. Because it was an interesting presentation this morning here at HR tech, which was about utilizing talent frameworks and data to become more inclusive. So, it was a DEI focused session. And it was really interesting because they really put the finger on the value of utilizing dat. Because then you can see people who you don't see otherwise. Because the differences, because you're different as a person, but you have the skills. So, if you look at skills instead of just people and rely on your managers to choose who should take your next, you know, the next step and take the next job.
Wayne Hoy: Yeah. I think that is underlying. Over the last two years, the rise in popularity of talent marketplaces is the ultimate level playing field, right? Every employee has the opportunity to go in. They can write down, this is what I can do, this is what I want to do. And, you know, your internal opportunities are supposed to be there, and it's supposed to find the best people for each of the projects that you have. At least that was the promise.
Anna Carlsson: And that's in the end is a type of data that we didn't have before. That you can analyze.
Wayne Hoy: Exactly. It's, you know, it's in a way kind of a classic matchmaking problem. Right? If you have these three things and I need two of those three things for our project, then we should get together and talk. And that software should help you do that.
Anna Carlsson: Yeah.
Wayne Hoy: So, it is uh, you know, they are trying to sell software, of course, They're trying to sell a tool. But the tool is driven by these aspects of data that exist even without the tool. People have skills that they can do, they have desires, they have goals. There are projects that need certain capabilities to be done. You can hope that they meet in the coffee room and talk, or you could codify it through data and find a way to make them meet. And ultimately, I think that's, you know, a way in which organizations can be far more productive than they are. How many, how many hidden superstars are there in all the organizations across the world?
Anna Carlsson: I think a lot of superstars that we don't see. And maybe they get mad at their manager for not seeing them, so the manager can connect them instead of having more data and tools to support them. But let's get into the different levels of, I mean, data and data mature. What are really analytics? Can you explain that in an easy, understandable way? Because if you don't work with data, how should you know the difference between, i mean, utilizing it in a mature way versus simple reporting?
Wayne Hoy: Before this podcast, I have to admit I went and googled data maturity frameworks. There's a lot of force that frameworks out there. The one that I remember the most is a little bit more of a technical take on it, but I'll try to map it to some of the more strategic ones, the more business ones. You know, there's this four level. You think of it as a staircase and you're climbing the staircase to get to the top, but there's four steps. The first step is just reporting, essentially looking back, what happened? Using data describe to describe what happened in the past. So how many people did we have? How many of them were women? How many women did we have in leadership positions? How many people left and how many people did we hire? Those sorts of very basic things. It's a technical first step in the sense that it's just very basic data, very basic use of the data. But it's also, I think, a sort of mental first step. Right? If all I'm doing is looking back, I'm creating a report card for myself and my organization. And if all it is, is interesting, then you don't get much more out of it and then you stop. So that's really the end of the first step.
The second step is, I think sometimes called diagnostic. It's all right if that's what happened in the past, using more data and using analytics and people to ask questions, well, why did it happen? So, I can see that my percentage of women in leadership positions went down from 40% to 36%, even though we wanted it to be higher. Why did it happen? And using more sort of people data and HR data looking at, well, these are the people that came in, these are the people that left, these are the people that moved in. There was this many retirements and we filled them internally with men. And that's why it went down right. So, explaining what happened is kind of step two. I think the word that a lot of these frameworks used for that step two is sort of diagnostic.
The third step is now a big job. The third step is usually called predictive. So, if I know what happened, my step one, I have my report card. I know why it happened. Can we use machine learning statistics AI now to predict what will happen? Will it happen again? Is it going to get worse? Is it going to get better? And here's where it starts to get interesting. Because once you start making predictions, I think if you're going to invest in making predictions, you're less likely to just look at it and say, oh, interesting. You're probably going to do something about it. Because if the prediction is bad now, you're going to change your decisions. And this is where it's a data enabled business decision. And that becomes much more interesting.
