Human Capital Data matters more than ever (Guest: Paul Rubenstein)

Paul Rubenstein: And this is not meant to be a political statement. The biggest challenge with any analytics is if you see a trend but you yourself have not experienced it or it is countered to your beliefs or to your political or economic agenda. How does it change your action on it?

Ben Newton: Welcome to the Masters of Data Podcast. The podcast that brings the human to data. And I'm your host, Ben Newton. Welcome everybody to another episode of the Masters of Data Podcast. Another episode that we're recording this from our home during the pandemic, but I think this is fun to keep having these conversations and keep connected with all of you. And today I think we're going to have a really interesting discussion that is different than some of the one's we've done before and I think you guys are going to all really enjoy it. I'm really excited to introduce our guest today. It's Paul Rubenstein, he's a chief people officer at Visier and welcome to the show. Good to have you here.

Paul Rubenstein: Thanks Ben, good morning.

Ben Newton: And I think based on some of the conversations we've had before I think this is going to be a really interesting discussion and come at some different things we actually haven't been able to talk about in the podcast before. But before we even get to that, like we always do we love to humanize our guest. Just talk a little bit about who you are and what your background is, and particularly considering what we're going to talk, how you came into that. So, Paul what's your story?

Paul Rubenstein: Yeah, first time chief people officer and I got here in the most circuitous route. I had an HR job when I really young and then I was like" The office is in the basement, I don't know if I like this, the money's not so great." And at that time the first human capital management systems were coming out. I was like" Oh, this is interesting." And I found myself suddenly running one of those old school HR transformation projects, the ones that consulting firms and so many software firms are built on, the old" Hey, how do we make the HR more efficient? How do we put in systems instead of paper?" And so I did and I woke up one morning and found that I was good at helping consulting firms conceive the project and sell the project, which hey, that's fun, pays the bills, it's nice. So, I did about half of my work were those HR transformation projects where you in- source, outsource. I spent some time in outsourcing. And about half of my work was real straight up talent strategy. If the left hand is figuring out what the HR function should, how to optimize the HR function, the right hand is" Well, what are you optimizing it for? What is the business? What kind of talent portfolio do you need? How should orgs be structured? how should you govern?" And then you do deep dives into talent acquisition, learning, basically taking the whole tour of the HR function on the consulting side. Then I did some other cool stuff when I was at one of the big consulting firms that involved looking at our portfolio of businesses. I'll never forget one day that CO, we needed lots of data at that firm, right? Because it was both consulting and outsourcing. He was like" Hey, you know about this big data? It's going to be big." I'm like" Yeah. What are we going to do about it?" He's like" I don't know." and I went on this tour and I don't know if you remember about eight, nine years ago people were really starting to getting beyond data warehouses and visualization tools. And the first visualizations were becoming popular and people were doing cool stuff. So, I went on that tour of everyone's magic beans and I met the business guys and I was like, " Oh, my god they were so far ahead of everyone else just in terms of their thinking and how to make the tools available for everybody." And instead of the old school" Hey, let's build a data warehouse, build as much data together and figure out what we can see," they flipped the model. And by the way these are the guys who created some of the monstrosity of this big data warehouse projects. These are the old crystal reports, business objects guys. And they were like" Hm, how much value if we created consulting firms and how often do those projects fail? What if we turned it on its head and started with the questions?" Because 80% of the questions people ask are pretty much the same, especially if you... So, how do you prove out this question based packaged analytics where it doesn't require an expert and you build up the question model over time and you build the tools in a way that anybody can see them and it's all in the cloud, not just the tools, the data warehouse and it's all there and you work across systems, so you're not worried about being anyone's particular tech stack. It can't be all SAP all day or Oracle all day, be [inaudible]. And their vision of it was really cool because it was like after doing all this HR transformation work I saw this data and saw their approach to the tools as the first time HR transformation was not about cost shift, right? This wasn't an investment that HR would make about making them more efficient. This was about HR reinvesting in something that truly served the business.

