Announcer: 0:03

Here’s an experiment for you. Take passionate experts in human resource technology. Invite cross industry experts from inside and outside HR. Mix in what’s happening in people analytics today. Give them the technology to connect. hit record for their discussions into a beaker. Mix thoroughly. And voila, you get the HR Data Labs Podcast, where we explore the impact of data and analytics to your business. We may get passionate and even irreverent, but count on each episode challenging and enhancing your understanding of the way people data can be used to solve real world problems. Now, here’s your host, David Turetsky.

David Turetsky: 0:55

Hello, and welcome to the HR data labs podcast. I’m your host, David Turetsky. And like always, I try and find people in and around the world of HR to talk to you about the world of HR data and analytics. Today, I have with me, my friend, Dino Zincarini. Hello Dino. Hello. And I have Vikas Saini from Willis towers Watson Hey Vikas. Vikas if you don’t know him, is a technology product leader. And he has incredible expertise in the world of HR, technology, analytics and big data. Vikas, what can you tell the listeners about your background?

Vikas Saini: 1:31

Hi. So I started my career in the technology industry what really seems like a long time ago. And during the course of my career, I got the opportunity to create and deliver various HR and talent management types of technology, which eventually led me to creating my first people analytics platform. And ever since I’ve had the opportunity to do this a few more times learning along the way. And I would love to share what I have learned with our listeners today.

David Turetsky: 1:59

Awesome. Well, we’re going to get there, I promise. So for those of you who don’t know Vikas, or even for those of you who do know Vikas, he has shared with us something fun and exciting that not many people might know. Vikas has a collection of old coins, some over 100 years old. Vikas very exciting.

Vikas Saini: 2:21

Yeah, I actually stareted doing this a little while ago. So I think what happened was some of those coins turned all by themselves.

David Turetsky: 2:30

I started collecting 100 years ago now.

Vikas Saini: 2:34

I used to I used to really love collecting coins. But my one criteria was that it can’t be just any coin, it has to be really old. And in the process, I was lucky enough to get my hands on some points that were minted over 100 years ago. So I still got those saved somewhere passing on my son. Some point, I guess

David Turetsky: 2:52

That’s cool. That’s cool. At one point, my dad actually collected old stamps, until his son ruined them all, and was permanently sent to his bedroom for the rest of his life.

Dino Zincarini: 3:04

And that’s where you’re at right now.

David Turetsky: 3:07

Exactly, exactly. Hopefully, he’ll let me out someday. But anyways, interesting. Our topic for today is talking about building your data analytics technology capability, what you should build, how you get started, and what to avoid. Vikas, the first question is, how do we get started even creating an overall analytic strategy?

Vikas Saini: 3:31

Yeah, so I think the first thing to understand is that analytics is not just a one time thing. It has to be treated as something that you will use in a continuous cycle in order to improve your business processes and performance. So as you start building out your strategy, which is obviously really important, begin by asking the right questions by interviewing your stakeholders, and understanding the types of insights that they require in order to execute their business strategies. In turn, this will help you lay a really solid foundation for your analytics initiatives. You should also use your overall corporate vision as a guidepost because it may help uncover additional insights that may or may not have come up in these interviews.

David Turetsky: 4:34

So Vikas where do you find these stakeholders that you’re talking about? Who are they? Are they the people you’ve normally given report to in the past? Or are they a different audience? Yeah, great question.

Vikas Saini: 4:45

So these users can can be all over your organization. There can be people who have you know, come to you with requests in the past, there can be business leaders that can be line managers, etc, who run their businesses and they really need data and insights in order To go ahead and execute on the corporate strategy on their business strategy, so it’s really important to understand their needs. The one thing that I would also add is that it’s really important to understand the capability of your users as well, because they will come in all shapes and sizes, and you will have users who are going to be an A, give me all the data, I will do what I need to do with it. And then you will have some users who will just need nuggets of information and some pre-generated insights that they can and will just run with it. All they need is just insights.

David Turetsky: 5:34

So when you talk about having an overall analytics strategy, you’re really talking about really understanding who it is that your audience is first, and then being able to either solve or try and find the ways in which you can give them the kinds of insights they need, without trying to be either overbearing or not help them enough, correct?

Vikas Saini: 5:58

That’s right. And one more thing, if your organization is new to this, you will need to drive a cultural change. So as you’ve seen, not all organizations are ready to take this change. So you’ll need to build a fact driven culture where business leaders don’t just trust their gut, but they rely on actual data, which in turn is readily available for them, because that’s really the point of building out the analytics platform and the journey that you’re about to undertake.