And then the very last step I think is potentially super aspirational. And I think the number of companies that get to that, this step is very low. It's one way to say it, I think, is prescriptive, where the data tells you what to do. And it's not like you're listening to how the computer from the movie and just doing exactly what it says. But I think it means is your strategic decision making is not just informed by data, but it is almost based entirely on the data. Josh Bersin, I think, calls this like strategic air. Right. So, an example of what might be sort of prescriptive or strategic HR would be workforce planning, probably belongs in that category. Workforce planning is how does this organization decide to compete for talent in order to compete in the products and services in which it's chosen. There's so many MBAs out there. I'm allowed to say that because I am one. There are so many MBAs out there who will talk about competition, the market, environments, political trends, those sorts of things, internal capabilities. But they'll stop at calling it internal capabilities. And they match what's in the environment, what the competition doing with what we're able to do to try to find the best combination for us to succeed. Ultimately, I think that's what sort of classical MBA strategy is. But inside that internal capabilities bucket of what are we able to do? Is your talent, like it's 90% talent. It's not machines anymore. It's not the industrial revolution, right? It's people. It's people doing mostly knowledge work in a lot of cases. So, what might be prescriptive or strategic decision making around using data around your people strategy. Well what skills do we need? Are we competitive for it? Or like if we need a lot of data scientists, are we going to be able to recruit Amazon? Okay, well, maybe not. If we can't recruit Amazon and I still need those skills. How else can I get it? Are there substitutes? What schools are around? What location should we be in? How much should we pay? How should they report to each other? So those are, I think, the very aspirational but important data driven decisions around how to design your workforce, strategic workforce planning. In order to execute that strategy that the consultants and MBAs have devised.
Anna Carlsson: But you can also do a lot with the previous steps as well. The understanding what's happened only I think is more understanding. But also getting an understanding why it happened. Then you can make decisions as well. But I would love to see more predictive or also, you know, if we do this what will happen then? If we analyze like, okay, so we need do less in this area. How? Who should we be moved somewhere else? Or how should we change the structure of the team? Or. I mean, there's so many things to get advice from. And now when we're so used to getting advice from our HR supported kind of ChatGPT or others and thinking in different ways. I mean, you should be able to do that with your own data, I think.
Wayne Hoy: Absolutely. You know, there's a couple of sayings in English, you know, don't let perfect be in the way of really good. But also, I believe in a framework like these four level maturity frameworks, I don't think you can skip steps. Each step builds upon the previous one. You can't jump straight to predictive if your data assets are poor, because what would the model predict on. Unless we're saying just let, I run my company, then, you know, maybe that's going to happen someday. But if we're not going to say that and that there's still role for human decision making and how organizations are run. You need a good data foundation before you can get to each of the next steps. So, I don't think you can skip steps if you haven't started, you know. Don't let sort of that endpoint be so scary to stop me from starting. Just start.
Anna Carlsson: Yeah. And I usually say that about your digitalization strategy, about how you need to have the foundation. I usually talk about building a house. You need the ground. You need to have your plumbing, and your electricity built first to be able to create the house. And that's the same in digitalization. You need to have your HR system in place somehow, because otherwise you can't build it up. And you don't have data then, and then you can evolve.
Wayne Hoy: And not just the technical infrastructure. The maturity of the people in the organization and the willingness to consume that. And to consider the insights that the data is showing you or the analysts shows you, jumping right to the end with business managers who have never been exposed to people data before, and telling them this is how you need to restructure. They're probably not going to listen to you. So, besides the technical journey, there's also a sort of a cultural journey and an educational maturity journey amongst the leaders in your organization that you have to go through as well. And it's a good thing is they can go together.
Anna Carlsson: And I've seen that. And then they start to ask about ask for more.
