WEBVTT
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Involve Solve Evolve. Welcome to cloud
Crunch, the podcast for any large enterprise
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planning on moving to or is in
the midst of moving to the cloud.
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Hosted by the cloud computing experts from
Second Watch. Michael Elliott, Executive director
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of Marketing, and Fred Bliss,
CTO of all Things Data at second Watch
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and now here are your hosts of
cloud Crunch. Welcome back to cloud Crunch.
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This is Mike Wellly, your host
of cloud Crunch. Joining me as
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always is my co host, Fred
Bliss, CTO of all Things Day to
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here at second Watch. Welcome Fred, Hey, good to be here always.
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And we have a special guest today
joining us as Paul Corney, private
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equity practice director here at second Watch. Welcome Paul, Thanks, Michael,
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glad to be on absolutely Paul.
So for the audience, can you give
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us kind of a quick synopsis of
your background in the technology consulting space for
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almost forty years now and very exclusively
focused in data and analytics. In the
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last ten years, I lead a
large data analytics consulting firm called Apptitive that's
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now a Second Watch company. All
right, In today's episode, we're going
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to cover cloud analytics for the private
equity industry. Now, private equity have
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probably been on the forefront of using
data to create competitive advantage since probably since
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their inception, because I think that's
a part of it. Now, they're
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always on the lookout, I would
imagine, for new investment opportunities, and
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most have a system in place to
help make those decisions. But Paul,
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I'd like to kind of kind of
explore a little bit around, you know,
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how does a private equity firm really
go about understanding those investment decisions?
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What are they looking for? Well, they're obviously looking for a return,
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but really there's two primary work streams
that private equity firms go to as they're
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looking at their investment. And one, it's just kind of deal sourcing.
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You've got to understand industries and markets
and company lists and something that you can
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about the financials to just understand which
might even be potentially viable. And then
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there's the actual due diligence process,
Michael or you're trying to drill into what
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is the real potential of this acquisition
and what kind of returns could we generate.
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Now, I imagine there's also another
side to that as well. Of
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the investments they've already made and understanding
how those companies are doing. Is that
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a part of you know, how
you pull data and analytics or just the
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data of those companies it is,
I mean, in the private equity industry,
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it's a huge data consumer and also
consumed a lot of human resources trying
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to analyze that data. Your traditional
private equity firm managing a bunch of portfolio
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companies is just still relying on Excel
to pull that data in and hugely data
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intensive tons of number as they want
to see on a daily, weekly,
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monthly basis. The monthly reporting package
is really burdensome, and so there's a
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lot of upsite opportunities from better analytics
in the industry. Well, let's let's
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talk about those upsides where cloud comes
into play with that. Well, cloud,
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you know, the one thing that
cloud enables a couple of things.
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It allows these small portfolio companies to
leverage data and analytics that you couldn't otherwise
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do if it was still all on
premise. So these cloud analytics solutions,
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I think they're a game changer in
the industry and allowing a private equity firm
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to have a bunch of smaller portfolio
companies that really can, I like to
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say, punch above their weight class
and operate as a much larger enterprise through
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the use of these advanced analytics solutions. And for the portfolio parent or the
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operating partners limited partners, you just
get better transparency and visibility because this data
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is easily accessible and presentable in a
format that they can see operating dashboards and
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operating metrics of their investments. And
Paul, from a privative quity parents standpoint,
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how much technology adoption are you typically
seeing at the average midsize private equity
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firm. Yeah, it's interesting.
It's a real challenge because you're if you
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look at a lot of private equity
companies, they're investing in companies not necessarily
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a single industry, and so the
financial that even the g L is all
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over the place, and otherwise the
ten different portfolio companies that they own have
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different structures and different costs that feed
into your net margins, gross margins,
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profitability, and so as a result, they've still relied a lot on a
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lot of Excel kind of base reporting
to pull in the data from all of
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their investments. So the technology adoptions
have been hard because you are trying to
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manage ten different operating companies or portfolio
companies with all different industries. Potentially we're
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working one on private equity company now
that invests primarily in food and beverage companies,
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but even those are very different.