Ben Newton: That's interesting.

Paul Rubenstein: Yeah, and so I fell in love with these guys and I went back to that consulting firm and I was like" This is the future, we should do this." And they were like" No, we have a..."

Ben Newton: Model for making money.

Paul Rubenstein: No, but even internally they were like" Look what we're building with data warehouses and we have all these people and all the inertia around it. We [crosstalk] cost." A couple years later I was like" No way man, this is the future." And they made me the head of value engineering, so I went in and really did two things. I looked at what was that moment at which the people saw data and acted differently? How do you take analytics around human capital, how do you begin to solve business problems with them? How do you distribute the analytics so it becomes part of an operating model? So that, if you have a long arc strategy around your human capital, how do you put data in front of people so that a small decision is actually based on a trend or strategic direction or you stop guessing at things. You stop using intuition around people and you start using data.

Ben Newton: Right.

Paul Rubenstein: By the way this is a game changer, right? This is for our customers, it's changed the way they avoid layoffs in many ways because they're able to have confidence in natural attrition, they're able to give voice to the quiet employee who is actually more productive than other employees. And it's just been a great ride and what's really changed things for us, unfortunately, has been this crisis because the notion of people data, it's gone from something that was a nice to have that with" Oh, we'll get to that later," to the must have because the decisions people are making... companies are making decisions with data around people that is the difference between whether a worker will get sick or not or whether they'll be able to deliver services or not or whether they'll be agile enough to move to a new business model or not, right? Boy, that was a long intro huh?

Ben Newton: No, that was great, I think it's fascinating because myself I've been in the big data space for a while and I really like the way that... there was a couple of things that struck me, how you were talking about it. Number one, I love the way you talk about asking the questions first and then going to data because I think there was... and this isn't just a big data movement, but I think for a long time it was basically gather all the things, gather all the data and we'll figure out the rest later. And what would happened is you'd be overwhelmed with data you didn't know what to do with. And when I was a product manager, some things that I've done in the past where I learned the hard way that you really need to start off, " What are the questions that I'm trying to ask? What are the actual business questions? What are the human questions? What are we trying to understand?"

Paul Rubenstein: What problem are we trying to solve?

Ben Newton: Exactly, exactly. Instead of trying to make the data fit the problem or go the other way I think that's really interesting. And also, in particular one of the things we've delved in this a couple of places on the podcast, but a lot of the data work, a lot of the talk about that is a lot of times in IT and security and some of these areas where they just naturally create data. But I think some of the most interesting applications are the ones like this they're not where everybody's mind first goes but some of the places where you can actually have the biggest impact in the business because there's a lot of untapped potential there.

Paul Rubenstein: Well, Ben what's left? What's left in corporate performance anymore? Corporate finance, there's not a lot of innovation in that. It is a great science, we've conquered that. They conquered that in the 60s and 70s. Supply chain, we've reached a mastery point in supply chain where just give it to XPO or to Amazon, right? Even automation, that's come a long way. The last indivisible element of performance is human for a company, right? So, our ability to use the data in meaningful ways to make fact based decisions, this is the golden age of it. There's a lot of things that have helped it along, right? But we're sitting here at Visier and we're like" Look, why do we even exist? We're here to help people see the truth and create a better future now." And this notion of truth, when you are able to take, especially people data or any data, decisions are fairer, it's a great equalizer, it gives voice to the quiet. People have been talking about how to advance diversity, numbers don't lie. They're are truly without bias, still honest and fair. And then the second part is to create a better future now. One of the things that has always frustrated me and has been really interesting to watch during the crisis is these long... " How long's it going to take to implement this system?" " Oh, first we got to get the steering committee, then we're going to have to have lots of [inaudible] security review. And then we're..." blah blah blah blah blah. I'm getting these stories of our customers who spun up data collection systems in two, three weeks. We did a release in two weeks of all new features. There's nothing like a crisis to help you shed so of the bureaucracy that's in the way of speed and agility.