Dino Zincarini: 6:27

So isn’t there a bit of a challenge here, though, especially with that last point, you know, building a data driven culture is not something that happens just because you want it to right, like, I guess what I’m getting at is, isn’t the analytic solution, part of building the analytics culture? So isn’t it a chicken and egg problem here, you want data driven culture, but you don’t have that until you have the analytics? And if you don’t have the analytics? Well, nobody really knows that they can have a data driven culture. So you know, what are your thoughts there on on kicking that off? Getting it started? Do you have any thoughts on that?

Vikas Saini: 7:05

Absolutely. And I think I have experienced this firsthand. Oftentimes, what you will find in organizations is these hidden champions who are trying to solve problems with whatever means that have, so they’re, they’re actually trying to, you know, get all the data that they can get their hands on, build some insights, build some reports, build some dashboards, and they’ve done these one off things and pass them along to business leaders who are utilizing them. But they’re often being done in silos and not being done at an enterprise wide scale, which is really the objective that you want, you want this to, you know, be widely used within the organization and not just be limited to certain teams. So identifying those resources, those team members, those champions, and then leveraging their expertise, because they also understand the business problems, you know what their users really want. That’s a great starting point. And if you can identify even a few folks who are doing that, I think that can give you a good head start.

Dino Zincarini: 8:08

So I’m hearing is really elevating the personal brand, of those people within the organization that already the go-to people for insights, absolutely, absolutely, is not new, it’s been done for decades, it’s usually been done, as you said, not at scale, and not in a consistent way. But it’s been done by individuals. And so finding those people that everybody already trusts and knows about, and elevating them and using that brand to help build the trust and the excitement and what you’re doing, I think that’s a great idea.

Vikas Saini: 8:38

Yeah. And it’s just the enabling technology that people just haven’t had their hands on. And your goal is to make sure that that you can get it in the hands of people who have not had it in the past and have often tried to rely on other means.

David Turetsky: 8:51

So Vikas that kind of stirs in my mind. So how do we get started creating that overall strategy, then? Are we trying to boil the ocean? Or are we trying to solve for those executives who have already started using it? Or are those leaders who’ve already started using something? And we are kind of riffing off of what they’re doing?

Dino Zincarini: 9:12

Yeah, I think it’s kind of going back to the previous point, right? Those individuals that have already been building and creating and teaching, taking some of that content, and putting it into whatever structure you have, because remember, the whole reason we’re doing this is to make it scalable, to make it accessible, to get it into the hands of more people than just those who happen to know the right person or on the right distribution list. And so starting with what’s already known what’s already trusted, and using, in my experience existing tools wherever possible, it’s kind of hard to, you know, ask for a budget for some big fancy new tool, if nobody’s really using it, and the company’s never had it before. And hey, we’ve been surviving without it thus far. So why do we need this new thing, so instead, like use what you got, even if it’s Excel, but take what is already out there, but maybe In an approachable, accessible format, or use a tool that’s already standard at the company, even if you’ve got something else in mind, and use that to kickstart this process, because the key thing is I think getting that adoption and that momentum and that excitement that because was talking about before, that’s what we want to start the process of building.

Vikas Saini: 10:18

I totally agree with you Dino, I think what can easily happen is that once you can even take a small, you know, piece of technology that is already being used, and you start extending it, and then showcasing it to other managers and other leaders in the organization, you can quickly gather momentum. And once the whole effort catches fire, the budget will follow the technology will follow and you know, the users will follow. So so I think that’s a great point.

Dino Zincarini: 10:46

I want to bring up one thing, though, that I’ve seen as a as a problem, both, you know, somebody who’s built analytic solutions, and also as a product manager, which is, when you ask somebody, what do they want? They say what you said before the costs, just give me everything, which are useful to me, right? trying to build something, I need, oh, I need something a little more specific, or worse, they just don’t know. Right? They don’t know they’ve never had this before. And sometimes when they say give me everything, it’s because they don’t really know how to answer your question. They do things on a very ad hoc basis. So do you both have any ideas on how you kind of get past that? Because sometimes you ask, What do you want? And the answer is,