One can be a discrete manufacturing one
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could be a process manufacturer, and
they're just different ways that you would um
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want to manage that business, different
operating metrics and stuff. So it's quite
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a challenge. So how do you
go about solving those challenges? Well,
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again, you have to do a
little bit of data modeling, Michael,
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but there are some common metrics that
you can develop and then build financial reporting
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capabilities. But one of the first
things we always find is so helpful,
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it's just to ingest all of that
data from those portfolio companies and then you
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can use that both for building it
has to do a purpose. That's one
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of the things that we see real
value from is that when I have that
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data, I can I can use
it to build operating dashboards and metrics dashboards
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and KPI dashboard for the portfolio company
sea level exacts. But it also gives
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that kind of transparency and visibility because
I can now show those uh KPIs and
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metrics to my uh, the portfolio
company, parent, my limited partners,
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my general partners, everybody can see
the numbers and how the the investments are
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performing. And when you're talking with
the with the various investors and p firms
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about these KPIs and metrics, how
often do they actually know what they want
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versus helping you helping them define?
It's really interesting I find. What I
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find is they they sometimes think they
know what the key metrics are that more
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offered than not. By by pulling
in DA the metrics and data in and
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building some of these data, they
learn more about their business, and they
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learn more about some of the cause
and effect, and they learn how to
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do the analysis when things are going
awry. So UM to us, it's
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kind of this data journey they have
to get on so that they're automating this
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and digitizing all of the data and
then doing the analysis and adoption. We
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call it. I call it driving
adoption. Yeah. I mean, even
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if you look at just the public
stock market, right, there's accounting is
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not necessarily a factor of a story, right, there are different ways to
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represent that. And I imagine operational
metrics that are commonly defined across many many
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porcos. Yeah, it's not something
you can really change, right And I
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like I think of it like somebody
who starts to learn how to read accounting
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statements and you get pretty good at
reading an income statement or a balance sheet.
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And the same true for private equity
company. Right after a while,
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you could pretty quickly translate ibada into
cash flow in your head because you've you've
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got this understanding of how these things
translate. I want to go back.
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You talked about Traditionally it's you know, importing into an Excel spreadsheet. Now
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now you're talking about, you know, how do you bring in all this
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data? What are some of the
tools? I mean, ingests is one
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part of it, But what are
some of the tools that you apply the
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enable you to get inside out of
all this data? Well that you know
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they're they're dashboards of course we build, but the it really starts with ingesting
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this data and put it in some
sort of cloud data a warehouse. And
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for us we use tools like red
Shift and Snowflake are the two leading ones.
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We use sometimes Azure as your synapse. But then it's building dashboards,
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Michael, and that's the business intelligence
side of it. I imagine of building
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those dashboards. So how how is
that process kind of can you describe a
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little bit about that work? Yeah, we you know, my h the
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second watch, we kind of favored
this agile development process where we build something,
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get go live with us with kind
of a quick solution, and then
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we learn and adjust and add to
that over time. So the dashboard work
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is often let's get all the data, put it in a cloud data ware
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here else, give you a handful
of dashboards to manage your key metrics,
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and then as you use the dashboard, you kind of decide maybe you want
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a different way of viewing it,
or you want to add some more analysis
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or some deeper drill down capabilities,
and so we'll take that on in a
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second phase or a third phase to
kind of add those capabilities over time.
