 From theCUBE Studios in Palo Alto in Boston, connecting with thought leaders all around the world, this is a CUBE Conversation. Hello everyone, welcome to this CUBE Conversation. I'm John Furrier, your host of theCUBE here in our Palo Alto Studios. We've got our quarantine crew, getting the remote interviews, getting the conversations at Matter, a great guest, Chris Eldridge, Senior Director of Business Intelligence at Paycore. Chris, thanks for joining today remotely from Ohio. Thank you very much. Thanks for having me. It's great to be here, John. I was just joking with a friend. You know, football season's up in the air and I know Ohio, you're stuck in between all the different cities there. And East Coast, certainly there's a lot of football madness might be canceled. So as COVID-19 has hit us hard and I hope everyone's safe out there with you guys. Yeah, yeah, we're staying hunkered down. So tell us more about Paycore. What are you guys doing to make a difference? Good question. Paycore is a software as a service company that focuses on payroll, human capital management, time for small and medium businesses. And we are a growing company. We've got nearly a few thousand employees and we've been in business for about 30 years. But it really does feel like a startup every day because we have such a focus for our customer and the technology is improving all the time. You know, Business Intelligence has been around. You have the old school guard companies you've been around for 30 years, you've seen them. The technology has changed and with that there's been more data. Okay, now you add on the pandemic and more surge of demand in computer science, data science, data engineering, one of the hottest categories on the job market front all point to the same thing. There's a lot of challenges and a lot of opportunities around data and the implications for businesses. You guys are on the front lines. Give us your take on how you see that evolving because more than ever, data is at the center of the value proposition and even more now and it's changing very rapidly. What's your perspective? Absolutely right. You know, you would think when you have a pandemic you would really slow things down, but in fact, we did not. In fact, we actually went from an environment where we were doing monthly and occasionally weekly reporting and then all of a sudden we needed daily updates. You know, as if you remember when COVID first started out and everything started closing, you know, people wanted information fast and furious and nobody really knew what to do. Luckily because about a year before that we had put in place a very dynamic and incremental approach to how we're going to enable data strategy at Paycore. We were able to just in a matter of days give them a daily dashboard of over 50 metric. What are some of the challenges that you guys have faced because now you guys also have to move in real time. You got to put out the insights. They got to be actionable, but data is not that easy to work with depending upon how it's built. So how are you guys facing the challenges and what's the implication for your business? A great question again. So when I first came to Paycore in early 2019 we really had an environment where it was the Wild West of data. There were lots of folks that were calling together their own data sets. People were rolling up products and customers any way they wanted to. And you had just incompatible data. Well, we really had to focus on there and what made a difference to kind of unwind this tangle of data was really to just talk to folks, get them aligned, get people to understand the true impact of data. And if you can govern it, master it, then you can set it up in such a way that people understand how it mixes and matches with other data sets. You have power and that was the real key is helping people understand the power of what they could unlock with their data and then being ready to unleash that when it's needed. You know, I talked to a lot of data pros, obviously Informatica, all these other companies certainly cloud scale helps. When you start to get into data that has to be integrated across different platforms or applications or new insights that are emerging out of new data sets, whether it's unstructured data or whatnot, you get more stakeholders or more people in the equation, more fingers in the pie, so to speak. As that happens, which is natural, by the way, you've seen that with virtual events and how people once the department now is the whole company, a lot of stakeholders to please. How did you guys approach that? Because you got to align the business. You got to try to please everyone and that's really hard to do. How do you get that done? Well, John, you have to be careful with what you do because success is contagious. And once you start setting up one department or one function in a company that they're happy with their data, you know, most places that I've been and I've worked in this a long time, no one's ever happy with their data or their reporting, right? And so once you get people that are happy with it, all of a sudden now that kind of explodes and everybody wants your time, everybody wants your attention and everybody wants, you know, their data to be accessible as well. And so you have to think about what does that scale look like? How do I go from five metrics to 500 metrics and how quickly do I need to do that? Those are the kind of things you have to think about. It's interesting, you mentioned the scale. I'll throw another word at that and I want to get your reaction to it because scale matters, speed also matters. You got scale and speed. These are becoming requirements. What's your reaction to that? Yes, not just scale and speed, you know, but the technology itself is changing. If you think about predictive modeling, if you think about data science that people talk about and how to operationalize artificial intelligence, you know, if you don't have a good data foundation, none of that's going to help you. So you really have to enable speed by, you know, having processes in place where as the business changes, you can make sure that you have the right data. You have to enable scale and that, you know, you could go easily from, you know, a couple thousand records to millions and millions of records depending on how the data needs to be structured, how you want to think about the data and what kind of features you would want in let's just say a predictive model. You know, that can really proliferate your data. So you need all three of those things. Take me through the play by play on how you guys modernize with Informatica because you had to kind of take a step back. You got to look holistically at things. How did they play that role in your modernization of the business intelligence environment? Well, essentially we took a look at the landscape and we saw that most people were self