Vikas Saini: 11:26

I’m not sure. This is a this is a very interesting dilemma. And I kind of want to step back a little bit and talk for a moment about how a lot of these analytics projects start, there’s really two types of projects. There’s, there’s one where you have a lot of data, you don’t really know what to do with it, but you have the budget. And then and you know, some executive telling you, let’s build an analytics platform, and then perhaps, we’ll do something with it, because the competition is doing it and you know, my neighbor next door is doing it. So we should have an analytic strategy in a platform. So that’s one the other one is where people have really, really specific needs, they’re struggling, the data is coming along with it’s coming along really slowly. It’s not structured, etc. And you want to put together a platform to answer those specific questions. Now, I say that because your question specifically is for the second piece, where we already know that there are some specific problems that we are looking to solve, what you can do is you can start prioritizing these business problems, you can start looking at the impact that you can create, if you solve say, the top three things, and then start building out the analytics around those business processes. And then yes, there will be a lot of noise, the more people who you interview and talk to the more requirements you’re going to get. And you can easily get into, you know, a situation where it takes forever to build out a platform. And oftentimes, the people who asked you for it have already left the company and gone somewhere else. Sure. Like I built this for John, but he doesn’t even work here anymore.

Dino Zincarini: 12:54

No I agree. I think one of the challenges that I’ve seen too, is that when you ask people, what do they want, what they end up describing is what they’ve had in the past. And you know, they need some help, they need some help. Seeing this new future, you’ve obviously got a vision, right as why you’re, you’re leading this thing, or you’re trying to champion it. That doesn’t mean everyone else does, they’ve got their own jobs to do and they see analytics is just another tool to get that done. So they don’t have the creativity necessarily, or maybe haven’t worked someplace where they’ve been exposed to the kind of thing that you have in mind. So sometimes you have to prime the pump a little bit I find and if you ask somebody, what do they want? And the answer with give me everything? Are they not really sure, sometimes the best thing to do is to just stop talking about analytics, I think and start saying things like, Well, you know, how do you decide, you know, if they’re in a finance position, how do you decide how to allocate budget? What data do you look at? What information do you gather, and then kind of reverse engineer even a simple little graph or a simple little visual? That starts to answer that question. Sometimes you have to ask questions from a different direction I find.

Vikas Saini: 13:59

Right to reveal those requirements, right? And really, the the questions to ask are not what you want, but what are you going to do with it? Right? What do you do with it is what is the impact of whatever you have created? And that’s where you start working backwards from?

Dino Zincarini: 14:15

And the good news is that it’s usually easier for people to answer that question. It’s, you know, what do you want is an intimidating claps and I’m gonna hold me accountable now. But if you ask somebody to describe what they do and why they do it, that’s much easier for me to get into.

David Turetsky: 14:45

I think this brings up the next question. And so the question that I want to ask both of you is, so now you have a good understanding about what it is that you want. How do you make the decision to build or buy the technology or the capability.

Vikas Saini: 15:02

So this is a very interesting and often very debatable topic. Personally, I think we can lay out a few very high level criteria on the table. So things like resources, for example, do you have the engineers, you have the data scientists to build the analytics platform, then a really important one is cost. Right? So this is, do you have the budget, oftentimes, not just building the platform, once once you build it, you have, you know, you have a recurring cost of it, you have to enhance it, you have to maintain it. And there’s a third one that is that I think, is really important, which is time to market, as I said, you know, has john already left the company, right, because if you need to get something up and running quickly, you might want to leverage a platform from a third party vendor, which is already available services, trying to build something yourself, which can oftentimes take like multiple quarters.

David Turetsky: 15:51

Sure. I think one of the things that we forget, is that though that even when you finish, you’re never finished, there’s always going to be scope that’s left on the table, there’s always going to be additional functionality that’s required or even maintenance, how many times do data models shift and change, and therefore the product that gets produced needs to shift or change. And therefore, this is not a short term investment. This is a very long term investment, whether it is a commercial off the shelf tool, or something that you buy by building it,

Dino Zincarini: 16:21

I think it’s also important to break this down just a little bit. Because when we are talking about build versus buy in the analytics context, we’re really talking about at least two different things. There’s the technology, you know, the technology that lets you serve up a report or a visual, but then there’s the content, what am I showing what we were talking about before, right? What is the? What’s the definition of turnover? What pictures should I use to show this particular number, that content that you build using the technology that you’ve chosen, both of those have to be built, or bought? And so when we’re looking at the build versus buy equation, I think I agree with what you guys were saying about thinking about the cost and the time. But I think it’s also important to consider whether you actually know what to build, right? You actually know the content. We were talking before about asking people and not everybody may necessarily know, it’s possible to buy the content, it’s possible to buy a predefined set of metrics and visualizations and answer common questions, especially in the HR space. So you know, it’s good to be honest with yourself to write if you really need speed, if you really need to impress and teach the organization, something, you may not just need to buy the tools, you may need to buy the finished house, right, not just the library and the nails, but give me a house. So that’s something to consider both sides.

