Sometimes in analytics we can get so absorbed in the numbers that we forget about the value they should be upholding. John Tardy is a leader and an expert in business analytics. John sets himself apart by focusing on delivering BIG value through technology, data analytics, and business intelligence, as well as building trust and confidence among stakeholders and guiding cross-functional teams and leaders to business insights and clarity amidst ambiguity and complexity.
I can’t wait to dive into this conversation and learn the concept of big value, not big data from John.
[00:01 – 03:15] Opening Segment
- I welcome and learn more about John Tardy
- Today’s topic – big value, not big data
[03:16 – 09:44] What is Big Data
- Examples of ‘Big Data’ and attempting to define it
- The importance of finding value in the data
[09:45 – 15:33] Big Value, Not Big Data
- John talks about the misconceptions of going towards the ‘hype’ of data
- How to measure value based on a goal
- The importance of education and focusing on business orientated value
[15:34 – 28:40] Creating Value through Data in HR
- It all starts with the business questions; all about mindset
- Examples through turnover, diversity, and absenteeism
[28:41 – 37:55] Closing Segment
- Summary of our conversation and final points
- Final Words
Resources:
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“As we get into data that expresses more of those ‘big data’ characteristics, we benefit from different tools.” – John Tardy
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“Ultimately, HR is working to tell a story of the impact of their activity on the value metrics that mean the most to the business.” – John Tardy
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“It’s not to chase the big data, it’s not to chase the ‘shiny nickel,’ but focus on the business question… There is so much value to be had here, in the data you already have in the organisation.” – John Tardy
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, pour 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: 0:53
Hello, and welcome to the HR data labs podcast. I’m your host, David Turetsky. Like always, I try and find fascinating people in and out of the world of human resources to talk to us about HR data and analytics. Today, I have my friend john tardy from Turetsky Consulting. Hey, john, how are you? David? Hey, great to be here with you. Awesome. I know, we’re gonna have a great time today. John, why don’t we start with who you are. So you have a broad technical background, you have a engineering degree from Rutgers, go big red. And you have an MBA from Georgia Tech. Why don’t you give us a little bit more about your background?
John Tardy: 1:26
Yeah, you know, I do have that technical background, my niche has always been like in the interface, really, between the business and the technology, really unlocking the value that’s in the technology. And in the in the data for the business. I’ve had considerable focus on on HR and payroll, but other areas of operations as well, field service supply chain, facilities and a number of others.
David Turetsky: 1:49
So you’re well rounded in the world of business and data. I’m a utility player, that’s great. Every team needs a good utility player. Awesome. One fun thing that you may not know about john, you are in training to become a pilot.
John Tardy: 2:05
That’s right, I have I have completed all the training requirements, I just haven’t taken the final exam. Oh, my goodness. Yeah, it’s always been a passion of mine really aviation and spaceflight, something great that people can accomplish together. And, you know, allowed me to really even feel closer to those experiences. And also, I feel like has parallels to my work in technology and data?
David Turetsky: 2:28
There’s nothing cooler and taking off, I imagine.
John Tardy: 2:31
Yeah, you know, I think for me, there’s this magical quality of flight. Sure. And, you know, getting closer to and really having that personal experience in being able to control the plane and flying helped me to understand really, but it’s not magic, that it’s physics. And actually, you know, at a deep level, it’s, you know, some point it’s that the plane is presented with certain conditions, it has to fly. And, you know, for me, you know, here we are talking about, about data and big data, machine learning and AI. And there’s, there’s really a parallel there, I think, because so many people see those as as magical, magical tools. And it’s math. It’s statistics. Right?
David Turetsky: 3:12
Right. Yeah. Cool. So today’s topic, as you’ve hinted is around the concept of big data. But it’s more about the value of data. So the way you’ve described it is big value, not big data.
John Tardy: 3:27
That’s right. That’s right. You know, I think there’s there’s a lot of excitement around big data. And I think for starters, as a technologist as a, as a data professional, you know, the first thing that I think is important to understand is it’s not a technical term. There’s no classification of Oh, this is big data. And that’s small data. Right?