Wayne Hoy: That's when it gets special. That's when you know you're doing something right. That is the twee would push out. And we sort of we tried to make them look good. They helped make us look good. And it was a virtuous partnership. I remember in my job at the hospital, we had started putting out some standardized reports. Well, I was a data analyst, and we had a team of dedicated data analysts. Back then our approach was to do everything manually. So, we didn't need a group of people with very particular data skills to be able to gather the data, clean the data, put it in the charts that show the right calculations and the right experiment to be able to give to our customers. I think that these days that there are probably technical solutions to that. I think Visier is one of them, but there are others. But at the same time, there still needs to be, I think, someone in your organization that can look at what the machine is doing. How are you calculating turnover? What's the numerator? What's the denominator? Does that fit how we believe turnover should be calculated. And to make those adjustments to be sort of the advocate across the organization. So, at some point you do still need some people with very strong data skills, hopefully through technology, if you were to buy a platform of some kind, you need fewer of them. They are hard to get. I was amazed that we had a team of seven people with the diverse sets of skills that we needed to have that team running. Whenever someone left, it was really hard to find a replacement. So maybe that's where technology can help.
Anna Carlsson: But can you explain a bit what this year's product does? Because, um, you provide an intelligent people analytics platform, I think? So, when do you utilize that and what is it?
Wayne Hoy: I'm the world's worst salesperson, so I'm not going to describe it in a way that will make my boss happy with me. But let me. I think I talked a little bit about the diverse skills on our team. So, it can contrast what Visier does with what you would need to do if you were to do everything manually, like the way I used to. You need to pull data from all your source systems. So workday SAP, your payroll system, maybe ADP, maybe your recruiting system, maybe your survey system. And you need to pull them and put it somewhere. Preferably not spreadsheets. We used to just back up everything into an SQL server database. So, someone with some technical skills to write a bunch of SQL code to automate the copying of that data into one place. All right. So, what benefit have we achieved by putting it in one place? Any time I need to answer a question for someone, I don't have to go to ten places. I now go to one place. I've saved. honestly many hours. So that part is automated or at least made a little bit more convenient. The second step for human is to then pull the data that they need out to answer the question that they were asked. That tends to be SQL programming. So now I need to find someone who's really good at databases, right? And I need to find four of the seven people on my team. Need to be really good at this. Okay. now I'm competing with software development companies and those sorts of things that need database analyst. Now I need to pull that data out. Okay. Now I need someone who can put their brain away from the database and into the mind of the business and the business user. They ask this question, what do they really want? What do they mean? I can give them a diversity number, but what problem will they really trying to solve? Is it really a retention problem? Is it really a talent attraction problem? Can I serve them better? So, someone to translate the requests and to work with the business. To translate it into a data experiment that you can then run. Then that person then needs to use whatever tools they need to do to answer that question that has been mapped from, you know, plain language, business language. And that might be visualization, that might just be like Tableau, power BI, maybe Excel, maybe now's the time to use charts in Excel. Maybe it's something more advanced. Maybe I need to write a regression. Maybe I need to write a monte Carlo simulation. Maybe I need to write some other sort of machine learning. Or now an AI model. Now I need data scientists to. And then finally, you give the information to the person who asked for it. And congratulations, you're at step one of four. You've given them their report card. If you want to do more now, you have to do even more. What Visier does, and honestly several other analytics companies do is they automate a lot of the first part of what I said. So, pulling, aggregating, collecting all the data in one place. That's the very first thing that needs to happen manually otherwise. They can make it easier to do that. One of the things that visier does really well, and I think others as well, is we also automate that query rewriting part. So, I've got the data in one place, but now I need to get it into a format that I can draw a chart with. Visier automates that as well, because we've modeled specifically HR concerns. That long life cycle of a person is core to the way that we model the data. We've hooked up a major sort of work milestone events. This is when you got hired. This is when you quit. This is when you got a raise. This is the one you got a performance review. And it's all part inside the software that we do. So that you don't need a person to write. You don't need a database administrator with five years of SQL experience to write a custom query every single time. It's already pre-built, you just get to see the chart. So, we do a lot of other things beyond that. But the biggest sort of two of the big areas of efficiency are collecting the data in one place and automating that query writing. There are some other things that we do that we didn't really hit on is, you know, in my prior life, doing everything manually, everything was kind of concentrated amongst the team. And even though we served a lot of people, I don't I never felt we served them as much as they needed or wanted, or we wanted. What you really want to do is to make this data available to everyone who needs it, if it's secure. So, knowing that that manager over there is allowed to see that organization, they're allowed to see that type of metric, but they're not allowed to see ethnicity. So, security rules around that, something that we used to do by hand by knowing what the rules are.
