Let’s discuss how Private Equity (PE) firms can do more with their data using Snowflake. The Snowflake Data Cloud empowers organizations by offering them a Single Source of Truth solution, which enables comprehensive Analytics that provide a holistic view of the business across the entire Private Equity portfolio. Along with benchmarking and due diligence, financial, market, operational, and exit analyses help Private Equity firms track progress, make informed investment decisions, and improve overall performance.
In our webinar below, we cover how modern data-driven PE firms:
- Bring together marketing, operations, and payroll data to drive actionable insights
- Incorporate a modern data infrastructure that can scale with growth
- Build data teams and reduce engineering time from weeks to days
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Transcript from the Meetup event:
Everybody, thank you for joining our Snowflake for Private Equity webinar presented by AICG. So a couple of things related to private equity. We’re going to be focusing on the data challenges related to private equity along with the analytic challenges.
We’ll talk about common problems, and then we’ll talk about, well, what’s a different or modern way to approach data and analytics within private equity? Really even taking it to another level of focus around operational efficiency.
And so that will be the framework for our conversation this afternoon. So some of the common challenges we see within private equity from data and analytics is that firms need data and analytics. But why do they need data and analytics?
Well, one of those reasons is when you think of it from an operational excellence perspective, that instead of just, all right, we’re going to have a lot of Excel spreadsheets and Excel workbooks. After a while, those grow into monstrosities and are very hard to maintain.
And a lot of times you’re scraping aggregated data from different systems and trying to make sense of it. And it’s very, very difficult and hard to share that information with others along with even getting to a base understanding of, all right, let’s understand from a top-of-funnel perspective, where are my leads coming from?
How are those leads converting into members or customers or visiting some of your locations? Then once they’re visiting, what are they doing within your individual, let’s say, portfolio company? Are they highly engaged?
Are they somewhat engaged? How much are they spending? What’s that lifetime value for that individual member or customer? And if we’re to offer certain incentives for them to be repeat customers, how do we know that’s being effective in really driving top-line growth?
Conversely. When you’re starting to use data and trying to get more out of your data, you need to get down to that transactional-level detail. Even if somebody aggregates their data at the highest level, you’re still going to have questions of, well, how did we get to this roll-up of this particular revenue, for example, or the number of new members for this month? You want to be able to drill down and potentially look at individual members for the new month.
Where did those individual members come in and what did they end up buying? How many sessions did they sign up for? How much additional product did they end up spending within their 1st, 30, 60, 90 days?
You need that detailed level of data in order to be able to answer those types of questions. And naturally, when you do get that data and you’re able to make sense of it and really starting to drive operational efficiency now, data has turned into an asset.
And that asset. When you think of when it comes time to exit a particular fund or a particular investment, now you’re able to bolster that price because you have the data that you’re able to sell off as part of the transaction and say, here’s how we’ve been able to run this particular brand or portfolio company for this period of time.
Here’s the proof of what we’ve been able to do, how it’s been able to grow, and here’s how we understand our membership. That makes you as a private equity firm, all the more valuable at the end of the day.
And of course, not just from an exit strategy, but even from fundraising, you’re able to tell the story of how are you as a private equity firm different than others. So not uncommon that we also see this because there are so many different systems within a private equity ecosystem.
You’ve got point-of-sale (POS), CRM, and other marketing types of sources that might be third-party sources. Think of Facebook or Meta ads. You can download that information, you can tap into other applications.
You have call center and HR data. Traditionally, what we’ve seen within private equity is this data gets pushed all over the organization. And it’s very hard to make sense of what the data is, where the data come from, what that common understanding of that data is.
And it just becomes very difficult for PE firms to really make the best operational decisions that they can. Exactly. Mike, great laying out the framework there. And now let’s talk about the data analytics solution strategy that really works for private equity firms in this modern era.
One of the stories we’re told by the private equity clients that we work with is the challenge of lack of access to data. And that lack of access to data leads to resources in the organization wrangling that data.
But the time that they’re spending wrangling the data, it really reduces the time that they have available for strategizing and reaching more operational efficiencies for the organization or for that asset.
And the solution to that is really what we call data replication or data synchronization, going after the source data or the transactional data and automating that process and that data flow. One of the other parts of the story is that when resources are wrangling the data, they often wind up with different key performance indicators or different data definitions of the data that they’re struggling with to find or the data they’re wrangling.
Then this often also leads to confusion within the organization. So one of the solution strategies we recommend there is providing some level of data governance that will help reconcile the data and provide data quality.
Moving forward through a typical problem we see is really with those different KPIs and struggling to bring those data definitions together that’s going to lead to report inconsistencies. So different reports that are spread potentially all over the place, perhaps in different formats like perhaps Excel, perhaps another bi tool.