David Turetsky: 3:46
Yeah. I mean, well, when I was doing my big data certification, on Coursera, one of the things that kind of struck me was there’s just a ton of unstructured data out there, right, that if you wanted to wrangle, you’d have to wrangle, and the concept of big data to me was always this, you know, big unstructured data. Is it really, or is it something else?
John Tardy: 4:11
I think that’s part of it. They talk about the the V’s, you know, varieties, one part of it, there’s a structured data and unstructured and, you know, a couple of other components are the volume, right? Just the size of the data certainly is one aspect of it. And then something we call the velocity, the the rate at which the data is coming in, or the rate at which the data is changing, right. And but these are characteristics. And so the more that a set of data is expressing those characteristics, the more it leans towards big data.
David Turetsky: 4:40
So John, what are some examples that we might find in the world of HR around big data?
John Tardy: 4:44
Yeah, I think there’s, there’s a number of areas. The first one that comes to my mind is around recruiting. And the reason for that is that we have many more applicants than we have employees, sure, and the interactions that are available there. So you know, starting with the company portal that a candidate would go to to complete an application, right, these web analytics later itself, what sort of interactions are we getting there? There’s opportunities to see how a candidate is responding to the information that the company is putting out. Right. resumes unstructured data. Absolutely. Right. So that fits in perfectly in the kinds of value analysis, analysis that we can do on that. Yeah, if you saw my resume, you’d know for darn sure that it was totally unstructured. That’s a whole other talk.
David Turetsky: 5:33
Yeah, I don’t know, the topic for a different day.
John Tardy: 5:36
And then you know, and then there’s all these different channels. So resources in which we have candidates coming in. So which one of those channels is performing the best for the company? Right? Yeah. Right.
David Turetsky: 5:46
And there are other ones as well. And I know that you have some examples. The examples I like to talk about are when people search the web, and you mentioned recruiting, recruiting sites, obviously. But there are 1000s of recruiting sites, and there are 1000s of postings on each one of those recruiting sites. And I know there are there are literally companies that scrape all of that data, and take all that unstructured data and put it into analyses to understand what are the hot jobs? Were companies hiring, what industries are hot, what industries are hiring? So there is a there’s a lot to be said for all that unstructured data?
John Tardy: 6:22
Absolutely. Another area, like looking more inside the company is around employee engagement. Right? So here, I think, you know, one area that’s really neat is looking at network analysis. So what is the connection between different employees? How connected is this employee to the organization? Whether it is within a particular department? Or even how cross functionally do they interact? Right? Those are key aspects. You know, you could look at sentiment analysis, how positive are those interactions? And you mentioned unstructured data, I mean, really a variety of data sources that you could look at, you know, email, certainly. Right. You know, the employee surveys are one data point, exit interviews would be another sharp, unstructured opportunity.
David Turetsky: 7:08
But I think that starts to get into once you start talking about exit interviews, you start talking about not as much on the volume side, I mean, yes, if especially, we’re talking about large employers, it is but but even then we’re still not talking the types of volume that are in the world of big data, right? Those are much smaller sets of data.
John Tardy: 7:28
Exactly. And I think that goes back to one of our original points. Okay, do you call that big data or not? Well, it could be depending on how you collect it. It could be unstructured, or it could be notes by the exit interviewer, and then somebody may consider that is the get what, you know, let’s, let’s talk about that for a second. Sure. What’s the difference?
David Turetsky: 7:46
Yeah, who cares, right? It’s
John Tardy: 7:48
big, or not big data, right. And I think the WHO CARES is that as we get into data that expresses more of those big data characteristics, we benefit from different tools. So as a data expert, that’s where it starts to come in is the same tools that are effective on small data in and strictly relational data start to break down when we get into more of the Big Data realm.
David Turetsky: 8:14
And I think that’s a really good point, because where you’re storing it, it shouldn’t matter, not these days, not with storage, being as cheap as it is, and with the availability of platforms like AWS, giving us the ability to, to really, you know, be extremely creative in how we not only take different forms of data and store it someplace, then figure out how to use it. So there really is, you could definitely say there’s the storage world has kind of revolutionized the types of data we keep, and then allow us to be much more creative around the tools we use to then be able to go in and look at it.