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And usually once we've built some basic
operating debtford, you start to take a
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look at um what we would call
more advanced analytics solutions AI or data science
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or predictive analytics, machine learning models
to predict customer churn, all all the
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kind of uh fun, really cool
advanced analytics things. So and we've talked
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a lot about analyzing the assets that
you have, but I imagine when you're
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when a private equity company is looking
to purchase a company, they're also trying
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to bring in some of those outsourced
data. UM outsource. Take that back,
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When a private equity company is looking
to acquire a company, they're looking
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at sources of data that may not
be readily available with the company. So
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how do you start to bring in
external data sources into that model? Yeah,
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we tend to call it alternative data
and the industry and and that's really
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the the you know, the to
market places that exist with Snowflake for example,
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that it uh and we find almost
every single one of our implementation cloud
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data warehouse imple pitation that there's a
lot of external data that they can take
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advantage of. And sometimes even it's
as simple as weather patterns. If you're
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a seasonal business, there's probably a
period where you're going to spend a lot
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on marketing because you're gonna be acquiring
new customers at the spring season when the
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weather changes, and so UM,
even basic data like that is really valuable
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to you. And anytime you start
talking about models of any kind, the
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more data that you have, the
better. And if we're trying to do
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not now we know that the weather
is changing, and we know we want
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to invest in marketing the questions with
zip coates, we want to spend that
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on and so understanding the age of
houses and typical income again another external data
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source. And so with these market
places that come with most of the cloud
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data warehouse solution, you can instantly
ingest that external data and use it in
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building your models. Yeah, imagine
the problem is not as much is enough
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to is there enough data out there? But more trouble understanding what data is
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and what's going to be important and
how do I combine that with the data
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I have already? Yeah, well, I like to use the word actual
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insightful data. Right. It's easy
to discope, get data. The question
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is what do you do about it
and what's the what's the data that helps
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you make smarter, faster, better
decisions. So there is an art to
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that and there is a process you
need to go through to figure that out.
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And even in the I would imagine
in the the due diligence process,
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with there being so much manual work
and excel um even with the rise of
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cloud solutions, it's probably not really
reducing the time there so much as it
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is um having better access to some
of that alternative data you mentioned, right,
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Paul, Yeah, yeah, I've
I find some. I think I
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think private equiferns could do a better
job of an upfront assessing what the opposite
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opportunity is from data and analytics.
You know, you can monetize the data
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or you can just take better advantage
of it and use of it in these
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portfolio companies that you're buying and investing
in, and there's just so much opportunity
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up front to say, what could
I do with this company? What how
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much better could we be there?
If you couldn't drive ten or improvement and
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an operating profits and EBITDA. I
think you could at the minimum you can
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achieve in first year if you just
had some of these operating metrics at your
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fingertips. It's funny. I was
talking to one VC investor UM a couple
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of months ago, and he had
mentioned that one of the interesting things that
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they do at his firm is UM
creating almost data clean rooms where they're uploading
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the the acquirer and the purchase and
the UM company being acquired are essentially sharing
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data in the same space and using
that as a way to communicate with one
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another. How often do you see
that in this world? And what is
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the upside there? The clean rooms
are much more popular these days, Fred,
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and and we see more of it
where we're at least taking the data
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in prede pre close. But I
still think some of the private equity firms
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suffer from a lack of a deep
technology adoption around analytics. So something they
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could suck in all the data pre
closed, but they don't even know what
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to do with it in terms of
figuring out what the what the targets and
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the improvements that they want to achieve
from it are there there. You ought
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to have a template, and you
ought to have a checklist of things you're
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thinking about in terms of what the
upside is. Every company in every industry
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has some key leverage points or leverage
areas where you can improve the operating results
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of the business by by taking a
vania. It could be customer retention,
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could be customer churn, customer acquisition. We work with one roll up company
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that customer acquisition was key to them. And so you know, if you're
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going to invest in a a bunch
of portfolio companies or roll up a bunch
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of them, build a strong customer
acquisition model, and and factor that into
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your valuation and and the multiples you're
planning on achieving, and what are you
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what are you seeing in terms of
the balance of technology solutions maintained by the
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parent company versus the portfolio company,
because I imagine you've got quite a bit
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of a mix. Some portfolio companies
are probably more advanced than others, and
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some parents are probably some more more
advanced than their port cas. Yeah,
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size has a lot to do with
that. I mean, if you're really
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small portfolio company, uh, you
know, a hundred million or less or
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even up to a couple hundred million, you may not have the deep talent
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needed to support some advanced analytics kinds
of solutions for example, and so uh
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and then depends on the resources of
the of the parent portfolio a parent private
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equity company how many how many of
their team are really data scientists or data
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engineers that are needed to build these
kinds of solutions. We see it.
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The biggest private equity companies threat that
they have the resources and the talent to
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go uh firm that out to their
portfo leo companies and help them build solutions.