Anna Carlsson: You saw everything then?
Wayne Hoy: Yeah. As an analyst, we were in extremely privileged positions. If we were not in such a privileged position, I don't think we could have served our organization at all. But, you know, whether it's Visier or other analytics platforms, you know, having that security be there as well. Being auditable governance reviewed by IT is super important.
Anna Carlsson: Because that worries me. Even if they use some kind of tool. I mean, there's usually an analytics platform like PowerBI, for example. But how do you make sure that granularity and what you are allowed to see on the people data, as it's so sensitive?
Wayne Hoy: Yeah, I mean, that ultimately comes down to the organization defining the rules first, and then executing those rules in whatever technical platform that you have. I think luckily, in a way the privacy rules around HR data are fairly straightforward, or at least they're fairly formalized. There's not a lot of variability. Right. There's some country variability. Europe, for example, if the ethnicity example was very specific. So, in North America we report on ethnicity all the time. That's just what we do. For the, the American EEOC. Also, in Australia and Asia Pacific, very open with that. Europe not so much. But these rules are generally quite well known, and they serve as a good starting point. So, when we go and meet with our customers, assessing and deciding on the boundaries of what is the secure information is part of our implementation process. And our security model again, that is a feature of the platform, and it should be a feature of a lot of platforms. Otherwise, you leave it up to chance or you leave it up to humans behaving well. It's codified there, and there's really no way to break those rules. Once they're coded.
Anna Carlsson: And now we also have a new regulation in place in Europe called the Corporate Sustainability Reporting Directive, CSRD. Which is already taken into, it's in effect since 1st of July now. And companies need to start reporting in different levels. And this is also something to get the data out to these types of regulatory needs as well as yeah, let's stay with that. But the regulatory needs of different reporting. So that you have as well?
Wayne Hoy: Yeah. I mean, the CSRD has ten parts. One part is about HR, HR data is always about employees. So, in a sense though, the CSRD, even just for HR, is so much broader than what we've talked about. We've talked a lot about diversity and headcount and turnover, and those are in there as well. But CSRD is that times three. Because there's safety issues in there. There are grievances. Human rights violations are part of it. Living wage is part of it. Family leave is in there as well. So, it's extremely broad. But ultimately it is about the experience of employees in your organization. And that is something that we happen to have designed a bit of a solution for as well. We want our aspirations to have all the information you need and want about your employees. To make strategic decisions, tactical decisions, or even just compliance reporting like CSRD. But, you know, if I were just starting out at a small company with analytics and I looked at CSD, I would be a little bit scared about how broad it is. It's not the first step. It's past the first step. It's not the second step. It's maybe step one and a half. By virtue of how many data elements they're asking for.
Anna Carlsson: Yeah. And I don't think everyone understands the impact yet. It's a bit the same as with GDPR. Took time. Some were on top of it already from the beginning. The rest are still coming. I think it's an opportunity to get your data in order and work on this. So, take these steps to really just start from the from the beginning at least. And who do you see have done it well? Do you have any success stories of companies not on CSRD but more on the analytics?
Wayne Hoy: Oh yeah, we have examples of both. We have a couple of large European customers who essentially bought us because of CSRD. And they've been on the CSRD journey for two years. And even as prescient as they were two years ago to get started, it's also been a difficult journey for them. Because it's hard. So, we have examples in Europe, I can think of two, a large sort of media company and a large manufacturing company. That have used us for CSRD already have been successful. We have many examples of large success stories in North American. Otherwise, I think of one very large financial institution that has, I don't know what step of the four, but they've maybe step two, two and a half, only. Not so much predictive. But they've gone broad. They've taken HR data and made it available to like 5000 managers.
