Cloud Crunch
Cloud Crunch

Episode · 1 month ago

S3E6: Application Modernization Trends for 2022


As we wrap up Season 3 of Cloud Crunch, we will be discussing “What is driving Application Modernization? And the digital trends and business imperatives for the industry”. How can our listeners know if they are ready for these trends? What are some questions they can ask themselves? This powerful conversation is led by our CTO of all things data at 2nd Watch Fred Bliss, and Director of Marketing Michael Elliott. We are also joined by our honored guest Jesse Samm.

Involve Solve Evolve. Welcome to cloud Crunch, the podcast for any large enterprise planning on moving to or is in the midst of moving to the cloud. Hosted by the cloud computing experts from Second Watch. Michael Elliott, Executive director of Marketing, and Fred Bliss, CTO of all Things Data at second Watch and now here are your hosts of cloud Crunch. Welcome back to cloud Crunch. This is Mike Wellly, your host of cloud Crunch. Joining me as always is my co host, Fred Bliss, CTO of all Things Day to here at second Watch. Welcome Fred, Hey, good to be here always. And we have a special guest today joining us as Paul Corney, private equity practice director here at second Watch. Welcome Paul, Thanks, Michael, glad to be on absolutely Paul. So for the audience, can you give us kind of a quick synopsis of your background in the technology consulting space for almost forty years now and very exclusively focused in data and analytics. In the last ten years, I lead a large data analytics consulting firm called Apptitive that's now a Second Watch company. All right, In today's episode, we're going to cover cloud analytics for the private equity industry. Now, private equity have probably been on the forefront of using data to create competitive advantage since probably since their inception, because I think that's a part of it. Now, they're always on the lookout, I would imagine, for new investment opportunities, and most have a system in place to help make those decisions. But Paul, I'd like to kind of kind of explore a little bit around, you know, how does a private equity firm really go about understanding those investment decisions? What are they looking for? Well, they're obviously looking for a return, but really there's two primary work streams that private equity firms go to as they're looking at their investment. And one, it's just kind of deal sourcing. You've got to understand industries and markets and company lists and something that you can about the financials to just understand which might even be potentially viable. And then there's the actual due diligence process, Michael or you're trying to drill into what is the real potential of this acquisition and what kind of returns could we generate. Now, I imagine there's also another side to that as well. Of the investments they've already made and understanding how those companies are doing. Is that a part of you know, how you pull data and analytics or just the data of those companies it is, I mean, in the private equity industry, it's a huge data consumer and also consumed a lot of human resources trying to analyze that data. Your traditional private equity firm managing a bunch of portfolio companies is just still relying on Excel to pull that data in and hugely data intensive tons of number as they want to see on a daily, weekly, monthly basis. The monthly reporting package is really burdensome, and so there's a lot of upsite opportunities from better analytics in the industry. Well, let's let's talk about those upsides where cloud comes into play with that. Well, cloud, you know, the one thing that cloud enables a couple of things. It allows these small portfolio companies to leverage data and analytics that you couldn't otherwise do if it was still all on premise. So these cloud analytics solutions, I think they're a game changer in the industry and allowing a private equity firm to have a bunch of smaller portfolio companies that really can, I like to say, punch above their weight class and operate as a much larger enterprise through the use of these advanced analytics solutions. And for the portfolio parent or the operating partners limited partners, you just get better transparency and visibility because this data is easily accessible and presentable in a format that they can see operating dashboards and operating metrics of their investments. And Paul, from a privative quity parents standpoint,... much technology adoption are you typically seeing at the average midsize private equity firm. Yeah, it's interesting. It's a real challenge because you're if you look at a lot of private equity companies, they're investing in companies not necessarily a single industry, and so the financial that even the g L is all over the place, and otherwise the ten different portfolio companies that they own have different structures and different costs that feed into your net margins, gross margins, profitability, and so as a result, they've still relied a lot on a lot of Excel kind of base reporting to pull in the data from all of their investments. So the technology adoptions have been hard because you are trying to manage ten different operating companies or portfolio companies with all different industries. Potentially we're working one on private equity company now that invests primarily in food and beverage companies, but even those are very different. One can be a discrete manufacturing one could be a process manufacturer, and they're just different ways that you would um want to manage that business, different operating metrics and stuff. So it's quite a challenge. So how do you go about solving those challenges? Well, again, you have to do a little bit of data modeling, Michael, but there are some common metrics that you can develop and then build financial reporting capabilities. But one of the first things we always find is so helpful, it's just to ingest all of that data from those portfolio companies and then you can use that both for building it has to do a purpose. That's one of the things that we see real value from is that when I have that data, I can I can use it to build operating dashboards and metrics dashboards and KPI dashboard for the portfolio company sea level exacts. But it also gives that kind of transparency and visibility because I can now show those uh KPIs and metrics to my uh, the portfolio company, parent, my limited partners, my general partners, everybody can see the numbers and how the the investments are performing. And when you're talking with the with the various investors and p firms about these KPIs and metrics, how often do they actually know what they want versus helping you helping them define? It's really interesting I find. What I find is they they sometimes think they know what the key metrics are that more offered than not. By by pulling in DA the metrics and data in and building some of these data, they learn more about their business, and they learn more about some of the cause and effect, and they learn how to do the analysis when things are going awry. So UM to us, it's kind of this data journey they have to get on so that they're automating this and digitizing all of the data and then doing the analysis and adoption. We call it. I call it driving adoption. Yeah. I mean, even if you look at just the public stock market, right, there's accounting is not necessarily a factor of a story, right, there are different ways to represent that. And I imagine operational metrics that are commonly defined across many many porcos. Yeah, it's not something you can really change, right And I like I think of it like somebody who starts to learn how to read accounting statements and you get pretty good at reading an income statement or a balance sheet. And the same true for private equity company. Right after a while, you could pretty quickly translate ibada into cash flow in your head because you've you've got this understanding of how these things translate. I want to go back. You talked about Traditionally it's you know, importing into an Excel spreadsheet. Now now you're talking about, you know, how do you bring in all this data? What are some of the tools? I mean, ingests is one part of it, But what are some of the tools that you apply the enable you to get inside out of all this data? Well that you know they're they're dashboards of course we build, but the it really starts with ingesting this data and put it in some sort of cloud data a warehouse. And...

