With people data and people analytics tools becoming more readily available, there’s a trend toward wanting a single solution that handles “all the data”. Unfortunately, this isn’t our reality quite yet. A holistic approach to people analytics can only be found when technology vendors and services players openly collaborate. This kind of synergy is what improves the lives of employees and fuels improved performance for businesses.
[0:00 – 3:28] Introduction
- Welcome, Nigel Stoodley and Dino Zincarini
- Today’s Topic: On Bringing All People Data Together for Businesses
[3:29 – 16:27] Lessons from Focusing on People Analytics for the Last 3 Years
- Why People Data is Often Undervalued
- How Businesses can Avoid Skilled Worker Shortages
[16:28 – 27:33] Proving Guidance on “People Intelligence 2.0”
- Getting in Front of Business Leaders
- Digging into People Data to Find Gold
[27:34 – 32:57] Connecting All This Disparate Data
- Is a Full Stack Solution Out There?
- Finding the Right Tool for the Right Job
[32:58 – 38:20] Final Thoughts & Closing
- Episode Summary
- Nigel and Dino Share Their Closing Thoughts
Connect with Nigel:
Connect with Dino:
Connect with David:
Connect with Dwight:
Resources:
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“It actually has stunned me for years that there isn’t really a strong discipline around analytics around people because increasingly, businesses are more and more about people.”
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“Because HR data is not HR data—HR data is business data. And one of the problems we have is we hold on to it too tightly. … we’ve heard the term democratizing data, and that’s just wrong. It’s not about democratizing. It’s [about] letting it go.”
Announcer:
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, chord 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:
Hello, and welcome to the HR data labs podcast. I’m your host, David Turetsky. Today we’re going to try something a little new. We’ve got co hosting Dwight Brown. Hello, Dwight.
Dwight Brown:
Hello, David, how you doing?
David Turetsky:
I’m great. And Dino Zincarini. Hey, Dino!
Dino Zincarini:
hey again.
David Turetsky:
Today, we have a very special guest with us. And what we wanted to try and do is have the best person actually present him. Dino
Dino Zincarini:
That was very kind. And I’ve had the pleasure of working with Nigel various job incarnations over the last. Oh boy. This is getting awkward. What, 10, 15 maybe? years. So I’m really excited to have Nigel here. I pushed my way on to this podcast just so I could be virtually close to him. So Nigel, why don’t you tell us a little bit about yourself?
Nigel Stoodley:
Thanks, Dino. Actually it was 2003 when we met. So that’s when we that’s when we first met. So anyway, my name is Nigel stately. I’m the chief customer officer at Visier. I’ve had the pleasure of working with HR data specifically for the last three and a half years. And it’s been a learning experience. I’ve My background is I started years and years or decades ago as a mainframe Report Writer. Then Then I was went to work for crystal, just tiny little reporting company in Vancouver. And then crystal was acquired by business objects. And I learned more and more about analytics there. And then SAP and then I like the smaller companies. So I went to join a little startup in Seattle called Tableau and I worked there for seven years, and had a really great job. And after I’d finished with tableau, a few friends of mine who we’d all worked together at Crystal asked me to come and help them out at visier. So I got together with Gosh, a bunch of people that we first met when we were young and full of innovation, tons and tons of work ethic and we we sort of came home and we’re doing it again.
David Turetsky:
That’s really cool. One fun thing you may not know, though about Nigel, I don’t even know, Dino may not know this, that you started out as a window washer.
Nigel Stoodley:
I did. I started out as a window washer. And I started out in houses, which wasn’t bad. But then I started doing buildings. And then I and I was afraid of heights. And so I was sitting on this building and I watched a bus go by that said join the Navy. And I did.