But the data in those tools might be inconsistent. So the typical scenario is people meet once a month or once a week for some sort of team or management meeting and everyone’s in the room with a different number wondering where each other, each person received that value or that information or that number for the same type of metric.
And so when we think about report inconsistencies, one of the other problems is that the people that receive those reports, they’re often needing to go gather other data to combine or enrich the information because they’re concerned about these.
Inconsistencies or they’re trying to just simply get an understanding of the bigger picture of the data as it relates to their particular department or their particular responsibility. And then typically that bubbles up to the executives or payroll or finance or some other part of the organization where they’re dependent on that operational data to do things like payout incentives, so or bonuses.
And those groups typically wind up also doing more wrangling. So when you think about the solution there, what we typically need to provide or implement there is some sort of centralization of that data into a commonplace with these common metrics that are governed.
And you have a central location from which certain people or all the necessary people have access. And that data centralization or that single source of truth repository can take different forms, but typically it’s a very curated repository of data, often called a data warehouse or a data vault.
If we continue to follow this problem-solution strategy, wrangling the data at the executive level or one of the higher levels in the organization, typically will lead to delays in monthly and quarterly closed cycles.
So these delays then just really compound this incomplete view of the entire organization’s operations missing out on efficiencies that could have possibly been gained or gleaned. And that, without a doubt, leads to questioning of the information that’s been delivered in the organization.
And once there are chinks in the armor of the information, then all types of different fire drills can start happening. But those fire drills typically start happening at the executive level because those are often the end consumers of the information that need to deliver that to the board or to the street.
And so in order to move forward with that at the end of the cycle, let’s say that’s the end of the month of the quarter, only then can the true problems be realized. And so then what happens is typically the problems just repeat again next month.
So the doubts that are surfaced, the solution option that we would have for that would be to provide additional layers of data quality, data transformation, data quality, and then, of course, a solution for repeating again next month, once we’ve provided an overall capability.
Both all through people process and technology that can really enable the business to start doing things, focusing on higher value targets, providing advanced analytics, and providing trend reporting that are going to really create those operational efficiencies.
So really this is becoming a new norm for private equity companies that have moved into becoming a data-driven firm. It enables them to deliver an overall vision for their operating companies or their portfolio companies, their asset brands.
They’re able to deploy this holistic view across all of their assets and investments so that from an investment partner perspective, from an executive level, the leadership can count on having a consistent view of the assets and brands.
When they have a question, they’ll know exactly where to go or from whom to get the information from. And those individuals that lead each asset or investment, they can feel confident in the data that they’re delivering to the executive team or the board.
But this new strategy also allows the firm to minimize any It capacity planning for software or hardware by deploying the solution into the data cloud. It also allows companies to focus on analyzing the data, focusing on those high-value targets, and strategizing instead of spending time wrangling the data and centralizing the data into a single source of truth data warehouse repository means that there’s going to be consistent meaning across all data points within that asset or that brand. And that easily rolls up in the hierarchy to more of the executive perspective of the investments.
Lastly, it’s a great way to use data as an asset instead of a liability. And also it can be used as a recruiting tool not only for some of the key resources that you need in the organization, but also in any speculative type of research that’s being done for other investments.
So it’s very powerful looking at a data analytics solution strategy inside of private equity. And so if we think about some of the key areas in which we’ll provide quick wins for a data analytics strategy, we like to look across the organization, across the enterprise, across different types of processes that will allow for the data analytics strategy to be very comprehensive.
And those things include data literacy, user training, providing a type of data catalog or metadata dictionary, looking across the type of resources that are involved, such as building a data analytics team, and providing different types of transformation capability so that you can understand the lifecycle and the journey of the customer from the data forward.
And then also looking at things like cloud architecture and the overall analytics implementation. So when you looked at that slide earlier with the chaotic spread of data and data points across the organization being fed from different operations systems trying to find the way into the actual business function what we found at AICG is by providing our private equity data analytics strategy as a service.
What starts happening is that the business is able to start extracting. Data from those operational source systems, bringing that information into an enterprise data warehouse or single source of truth repository and bringing that into a very modeled curated structure inside of Snowflake.
And then that allows the organization to pick any of the existing bi or analytics tools they have currently in place, or perhaps look for another tool that fits a particular need that they might have as part of their strategy and then layer that on top.
And this way you have a comprehensive view of your investments, of your assets, of the firm in a centralized automated manner. And this will enable the operational excellence that all P firms seek out.
So, just wrapping up a little bit here on the analytics strategy. If you think about the private equity data analytics strategy that we’re talking about, it really does provide a comprehensive view for the data that is necessary to run the operations of the assets as well as the private equity firm itself provides a holistic view across all the operating companies and portfolio companies.