John Tardy: 8:48
Absolutely the storage is it was a game changer. The compute available. Cloud is a huge game changer in what we can collect. And we didn’t even touch on this yet. But you know, just look at all the data we’re collecting. Right? I mean, you know, I got I have my Apple watch on it’s tracking how much how many staff you’ve got one, two, right? So it’s like, you know, how many steps did I take? How long did I work out today? What was my heart rate? That’s all data.
David Turetsky: 9:14
It is and the ability to kind of maximize your return and find some return on that data. It’s really kind of up to the creativity of those who are trying to figure out where do they find the value in that data? So if we’re trying to find value with data analytics, do we need to be talking about big data? Or, you know, can we just be talking about all these sets of data?
John Tardy: 9:55
Yeah, I think that I think that’s a common misconception really is that people feel a pressure. To need to go to the latest and greatest the big data because that’s the that’s where the hype is right? I don’t believe that, I believe Really? Well, first of all, in HR, there’s lots of data that is very valuable, right? That is not big data, right. And, you know, my focus we touched on this earlier has always been working between business and technology to understand where the value is, and how we can use the technology to drive that kind of value. And so in order to do that, you need to speak to and understand the business. Sure. And in HR, you need to understand the business of HR, and then the company’s business as well.
David Turetsky: 10:38
Yeah, you know, I always love when I get into conversations with HR people, and we start talking about the drivers of business, and looking at their clients in a different light, not as being just and my clients being report writers or order takers, but better consultants, and better question writers are better question askers. You know, what is it you’re trying to solve? What business problem are you trying to solve? How can we take the data that you are generating every day, and then marry it to something that the business cares about more like revenue or sales or something like that? And then be able to figure out, what kind of patterns are we seeing in the data? And how can we drive value by maximizing the interactions with our people to be able to drive value?
John Tardy: 11:24
Absolutely. I think that that’s the keys which you hit on is that it should all start with the business question. Right. Right. Not the technology, not whether it’s big data or not start with the business question. And how you measure that that depends on the goal, right? For the business? Sure. There is a I like this quote from, from Elon Musk, you know, saying what is the company? What is a business? It’s really just a bunch of people working together in pursuit of a goal. Right? So depending on the goal, there are different types of measures that would indicate value, right? You touched on revenue? Yeah, right. Expenses are clearly you know, productivity. Sure, maybe it’s around customer experience, or the employee experience, right.
David Turetsky: 12:05
And one of the things I think that we all miss understand is, everybody thinks of HR as just a cost center, and not to worry about what the business is doing, you know, pat the good HR person on the head, say you’re doing a great job, you’re keeping our valuable resources. Now, let the business people work here. I think that’s been a common misconception. And I think HR some of the most brilliant business people in the organization, because they are managing, and they are helping to take care of those precious resources we call people. And they can drive a relationship between the people and the outcomes, in business, if we just had a chance. And with this data, I think we’re given the opportunity to tell stories that the business just wouldn’t have if they didn’t have access to this type of information, this inside.
John Tardy: 12:53
That’s the really exciting thing here is being able to make that connection and tell that story of the impact that HR has on the buiness. I mean, the business is all about the people. Yeah, right. That’s what’s driving this activity. Right? You know, why doesn’t that happen so much right now? Because it’s not always easy.
David Turetsky: 13:12
It isn’t, it isn’t. And especially if we’re not asking questions, like, some of the questions that we hear are, you know, what columns Do you want in that spreadsheet? That’s not the question you should be asking. Somebody says, Hey, I need a, I need a spreadsheet of people. And I need their date of hire, I need their job title. I knew their job code. Okay, so you’re looking for a demographic analysis? What is it for? How can I help you with it? What other questions should we be asking to be able to get that spreadsheet to become an analysis?
John Tardy: 13:40
Right? And I think that the key here these components of this analysis that you’re talking about? These are not the expertise of HR, generally
David Turetsky: 13:48
not they have not been in the past? That is correct. Yeah.