Anna Carlsson: Oh, wow.
Wayne Hoy: It's amazing. That that level of scale. I try to think of how I would have done that for like, we were doing it for 150 managers, and that was a ton of work. They're doing it for 5000, and it's all the HR data about their teams. Who's there now, how it's changed, that you can ever want. I remember at our user conference in the spring of this year, uh, some of our customers showed off. We had like a dashboard contest. We called it a guidebook contest, and we showed off, um, the dashboards they've designed in our product. And, you know, the winners just blew me away. With not only just how good they look. They look like, you know, The Economist or the Guardian. Like, they looked like newspapers. They were very visually engaging. But again, kind of like that financial company, what they represented was they weren't making these just for fun. They're making these because there was demand from their users, their business users, non-HR users. So besides just HR's BPs, business unit leaders, frontline managers, people who don't deal with HR data every day, people who have a different job. People whose job it is to write software or to write marketing campaigns, as consumers of this data. And that really makes me happy to see that, you know. Putting that data in front of them, removes the barrier to them making really good people centric decisions.
Anna Carlsson: So, just coming back to that. Because we are going to have a webinar. I'm not sure. I don't think you are going to be in that webinar, but we're going to have a Visier webinar with Pandora on 14th of November. So, if people are interested, they can hear from an organization that really utilizing your platform as well. So that's going to be interesting. I'm going to be there.
Wayne Hoy: Great. Yeah, I don't think I am, but I'll tune in.
Anna Carlsson: Exactly. It's 4:00 in the afternoon in Swedish time. So, it's early morning. But you will probably make that. But how would you guide an organization who are not so mature in their data utilization?
Wayne Hoy: Find some way to get started. That probably means asking for money. Honestly it does. It's hard to do this on the side of your desk. But if you have to start on the side of your desk, if you're interested in it, then start on the side of your desk. But if you can get some budget and if you can spend it on a person, parts of people. The best tool that you can afford and that can be Excel if it has to be, that can be access if it has to be, that can be a basic snowflake subscription. If it has to be. Like you don't have to go big because it is both a technical journey as well as a cultural journey. Right? Don't underestimate like the people change management aspect of it. But you do need to get started. So going back to some of that, that sort of first step. Because I don't think you can skip steps. Figure out what metrics belong on that basic report card. And I say report card kind of jokingly like it's not meant to punish people. But the basic numbers about your workforce that someone would find not only just interesting, but eventually indispensable to the way that they manage their team. And then put them out there, start putting it out there. And your champions should come to you, hopefully, and then work together with them, to drive better success for them. Make them look good. They'll make you look good, and drive or appetite organically improve success that way. So, start with as much budget as you can get, find a person or parts of people, some kind of tool. It's really hard to do it in Excel, but if you have to then do that. And collect the data in one spot. Publish the 2 or 3 most important metrics that would get people's attention.
Anna Carlsson: Yeah. That's interesting. So, I do believe that some people already are. I mean, they are gathering data, reporting on some data. But starting to dare to show it or make a story around it and making people want to have it. So, it's a change process.
Wayne Hoy: It is. I just had another idea. Every vice president has things that they get bonuses on. Hook up with that. Maybe one year vice president's bonus will depend on their ability to retain female managers. There's your metric. Give it to them every two weeks. Don't just give them the number, right? Just the first step. Give them that second step. Well, explain what happened. Once people's careers and bonuses depend on things, then suddenly becomes important. So that's you know, I think earlier I described a very organic bottom-up approach. This might be a top-down approach, right? Find someone who already has a number that they care about for other reasons and try to make them successful.
Anna Carlsson: Thank you so much Wayne, for coming, or for meeting me here in Vegas.
Wayne Hoy: We both came to Vegas.
Anna Carlsson: Yes, we both came to Vegas. So, thank you for today and thank you for joining the podcast.
Wayne Hoy: It's been my absolute pleasure. Thank you.
Anna Carlsson: Thank you.