David Turetsky:
Here you go. A power of suggestion there. Yeah. So Nigel today, we want to take all that great background that you have and talk about the world of people analytics from a perspective of someone who’s been in the analytics world more generally, and then came in to people analytics. And now you have a really good understanding about what is the need and what people have been experiencing in what let’s just call people intelligence one dot O and what people might need for what we might call people intelligence to dot O, yeah,
Nigel Stoodley:
I’ve been thinking about this for a while. And it’s actually one of the things that intrigued me and continues to intrigue me about people data is AI working in this industry, I’ve seen that if people are using analytics on every part of their business, and a lot of the focus goes on to operations, marketing, sales, finance, the things that businesses are either regulatory, like required by the regulatory reasons to track or the way the businesses are measured on the market, either from an equity perspective by shareholders by owners, and you know, that generally comes to things that directly affect the p&l that are clearly attributed to the p&l. And it actually has stunned me for years that there there isn’t really a strong discipline around analytics around people, because increasingly, businesses are more and more of People, it’s there, there still is huge capital investments. Absolutely. You look at places like Amazon and, and other companies and you know, modern warehouses. Yeah, there’s there’s tons of robotics and there’s tons of capital labor. But increasingly, there’s more and more brain labor happening inside these companies. And businesses don’t really know how to manage it. And I kind of use the quote is, most companies are many companies, IP walks in and out the front door every day. And they have no idea how to measure it.
Dino Zincarini:
That’s interesting. You said, well, you always say interesting things. But you said one thing there that really piqued my interest. Because we’ve all been lamenting this lack of adoption, or acknowledgement, perhaps, of the urgency and importance of people analytics, compared, especially to all these other areas where we fully embrace data. And you said something, they’re very profound, you said that there’s a lack of discipline around it. And I’m wondering if you want to, or if you could expand on that, because I think that’s a really good point. I wish I’d thought of it, the idea that we can’t adopt something, if it’s not well defined, if it’s not well understood, if there isn’t an established practice about it, it makes it like we have to build everything from the ground up each time. Maybe you can elaborate a bit on what you meant.
Nigel Stoodley:
Yeah, so when I kind of think about, you know, in many places, analytics, getting the ground hold is things that, you know, sales process is a great example, where there’s a process, it’s very material to the bottom line of the company, or the top line of the company, and they measure each and every single opportunity, or, or, or even lead and track them all the way through, like, they track that whole lifecycle of that opportunity, so that they’re constantly optimizing their processes, or a company’s process. Same with finance, you know, that you look at it, that we’re constantly measuring how the money’s flowing in and out of other companies, because it can affect both the top and the bottom part align, people data needs to be linked to business data, because the reality is people make the businesses. And the big challenges is, if we don’t see people data, as integral to the business data, there’s not going to be a discipline about it, and then you’re not really going to be optimizing your people to your business. And I look at planning as an example, you know, boy, wouldn’t it be great if we could actually plan out our, our attrition rates into our, into our business forecasts, and then also our recruiting rates back into our business forecast. So we can actually better plan for the quote, unquote, unscheduled departure. But the reality is, you know, those are all predictable based off past histories, and also what’s going on in the in the business in the market. And people can have a tremendously impactful piece of the business, I look at my and I do think tying the people data to business data is super, super, super important. And it can actually allow you to get people to be more productive, and not need big brother kind of way. But in a in a helpful kind of way, I look at it. So I run customer success. One of the measures, I have this net promoter score, I use Net Promoter Score all the time. And I can track that down to individual people. And I could say, look, you, on average, are having some problems with this type of customer. And now we can help either through skills development, or you know, even account selection, and get people so that they are able to be the most efficient that they can be by just looking at one business metric combined with all the other types of people data that I have on the system, whether it be how long they’ve been with the company, what their engagement level is, with theirs, what skills they have. And so you can really optimize the employee experience and the business experience.
David Turetsky:
And Nigel, you just actually brought up one great example because you could tie the return on investment of that training, and client selection to the outcomes. You can see the before and after. And it’s clear.
Nigel Stoodley:
It is clear. And you know what’s interesting, there’s so many guesses out there, oh, if we do this training, it’ll be you know, it will, people will be way more effective. If you actually look at some of the statistics, you see from some of our customers, I had one customer who showed me some data. And their data said that when we invest in this training people leave the business. And that’s not exactly what the desired outcome of training is, especially spending a bunch of money on people, and then their net result is to leave. So that means it’s totally causal. And and, you know, you’ve now said maybe we shouldn’t be doing this training.
