Don Weinstein – Using HR Data Analytics to Drive Business Insights

Connect with Don Weinstein:

• 

Connect with David:

• 
• 
• david.turetsky@salary.com

Connect with Dwight:

• 
• dwight.brown@salary.com

Podcast Manager, Karissa Harris:

• HRDataLabs@salary.com

 

Production by Affogato Media

Resources:

• Consulting Practice
• HR Data Labs Season 6 Episode 25
• HR Data Labs Season 6 Episode 23

• “When [HR] has the facts, and the analytics, and the data, [they] can make a real impact in an organization.”
• “Many organizations are still suffering from limited adoption [of HR data analytics] and one of the things that’s still hindering them is poor data quality.”

Announcer: 0:02

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, that 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: 0:46

Hello, and welcome to the HR Data Labs podcast. I'm your host, David Turetsky. And with me, like always, my trusted friend and partner from Salary.com, Dwight Brown. Hey, Dwight, how are you?

 

Dwight Brown: 0:56

I'm good, David, how you doing?

 

David Turetsky: 0:57

I'm doing okay, although it's rather cold outside. But we're not going to let that stop us from having a really good time. Because today, we have on a really brilliant person who I had the pleasure of working with for a very long time in my career. And to say this person was a mentor and a friend, I think kind of downplays it. But today we have on Don Weinstein, who had been kind of you could think of him as the strategy guy gone HR. And done, I want you to give a little bit more background on yourself. But hopefully that doesn't take away the fact that you've accomplished a tremendous amount in your career.

 

Don Weinstein: 1:33

Oh, well, that's kind of you, David. And, and it was a long career. So I hope I can accomplish something, you know, 35 years in the fortune 500. But, you know, certainly more than half that time in HR. And really, I think it was a great run for me. Certainly at ADP for many years where we, you and I worked together. And we had a lot of fun with data there. As we we always like to say data was our middle name. And we just had to, we just had to bring it to fruition.

 

David Turetsky: 2:04

Yes. And we're gonna have a lot of chance to talk about that today. But Don, what's one fun thing that no one knows about you?

 

Don Weinstein: 2:13

Other than the fact that I'm not that much fun.

 

David Turetsky: 2:15

It's not true!

 

Don Weinstein: 2:18

I don't do a lot, but I will I will say like, you know, I've had my share, I talked about 35 years in the Fortune 500. But before that started, I had my share of a really odd jobs when I was growing up. Anything to anything that make a dollar. You know, summer breaks, you know, after school jobs, so I was a busboy. I actually worked the grill at a sizzler restaurant. That's where I learned how to cook. I painted houses. I even spent one summer working in a sand mine. Yes,

 

Dwight Brown: 2:52

A sand mine? that is a real

 

Don Weinstein: 2:53

That is a real thing. We mined sand for Yeah.

 

David Turetsky: 3:00

Really? That's where it comes from.

 

Don Weinstein: 3:02

Well, for you know, industrial applications. Absolutely. You need you need high grade high quality sand that nothing better than a 18 year old with a shovel to get at.

 

Dwight Brown: 3:12

So when someone tells you to pound sand, you you actually know how to do that.

 

Don Weinstein: 3:18

I did pound my fair share of sand! Yes, I did.

 

David Turetsky: 3:23

And I was going to ask, Don, so you didn't actually go to the beach, take a truck and just kind of shovel it sand into the truck from the beach.

 

Dwight Brown: 3:31

Don stole the beach.

 

Don Weinstein: 3:36

Beach would have been nice. That would have been a much better working environment than than the types of plays like the bottom of a sand pit or a sand wash as we used to call it. Yeah. I will say that, like that's real work. All this other stuff that you know, I've been doing ever since. You know, that'll teach you to know the value of honest hard day's work.

 

David Turetsky: 3:57

But the Sizzler! You know what, I don't know if you ever saw the movie Waiting with Ryan Reynolds.

 

Don Weinstein: 4:02

Oh, sure! Love it!

 

David Turetsky: 4:02

But that kind of that came to my mind. That never happened. Right? That stuff that happened in Waiting. That stuff never really did occur, right?

 

Don Weinstein: 4:11

We'll leave it like that. Yeah, that that never, that never happened.