Ben Newton: Yeah, no, you're absolutely right. I was actually just reading something about it the other day, if you focus you can always... Chambers out of Cisco, he was talking about that. But one thing that's really interesting here too Paul, when you're talking about this, is I think conceptually this makes a lot of sense because, like you're saying particularly, in this time people are the area that maybe measurement has not has been done as good about. But talk to me about the actual data because number one, you use this word human capital a lot, what does that mean? And when you actually talk about human capital data, the data in that space, what is that data? What does it look like?

Paul Rubenstein: I love when I use... I think I've been in consulting too long, I can't get passed jargon. I think part of this, for your audience, let's just take a little look at the history there. Remember the old, there were payroll systems and then there were early days mainframe HR systems like Tesseract, Genesis but they were all sort of payroll centric.

Ben Newton: Yeah, I had to work on one of those early in my career.

Paul Rubenstein: Yeah, my green screen. It's funny, you know how I got into this? And I'll never forget this, my first business case for an HR system was because we had a COBOL CICS homegrown HR system when I was working at Home Box Office and it would take a couple of hours to give me a feed of data that we went through hoops to get into a Lotus 123 spreadsheet. I'm showing my age. And then I would look at the data and the data would be wrong. And they were like" That's because..." these were before the modern transaction systems. That's actually how I wrote my first business case which wound up getting me into consulting later on, it's funny. And the best thing is doing early diversity data, so things came full circle. But anyway, then you had PeopleSoft and SAP coming out with all their human capital systems and that was all in the client server days. It was like, okay how do we... And all of those business cases were built on transactions because remember, HR departments would have forms, remember paper forms and files? So, the first wave of digitization that made it affordable for most companies was client server technology or they outsourced the ADP, et cetera, inaudible. And then it was like" Oh, no you can't do that you have to move it to the cloud." So, there was this second wave of... actually, between that there was this wave of outsourcing. And then" Okay, let's make it more efficient, let's do more self- service in the cloud and put things on mobile." But it was always about transactions. It was always who was hired, who was fired, who was promoted, what was their salary? Alongside of that you had a whole set of technologies when you had to, okay here's a jargon- y term, OFCCP, the office of, I can't even remember what it stands for anymore but it was all of the records we've had to keep to make sure that you were compliant in your hiring practices.

Ben Newton: Ah, right, right, right okay.

Paul Rubenstein: That gave rise to the ATS, right? Remember Taleo, BrassRing, all those. Now we have a whole new set of cloud based modern ones. Then there were the benefits administration systems, especially prevalent in the U.S. so that once a year you could forget your login and remember how to pick the plan that was confusing in the first case. Then, wait, then there was learning administration, right? Where did you take the course and how did you have a record of the course? All of this slow... oh, and let us not forget what I actually think is one of the most valuable ones in large calibrations, time and attendance. Who showed up and what did they do with it? Who shared up and what kind of work did they do? Which actually had some early innovations. Those were transaction systems that weren't connected and the organizations that supported them, all of those same silos, the tech nerd, the org structures and the siloing of HR and reinforced it. The whole thing was" Okay, now we can get rid of HR generalists who do transactional work and make them more strategic and HR will have this seat at the table because their capacity has been created for them to be more strategic." Not necessarily the capability, but capacity. And so, now we're on our third cycle of that. Along the way things got better. I would say neuroscience got better, social sciences got better, assessment science got better, engagement science moved from this once a year giant effort, which took months to be actionable, to continuous listening, right? To see innovation in network analysis, who actually talks to who. Give me an org chart of how information really flows through an organization. We've come a long way, so you see this progression in the data in HR over a period of time. Now mashups happen, right? We start to answer questions. The guy, he's the head of HR at Patagonia, this guy Dean Carter. What a cool job, right? He was at Sears and I think it was under his leadership that one of the first things they did at the time clock was ask somebody" How do you feel today? Frowny or smiley?" And they were able to see how store sales were able to connect to that. So, now you have the inaudible sales force effectiveness and there were a lot of consulting based, single point in time projects to understand productivity. But then how does it become persistent and actionable? That's the evolution, that's where companies are now. How do we with the same regulatory that we published the PNL do we publish information on people so that you can make better decisions? So that you can make thought based decisions? If you've ever run a PNL you know that the data comes in and you're like" Oh, crap I'm over budget. I better cut some hours or cut some heads." Where's the cycle of information that helps you understand your labor patterns before the end of the month so that you're not always inaudible things like that.