David Turetsky:
But in the same way, though, that we make good decisions around financial investments in other things, especially in the capital you were talking about before. We have to be able to test those hypotheses As almost scientific experiments as a, as a course of business, do we want to spend more money on training? Do we want to invest in certain training? Do we want to look at our, our planning, our headcount planning, and be very precise about it? All those things can be tested in a in the same way that we test other things, especially around finance. And why haven’t we what’s been the lack of discipline? And I think where you’re going is is that, do we have people in the organization that would tell me if you if you have this, do we have people in the organization who are trained in the people analytic space, to be able to do those kinds of experiments.
Nigel Stoodley:
We’ve started to see it, it’s been a slow change, I actually think to one of our customers who works in, they work in construction, construction materials. And they were talking about how they were getting to the point now where, when they took on an HR initiative, they were starting to define how they can see that in the data, the effectiveness of it, and that experiment. So there’s the experimentation, or there’s the discovery process, which allows people to sort of seem, you know, come up with educated or informed ideas. But then you take that idea, you determine whether it’s valid in the data discovery process, and then you come up with, I need to do something about this. And when you decide to do something about it, if you can, that’s the time you define your measurements. Because if you initiate a program to let’s say, address engagement, well, you need to be able to measure that over time, and make sure that it’s having the desired effect. And sometimes it’s really hard because people are, are kind of different, because you may have tried, you may have done a program that you thought was solving the problem. And the metrics may indicate that but it may be something else, like your stock price went up. And everybody’s restricted stock units are now like, super valuable, that that’s why you’ve affected attrition rate or something like that. So there’s lots of different things. And but being able to see all those different, all those different factors towards people is actually important, because and that’s even say, I went to HR tech, and it stunned me how many vendors there were at HR tech, like I, the only place I can think of any other place was like, marketing events where you see that much technology and disparate technology. And so this actually is another reason why people data is awkward
David Turetsky:
Yeah,
Nigel Stoodley:
It’s in every disparate system. And you know, there are these vendors coming out there that are saying, Oh, yeah, we can manage all your people data and all this stuff out there. But the reality is, innovation is happening in small pockets. And people are able to solve tactical problems or small business unit problems with tactical solutions. And there are 1000s of out there if you go to HR tech, and and that’s where disparate data just gets messy. And so you got to you have to clean it, you can this is one of the things I learned in my whole career is I’ve always been torn between the idea of super clean data, and the cost of getting super clean data, versus the agility of just having really smart people that can slice and dice the data and drill into data and your work in Excel and all these other tools. And the answer is both, you need both. If you want to do it with really bright people, great, you can get some really great people. But if you want to do it at scale, and you want to push it out to the business, then you actually need some clean data that has some history that so people can look at the past to look at the future. And I think that’s the dichotomy. Sure, of people analytics.
Dwight Brown:
I think we’re definitely in the wild, wild west right now of people analytics. I mean, you think about it, and and it really is such a young specialization or industry or whatever you want to call it. Partially because we’ve always looked at people as expenses. And we haven’t looked at people as assets. And so when industry finally caught on to the fact that, oh, people are assets, and we need to start to look closely at those, then it you move into that next phase, which is the wild wild west phase, where everybody’s trying to figure it out, and everybody’s coming up with their solutions selling their solutions. And, you know, but everyone’s trying to get they’re trying to get their arms around things right now. And so when you talk about HR tech and the number of vendors out there, it we really are in this wild, wild west phase of trying to figure this out and start to put order to and whatnot with that.