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But at the mid size that it
really really can be a challenge to
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get that kind of talent on board
and delivering the value that you want.
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So, if you're a mid sized
private equity firm and you've got varying degrees
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of I guess data saving us within
your portas, and you want to do
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more with analytics, you're you want
to start to measure your operational KPIs,
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you want to do a better job
of reducing the amount of human effort involved
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in doing some of these financial roll
ups. What's the best way to get
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started? Where should you start?
Well, we strongly advocate starting with a
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little bit of a data strategy so
you can figure out what's the right architecture.
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This is a rapidly moving industry.
There's new tools come out every day,
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every week, every month, and
so it's important to pick the right
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ones. Ultimately, you know you're
gonna buy and hold a portfolio company for
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five years and then sell it,
and part of the evaluation would be debased
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on did you pick the right product? Did you pick the right cloud data
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warehouses? There's still a strong,
viable product in the marketplace, and so
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that strategy phase get you started fred
with a good architecture. The right products
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and then quickly build something quickly in
just some day to build a couple of
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dashboards, a couple of models and
start to use them and apply them.
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And then the other thing that quickly
drive value is to have aggressive adoption program.
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And what are the dangers of just
building? And I guess what are
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the most common reasons that some of
these projects don't go well. Talk to
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the CEO of a private equity firm
that we built a solution for and asked
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them after the fact, kind of
how happy it would the work that that
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appitive did for you? And he
said, loved everything we built so valuable
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for the company. The only thing
is I wish we the private equity parent
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had worried more about adoption. And
so the rate of adoption drives the value,
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and the faster you can get your
management team you're engaging these new analytics
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and making decisions in a different way
the fact you're going to drive value.
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So it is one of the biggest
challenges we face. You take a really
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small company, you probably just barely
had decent reporting. Let's take a hundred
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million dollar manufacturing company. The reporting
is probably not that great at that company.
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You probably have difficult to get in
your hands on the right data to
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drill into problems and solve problems.
And so having a different set of analytics,
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a different set of data, and
being able to use that and leverage
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it two more quickly resolve problems.
That's just a different way of thinking and
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acting. And so you've got to
kind of make that transformation, make that
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change in your management team and your
use in adoption of analytics. So last
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question for you. You know,
as a private equity company starts to embark
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upon an acquisition and thinking about that
data strategy they want to bring in.
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What is some of that advice at
top level advice you would give that private
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equity company, I think twofold Michael
one would be there's a ton of value
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you can drive and your investments with
better analytics and data. And two that
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there are some even higher returns and
better investments with some of these advanced analytic
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solutions. Even your smallest company can
do a lot better, be more effective,
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more efficient, higher cat flows,
higher investment returns with some of these
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advanced analytic solutions, and they are
coming down in size and scale so that
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the smallest company could start to apply
them. And I think that's a key
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of what data insights have really brought
to those smaller companies, giving them the
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power that they never had before.
Beyond just an Excel spreadsheet from a large
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enterprise, they had all this power
and technology and capability. This really does
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bring the power to those smaller firms. Yeah, we had one again,
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a different private equity company was rolling
up smaller you know, million dollar companies,
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but rolling them up into a billion
dollar enterprise. But they introduced customer
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attribution model to tell them why their
customer came to them via a website,
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online ad, print ad, direct
call call campaign, so they could do
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a better job of targeting customer acquisition
and getting new customers and driving up revenue.
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And once you're a little company and
you could do that more effectively,
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you can also spot invest in that
you know, you wait until you have
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service technicians available to do a job
and then drive some instantaneous kind of marketing.
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And you could not do that as
a little, tiny, million dollar
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company in the past, but now
these tools will let us do this.
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And the great thing about the cloud
is that you're paying only for consumption.
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You're not buying the giant analytic licensees
You're just paying when you run them,
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so you only paying for what you
use, which is awesome. And that
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is the power of the cloud,
of that pay as you go type of
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capability that it brings. So I
want to thank the audience for tuning in
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to cloud Crunch, covering cloud analytics
for the private equity industry with special guests
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Paul Corny, private equity practice director
for second Watch. Paul has been an
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absolute pleasure. I appreciate you joining
us today. Thanks Michael, glad to
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be here. You've been listening to
cloud Crunch with Michael Elliott and Fred Bliss.