Ben Newton: Some of the things you bring up are the things that we kind of know about, maybe not the best experiences with because I definitely remember interacting with inaudible. But I think that, it's really interesting when you frame it like that in that timeline. It is very interesting to see how it lines up with a lot of changes that have happened other places in the business and how the data's transforming what we do.

Paul Rubenstein: Look at marketing, okay? Marketing is probably one of the best at inaudible data in most organizations in their transformation.

Ben Newton: Right.

Paul Rubenstein: I have always aspired to follow marketings transformation and data with HR's transformation.

Ben Newton: Yeah, that makes a lot of sense. In particular, I say even with marketing it's not just collecting the data but it was how you act on it because I think there was a tendency in marketing and other places to collect a lot of data and then you just sat on it. But now moving into these more agile models where you say" Okay, why don't we try something and see what happens?" And be able to measure that and then make data backed decisions. The actual decision making people process took longer than actually getting the data, right?

Paul Rubenstein: Okay, HR data. I remember doing my first, I'll call them time in motion studies in consulting. By the time the business asked the question you would have a... they would ask an HR business partner who would go back to some specialist and analyst who would dump something into Excel who would correct the data, who would then show it to the HR business partner who would have it just in time before meeting. And the person they were presenting it to had better analytical skills with data than the... they would be like" Oh, let me put this into a pivot table." But the HR business partner may not have that skill. And then the second challenge was" Oh, I see one error in the data, how can I trust any of the data?" Make the HR business partner go back inaudible. And this is the cycle that HR has broken out of as the data has been more prevalent to everybody and everybody owns the data, right?

Ben Newton: Yeah.

Paul Rubenstein: And the second thing is, people are starting to use trending. But here's the rub. And this is not meant to be a political statement. The biggest challenge with any analytics is if you see a trend but you yourself have not experienced it or it is countered to your beliefs or to your political or economic agenda. How does it change your action on it? No government acted fast enough when they saw the COVID data. " Oh, this can't be true. Oh, this is too disruptive. Oh, it's not that bad." We all could've acted faster. And I'm hoping that the lesson here is, especially around people data, if we see a trending engagement... I'll walk this across to the classic" Hey, sales leader, I see a pocket of engagement challenges." " Oh, don't worry about those people." " Oh, take a look." " Oh, don't worry about those people they're closing deals." Okay, but if you look closer, they're not actually doing anything in their early pipeline. And so, which came first, the disengagement or the early pipeline fall? How you can start to see the patterns and take action early and get people to rely on... I'm not saying you should be completely data driven, but it'd show you where to hunt and challenge your intuition.

Ben Newton: Yeah, and I've had a couple of discussions about that on the podcast and I think it is always that balance because you still need the experienced leaders that can process the data. I've even heard it said one of the things that an experienced leader can do is that they can look at data and because of their life experience and their work experience, they can actually incorporate that data and process it and make decisions on it much quicker but the thing is they still have to use their human experience and their intuition to measure it. But to your point, these things where you have to be proactive and you have to get ahead of it, that is very hard to do without data indicators driving you because otherwise you're not going to see it. There's a lot of things you are not going to know what are going on unless you have data telling you, right?