Nigel Stoodley:
I think we in the Western world here are you know, but For the pandemic, the there was a there was a huge shortage of workers. Like I can’t remember the number of jobs that were open on the job market. That was just insane. I think it was like, there were more was it? No, it wasn’t it was it was a decent percentage of, of open jobs compared to the total employment rate in the US. And, you know, same with Britain, you look at most of the Western world, our demographics, there’s gonna be a shortage of labor. And that means companies, yet, you looked at it, you had this young college group of kids, people that were coming out, couldn’t get jobs. We’re all as Gen X. But baby boomers and everything we’re sitting in jobs are all quite happy, fully employed, if we want to be, and, and then there’s this younger generations, that’s really struggling. And I think that companies are going to have to invest in employees like assets. And they’re going to have to take young people right right out of college, they’re going to have to make investments in them. And like any other investment, you need to make sure that that investment is going to pay off to your business. And I think this is going to be great for employees, because employees are going to find companies where people invest in them, train them. And that’ll be an important part. But then those companies are going to have to work to retain those employees. Because there will be another bunch of companies out there that say, I’m not going to bother making that investment. I’m just going to harvest what others produce. And I think that it’s a really interesting dilemma. And it’s all going to center back on people analytics because people will be more valuable than capital.
David Turetsky:
So Nigel, let’s talk about from what you’ve seen before you came to people analytics, and then learning what you now know it people in the world that people analytics, what kind of guidance would you provide? What kind of advice would you give companies who are trying to get their feet wet with what people analytics has become what those people intelligence to Dotto is now
Nigel Stoodley:
You know, it’s actually funny being in in analytics, and Dino Can I actually probably attest to this is the hardest problem with analytics is actually getting people to consume it is you know, you can build the best analytics in the world. And it’s, it’s hard to find, it’s, it’s, it’s not perfect, it tells you the answer. But somebody may not be familiar with the tool or the presentation or things like that. But I also think it’s also incomplete. And that’s another piece of the challenge. But I always ask, the question is, do I want the people who are doing a job? Do I want them doing analytics? Or do I want them doing their job? So Richard Rosenow, I was trying to produce the best analytics for Nike, but it’s never going to be 100%. Right. And, and, and the fact is, you really want people at Nike, either selling shoes, making shoes, designing shoes, and other apparel, and golf clubs, and everything else Nike does, but you know, you don’t want them like all everybody in the company becoming data analysts, you know, that’s, that’s not what they want. So it is always a challenge. And getting adoption is hard. It is really, really hard. But I do find the best way to get adoption, is to get it out in the hands of business users, and get it in a very simple, approachable, accepting the incomplete, but just allows somebody to get some information and do something. And and you know, embedding it into some sort of process process. But Americans and Canadians say that we’re differently. So I’ve just outed myself about the, but you get that, you get it tied to the business users. Now the business users are more informed, and they can make better decisions. And I think this is one of the things that HR can really learn is get the data and get the business data in with your people data. Like you have to bring the business data in, because people are affecting the business. And I as a business leader, myself, people data in isolation, I maybe look at once or twice a year, you know, kind of performance review time salary review time, you know, odd request, I’m, I’m busy running my business. But when I have my business data tied into my people data, all of a sudden my people data is that much more vibrant. And then I can ever ask questions about people like I remember when I was at Tableau, I had I had a I had attrition problem. And I was I was complaining to the CIO and the CFO about it and I said, Look, I got I got a salary problem. And I said, No, you know, it’s only a ship problem. And he said, you know, you need to it’s always a leadership problem. Money is never the problem the solution to these problems and you know what, we ended up doing a very expensive compensation analysis and found that we were paying below market and and you know, it was it, and it wasn’t that and we said, well, why why is this suddenly the case? It wasn’t that the market had changed. The reality is, we had some changes in our stock price. And the RSU weren’t necessarily so valuable for people. And so people had, we didn’t know this, we’re just catching up there as us to every every quarter, to supplement their salary, sir. And so but that was a bit of people analysis, we were able to solve that problem. And also, another time is recruiting I, you know, Tableau was an awesome company to work at, in the fact that I went here, I had to grow by 100%, my staff around the world. And it was, it was crazy. So my number one friend was my recruiting my recruiting team. And I used to meet with them all the time, and I’d be talking about pipeline, I’d be talking about efficiencies, where, where we’re recreating drag in the system, and in the process as a business leader, like, and they just didn’t have the data for it. And I went and talked to the CIO, and I said, Look, I’ll pay for this, I don’t care what it takes, I need to get this information. So otherwise, I’m not going to be able to hit my my growth targets, and I’m not gonna be able to bring the revenue in or support the customers, the way the company needs me to do it. Right. And so, you know, I think that business leaders are very, very powerful ally, for the, for the CIO, in getting some of these initiatives off the ground.