Vikas Saini: 17:39

absolutely right. And you know, on the on the flip side, if you are a large, complex organizations spread out globally, and you have the resources, you know, you have deep pockets and you want a really, really custom solution, you have subject matter experts who actually know what they want to show and to whom, then it might make more sense for you to go ahead and build your own platform and you can evolve with it. Right, you can enhance it, you keep customizing it. So it really depends on the type of organization that you are, you know, how complex are you? How big are you? Do you have the time? Do you have the money? Do you have the resources?

Dino Zincarini: 18:15

I agree. And it’s what’s interesting is that, you know, the answer to this question, I think was different even just five years ago? Sure. technology space has been evolving so dramatically, in terms of more intelligent tooling, right? Even programming languages have gotten easier and more flexible to use, right? When I was in school, you know, c, c++, those are hard languages to develop it even if they’re efficient. Now, we’ve got things like Python that are much easier to approach. We’ve got databases that are far more flexible and forgiving, as opposed to the original ones that we started relational databases with. So you know, the technology has become much more approachable and easier to work with, and more forgiving if you don’t get it all right the first time. So the technology side of the question, I think has gotten a little easier to answer and perhaps more economical even for people to build their own applications. I still think the content one I’m going to get on my little soapbox here. content, is that something that companies underestimate what they need there, but I think on the technology side, the equation has balanced out quite a bit between build versus buy.

David Turetsky: 19:23

Let me just add one thing to that, Dino, and I think that the concept of storage cannot be under appreciated here. The world of AWS, the world of Amazon’s Web Services has made it so much easier and cheaper to now store much more data about people than we had in the past. But it also enables us to bring more data into the analyses that we may not have been able to in the past because we wouldn’t have been able to either afford it or it wasn’t available to us to actually bring in and to mash up with our HR data. And so therefore, I think that the or the shrinking cost of data, or at least keeping data has now given us the ability to see more things in the HR equation. And of course, that’s oversimplifying it, but there’s more availability of data for us to be able to look at to be able to draw better conclusions, because now we have a richer picture to look at.

Dino Zincarini: 20:20

It’s totally true. I mean, the whole premise of a relational database was to save space. That’s the whole reason it’s complicated and hard to work with is laughable. Think about that right? To save space, as much as I want. Sorry, once a relational database. even know is that you know, we can, we can revisit a lot of old assumptions here. And yet, sometimes, we’re still building things the way we used to with the same framework or the way of thinking about it, because you’re stuck in that mode.

Vikas Saini: 20:50

And just just to finish that thought, I think, to your point, David, business data today is incomplete without people data, and people data is incomplete today without business data, so don’t go hand in hand and, and the more technology you can leverage to merge the two, that’s how you get the complete insight about your organization.

David Turetsky: 21:08

Agreed.

Announcer: 21:09

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David Turetsky: 21:27

So Vikas, what are some of the challenges that people may face in this decision? And what could go wrong?

Vikas Saini: 21:34

Great question. And I think we’ve talked about this a little bit already. But you know, there are many things that can go wrong if you’re not careful. So I’ll talk about a couple of those to begin with. So the first one is, is data quality, your analytics will only be as good as the data that is being fed in from various source systems, we as humans generally have a tendency to overestimate the quality of these types of data and underestimate the amount of effort and governance that needs to be put in, in order to ensure that your users can get the quality of data that they desire. With people data. This is especially important because you know, you could be making important decisions about pay about benefits, etc. Another issue that I have seen pretty commonly is more technology focused where we tend to get a quick solution out of the door, but we are never able to come back and enhance it as we would have liked. Right. So it’s just like that project where phase three never happened. This can really lead to alienating our users who need some specific features and functionality to conduct their jobs. On the other hand, by the way, as we talked about earlier, there are instances where you can strive for perfection and take too much time to build a solution. And oftentimes, you end up building feature functionality that no one really needs are and people never use it. So striking a really good balance between these two things is key.

David Turetsky: 22:56

I’ve worked with many companies over the years who have done their best to create the best people analytic solution, only to realize once it’s released, that they over engineered something that they actually didn’t do their homework on that you were talking about before about talking to users. And that over engineered tool literally met with a gigantic yawn. And they wasted millions of dollars, creating something that no one needed that no one asked for. And that was overly complex and virtually unusable. And then at the end of the day, they basically were looked at as being innovators, but with a question that nobody asked. And so it was it wasn’t good. It wasn’t wasn’t good at all. And they had waste a lot of money and time when all they really needed to do is look around and ask the questions that they needed to ask me we talked about before, and trying to find and I know as product people, we all hate this, what’s the minimum viable solution to get out there so that people can start playing with it and asking the right questions and understanding whether or not you’re actually on target or not.