John Tardy: 13:51
Yeah. And so we, you know, we’re bringing this is a melding of the HR and the technology world, and very quickly changing area of data and analytics, and it’s bringing it all together to really drive the real value.
David Turetsky: 14:07
And I think we’ve started to see that we, we’ve started to see HR people, especially people in the analytics function, who have done a lot of work on being able to become much more advanced in their understanding of the business. And you can even say, generalists have been doing this for many years to get that seat at the table. generalists have had to learn their business so that they have much more intelligent conversations with their leaders with the people that they support. In order to be able to find the right recruits for them and able to help the people understand in the field in business, help them understand what is the leader trying to accomplish. And so the HR person has kind of started to generate that understanding of the business. But we need them to get further in order to be able to ask those better questions to produce those better analyses. Right. And that’s the great thing about what you’re doing here,
John Tardy: 15:02
David, with this podcast, it’s education. It’s all about educating, and in helping to close that gap so that people can can utilize these tools and these techniques, and bring these approaches really to bear for the business.
Announcer: 15:16
Like what you hear so far, make sure you never miss a show by clicking the subscribe button. Now, this podcast is made possible by Turetsky Consulting and listeners like you. Thank you for your support. Now, back to the show.
David Turetsky: 15:32
What can we or how can we create value with data in HR? What are the examples that you’ve seen, in your experience that provide people with value in not the big data but in smaller pieces of information?
John Tardy: 15:48
Yeah, yeah. So you know, I think, like we said before, it all starts with, with starting with and focusing on the business question. Jim Barksdale, from from Netscape had this this quote, If we have data, let’s look at data, if all we have our opinions, let’s go with mine it’s a mindset. Right, right. It’s like, you know, let’s get to it. Let’s get let’s get down to the data. So what do we have here? You know, classic turnover? Sure. Right. So we’re classic, you know, today, we’re talking about how we can go deeper in these kinds of analytics to really drive value out of this information, because everybody has turnover metrics. But, you know, the first there’s many definitions of turnover, right? So we can start there. You know, if we start by counting turnover, how many instances of turnover, that’s information, but there’s no real insight there? No. Okay. can’t compare it to anything? No. So then we start out very simply by, you know, normalizing those into rates so that we can actually compare, say, across organizations, that’s a first step. But then, you know, going deeper into that, what kind of turnover do we want to focus on? Do we, for example, segmenting, you know, looking at our highest performers, that are choosing to leave on their own? Right, and looking at those rates?
David Turetsky: 17:06
Yeah, I mean, I would go a step further and to say, you know, when you start talking about turnover, you first have to filter out a bunch of noise, right? So first of all, there’s seasonal turnover. There might be interns, or seasonal workers, or transient workers who, you know, come and go like temporary workers, that, you know, they’re probably not the best population you want to have inside of the data. So you remove those, and then you start focusing on your core, right? And then you say, okay, we’re only going to look at people who voluntarily left the organization, obviously, right. We know that there has been some involuntary turnover for every company, they have, you know, layoffs, and growth spurts and layoffs. And what we then need to do is look at what so we’re just going to look at voluntary turnover. So once you start getting that population set, you know, then you start looking at the ones you’re talking about, which are performers, and you start looking at the different demographic groups, like, you know, the jobs, what type of job functions and families and you start looking at different demographic groups to say, oh, my goodness, are we losing people in certain race ethnicities, or genders or age bands? You know, what, why are we doing a bad job in certain places? So that’s definitely a way of getting to, you know, finding value and turnover, which we may have missed if we hadn’t gone to those depths.
John Tardy: 18:33
That’s right. Yeah. And I think the questions that you just went down there are very specific to the business situation. Sure. They can’t be they’re not generic. You talked about seasonality. That made me think of trends. Right. So you know, often we’ll look at trend lines. Okay. And well, you know, for, for starters, is that a statistically significant trend? Is this something that we should react on? Right? If it’s seasonal, if the same trend happens every year? Yeah, then no, right?