 

David Turetsky: 4:17

There you go. There you go. We're gonna leave it right there. So our topic for today is one that's very near and dear to Don and my heart. And Dwight, I know that you love this too. This is kind of the reason why we actually started the HR Data Labs podcast, which is talking about data and the journey that companies need to go on and how it works for you. And so Don, I know you what we want to do today is talk a little bit about the journey that even we took when we were at ADP of making data, actually into insight.

 

Don Weinstein: 4:59

Yeah, for sure. And for me, it goes back, you know, to the beginning. So I, I skipped over a little bit in my my background, but I was I was an engineer by trade, I actually started my career, as an aeronautical engineer working for General Electric aerospace on things like like GPS, right? So,

 

David Turetsky: 5:18

Wow! You were a rocket scientist is what you're saying?

 

Don Weinstein: 5:20

Yeah. You could, you could say that. I tend not to, but, but I was an engineer. And so, you know, people always ask, like, hey, you know, engineering to the world of payroll, and HR, that's quite a leap. But, you know, there were a few stops in between. But when I came into the world of human capital management, it was surprising to me, again, coming from a data centric world, like engineering, how underleveraged data was in the practice of human capital management. And, in particular, I think, there, there were a lot of other functions, not just engineering, but other enterprise functions that were in the process. And, you know, we're going back, like, you know, two decades now, that were in the process of being completely transformed by analytics, I think about sales and marketing as a good parallel. And, you know, there used to be that old marketing adage about, well, you know, I know, half my marketing spend is wasted, but I just don't know which half. And look, you know, in the in the pre digital world and the analog world of things like billboards, and you know, I remember buying Yellow Pages ads, you know, compared to where we are today, where I'm buying keywords on Google, and I know, to a fraction of a penny, what my cost per click, what my click through rate is what my cost per customer acquisition, you name it. And I saw the way that analytics was transforming other enterprise functions. And it just seemed like, like HR was behind the curve. And, and not only was HR behind the curve in that, in terms of limited use of analytics, but a lot of the limited analytics that were out there that were being peddled at the time was was bad analytics, it was a lot of junk science. Sometimes I use the term malpractice out there, it was quite, quite embarrassing. And so we saw an opportunity, we thought we could do something to change that and change it for the better.

 

David Turetsky: 7:14

I don't know if you remember this, but there was one client that we worked on, in particular, who had invested millions in trying to create HR dashboards. And they basically, were taking all their data, dumping it out into a data lake, essentially, and trying to actually give managers a view into the data. And they would literally give these HR dashboards to managers without any insight without any training without any context at all. And they got back a gigantic thud, you just wasted $2 million. And so one of the lessons we took from that is, don't repeat that and be be at least a little thoughtful, in how you're developing this and how you're delivering, right?

 

Don Weinstein: 7:57

That's exactly right. I mean, I think there were so many lessons we've learned in the early days. And, you know, we were we were rolling out data tools and data products, you know, starting like, almost 15 years ago. So we've been at this for a little while. But a few things really jumped out at me, you know, first was, I think just how bad some of the data in human capital management systems actually is, you know, things like job titles and position descriptions. But I think inside my own organization, we had, like one and a half job titles per person as an example.

 

David Turetsky: 8:30

That's actually not bad!

 

Don Weinstein: 8:32

Yeah, sure. I'm sure you've seen a lot worse. But I mean, how do you how do you operate in a world like that, where you have more job titles that actual people working it's it's mind boggling. Skills data, another area is just so fraught with, with just bad data. I'll still remember we did, that was coming out of the technology organization, where we did a Skill Survey. And, you know, like, the world of technology evolves very quickly. And so you're, it's, it's a thoughtful, it's not a bad idea to say, hey, let's just make sure we do an inventory of what people know and what people don't know. But the survey design was so bad and it was so painful that I remember going through it myself, and I was just a participant at the time thinking, How do I get through this as quickly as possible? And what I realized, first of all, was, anytime I checked the yes, it asked me a follow on question where wanting more information. So I don't want to do any of that. So I just said no to everything, I have no skills whatsoever. Other people spent like, well, you know that it's a little bit of a risky move, I grant you but it was it was that painful. And like I know other people who went, you know, completely other directions, spent hours and hours and hours, trying to document all the skills that they had, which of course, even if it was accurate, it was going to be out of date within six months, even though he asked me to follow on question, well, what did we ever do with that? Zero. nothing like whatsoever. So bad.