David Turetsky:
I think one of the problems with the data that you’re describing, though, is that HR who has typically made the mistake of being very siloed, and of being too captive of the data and of the processes and trying to make them to HR. And that has made, like you mentioned, performance and compensation is the two processes that you thought of as being HR, you know, processes you had to deal with. That’s the problem is that we made business leaders hate us in HR, because we said, those are the two times we want your input, which should have been all the time, we should have been talking to you all the time, we should have brought the business leaders in and said, Hey, this data is yours. Because HR data is not HR data, HR data is business data. And one of the problems we have is we hold on to it too tightly. It isn’t ours. So, you know, we’ve heard the term democratizing data. And that’s just wrong. It’s not about democratizing, it’s letting it go. letting our grip off of it so that the business leaders can feel like it’s part of what they should have been looking at all along. Absolutely. I know, Dino, you love this topic to write about giving up this control.
Dino Zincarini:
I love all of our topics. But I actually wanted to segue a little bit to a topic that you just you mentioned something there, too, that that I wanted to, to think about a bit which is, you know, the inside of your example, I love that example, right? Because I think of all the departments that HR oversees, especially recruiting is the one of the ones that is most tied to the business operations. And it’s very tangible, every business leader can relate to the importance of that data insight that you wanted. But to be able to serve that need that you had zoom in, you’re not an idle stutely and haven’t had 20 years in analytics to know that this is something can be solved with data, it requires you to know the business problem, the business problem there was it I need to identify bottlenecks in the recruiting process that I can control? Right? It’s it requires us to get to that level of understanding of knowing the business problem of our business partner, not just operating the process, not just helping them when they call up. But really getting into the details of what they’re doing and why it’s important, then I can figure out what metrics are important because you said earlier, you know, the consumption problem is real. And it absolutely is right. One of the ways to solve the consumption problem is to curate some of the content help make sure that the content is attuned to what that business leader needs. The only way to do that is to understand their business problem to understand their business question. So I love what you’re talking about. I think it’s a fun thing. It’d be fun to get to know our business partners better and I have a feeling most people in HR probably have a really good idea of what those business problems are. They may never have been asked to articulate them or to apply them in this way.
Nigel Stoodley:
You’re 100% and I would actually say I have been blessed in my career, having awesome business partners, HR business partners. I haven’t like I just I think back to every single company I’ve ever worked at my my HR business partner has been my ally in this regard. So I’ve never seen the Uber cop, you know, the the giant company, problems of business partners being spread too thin or not having domain expert experience. I’ve been lucky to work with awesome HR business partners who have been able to bring the data to the table in a conversation I also think I’ll speak to business leaders and not just HR HR people here is, you know, being part of the business partners, or the business leadership team is super important. When recruiting is a big, big issue, I invite the recruiting lead, my recruiting lead, and my HR business partner to every single management meeting, they are part of my management team. And I think that’s a really great practice. Because it gets them solving your business problems, it gets HR out of their silo that you mentioned, David, it gets them so that they’re participating, they are now part of my business. And they bring so much more value to the table in regards to, you know, business people can come up with all sorts of crazy ideas that are likely to drive up attrition as opposed to drive it down. And I think they bring that the the the brilliance of really good HR practices to a business leader who’s trying to solve business problems, and getting those two things aligned is which makes businesses super high performing, or even business units. It doesn’t have to be done at that corporate level. Like, I’m speaking to individual business partners here. I’m speaking to individual business leaders here, sir, whether you’re a team lead or department head or anything like that,
Dino Zincarini:
Yeah, it’s not all about the dashboard to the executives, it’s about getting close to where the business really happens as well.