David Turetsky: 19:04
Well, that’s why I like, you know, especially if you know, that there’s going to be seasonality in your data. I like to remove it so that, you know, we can be looking at baseline, you know, a good baseline of data. But you’re right, you know, we could look at overall trends year after year and see the seasonality in the data. And especially if you have multiple year data and a relatively steady state business, that you should see interesting trends in the data as you go through the different cycles of your of your company’s life. You know, when I worked at an investment bank, John, we knew there were certain times of the year when, you know, especially when we hired our analysts and Associates, after bonus time, we knew that there were lots of people that were going to come and go, and we kind of figured that into our business plan. So we just knew, and so when we brought up turnover, the first thing we did was we kind of threw out, you know, these are the populations we know we’re going to ignore when we go into looking at that kind of data. But it was especially funny. To look at, right after bonuses were given, no matter what it was, whenever we gave bonuses out, there were those people who left. And the funny thing is, you mentioned the higher performers in the world of investment banking, especially in a lot of the financial services world. It was always funny to see, you know, who we had tried to retain, especially if they were top performers, and who just left anyways. And that’s why as a comp professional, john, I always tell people, money never saves an employee who’s got one foot out the door and one foot on a banana peel. So
John Tardy: 20:33
yeah, there’s those trends in those correlations to different kinds of events that happen now are key. Yeah. The other thing is, sometimes you’ll see a trend and it could just be noise. Yeah. Right. So this is where that statistical analysis comes in every time the metric moves does not mean that some sort of action is indicated.
David Turetsky: 20:52
Yeah, absolutely. But it also needs to have that context, right. So that’s where if you have a generalist, or you have someone who you can talk to about the trends you’re seeing, and get their perspective on it, whether it’s the manager or the the HR person in charge of that area, get their perspective, so they understand what you’re seeing, and they can provide you with the color commentary on what’s actually happening there. So if there are trends, that they see that you see the go, these look interesting to go, Yeah, no, that was noise. You know, we knew we lost a couple of people there. That’s why it happened.
John Tardy: 21:27
Yeah, well, let’s talk about perspective. Sure. Is turnover bad?
David Turetsky: 21:30
That’s a great question. And I know my my answer is no, I always think of turnover as being great for an organization because it enables people to move up, take on different roles, take on new opportunities, and to get new thinking in leadership positions. What about you? What do you think?
John Tardy: 21:45
I think that’s true. And I think it does depend on the situation again, right. So it’s, it’s the the point here is having that perspective, that broader picture, understanding what segment of the business we’re looking at, what’s the right answer for the business, right? That’s and and keying in the analysis to focus on that. So we’ve gone much deeper than many organizations go in this analysis, this has nothing to do with big data. Sure, right. But we’re, we’re getting deeper and deeper to drive value out of out of this data. Another example that I like to go to is around diversity. Sure, most companies at this point are measuring some kind of diversity metric, you know, again, you can start very simplistic and just look at sort of ratios or percentages of different demographics in the workforce. And then then we can come up to similar, you know, statistical questions. Well, if there are differences, then there will be right, they’re not going to be all the same. Are they significant? You know, we get into what are what are the odds essentially, that this distribution happened by random, right, random variation? Right? That’s a key question.
David Turetsky: 22:53
And I think that brings up the ability for us to use big data to help us because there is a lot of big data about what population we’re measuring against, you know, if we’re looking at diversity, and we say, are the populations indicative of the populations that we’re hiring from, or the schools that we’re hiring from, or our target market, or even our what our clients look like, then it should give us a good indication by looking at big data that does show us what those percentages are? Of what should our distributions tend to look like?
John Tardy: 23:26
I think that’s a great point. David, I love the part about the talent pool. Yeah. Right. Because I think often there’s sort of a, there’s a tendency to jump to a conclusion, which is that if the diversity metrics are not what we think they should be, that that is being driven by decisions within the organization, right? And hiring managers, essentially. And if the distribution in the talent pool that we’re selecting from, is different, and you know, we could we could you can start with general population in the geographic area that we’re selecting from. That’s that’s a big, big picture, right. But then you get into more very specific things about the specific talent skill sets that we’re looking for, and who are the people that have gravitated toward those kinds of roles, and have have chosen to pursue those degrees?