 

David Turetsky: 9:58

I remember when that happened, and I think one of the the interesting things is, even before it got finalized, the survey got finalized. Technology changes were happening so rapidly, that you just basically had to call it a day and send it out. Because otherwise you would constantly be evolving the survey and never actually collect anything.

 

Don Weinstein: 10:18

Exactly. And again, even that which you collected was, you know, not terribly valid, you're asking people to, you know, self assess, and maybe they were maybe they weren't the best assessors. And so then they asked managers to validate the people self assessment, I have no idea, you know, like everything that you know, I might know what you're working on for me today. But you've worked on on more than that. So data was so poor, and it still is today. But there was an exception to that rule. And that was payroll data, right? Your payroll data is the one thing that we know, is digitally accurate to the fraction of a penny, because if it's ever not, it's effectively crowd sourced. It's crowd sourced, right? Because the crowd will tell you if it's right, or,

 

David Turetsky: 11:00

Or you're gonna get angry, call that, hey, you were off by 25 cents on my check. So.

 

Don Weinstein: 11:06

Yes, up to the up to the second, and accurate to the fraction of a penny. So, you know, I think we, we spent a lot of time there. And to your point about making sure it not landing with a thud. I think the focus were, what are those things that we incontrovertibly know to be true? Like, there are a lot of things, skills, positions, etc, very wishy washy out there, but I could tell you things about employment levels about income. And so as you know, we talked about the compensation benchmark was probably the most popular of those early things that we rolled out because it was it was true, it was accurate and it was valuable. And if people you know, the transparency aspect of it was useful. And then transparency wise, people are saying, well, where'd you get that from? Well, I got 26 million paid people out here. So so so I know a little something about it.

 

David Turetsky: 11:57

Across industry, across geography, across company size. So you could literally, if you needed to narrow down your focus and be more specific about what and who you're paying.

 

Don Weinstein: 12:10

That's right. Which is, which is enormously valuable. I mean, think about it, like you have a lot of organizations out there, who, who, on the one hand, don't have good insights into what's happening in compensation trends. Certainly the last few years that we've been through, right? Where we've seen, you go through these periods, where in some cases, compensation is somewhat stable and static, and you don't see a lot of dramatic movement, you know, month to month or even year to year. But then I look at the last two to three years, and we're getting these wild swings that were happening. And people were looking like, I have a compensation survey, to your point, you know, maybe they're getting it from some third party out there. But it's, it's from a year ago, like I don't know how, how good is it? It's changed a lot in a year. And so I think that was the other thing that was amazing about the payroll data is it was self updating every week, every two weeks, you're getting near real time, kind of kind of feedback. So I think that was one of the things that we we did early on that was really exciting is that we found sources of data that were, you know, they were unique, they were valuable, they were trustworthy, we could be transparent about it, to your point, we could show you all the underlying factors that went into it. I think transparency was a key learning for, for us early on as well, like people, I guess the world of HR analytics had maybe a poor reputation, and in part because of some of the, you know, the other poor sources of data out there that we've talked about. And so being able to come out and say like, here's, here's the dataset, let me unpack it for you. Let me show you all the sources so that you can have more confidence in it was a big step forward, I think, for the practices of HR analytics.

 

David Turetsky: 13:52

But I think one of the other things you mentioned, though, actually gave it more credence, which is that payroll is derived from this data, whereas we both know that HR data is flawed, and that there's lots of errors built into it, because we're not keeping this up to date as fast as we should. The analytics that they're using came from their payroll system. So if they trust their payroll data, then they should trust the analytics that are coming out the other side. So there was that and if it didn't, we help them fix it, or we showed them how to fix it, which made it feel more comfortable on the way out.

 

Don Weinstein: 14:30

Exactly right. Exactly right. Trust is so important in this in this area and that became a key differentiator. And that's what I think was critical to the acceptance of that of that analytics.

 

Announcer: 14:43

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David Turetsky: 14:54

What were some of the other critical lessons that we learned when we were actually going through from the early stages, and you talked about some but what were other lessons that we learned when we started the HR analytics journey.