You can reduce risk by having this type of visibility as well as focus on those high-value targets and strategizing of operations. You can also provide accurate reports to your stakeholders, executives and to the board.
And it’s just a general better overall way for portfolio management to function inside a PE firm. And then lastly, you’re able to make better investment decisions with complete data inside the organization, which can lead to higher revenue, obviously provide a competitive advantage, and then also produce a successful exit strategy or help for a successful exit strategy.
So, one of the things that we talked about from a common theme perspective, is really centralizing the data in order to create the analytics and insight that you’re really looking for within your organization.
And the key component to that is not only we talk about the centralization, it’s really Snowflake as the catalyst and platform. In order to do that centralization snowflake, the data cloud, we think of ingesting data, data from all of your different locations, whether that’s cloud, on-prem third party, maybe it’s a share that somebody gave you.
You need to govern that data, which naturally governing the data is applying consistent metrics and definitions and using that across your data belt along with controlling who has access. It’s not a one-size-fits-all that every person that logs in should be able to get to see everything.
You may have HIPAA and PII considerations. You also have very sensitive information in there as well. And so you want to make sure that you can control that access. Especially if you’re going to let the outsiders from your PE firm.
If you’re in a franchise or type of an arrangement where those folks are going, to need to come in. And whether that’s through some sort of an analytical tool or you’re creating data shares that they can come in and get direct access to their data, you need to be able to have the mechanisms for that control.
And then naturally over time, from a PE perspective, obviously funds come and go. A lot of PE firms are trying to raise larger and larger funds, which means larger and larger types of investment, which there tends to be a correlation with the amount of data and the amount of users continues to rise as you get larger and larger funds, investments going as well.
So Snowflake can help scale with that and really pay as you go versus having to think out x number of months or years down the road. So when you think of Snowflake, we mentioned you have different data sets.
Some of these are going to be on-premise, some of them will be in the cloud. You may be an organization that is generating a lot of data through a website or some sort of a portal or even through IoT.
You have devices generating a tremendous amount of data. That data is going to be in lots of different structured, some unstructured, some naturally structured. You can have streaming batching that data in.
You need to apply a level of transformation to take that raw transactional data and to make it more sensible so that you can share that data to be consumed via an analytic tool of some sort. You may have operational reporting, you may even have some individuals, such as data science tests that want to come in and look at perhaps they want to look at some of that aggregated data, but more likely those data scientists, they’re going to look at that raw transactional data.
And so having access to all of those different pieces centralizing all that data within the Snowflake data cloud is that core component to how do we solve this data challenge for private equity firms?
We’re here to chat with you more. Obviously, every PE firm is unique and different and it’s not, again, like I said, a one size fits all. But there’s definitely we have a framework and approach for PE analytics that we’re happy to walk through and talk to you about how it can relate to your specific fund investment portco brand.
However, your private equity is set up. So feel free to reach out via the email address there or our website. And so now we open it up to Q and A. So anybody that has questions, feel free to verbalize them or you can even submit them on the chat through the webinar here as well.
And we’ll give people a couple of seconds to bring those in. So the first question here, I’ll throw it out. Christian, you and I can maybe just take a spin at this one, all right, how do I get my data into Snowflake?
Well, all right, I guess that’s a fair question here. Christian, you want to take a first pass at answering that one? Yeah, absolutely. There are a number of methods to push data into Snowflake. From the perspective of our private equity analytics solution, we really use a tool that’s part of our process that enables the extraction of different transactional systems, such as point of sale systems, finance systems, such as a NetSuite or Sage Intact or other industry-specific systems, and maybe not industry-specific, but also payroll incentive systems.
And so we use a tool that allows the system to connect to those APIs or to those databases and then move the data into Snowflake. And then in the case of private equity analytics, we do have some pre-built models inside of the framework that we’re discussing here that will provide a quick set of aggregated principles and models that provide a holistic view of those transactional data points, both at the raw level and aggregated level.
There are some other methods that we can use inside of Snowflake for streaming data. So depending on the situation, there are a number of tools that we do apply to get the data into Snowflake. Yeah. Do you have anything to add to that, Mike?
I do. One of the common questions or made debates that come up with their PE customers is, well. In order to get that data in. Should I build something myself or should I buy a tool or a package of some sort?
And that buy versus build notation you think of. If you’re going to build something yourself, you now have signed up to maintain those different connectors. And if you say, you’re ingesting via an API, for example, all right, you now have to stay up to date on all of the changes of what is being exposed or newly exposed within the API.
You have to have the right engineering talent in order to be able to work with APIs and then to be able to pivot if something dramatically changes within your organization within that particular application.
So for a lot of organizations, we say, look, based upon the skill sets that you have and your direction for your PE firm, it doesn’t make sense to hire dedicated engineering talent for that because at the end of the day, it’s not a full-time job.