sourcing their data and they didn't really know how. And when something went wrong, they really struggled with, you know, how do I fix this? You know, how do I rerun it if I'm on vacation? Who's going to make sure that these datasets get populated? And so we changed the game with Informatica, but basically saying, let's pre-think all the things that we need to do around data movement into the data warehouse. And let's really think about not from a, how do I get it from A to B, but how do I get a production grade pipeline of data into my environment so that I can really, really have what I need and anybody in the business that is either part of the data team or part of the IT team can understand what we're doing, can troubleshoot what we're doing, and we can, you know, have a common language about how we talk about that data. So we put in place standards, we put in place, you know, common metadata, we put in place process that was aligned across and we made sure that we put protections in there using things like DevOps to make sure that whatever we gave the business in terms of data was bulletproof and was structured in such a way that it had been tested, it was, you know, was backed up and it was something that we could actually, you know, recreate if we needed to. That's kind of how we did it. When did this all go down? What, last year, this year, can you take us through the timeline? Well, we first started thinking about Informatica, the first half of 2019 and we first signed a contract with them in June of 2019. But before we did anything before we installed it, before we started writing the code, we talked to people. We talked to people for, you know, depending on which area you're talking about between three and six months before we actually implemented. And because we had set up the process, the procedure, we understood how the data model needed to be put together. We understood the expectations of the business, whether they wanted built through reporting or they wanted top of the house KPIs or things like that. We talked to them and once we knew what that blueprint needed to look like, Informatica really helped by making it very easy for us to pull that data in from a variety of sources all in the same way, all manage the same way and build our data warehouse. We essentially built a data lake where we tried to get data as close to what it looks like in the source system as possible but accessible by the data engineers. And then we built certified data marts that were structured in a way that was more useful for the business. You know, and all that really took less than a year. The actual hands to keyboard code took less than six months just because we had spent all the time preparing. And now we're in COVID. So take me through, because this is really kind of a change of landscape, BC before COVID, DC during COVID and then AC, which is coming after COVID, hopefully sooner than later. But this is the new reality. How has the impact changed your environment? If any, good, bad, did it change the trajectory? Was the solution in place before any benefits coming out of it? Because you're an interesting timetable here. You went in full planning, went into production and then COVID hits March. How did you respond to that? What happened? Be careful what you wish for and also no good deed ever goes unpunished, right? So because we had to quickly adapt our plan and go from, you know, weekly, monthly, typically monthly reporting into a daily environment, people wanted daily metrics every day. As you can imagine during the chaos of COVID, some of those 50 metrics changed or maybe weren't understood completely and needed to be adjusted so that they were more in line with the expectations of the stakeholders as well as matched the spirit of the other metrics. And so now going forward, we have to be prepared that as requests come in, as needs come in, you know, we might have new daily reporting and that means we have to figure out where that's coming from, figure out who owns that information, figure out what transforms need to be done to make sure that it's represented correctly and then figure out how to make sure that we get that same data pipeline blowing every single day. And that's a challenge, but luckily we were able to do that because we were able to, you know, set that platform right and we didn't have to guess about what we're going to use to pull the data. We don't have to guess about what kind of things we need to name it. We don't guess about where we're going to put that data. And so we know how to report it. What's interesting, you mentioned DevOps earlier in the interview, you have that DevOps mindset and now you have the business has to react to the environment, which is the pandemic. And a lot of companies are actually refactoring or resetting and then have to put a reinvention plan to get on the business side and then have a growth stretch which might mean completely change of metrics. So being agile with the data, super valuable. Sounds like that's what happened. There are folks out there that may or may not be in that same situation. So how do you share your best practice for someone out there who's saying, you know, I had an environment, I was in the middle of this or I need to rethink it. But because my stakeholders are saying, we need to change or refactor our business, I got to make the data agile. What do you say to that? What do you say to your peers and your colleagues in the industry? DevOps is a good place to start. And I bring that up because DevOps is a critical function. A lot of companies like Take or have dedicated people working on DevOps. But there are also other groups within the technical community that can have a hand in your success. There's database administrators, there's application development teams. There's enterprise level architects that are looking across. There's information security. We live in a world where privacy is becoming more and more important. And any one of those groups, if you're not aligned correctly, or if you haven't thought about it, really can cause your progress to slow down. And if you do work together with them and you build the right operating model, then you can actually figure out a way to make your progress speed up. If you have alignment with those guys, and let's just say a privacy or personal identifying information question comes up. If you've already thought about who needs to answer that, how you need to evaluate that, and ultimately how can you move something like that into a production environment? You're way better off. And it takes sometimes a matter of minutes whereas in some cases and some companies have been at, it can actually take weeks or months to work through those kind of issues. I want to get your personal take because one of the things that's a historic time for the tech industry as it continues to try to do good at the same time because