 

Don Weinstein: 15:06

But I think the, the fact that and you alluded to this, is about the also the need for help in rolling these things out. The function tends to be under populated by by STEM graduates, right? And so our clients needed help, first of all with just getting good data and knowing what good data is and what isn't. And then with interpreting it, and then finally, driving utilization. Just as the whole area evolved, there was there was appetite and there was interest, but maybe the appetite and interest exceeded the capability inside the organizations that we were working with, just because it was it was relatively new. And not a lot of people had a lot of history and learning with it. And that's where, you know, where we collaborated, I think quite nicely in building the professional services practice around, not just, Hey, let me, to your point, let me let me roll out some some interesting stuff here. But let me work with my my clients, to help them understand how to actually use it. And then taking that to the next level. I think probably my favorite learning in all of this was the the notion of pushing the information as deep into the organization as we could because one of the other challenges that I think I saw with many implementations of of HR Analytics was that it was being kind of contained within the HR organization, which which has some value, right has some value, no doubt. But at the end of the day, where do most of the people decisions get made? They actually get made in the field by the managers and the supervisor, I'm talking about who do I hire? How much should I pay them? Whom should I promote? Any kind of decision like that, that could be enhanced with analytics. Well, if you've got all the analytics sitting kind of captive, in a central shared service, it's it's not going to be democratized out to the masses, it's not going to be put in the hands of the people making the decisions in the moment of truth. So what we found was really going from that that world of, hey, we've got these cool dashboards over here that some people could use to actually embedding the analytic information, right in the hands of the users at the moment of truth. So think about we talked about compensation benchmarks, obviously very useful for for the compensation department of an organization. How about if I'm in a, in a recruiting workflow, and I want to make an offer letter, and I'm right there at the point of making an offer, pop me up the comp benchmark right there and give it to the user who's responsible? So I think, I think that was the critical learning of them all the most impactful one.

 

Dwight Brown: 17:55

And I would imagine that was that was a partial culture shift, too, because oftentimes, in organizations, the people who work with the data, who curate the data, tend to be very protective of it. And you oftentimes hear, what do you need, I will give it to you, because you wouldn't know what to do with the data if I gave it to you, you know, and it sounds like you kind of had that culture shift, you probably went through education, all of the follow on pieces that get you to that democratization that you talked about.

 

Don Weinstein: 18:29

Dwight, you could not be more right about that. It was it was cultural. In some respects, people were protective of the data. That's a nice way to say it, sometimes they were hoarding the data. A little bit, yes. You know, could be a little bit of job security enmeshed in that, like, I'm the data right here, right? What do you mean, you're gonna end this all out? And can we trust people with it, but clearly, you know, if you want to get the maximum impact out of it, and you move from, you know, from analytics, to insights to action, at the end of the day, you want decisions and actions, you have to put it in the hands of the people who are making the decisions, but not everybody was comfortable. Not everybody was comfortable. You are spot on, Dwight.

 

David Turetsky: 19:10

Even now, though, Don, when I'm dealing with clients, in fact, every single day, I deal with clients who are trying to get this information in the hands of managers. There's a technology issue, there's a permissions issue, and there's a culture issue that we have to overcome. Sometimes the technology doesn't allow them to do what they'd like to do. Sometimes they're the way they have permission set up, don't allow them to do that. And sometimes it's just a culture of, I can't give this out to these people! I want to, but I can't because of the ways in which not only is the data structured, our hierarchies aren't right in, in ADP or in any other system. But so they have to overcome those as well. And so, the I know you dealt with the technology issues a lot. What kind of cultural change does this have to, did you have to overcome actually selling this internally to the businesses that you are trying to drive to actually get that, that decision to actually let's push this out to those managers?

 

Don Weinstein: 20:08

Yeah, that's, that's a fair, a fair observation. You're right, there was a lot of technology behind the scenes. I'm glad you mentioned permissions, because you know, we spent an incredible amount of time on that, to get that right in terms of what you can see, and what you can't see. But like all of these, and it's true for probably most technology, that's it's those things are technical hurdles that can be overcome the cultural is, is by far the most the most challenging. And really, you know, we put it in the hands of let's, let's make HR the hero here.

 

David Turetsky: 20:41

Right.

 

Don Weinstein: 20:41

We hear this time and time again, you know, HR wants a seat at the table, needs a seat at the table, deserves a seat at the table. Well, I will tell you one of the main differences between the organizations where I see that happening and the organizations where I don't, is the data. And if you're viewed as an organization with with low quality, low utilization data, you know, nobody's really that excited about hearing a bunch of opinions proffered around.