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For more information, check out the
blog second watch dot com, forward
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slash cloud dash blog, or reach
out to second Watch on Twitter and LinkedIn.
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Involve Solve Evolve. Welcome to cloud
Crunch, the podcast for any large enterprise
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00:00:08.880 --> 00:00:12.919
planning on moving to or is in
the midst of moving to the cloud.
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00:00:13.439 --> 00:00:20.719
Hosted by the cloud computing experts from
Second Watch. Michael Elliott, Executive director
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00:00:20.800 --> 00:00:27.280
of Marketing, and Fred Bliss,
CTO of all Things Data at second Watch
5
00:00:29.120 --> 00:00:36.960
and now here are your hosts of
cloud Crunch. Welcome back to cloud Crunch.
6
00:00:37.119 --> 00:00:40.520
This is Mike Wellly, your host
of cloud Crunch. Joining me as
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00:00:40.560 --> 00:00:44.159
always is my co host, Fred
Bliss, CTO of all Things Day to
8
00:00:44.240 --> 00:00:48.679
here at second Watch. Welcome Fred, Hey, good to be here always.
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00:00:48.719 --> 00:00:52.840
And we have a special guest today
joining us as Paul Corney, private
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00:00:52.880 --> 00:00:56.880
equity practice director here at second Watch. Welcome Paul, Thanks, Michael,
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00:00:56.920 --> 00:01:00.719
glad to be on absolutely Paul.
So for the audience, can you give
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00:01:00.799 --> 00:01:06.439
us kind of a quick synopsis of
your background in the technology consulting space for
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almost forty years now and very exclusively
focused in data and analytics. In the
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last ten years, I lead a
large data analytics consulting firm called Apptitive that's
15
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now a Second Watch company. All
right, In today's episode, we're going
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to cover cloud analytics for the private
equity industry. Now, private equity have
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probably been on the forefront of using
data to create competitive advantage since probably since
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their inception, because I think that's
a part of it. Now, they're
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always on the lookout, I would
imagine, for new investment opportunities, and
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most have a system in place to
help make those decisions. But Paul,
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I'd like to kind of kind of
explore a little bit around, you know,
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how does a private equity firm really
go about understanding those investment decisions?
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What are they looking for? Well, they're obviously looking for a return,
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but really there's two primary work streams
that private equity firms go to as they're
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looking at their investment. And one, it's just kind of deal sourcing.
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You've got to understand industries and markets
and company lists and something that you can
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about the financials to just understand which
might even be potentially viable. And then
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there's the actual due diligence process,
Michael or you're trying to drill into what
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is the real potential of this acquisition
and what kind of returns could we generate.
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Now, I imagine there's also another
side to that as well. Of
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the investments they've already made and understanding
how those companies are doing. Is that
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a part of you know, how
you pull data and analytics or just the
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data of those companies it is,
I mean, in the private equity industry,
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it's a huge data consumer and also
consumed a lot of human resources trying
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to analyze that data. Your traditional
private equity firm managing a bunch of portfolio
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companies is just still relying on Excel
to pull that data in and hugely data
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intensive tons of number as they want
to see on a daily, weekly,
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monthly basis. The monthly reporting package
is really burdensome, and so there's a
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lot of upsite opportunities from better analytics
in the industry. Well, let's let's
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talk about those upsides where cloud comes
into play with that. Well, cloud,
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you know, the one thing that
cloud enables a couple of things.
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It allows these small portfolio companies to
leverage data and analytics that you couldn't otherwise
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do if it was still all on
premise. So these cloud analytics solutions,
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I think they're a game changer in
the industry and allowing a private equity firm
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to have a bunch of smaller portfolio
companies that really can, I like to
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say, punch above their weight class
and operate as a much larger enterprise through
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the use of these advanced analytics solutions. And for the portfolio parent or the
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operating partners limited partners, you just
get better transparency and visibility because this data
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is easily accessible and presentable in a
format that they can see operating dashboards and
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operating metrics of their investments. And
Paul, from a privative quity parents standpoint,
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how much technology adoption are you typically
seeing at the average midsize private equity
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firm. Yeah, it's interesting.