Paul Rubenstein: Look, we can't make everyone great but what I think we can do is raise the floor, right? By raising the floor inaudible across the entire enterprise and just exposing them to patterns. So, how long does it take for an organization to change their behaviors around data? I think if done right it can be as little as a quarter because if you think about it the quarter end we're conditioned to.

Ben Newton: Yeah.

Paul Rubenstein: And you're either a hero or goat at the end of it. And how many quarters can you go before your job is in jeopardy?

Ben Newton: You can't put up a goat every quarter.

Paul Rubenstein: If I show you a trend on the first of the month, the first month, the second month and the third month and you do nothing, how many quarters can you go before the pain of understanding how this data... consuming this data is no longer a vitamin, it's a pain pill, right? It's not a nice to have must have. Organizations, we've seen our clients get good at that rhythm and it makes a difference.

Ben Newton: Yeah. You mentioned something that I want to delve a little deeper into is like you said that how these changes happen, a lot of times there's external factors that drive it and you particularly bring up COVID. One of the things I've definitely seen in my own organization, and I've seen it, I've been around long enough that I... I was around in 2001 and 2007, 2008 and where I've seen these other crises of different types drive changes and behavior, what are you seeing from your perspective that's changing right now because of what's going on with COVID, particularly with what we've just been talking about.

Paul Rubenstein: Yeah, so can we stay geeky and talk about some of the data implications because that's where crosstalk.

Ben Newton: Absolutely, please go geek.

Paul Rubenstein: ...really been interesting. So, in the middle of this the people inaudible is a pretty strong one and I've been doing these interviews with people and here we did one on recruiting, we did one on engagement, we've done some general ones but I did... The heads of people on inaudible three of the largest healthcare organizations on yesterday. And the pre-interviews and the interviews, it just killed. It's really interesting because we had to spin up for them and they had to make very short order decisions on good people data. So, it's something that's simple. Somethings seem simple like how many people do we have? How many masks do we need? All of a sudden your headcount reconciliation process is gone from some finance versus HR budget drama to" We have a constrained supply of a critical element and these are life and death decisions." Then you dig into your data and you say" Well, who is a frontline worker? Who is not? Who is a critical caregiver? Who is not? Where does that data actually sit?" What is skills data? Skills data gathering has always been this monstrous project where" Oh, well who can we trust? Can we trust people to self- report? We have to have the certification and we have to have this annual process." And there's teams of people that create controls around this. And so, a lot of organizations don't do it because they don't want to be wrong or they can't get to the right level of granularity and specificity of crosstalk. " Yeah, I used to intubate somebody," right? How do you get that data? They went out and then they asked people self- report it as a starting point because you need that stuff in a crisis. They delayed a learning project to collect skills and re- implemented that module because they were like" Oh, that's going to take seven months and it's not a priority." All of a sudden that data becomes a priority. You know job codes and job descriptions?

Ben Newton: Right, yeah, yeah I crosstalk.

Paul Rubenstein: I need to know who touches terminals or not or come for payment. I crosstalk. Just think about all the elements of work that have to change and if you're trying to study those patterns and make decisions... One of my guests said it the best. We woke up and we realized that all of our systems had been built for administrative convenience, not analytical capability.

Ben Newton: Oh, wow. That's crosstalk.

Paul Rubenstein: Not the tech, it's not that they asked the tech. We spent all this money on inaudible. When you think about HR function's budget they spend so much more money on record keeping just to improve the record keeping and they never actually changed the data, right?

Ben Newton: Yeah.

Paul Rubenstein: Job code data and skills architecture and oh god, old school competency models. They'll pay a million dollars to have a competency model project that never actually reflects the work that the operational, analytics needs of the... right? They'll spend 10 times as much on record keeping as they will on insights and analytics. It's this big sucking sound. A lot of organizations are going to have to rethink that and that's been one of the things. The second thing is frequency of data. Cost data on payroll is slow, right?