David Turetsky: 24:19
Yep. I would argue though, John, what that enables you to do is to look for those populations where those skills are plentiful, and the candidate pool is diverse. And I know in the past, I’ve asked recruiters to give me diverse hiring bodies to be able to recruit from and because they used to use their you know, the sources that they’d go back to time and time again, they kind of pulled from those same sources and I asked them to choose differently. I asked them to choose, you know, more veteran rich and more ethnically diverse and more gender diverse and to try and find a candidate pool that look more like the world not look more like us and It wasn’t like I had resistance, I didn’t have resistance, I just didn’t get the pools that I was looking for. And I think that kind of speaks to one of the issues that you’re bringing up, which is that we need to be able to find the skill sets inside of those pools that we’re trying to recruit from, in order to be able to get the population or interview the population, we want to interview. And that’s why I’m saying Big Data can help you with that, because big data will tell you where to go to guide you, it can’t get you there, I can’t say hire Bob, because Bob’s got the background. Bob is ethnically diverse, gender diverse, and whatever. Well, he that’s not going to happen that doesn’t just exist, it just doesn’t. There’s no tools for that right now. And and by the way, it’s not fair, what we need to do is have a bunch of people that we can choose from making sure that we’re hiring for the right skills out of the population that gives us what we need.
John Tardy: 25:58
Yeah. And take the right actions. You know, before you earlier you alluded to, for example, educational partnering, you know, you may be partnering with the educational institutions to share Yeah, yeah, to promote and foster, to get a more diverse talent pool, that’s a completely different action, then then focusing on the hiring manager.
David Turetsky: 26:18
So John, when I, when I was in a previous company, we actually had partnered with a an institution, actually, it was Georgia Tech, to try and find people in the undergraduate world who wanted to go into analytics. And we had a people analytics incubator inside of Georgia Tech, in order to be able to grow those skills in a really good place, which had brilliant people, brilliant kids, and we got a lot of success trying to find people to then understand the business problem we were trying to solve. That is a great example. And did did data lead you to that approach. So when we got into that relationship with that institution, it was because the company had a relationship with that institution. But at the end of the day, it gave us an opportunity to find the right people with the right skills added at a time where we really couldn’t find people who understood HR analytics the way we wanted to do. So we were able to grow that talent in an institution that worked well, for everybody.
John Tardy: 27:24
That’s a great example. Yeah, another, you know, sort of going back and focusing back on inside the company. absenteeism, is another area that we could really focus on, and looking for correlations around why people are absent, how much are they absent? Or how much is that costing the company?
David Turetsky: 27:41
Yeah there’s a lot of that, in fact, you could draw a direct correlation between absenteeism, and overtime cost for people who have to fill in for absentee employees. So they are, there’s a lot of great data that you can get out of time data. In fact, time data is one of those pieces of information that could be as close to big data as you can get inside of an HRIS because there’s just so much of it, you know, every time someone clocks in and clocks out, that’s time data. And oh, my gosh, there’s just so much great insight to be able to glean out of that. We’re just scratching the surface, John, I mean, we could talk about this for hours. But well, I’m sorry, we don’t know that much time. Darn. So John, we talked a lot about how big data is and isn’t useful in HR, and how you can drive value out of data that isn’t big data, especially in the world of human resources. We also talked about some examples of how you can utilize not small data, but not big data in the world of HR. What other things did you want to talk about around this? Before we close?
John Tardy: 29:06
I think we covered a lot here, David, this this one key point, if there is one thing it’s not to chase the big data, it’s not the to chase the shiny nickel here, but focus on the business question. And that even if you don’t have the big data, or the tools or the people to approach that there is so much value to be had here in the data that you already have in the organization.
David Turetsky: 29:33
Outstanding. John, thank you very much. It was my pleasure. Awesome. we’ll have you back again to uncover more. Thanks, David. And thank you guys for listening. We really appreciate it. If you liked this episode, please hit subscribe. If you think you have somebody who might find value in this please forward it to them. And if you have any suggestions, please go to Turetskyconsulting.com slash podcast and leave us your thoughts. Thank you very much for listening. Take care and stay safe.
Announcer: 30:03
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