It's a real challenge because you're if you
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look at a lot of private equity
companies, they're investing in companies not necessarily
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a single industry, and so the
financial that even the g L is all
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over the place, and otherwise the
ten different portfolio companies that they own have
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different structures and different costs that feed
into your net margins, gross margins,
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profitability, and so as a result, they've still relied a lot on a
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lot of Excel kind of base reporting
to pull in the data from all of
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their investments. So the technology adoptions
have been hard because you are trying to
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manage ten different operating companies or portfolio
companies with all different industries. Potentially we're
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working one on private equity company now
that invests primarily in food and beverage companies,
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but even those are very different.
One can be a discrete manufacturing one
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could be a process manufacturer, and
they're just different ways that you would um
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want to manage that business, different
operating metrics and stuff. So it's quite
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a challenge. So how do you
go about solving those challenges? Well,
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again, you have to do a
little bit of data modeling, Michael,
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but there are some common metrics that
you can develop and then build financial reporting
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capabilities. But one of the first
things we always find is so helpful,
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it's just to ingest all of that
data from those portfolio companies and then you
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can use that both for building it
has to do a purpose. That's one
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of the things that we see real
value from is that when I have that
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data, I can I can use
it to build operating dashboards and metrics dashboards
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and KPI dashboard for the portfolio company
sea level exacts. But it also gives
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that kind of transparency and visibility because
I can now show those uh KPIs and
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metrics to my uh, the portfolio
company, parent, my limited partners,
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my general partners, everybody can see
the numbers and how the the investments are
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performing. And when you're talking with
the with the various investors and p firms
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about these KPIs and metrics, how
often do they actually know what they want
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versus helping you helping them define?
It's really interesting I find. What I
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find is they they sometimes think they
know what the key metrics are that more
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offered than not. By by pulling
in DA the metrics and data in and
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building some of these data, they
learn more about their business, and they
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learn more about some of the cause
and effect, and they learn how to
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do the analysis when things are going
awry. So UM to us, it's
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kind of this data journey they have
to get on so that they're automating this
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and digitizing all of the data and
then doing the analysis and adoption. We
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call it. I call it driving
adoption. Yeah. I mean, even
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if you look at just the public
stock market, right, there's accounting is
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not necessarily a factor of a story, right, there are different ways to
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represent that. And I imagine operational
metrics that are commonly defined across many many
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porcos. Yeah, it's not something
you can really change, right And I
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like I think of it like somebody
who starts to learn how to read accounting
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statements and you get pretty good at
reading an income statement or a balance sheet.
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And the same true for private equity
company. Right after a while,
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you could pretty quickly translate ibada into
cash flow in your head because you've you've
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got this understanding of how these things
translate. I want to go back.
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You talked about Traditionally it's you know, importing into an Excel spreadsheet. Now
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now you're talking about, you know, how do you bring in all this
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data? What are some of the
tools? I mean, ingests is one
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part of it, But what are
some of the tools that you apply the
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enable you to get inside out of
all this data? Well that you know
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they're they're dashboards of course we build, but the it really starts with ingesting
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this data and put it in some
sort of cloud data a warehouse. And
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for us we use tools like red
Shift and Snowflake are the two leading ones.
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We use sometimes Azure as your synapse. But then it's building dashboards,
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Michael, and that's the business intelligence
side of it. I imagine of building
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those dashboards. So how how is
that process kind of can you describe a
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little bit about that work? Yeah, we you know, my h the
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second watch, we kind of favored
this agile development process where we build something,
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get go live with us with kind
of a quick solution, and then
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we learn and adjust and add to
that over time. So the dashboard work
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is often let's get all the data, put it in a cloud data ware
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here else, give you a handful
of dashboards to manage your key metrics,
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and then as you use the dashboard, you kind of decide maybe you want
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a different way of viewing it,
or you want to add some more analysis
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or some deeper drill down capabilities,
and so we'll take that on in a
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second phase or a third phase to
kind of add those capabilities over time.