...for us we use tools like red Shift and Snowflake are the two leading ones. We use sometimes Azure as your synapse. But then it's building dashboards, Michael, and that's the business intelligence side of it. I imagine of building those dashboards. So how how is that process kind of can you describe a little bit about that work? Yeah, we you know, my h the second watch, we kind of favored this agile development process where we build something, get go live with us with kind of a quick solution, and then we learn and adjust and add to that over time. So the dashboard work is often let's get all the data, put it in a cloud data ware here else, give you a handful of dashboards to manage your key metrics, and then as you use the dashboard, you kind of decide maybe you want a different way of viewing it, or you want to add some more analysis or some deeper drill down capabilities, and so we'll take that on in a second phase or a third phase to kind of add those capabilities over time. And usually once we've built some basic operating debtford, you start to take a look at um what we would call more advanced analytics solutions AI or data science or predictive analytics, machine learning models to predict customer churn, all all the kind of uh fun, really cool advanced analytics things. So and we've talked a lot about analyzing the assets that you have, but I imagine when you're when a private equity company is looking to purchase a company, they're also trying to bring in some of those outsourced data. UM outsource. Take that back, When a private equity company is looking to acquire a company, they're looking at sources of data that may not be readily available with the company. So how do you start to bring in external data sources into that model? Yeah, we tend to call it alternative data and the industry and and that's really the the you know, the to market places that exist with Snowflake for example, that it uh and we find almost every single one of our implementation cloud data warehouse imple pitation that there's a lot of external data that they can take advantage of. And sometimes even it's as simple as weather patterns. If you're a seasonal business, there's probably a period where you're going to spend a lot on marketing because you're gonna be acquiring new customers at the spring season when the weather changes, and so UM, even basic data like that is really valuable to you. And anytime you start talking about models of any kind, the more data that you have, the better. And if we're trying to do not now we know that the weather is changing, and we know we want to invest in marketing the questions with zip coates, we want to spend that on and so understanding the age of houses and typical income again another external data source. And so with these market places that come with most of the cloud data warehouse solution, you can instantly ingest that external data and use it in building your models. Yeah, imagine the problem is not as much is enough to is there enough data out there? But more trouble understanding what data is and what's going to be important and how do I combine that with the data I have already? Yeah, well, I like to use the word actual insightful data. Right. It's easy to discope, get data. The question is what do you do about it and what's the what's the data that helps you make smarter, faster, better decisions. So there is an art to that and there is a process you need to go through to figure that out. And even in the I would imagine in the the due diligence process, with there being so much manual work and excel um even with the rise of cloud solutions, it's probably not really reducing the time there so much as it is um having better access to some of that alternative data you mentioned, right, Paul, Yeah, yeah, I've I find some. I think I...