Vikas Saini: 24:11

I would just add to that, even today, one of the biggest issues with analytics systems is that we go back and we start tracking usage because they don’t gain traction. And the reason is mostly, you know, your users don’t really know what to do with the data that is in front of them. And when you track usage, actual usage of the system, you end up discovering that it’s about 20% of your product that is being used most of the time and the rest of it is really never getting used. So how do you ensure that that that 20% is what you keep on enhancing and making more usable and attracting more users to that, you know, feature or functionality that you’ve created? called?

David Turetsky: 24:55

You can keep asking. I mean the there is no harm in asking. You do the research. Look at how people are using the product. And you ask them, What can we do to make it better? What can we do to make your life better? I think you guys talked about it before we were talking about, you know, finding those champions of the process and of the need, and constantly revisiting it, because john might leave. And then you need to go and find who’s the next person who might be the champion of this, and constantly keep finding those people in your organization. Vikas I think one of the other problems that people might have, when they go down this potential build versus buy decision, is that I think you had brought up the big, large, complex company who has a lot of money and has all the time in the world to be able to create a solution. I think one of the things that I’ve heard from those kind of companies are that they change leadership, and then they realize there were commercially off the shelf tools that actually did what they needed, and potentially cost less. I think that’s one of those big challenges that anybody going through the build versus buy, need to do their due diligence. What do you guys think?

Dino Zincarini: 26:02

Yeah, I agree. I think we underestimate the cost, or perhaps overestimate the effort on the side of the technology and not on the side, as we were saying before, of the content, and also have the adoption, those two things. I mean, the reality is, I think most analytics projects die, because of those two things. You know, people don’t use this stuff, or it’s just not answering the right questions. And so I think, sometimes buying a product that solves that, right, that already has the pre built content that is exciting and interesting. And, dare I say, even fun to use, so that adoption happens more easily, more quickly. Those are important considerations to think about, in addition to just, hey, we’ve already got a bunch of tools, like maybe you use what you’ve got to start the process. Don’t be afraid to change direction later. Once you’ve got some momentum, you’ve got some champions, you’ve got some attention, that’s what you can ask for some budget to go get a tool, perhaps that takes you to the next level.

Vikas Saini: 27:00

Yeah, and I think adoption and evolution will go hand in hand, if your tool cannot evolve with the times you are going to start, you know, you’re going to start losing users. And that’s really like a slow death for your system. So what you really need to ensure is, as new applications, you know, are being adopted by your say your corporate it, you need to make sure that data from those systems are flowing into your analytics platform. And, and that way, you’re actually increasing the number of users as opposed to decreasing them, right.

David Turetsky: 27:51

So Vikas, you know, we’ve talked a lot about building versus buying and creating analytic strategy. We also talked about some challenges that people could potentially face when walking down this path. Any other things you wanted to talk to? Before we end the program today,

Vikas Saini: 28:09

I would just add one more thing that you know, if you’re struggling with adoption, and if you’re struggling with usage of the system that you have created, do not hesitate to, to get help leverage the experts, there’s there’s plenty of people, perhaps even outside your organization, who can

David Turetsky: 28:27

turetskyconsulting.com, turetskyconsulting.com

Vikas Saini: 28:30

you know, you can, you can definitely leverage somebody who can come in and actually get you kick started help people understand how to use the data, how do you ensure that this can then be used to improve your business process. And I think that’s critical a lot, a lot of times people just hesitate from doing that, because they don’t know what they’re getting into.

David Turetsky: 28:49

But seriously, that’s the reason why consulting firms like ours exist is to help companies get back on track with their people, analytics strategies, and be able to rethink them or to change course, and to find those people that you were talking about before those champions, those users, the people who are critical, and re engage them and reinvigorate them to relook at their platform and help us figure out what’s going on. Dino?

Dino Zincarini: 29:17

Yeah, I think it has to be a continuous process, you know, the types of data the uses of it. And nobody was talking about a lot of the sort of algorithmic applications of it a few years ago that today is the all the growth in Linux, right? You got to keep an eye forward and make sure that you’re anticipating if not even kind of inspiring your users to try new things, things differently.

David Turetsky: 29:43

Sure. That sounds great. Guys, thank you so much. Vikas, thank You for joining. Thank you for having me. My pleasure. Dino. Thank you for joining. Welcome. And thank you for listening. Please, if you enjoyed the podcast, please hit subscribe if you have someone that might find this funny. Fascinating. I hope you do, please forward it to them. And thank you very much for paying attention and listening and for joining the HR data labs podcast today. Please stay tuned for further episodes and stay safe.

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