Nigel Stoodley:
Exactly. And, and I work at Visier right now. So it’s, it’s always, you know, they’ve got the data, and they know where all the data is, I probably could look for it and find it, but they bring it to the table for me. And then we very quickly pull it together with the business and Visier, we’ve actually brought our a lot of our business data into our Visio system. Sure. And so my business data is aligned to my people data, and I can see it all. I do think that HR should also look for business processes that I have, and figure out how to get people data into it. Because I you know, I would love to be doing more in when I’m doing salary reviews, I’d like to be able to see, you know, engagement scores, I like to be able to see performance, I’d like to see analytics and be able to do what if analysis when we’re doing, you know, people without see what’s happened to an individual over time, often these are just set, what is their performance today? Well, let’s go back and look at an individual’s performance over time.
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David Turetsky:
That brings us to our third question, which is, there are a lot of systems with a lot of data. And to your last point, as a business leader, you want to have them all brought together. So you can make better decisions, not just in HR, but outside of HR, having the full stack of data with all of this disparate data, how do you bring it all together?
Nigel Stoodley:
Well, without making a plug for my current current. But, you know, I I do think that there’s there’s obviously data warehousing strategy. And going back to what Dino said, and what we were talking about democratization. I think democratization is, is important, but let’s be clear, not all the financial data is sitting in the data warehouse, there’s a bunch of data that’s confidential, there’s a lot of the sales data that or or, you know, that is not kept in the in the sale system. And so there, the data warehouse is generally a great place for detailed level data. But there is going to be stuff that you can’t share with everybody. Unless you’re working for a company that has, you know, complete visibility into people’s salaries, salary history, potential, you know, incidents that people may have been wrapped up in, you know, either performance or, or something like that. There is data that is necessary to the management of the business from an HR perspective, that shouldn’t end up in a warehouse. And I also think, with the increasing demand on privacy, like that’s actually the hard part, this is the this is like where suddenly your data warehouse becomes in conflict with your need for people analytics, specifically, these privacy control controls, like it’s hard. So I do think that you have to figure out how you have that, that that data warehouse that has everything in it. And then you put your way you want to have the detail be selective about what business business data you bring into your HR analytics system, and then also, you know, what HR data you’re putting into your into your data warehouse. And I think that’s the important thing. I’ve always said that as you need these. Often, there is no such thing as a single data strategy, like getting to my overall experience. You can’t have a single data strategy. Most companies will have a data lake swamp, he often it’s a swamp, they have the data swamp and in what they start building is analytical data Mart’s on topics. And what happens is the data just becomes more and more curated, and becomes more more accurate. But and I think that’s what companies because there is so much data out there, like all these machines are spitting out data, some of them might be really useful to looking at people’s performance. And, and but you know, some of it is very big brother. And so you just have to be careful, you know, keep it in the data swamp, if that’s where you want to keep it. But that’s, that’s an unmanaged mess, and then drain the swamp a little bit and put some nice cleaner data for brought for more broad scale access in a data mart, or an or an analytical solution, you know, and that’s where you can actually have that tiered data approach. And I think that businesses will realize that there will probably be a reckoning where there is people data inside a business, there is customer data with inside a business, there is money data inside of a business. And and those are the four core side or three core operations, I forgot about operations, you know, making widgets, or whatever you do. The those are the central views. Those would be your your Mart’s. And I think companies are going to have to realize that they’re going to need to get to that state.
David Turetsky:
Yeah, to Dino’s point before, and not all of the data, not all the divisions, not all the businesses need necessarily the same level or depth. So Nigel, what you’re talking about is absolutely true. Not everything needs to be there. And there are definitely privacy areas where you know, especially discipline and things like that, that definitely cannot be shared. But the you know, the the world that we’re getting to is more transparent. There are companies that are doing much more transparency, especially around pay. We had a podcast on that last year. And I think what the world is trying to do is to try and get to a place where the secrets are less. And the access to the data to make better decisions is more, because there has to be more discipline, more training, more understanding about what that advantage can be for that company who understands more about their world.