...think private equiferns could do a better job of an upfront assessing what the opposite opportunity is from data and analytics. You know, you can monetize the data or you can just take better advantage of it and use of it in these portfolio companies that you're buying and investing in, and there's just so much opportunity up front to say, what could I do with this company? What how much better could we be there? If you couldn't drive ten or improvement and an operating profits and EBITDA. I think you could at the minimum you can achieve in first year if you just had some of these operating metrics at your fingertips. It's funny. I was talking to one VC investor UM a couple of months ago, and he had mentioned that one of the interesting things that they do at his firm is UM creating almost data clean rooms where they're uploading the the acquirer and the purchase and the UM company being acquired are essentially sharing data in the same space and using that as a way to communicate with one another. How often do you see that in this world? And what is the upside there? The clean rooms are much more popular these days, Fred, and and we see more of it where we're at least taking the data in prede pre close. But I still think some of the private equity firms suffer from a lack of a deep technology adoption around analytics. So something they could suck in all the data pre closed, but they don't even know what to do with it in terms of figuring out what the what the targets and the improvements that they want to achieve from it are there there. You ought to have a template, and you ought to have a checklist of things you're thinking about in terms of what the upside is. Every company in every industry has some key leverage points or leverage areas where you can improve the operating results of the business by by taking a vania. It could be customer retention, could be customer churn, customer acquisition. We work with one roll up company that customer acquisition was key to them. And so you know, if you're going to invest in a a bunch of portfolio companies or roll up a bunch of them, build a strong customer acquisition model, and and factor that into your valuation and and the multiples you're planning on achieving, and what are you what are you seeing in terms of the balance of technology solutions maintained by the parent company versus the portfolio company, because I imagine you've got quite a bit of a mix. Some portfolio companies are probably more advanced than others, and some parents are probably some more more advanced than their port cas. Yeah, size has a lot to do with that. I mean, if you're really small portfolio company, uh, you know, a hundred million or less or even up to a couple hundred million, you may not have the deep talent needed to support some advanced analytics kinds of solutions for example, and so uh and then depends on the resources of the of the parent portfolio a parent private equity company how many how many of their team are really data scientists or data engineers that are needed to build these kinds of solutions. We see it. The biggest private equity companies threat that they have the resources and the talent to go uh firm that out to their portfo leo companies and help them build solutions. But at the mid size that it really really can be a challenge to get that kind of talent on board and delivering the value that you want. So, if you're a mid sized private equity firm and you've got varying degrees of I guess data saving us within your portas, and you want to do more with analytics, you're you want to start to measure your operational KPIs, you want to do a better job of reducing the amount of human effort involved in doing some of these financial roll ups. What's the best way to get started? Where should you start? Well, we strongly advocate starting with a little bit of a data strategy so you can figure out what's the right architecture. This is a rapidly moving industry. There's new tools come out every day, every week, every month, and so it's important to pick the right...

...ones. Ultimately, you know you're gonna buy and hold a portfolio company for five years and then sell it, and part of the evaluation would be debased on did you pick the right product? Did you pick the right cloud data warehouses? There's still a strong, viable product in the marketplace, and so that strategy phase get you started fred with a good architecture. The right products and then quickly build something quickly in just some day to build a couple of dashboards, a couple of models and start to use them and apply them. And then the other thing that quickly drive value is to have aggressive adoption program. And what are the dangers of just building? And I guess what are the most common reasons that some of these projects don't go well. Talk to the CEO of a private equity firm that we built a solution for and asked them after the fact, kind of how happy it would the work that that appitive did for you? And he said, loved everything we built so valuable for the company. The only thing is I wish we the private equity parent had worried more about adoption. And so the rate of adoption drives the value, and the faster you can get your management team you're engaging these new analytics and making decisions in a different way the fact you're going to drive value. So it is one of the biggest challenges we face. You take a really small company, you probably just barely had decent reporting. Let's take a hundred million dollar manufacturing company. The reporting is probably not that great at that company. You probably have difficult to get in your hands on the right data to drill into problems and solve problems. And so having a different set of analytics, a different set of data, and being able to use that and leverage it two more quickly resolve problems. That's just a different way of thinking and acting. And so you've got to kind of make that transformation, make that change in your management team and your use in adoption of analytics. So last question for you. You know, as a private equity company starts to embark upon an acquisition and thinking about that data strategy they want to bring in. What is some of that advice at top level advice you would give that private equity company, I think twofold Michael one would be there's a ton of value you can drive and your investments with better analytics and data. And two that there are some even higher returns and better investments with some of these advanced analytic solutions. Even your smallest company can do a lot better, be more effective, more efficient, higher cat flows, higher investment returns with some of these advanced analytic solutions, and they are coming down in size and scale so that the smallest company could start to apply them. And I think that's a key of what data insights have really brought to those smaller companies, giving them the power that they never had before. Beyond just an Excel spreadsheet from a large enterprise, they had all this power and technology and capability. This really does bring the power to those smaller firms. Yeah, we had one again, a different private equity company was rolling up smaller you know, million dollar companies, but rolling them up into a billion dollar enterprise. But they introduced customer attribution model to tell them why their customer came to them via a website, online ad, print ad, direct call call campaign, so they could do a better job of targeting customer acquisition and getting new customers and driving up revenue. And once you're a little company and you could do that more effectively, you can also spot invest in that you know, you wait until you have service technicians available to do a job and then drive some instantaneous kind of marketing. And you could not do that as a little, tiny, million dollar company in the past, but now these tools will let us do this. And the great thing about the cloud is that you're paying only for consumption.

You're not buying the giant analytic licensees You're just paying when you run them, so you only paying for what you use, which is awesome. And that is the power of the cloud, of that pay as you go type of capability that it brings. So I want to thank the audience for tuning in to cloud Crunch, covering cloud analytics for the private equity industry with special guests Paul Corny, private equity practice director for second Watch. Paul has been an absolute pleasure. I appreciate you joining us today. Thanks Michael, glad to be here. You've been listening to cloud Crunch with Michael Elliott and Fred Bliss. For more information, check out the blog second watch dot com, forward slash cloud dash blog, or reach out to second Watch on Twitter and LinkedIn.

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