Ben Newton: Yeah.

Paul Rubenstein: People put in time codes. One of the guys was telling me, well we had a very simple time code system because we only had a couple of codes because we don't want to confuse people. But it wasn't enough to understand the data as they were looking for patterns of why people were in, out, what they were working on. They had to spin up text analytics because all the interesting stuff was in open comments.

Ben Newton: Oh, wow.

Paul Rubenstein: When people entered the inaudible to understand what people were doing. They never built their systems for being able to handle this crises. So, never let a good crises go un- wasted." And actually here's the other one, people analytics teams interacting with all the other analytics teams. My favorite mashup at work is our human capital data with our sales force data. And our hospital clients, they love it with patient outcome data, you name it. We sell all kinds of people data mashups that are really... The COVID- 19 crises has just accelerated the frequency with which people are consuming... We have clients that were monthly that went weekly, weekly that went daily to their feeds.

Ben Newton: Yeah.

Paul Rubenstein: That's the thing that's the hardest. If you want to understand who's at risk, where can we reopen first, et cetera, you have to have the right amount of data, you have to the right granularity, right frequency and you have to be able to trust your models and get people familiar with models. First of all, how do you even return to your office? Who's essential? Who's agile? Here's another thing, what we keep about people, performance ratings. One of the themes I'm hearing in business around talent right now is we're going to have go back and when we rehire and take people off furlough we're not going to have the same economics that we had before. We're going to have to right- size our organization and our operations are going to change. The way we did the old work may have to change, restaurants, hotels, you name it. Is it who's tenured or who's agile? Right? Who has both agility and skills and mindset? Where is that data?

Ben Newton: Yeah, one of the things I find most interesting with the area that we're talking about when you talk about healthcare and all these other areas, because typically my background with data has been" Is this system broken? Is this particular application down?" And I think when you're really talking about the outcomes for people a lot of times I think the perception in the past has been that" Okay, big brother business is keeping tabs on me but I think there's a..." One of the things I liked that you said, the thing about optimizing for administrative ease, not for analytics. And really what that gets down to is that there is a trend right now across the businesses that they're realizing that if they can capture the right data and actually put it to the right analytics, basically put it through that right thought process that are actually winning. And this is not new, it's not like the companies that apply technology and things like this are the best or the ones that have always won. I mean, Walmart won because they have mainframes. It's always like that and it's really interesting that now the innovation is happening with the data and it's about driving different parts of the business come together. I find that endlessly fascinating, I think it's a really interesting area.

Paul Rubenstein: Ben, people have been doing it with customer data.

Ben Newton: Yeah.

Paul Rubenstein: Right? Think about what they do with customer data. People are a little bit afraid of people data. The norms around people data have changed. HR mostly is driven... old school leadership is driven around avoiding risk, right? They're the risk police inaudible. HR now has to become the" How do we take the right risks," drivers, right? If you think about this, work from home. I've watched mindsets shift around working from home.

Ben Newton: Yeah, yeah, yeah.

Paul Rubenstein: It's amazing what we can get done. And I do think there's a huge value in the collaboration of the office, don't get me wrong, right? But we're going to be like this for a while, people are going to have different attitudes about being in group settings, what ever it is. How are we going to have a connected workforce should we... if you've never done a network analysis before to understand who is actually... yeah, maybe it is about productivity. Here's the old joke, and it's not even a joke it's a true story. When I was 21 years old I... think about this problem, I was a manager, right? I could make long distance phone calls on the company phone, that was a big deal.

Ben Newton: I remember that.