 

Dwight Brown: 21:08

They don't take you seriously.

 

Don Weinstein: 21:10

They don't take you seriously, and why should they? Right? Why should they? Conversely, you know, when you have the facts, and you have the analytics, and you have the data, and you can make a real impact in an organization, and then you can back it up. And you know, talk about some examples there. Like you've talked about compensation benchmarking, I can show an impact where, hey, here's how much money we saved because we were able to right size, our compensation structures, because we had the the benchmarks. I'm, I'm speaking in the language of the rest of the organization, I'm speaking in the language of profit and loss, as opposed to you know, when HR gets a, sometimes an unfair reputation as being a fluffier type of function. And when you're coming with the analytics and the facts and the data, you can puncture that that old tape and earn the seat at the table. And I think that was the key to really overcoming that cultural impediment, in my view.

 

David Turetsky: 22:08

Hey, are you listening to this and thinking to yourself, Man, I wish I could talk to David about this? Well, you're in luck, we have a special offer for listeners of the HR Data Labs podcast, a free half hour call with me about any of the topics we cover on the podcast, or whatever is on your mind, go to Salary.com/HRDLconsulting, to schedule your FREE 30 minute call today. Let me take this a little different direction along the same lines, but a little different. And let me put it in terms of maturity. And so let's talk about the maturity of the HR function to be able to not just utilize analytics to be able to drive the business, but also, what do you see in the future? So it's a two part question. How do you see it today? Where's the maturity today on HR analytics? Where do you see them driving these kinds of insights in the future?

 

Don Weinstein: 22:58

Yeah, no, it's it's a fair question. And I do think we're still in the early phases of maturity, need to see more STEM graduates going into HR analytics. Need to see more STEM content being pushed into HR, you know, programs, whether it's it's university or continuing education. I do notice some some folks who are going after those postgraduate certifications and the like, and I think those are great from a continuing learning perspective. Those are still more the exception to the rule from, from my observation, I'd like to see more of that. I feel like the awareness much, much higher. So we've made great progress on the awareness, I think the interest is much higher. But many organizations are still suffering from limited adoption. And one of the things that's really still hindering them is poor data quality. And that's changing in some areas. But I think one of the challenges you have, again, if you have folks in, in the function, who don't have a good fundamental understanding of the data, like who is the steward of the HR data of the organization, then you're going to struggle with poor quality, the poor quality will lead to low adoption. And until we can get that one really figured out, I think we're still going to be lagging the other functional areas, who have much better stewardship of their of

 

David Turetsky: 24:28

Do you see that coming from the head of HR? Do their data. you see that coming from the head of payroll? Do you see that coming from another like the CFO, who is that steward who has to stand up and say, we require HR to do this for us?

 

Don Weinstein: 24:43

Ultimately, it has to be the head of HR. It can come from other areas, but I think for the function to make the kind of leap that I would like to see it make. I think that the head of HR has to be the ultimate steward of the HR data. And if it's, by the way, I'll tell you who it's not And I say this as a former head of technology, it's not the head of technology. I'm not trying to wash my hands of this thing. But it just can't, it just can't be.

 

Dwight Brown: 25:10

Right.

 

Don Weinstein: 25:11

It needs to be owned by the function, because they're the ones who have to take ownership, but then derive the value at the end of the day. And I've seen too many organizations where they try to push that more towards Well, that's a CIO type function. The CIO can help for sure they have a role to help. But the ultimate steward of the data of any function has to be the leader of that function, right? Who's the steward of the marketing data? It has to be the CMO, right? Who's the steward of the sales data? And again, they'll have people who help them. Right, but I'm talking about the, you know, the A in the RACI matrix, who's got the A, have to be the head of HR.

 

Dwight Brown: 25:50

Yeah. And definitely insistence from the business units too. You know, which gets to your adoption question that you've talked about. And once you've got enough credibility and adoption out there, then they also help, like you talked about, they help become champions and help the, the head of HR to be that steward.

 

Don Weinstein: 26:11

It's the virtuous circle.

 

Dwight Brown: 26:13

Right, exactly.