And so what are those folks going to do otherwise? It’s better to buy a pre-built tool and work with that individual vendor to say, all right, I have these dozen sources. All right, we bought another company within the fund.
Or, we just have now adopted a new application that we’re rolling out to a larger part of our organization, work with that data provider that has the tooling in order to replicate the data, to say, all right, I have this new source.
Can you please help me build out this connector? And they’re going to say, yes. And now you have someone else that you’re paying a low monthly fee for to really maintain those connectors forever and keeps you out of the business of having to worry about that.
And you just focus on making better operational decisions from that data. Let’s see. There’s one more question that I see here. You mentioned earlier turning data into an asset. What did you mean? Data becomes an asset.
I’ll take a first benefit, Christian, if you want to follow up after that. So when you think of data as an asset, it really is when you think of centralizing this data and really from again, that operational efficiency to be able to if you’re a franchise, or even a franchisee, for example.
And you’re trying to get information into the hands of your individual operators. Could be your franchise, or it could be a mom-and-pop owner of a particular location. You’re trying to give them the best insight into their business so that all of their questions are able to be answered.
And then larger organizations, other PE firms that your PE firm is working with, to really make that data available in such a manner that they say, wow, I need this in order to run my business and to really maximize not only my revenue, but it’s the bottom line at the end of the day in order to maximize that piece, which will now turn that data into an asset.
And you can determine, all right, if I’m going to let other parties use this data and consume this data, could I charge them a technology fee, for example, for giving them the transactional data and some curated data as part of how you go to market?
And so you want to make sure that you don’t have a burden from an It perspective of making that data available, and now becomes very cumbersome as to how you make that data available. Snowflake makes it very easy for you to set up data shares that you apply security so that person A logs in, they only see their company’s data, and that is it.
You have person B log in, they see only their company’s data. You have that peace of mind. And again, we talked earlier about the value of Snowflake, of having that security and that control over who has access to that data.
At that point, that data has become an asset because now you are also monetizing that data, not just making better decisions for yourself. So that was what we were conveying. Christian, if you have additional thoughts around this.
That great response, Mike. The only thing I would have to add to that is that just having the perspective that the information that’s flowing through your organization has some value is step number one.
And once a company accepts that mindset and they start believing in that mindset, that mantra of data as an asset, then they can start going down the road that you mentioned, right? Start taking advantage of the benefits of, for example, private equity data analytics as a service, start taking advantage of the efficiencies of using a cloud data warehouse and centralizing the data.
And then they can really start embracing this concept of their data to information process and looking at that as valuable in the organization just like an asset. And then they can choose to go down different paths which will allow them to realize the value, whether that be charging customers or vendors for data that they already collect and they just happen to curate or using and aggregating data that they have in such a way so that they can then perhaps.
You know, sell that data anonymously, even, for example. So all the things you said are spot on. And as as we know, the clients that really embrace this concept of not only data as an asset but becoming a data-driven organization, they are the ones that come out with those competitive advantages.
They are the ones that are leapfrogging their competition. And we’ve done this so many times at this point that we can see the journey for private equity companies and firms that embrace exactly what we’re talking about here, and then they’re able to reach that next level in a fairly quick amount of time and really make and have impressive results across the board.
So, yeah, there’s a great question, and we look forward to helping other companies take that data driven journey and really embracing data as an asset. So excellent. Fantastic. Thank you, Christian. All right, it looks like we’re out of questions, but again, feel free to ping us through those links you saw earlier sales@aicg.com or visit us on our website.
We got a couple of events coming up here just want to make folks aware of where you can learn a bit more. You can meet with us and learn more about Snowflake. Snowflake has this event concept called Data for Breakfast, where you go for a couple of hours, get to meet and greet folks that are either current Snowflake customers are thinking about Snowflake.
They’re on their journey of exploration. And you get to hear from not only the socializing aspect, but you get to hear from an actual customer success story. And really, their journey, that’s really what it is.
At the end of the day, it’s the journey that they are on and where Snowflake fits in. And yes, snowflake is technology, but it always comes back to the business. How does any technology help the business at the end of the day?
It’s cool if technology can do A, B and C, but if you can’t apply it from a practicality perspective to either help make more money or identify ways to improve what you’re doing today, it doesn’t really help anybody.
So check out Snowflake’s website. You can even just search data for breakfast. It’ll come right up. And feel free to sign up. These are free and they are all over the US. And the rest of the globe as well.
So check it out. So we hope to see you there. So thank you, everybody, for joining today. Again, feel free to reach out to us. We love to talk all about data at the end of the day here. And of course, we’d love to help you from a private equity firm perspective, in order to really maximize the data that you have in your purview and really help you get to the next level.
Thank You!