there's health issues involved. Every company has to think about the health of their employees and their customers being sheltered in place. We're living in a kind of historic time where this real agility and engineering around the architecture and thinking things through because there is a new reality and that is going to be more work at home which is edge of the network. More data coming out, maybe there's more sources. So a new wave of challenges is coming in. How do you see that? Based on your experience over the years, you've seen many waves. What's going on? What's some of the learnings that you've seen? What observations? Can you share any insight from the perspective of where you're sitting? So from that perspective, data governance becomes a lot more important. Whereas in the past when you were in an office, when we all used to work in an office, you could actually just walk to your neighbor or walk across to another department. You could have that conversation. Today that's a little bit more challenging in terms of you would have to figure out, are they online? Do you check them up? Do you use the collaboration tools? And because of that environment, because everybody is digital or online. Now you have to think about how do we make sure that the data governance manifests correctly? And so that means we have to think about data catalogs. How can I go to a definition quickly of what is this data set or metric mean? Metadata itself, how do I understand what I'm even looking at? We think about things like data lineage and how can I go into that and figure out where did this data come from? How was it changed if at all possible? Who's using it? That needs to be something that's accessible to a broader community. And especially now where you have collaboration tools, you have things like Slack and Microsoft Teams, you have things like Zoom and Skype. And you need to connect together, but you're also online and you're on the network. So if you can point to documents that are better yet, companies like Informatica have tools like glossaries and data catalogs and things like that where you can actually provide stakeholders. It makes it much easier. And that's much more important now because you're getting thrown so much stuff on the computer, so much data, so much information. You really need to understand how to parse through that. I think it's a great opportunity for someone to come out with some really new collaboration tools around the use case. That's what we're talking about here because you don't have the neighbor, you don't have someone you can just walk down the hall or jump into a conference room and whiteboard something. You got to do it online. It's like, what the heck? Crazy technology. It really makes you think and when you come into data, right? So I like to think about data as the data pipeline or how it gets sourced, right? I think about the data model and how you need to make sure that it's something that can be consumed by people. But then there's really the business intelligence side or the data consumption side. And those tools are changing and they're changing quickly. And we need to think about how we use those to communicate the way people are communicating now. Chris, thanks for coming on and sharing the insight. One final question, obviously as cloud and we've seen of the past decade of big data, unstructured data, as the world starts to become more horizontally scalable where data needs to be accessed by a lot of different things, but yet be needed with specialism around machine learning and automation, you've got this new kind of thinking going on that's kind of becoming more mainstream, which is, hey, I want the data to be everywhere and I want it to be specialized for machine learning. I mean, it sounds really easy, but it's not, right? So this is kind of the future architecture. Just get your perspective on that and we'll end out the segment. Because I think this is teasing out some of the things like the tooling, the workforce involved, how is the architecture going to be laid out? This seems to be something that seems to be more of a conversation now than ever before. What's your thoughts? Well, you've triggered upon a real-time discussion we're having at Paycore of how does the cloud, how does the introduction of machine learning across the different parts of any kind of data chain or value chain or process, how does that change where our focus needs to be? And in this case, if we're talking specifically around data and how do I analyze data, you really need to think about the foundational side. Machine learning, artificial intelligence is meant to make our lives easier. It doesn't mean it's easy to implement. But it also means that if you're giving up that control from a person to a machine, whether it's an algorithm predictive model or whatever it might be, you really need to make sure that the underlying foundation of data is correct, right? And so you change the focus. Whereas we used to have over the last couple of decades, we had a lot of people thrown at reporting. Somebody working on a report or with a business intelligence tool can interact with that and turn things around. If you give that to an artificial intelligence application, now, whatever they're looking at, you're not going to have those natural connections like, oh, that doesn't look right. And you need to make sure that you put your resources that are people on making sure that that data is as good as it possibly can be. That's a great point. You have these amazing fast reports that actually are wrong, right? You got to think foundational first. And it makes the humans more important. I mean, this is not, you don't lean on the machines if they're augmenting the humans. This is a big point. Close us out with that thought. Well, you absolutely still need people. It's just a matter of where does their focus change to? What can you now free them up from doing that was maybe tedious or maybe just busy work that was needed but not super value added into much more higher value added type activities? Chris, great insight. Thanks for sharing. Obviously, you're on the cutting edge. Business intelligence is changing. You guys have been working hard. Congratulations. Stay safe. And looking forward to catching up another time. Thanks for coming on. Appreciate it. Thank you very much. I enjoyed it. Chris Eldridge, Senior Director of Business Intelligence at Paycore, implementing some great data engineering, having the data warehousing. Now it's scaling in the right place at the right time as businesses reacting to it. And this is what everyone's facing right now. How do you make data agile as the business evolves quickly and excel on a highly accelerated basis? How do you become more agile to serve the business needs? This is theCUBE, bringing you remote coverage from Palo Alto. I'm John Furrier, your host. Thanks for watching.