 

Don Weinstein: 26:14

But you have to seed it, there's the problem, right? So it's a virtuous circle, you're 100% right. Because if you create good, if you if you get good data, and you create good analytics, and you push it out to the field, people will use it, and the people who use it will see the value in it. And they will then for participate in that virtuous circle of getting you more good data. And then the vicious circle on the opposite side, as you put crappy data out there, nobody, nobody uses it, they shun it, and it just gets gets worse. But so how do you kickstart that you have to seed it somehow, it's not going to be the functions necessarily, are going to be the ones to seed it it's going to be HR. Where I've seen that, in my experience, where I've seen the best implementations, it's been where the head of HR is the steward of the data, has a vision for how they want to use it, takes accountability for producing good quality, and then pushes it out of their operation, doesn't hoard it in the HR analytics group, pushes it out, and then people use it, embrace it, and contribute back. That's the pattern.

 

Dwight Brown: 27:16

Yeah.

 

David Turetsky: 27:17

But what's the next step Don? Where does this go from here? So we get STEM graduates coming in to HR, we get the technology in the data all sorted, we get HR becoming the steward, what happens to HR analytics, then does it does it just fall in line with where all the all the the marketing and the sales analytics and all the other the CFO based analytics, does it? Does it go that direction? Or is it it's something else?

 

Don Weinstein: 27:42

First of all, I do think it should, it should follow the pattern of other functions, right? Why should, why should HR, you know, be different? Think about the finance function. I mean, they have analytics for their own purposes, but they push lots of analytics out into the field. I can tell you, when I was a functional leader, I got plenty of analytics pushed away from from the finance function. So of course, there's some that you want to you want to contain for yourself, but I don't know why HR should be different than than any other function. And where we go from here, I think it's really a couple of different spots. So so first of all, there's still a data cleanup effort that hasn't yet happened at scale. And it needs to happen. And the way that takes place is going to be a two part play like one, we have to do a better job of training, future HR leaders, both the ones who are going through the programs in the universities today, as well as, you know, postgraduate with certificate and other programs, you know, in data, doesn't mean they have to be like PhD mathematicians, or they have to have just a good understanding of of data and understanding data quality. There's the data cleanup effort, that still has to happen out there in many organizations. And in particular, as you think about the future and the impact that artificial intelligence can have on again, every function, HR is no different. I think AI is going to have a huge impact on HR analytics, just like every other enterprise function. But, you know, my point of view is the algorithms will come and go. But it's the underlying data quality that will be the differentiator here. Said differently, if you had two vendors who were talking to about, you know, solutions, they had AI solutions, and one had a decent algorithm, but it had been trained on a pristine data set. And the other one had a whiz bang algorithm, but it had been trained on a poor data set. Which one would you be more likely to believe?

 

David Turetsky: 29:42

Oh, definitely.

 

Dwight Brown: 29:43

Yeah.

 

Don Weinstein: 29:44

And so I think the algorithms will come and go, AI will continue to evolve. People don't realize like chat GPT you know, was was version 3.5 of open AI, you know, we're on and now it's chat GPT four, so we're on the fourth generation this has been building for a long time. It will continue to evolve, it will continue to get better. But it will only go as far as your underlying data quality will take you. And I think that's kind of the dirty little secret in HR analytics. And the organizations that pay attention to it and that take ownership of their data, and steward it through are going to be the ones who ultimately prevail with having the best quality in the future.

 

David Turetsky: 30:23

Well said. Don, thank you very much. It was just a insightful conversation. It is always a pleasure to talk to you. But now everyone else will learn from how brilliant you are, and how cool it is to actually have you, you know, be able to help them understand the world of HR and HR analytics. So thank you so much for being on the HR Data Labs podcast.

 

Don Weinstein: 30:52

Oh, my goodness, my pleasure. Always a great to chat with you and catch up. And Dwight, it was a pleasure meeting you as well.

 

Dwight Brown: 31:00

Definitely a pleasure. I always love the opportunity to hang with fellow data geeks, especially at the end of the week, like we're at. It's been great. So thank you.

 

Don Weinstein: 31:09

What better way to spend a Friday afternoon?

 

Dwight Brown: 31:12

All right, exactly. Yeah, exactly.

 

Don Weinstein: 31:15

We should be having beers right now.

 

David Turetsky: 31:17

Well so for all our listeners, open up your favorite can of whatever, I've got coffee here and cheers. Take care and thank you and stay safe.

 

Announcer: 31:26

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