Nigel Stoodley:
Absolutely. People, people data, like you can make a huge difference of people data. We, my little team is a high performing team at my current company. We look at the end, we look at the engagement data, we look at the Net Promoter scores, and we look at and we can correlate those two things, people who are highly engaged, have way better Net Promoter scores with customers. And so that’s, you know, that’s a people it should go back and fix the engagement issue, you’ve fixed your MPs problem, right. And that is just shown up the data over and over again. And that isn’t super sensitive data.
David Turetsky:
So we had really good discussions around what is the problem of people analytics, and what actually Nigel has learned over the last three years of transitioning from bi to people analytics, specifically, we also talked about the things from his background, and from what he’s seen, that he can provide as practical examples of how actually you can get to people analytics. And then we talked a little bit about the things that he’s learned around connecting the disparate data, and some of the things that are good as well as bad about connecting data. Gentlemen, what else would you like to add? Nigel, you first what what else did we want to cover that you hadn’t already?
Nigel Stoodley:
I just think people data is so valuable, and so underutilized in business. And I think that will be reckoned with as business, just the demographics of the Western population is changing, and, and we need to invest in employees and invest in their ability to contribute to the company. And so that that for me is is number one. The second piece is talking about getting aligned to business, right is so important is and don’t be afraid the business will help you you know, the business leaders there. They’re not it’s not a it’s not a data war. It’s not I’m smarter than you are, I know this better than you. Gosh, we partner business is a partnership. And I think that’s the way we’ve got to look at it because HR people, you know, I know people so much better than business people who know their businesses, and bring that together super powerful. And then last but not least, it’s messy out there. I think if you talk to any sort of analytic vendor, they’re all going to talk about the multi tiers of data and having the right level of curation. For the right business problems is the right thing to do. And, and having that that multi stack approach. That’s my summary.
Dino Zincarini:
I know one thing that your summary because you said a lot of Really good things. And it’s hard to summarize it. But one that I learned today was that adding business data to people data is a one plus one equals three type equation, where the value of the data of both in discrete pieces of data is increased when they’re combined in your example of, you know, as a, as a leader in your support organization, having the Net Promoter Score next to the engagement score, right, that gives you a cause and effect type relationship that helps you run your department better. And so even for those people out there who have a people analytics solution, you have some reports, you have some dashboard, have you added business data into that? There’s more, that’s where everything is going. So it’s a challenge for all of us who are in the industry to keep pushing?
David Turetsky:
Dwight anything for you?
Dwight Brown:
Yeah, and I would say, you know, I’m, I’m the data governance, evangelist of the of our world and, and really touching on a point that you had touched on very early in the conversation, Nigel, and that’s around that idea of really looking at your data coming up with standardization of the data. And we’re just we’re, we’re not at a great point for that, because we are back to that concept of the Wild, Wild West. But we are getting there. Every day is a new day, and where we’re having more synchronization among vendors and those sorts of things. And the key underpinning of that is the data governance side of things. The data standardization, come up with, with agreed upon industry standards for data standardization.
Nigel Stoodley:
I agree. And I’ll just put a word of warning out there is AI. and machine learning is the or AI specifically, is the big buzzword in business intelligence these days. Yeah, ad. Ai frightens me, because I know the quality of the data out there. And if you’re making bet you’re making good decisions off that data. You’re not making good decisions.
David Turetsky:
Great if the AI was actually focused on fixing the data first. Yeah, yes,
Nigel Stoodley:
exactly. But I do I just see that you know, that people want to, you know, get people out of making the decisions. But today, a lot of decisions are that doesn’t look right. Let me go check. Right and, and so I that’s my little word of warning is about this governance. It’s super important. And it’s the springboard for all future innovation.
David Turetsky:
Well, the AI might be driving our cars, but they’re not driving the businesses yet. So there we go. Gentlemen, thank you very much, Nigel. Dwight Dino, thank you very much for joining.
Dino Zincarini:
Thanks David. Thank you. My pleasure. Thank you.
David Turetsky:
And thank you for listening. And we appreciate it. If you liked the episode, and you like the series, please hit subscribe. And if you have any comments, please go to Turetsky consulting.com slash podcasts and give us your thoughts. Thank you very much. Take care and stay safe.
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