Paul Rubenstein: Okay, so at 5: 30 one evening, it's a little quiet and I call my grandfather. " Grandpa, what's going on?" " What are you doing?" " I'm at work." " You're at work? It's after 5: 00." " Yeah." " Are you getting overtime?" " No," I wasn't even going to explain that one, right? I wasn't inaudible that. He goes" Oh, is the boss there?" I'm like" No, he went home." " How does the boss know you're doing a good job if he's not there to watch you." Okay, so if you think about management by walking around, which a lot of execs still do. " I want to walk out and see my dev team. I want to walk out and see what's going on in the customer service floor." How are we going to understand? What is our sensing mechanism going to be around this distributed workforce, right?

Ben Newton: Yeah.

Paul Rubenstein: Now we got the challenge of both analytics and data collection. Being able to take sentiment analysis, poll surveys. There's a company up here, inaudible, they have a product, I think it's called Better. me. After every piece of work a technical worker does, they ask" How was that? How did it feel? Was it easy? Was it hard? Did you like it? Did you not?" Talk about a high frequency of sentiment, so you can understand who's close to burnout or who is precursor to failing. Like I said, network analysis, who isn't slacking or calling anymore? Network analysis, have you ever seen one of these maps?

Ben Newton: Yeah, for different crosstalk, yeah.

Paul Rubenstein: crosstalk you strip the meeting data, you strip the Slack data, you strip the e- mail data. And by the way if you're working where everyone's forced to work from home then the quality of that data and the amount of that data is better than ever before. You see who are the go- to people in the organization, who's stranded. This is especially useful in onboarding new hires, which I worry about in this. Imagine getting hired and never coming to the office. inaudible socialization. And never being able to fly in for orientation. I look at work that was done at McKesson a couple years ago where they looked at analysis on sales people, like which are the better sales people? The ones who spend all their time externally or the ones who connect internally better? What does your intuition tell you versus not? And so, when we think about our sensing mechanisms for the new world of work and what we want to show up in our analytics, that's an important thing. Safety, right? Geez, I don't even know where to begin. Safety use to be the backwater of HR, it's the people who did investigations and insurance claims, et cetera and they would fill out compliance reports. Safety's at the forefront, man.

Ben Newton: Yeah, like you said this is something that we could really delve into for many hours. I think this is super interesting. So, let me tie a bow on this with one question to you. Why does the thing that you're thinking about in your area you're thinking is coming that you don't think other people are thinking about? What are some things that are rattling around in your head that you don't think other people are cracking?

Paul Rubenstein: Man, I don't pretend to have any magical insights. I'm blessed that work even some ... a whole lot of people are way smarter than me. But what am I tracking? I think there's a shift coming in the HR. Spend these multi- year implementations that are all still about process optimization. I think people are coming for work, inaudible, SAP, all those big ERP projects because it can't be their way or the highway and it can't be all one software anymore, right? I think that's coming to an end. I think the nature of HR transformation as a cost and efficiency exercise is done. The future is" Show me insights and give me analytics." I think that's going to change. I think new methods of data collection like we talked about are going to be continually popularized. Sentiment analysis, network analysis, being able to get beyond our human observation and use assessment science, which has come a long way. And I think the silos are going to break down. People data is business data and we talk about... remember the old" We're going to democratize data," We didn't democratize the tool yet. It's got to be for everybody to consume so that everybody has access to be a great leader, great manager, great decision maker. I think COVID- 19 is going to accelerate that. And the forced compressed rethinking of work and work models is going to have to accelerate that. People are going to have to get good at scenario planning, analytics. Workforce planning is no longer going to be some secret science, it's going to be what everyone has to do.

Ben Newton: Yeah, well I think that's a good note to end on, Paul. I know I personally learned a lot and I think this is really fascinating discussion and it probably makes sense to bring you back on a little bit later to see how things are changing in your opinion. But thank you for coming on Paul, this was awesome. I appreciate your time.

Paul Rubenstein: Ben, it's been fun. And what we've been telling people, stay healthy, stay safe and stay sane.

Ben Newton: I think I can do most of those, we'll see. But thanks everybody for listening and as always, rate and review us on iTunes so that other people can find us and thanks for listening.

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