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And usually once we've built some basic
operating debtford, you start to take a
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look at um what we would call
more advanced analytics solutions AI or data science
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or predictive analytics, machine learning models
to predict customer churn, all all the
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kind of uh fun, really cool
advanced analytics things. So and we've talked
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a lot about analyzing the assets that
you have, but I imagine when you're
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when a private equity company is looking
to purchase a company, they're also trying
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to bring in some of those outsourced
data. UM outsource. Take that back,
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When a private equity company is looking
to acquire a company, they're looking
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at sources of data that may not
be readily available with the company. So
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how do you start to bring in
external data sources into that model? Yeah,
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we tend to call it alternative data
and the industry and and that's really
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the the you know, the to
market places that exist with Snowflake for example,
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that it uh and we find almost
every single one of our implementation cloud
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data warehouse imple pitation that there's a
lot of external data that they can take
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advantage of. And sometimes even it's
as simple as weather patterns. If you're
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a seasonal business, there's probably a
period where you're going to spend a lot
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on marketing because you're gonna be acquiring
new customers at the spring season when the
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weather changes, and so UM,
even basic data like that is really valuable
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to you. And anytime you start
talking about models of any kind, the
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more data that you have, the
better. And if we're trying to do
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not now we know that the weather
is changing, and we know we want
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to invest in marketing the questions with
zip coates, we want to spend that
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on and so understanding the age of
houses and typical income again another external data
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source. And so with these market
places that come with most of the cloud
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data warehouse solution, you can instantly
ingest that external data and use it in
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building your models. Yeah, imagine
the problem is not as much is enough
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to is there enough data out there? But more trouble understanding what data is
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and what's going to be important and
how do I combine that with the data
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I have already? Yeah, well, I like to use the word actual
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insightful data. Right. It's easy
to discope, get data. The question
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is what do you do about it
and what's the what's the data that helps
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you make smarter, faster, better
decisions. So there is an art to
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that and there is a process you
need to go through to figure that out.
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And even in the I would imagine
in the the due diligence process,
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with there being so much manual work
and excel um even with the rise of
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cloud solutions, it's probably not really
reducing the time there so much as it
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is um having better access to some
of that alternative data you mentioned, right,
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Paul, Yeah, yeah, I've
I find some. I think I
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think private equiferns could do a better
job of an upfront assessing what the opposite
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opportunity is from data and analytics.
You know, you can monetize the data
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or you can just take better advantage
of it and use of it in these
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portfolio companies that you're buying and investing
in, and there's just so much opportunity
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up front to say, what could
I do with this company? What how
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much better could we be there?
If you couldn't drive ten or improvement and
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an operating profits and EBITDA. I
think you could at the minimum you can
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achieve in first year if you just
had some of these operating metrics at your
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fingertips. It's funny. I was
talking to one VC investor UM a couple
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of months ago, and he had
mentioned that one of the interesting things that
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they do at his firm is UM
creating almost data clean rooms where they're uploading
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the the acquirer and the purchase and
the UM company being acquired are essentially sharing
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data in the same space and using
that as a way to communicate with one
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another. How often do you see
that in this world? And what is
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the upside there? The clean rooms
are much more popular these days, Fred,
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and and we see more of it
where we're at least taking the data
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in prede pre close. But I
still think some of the private equity firms
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suffer from a lack of a deep
technology adoption around analytics. So something they
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could suck in all the data pre
closed, but they don't even know what
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to do with it in terms of
figuring out what the what the targets and
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the improvements that they want to achieve
from it are there there. You ought
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to have a template, and you
ought to have a checklist of things you're
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thinking about in terms of what the
upside is. Every company in every industry
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has some key leverage points or leverage
areas where you can improve the operating results
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of the business by by taking a
vania. It could be customer retention,
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could be customer churn, customer acquisition. We work with one roll up company
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that customer acquisition was key to them. And so you know, if you're
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going to invest in a a bunch
of portfolio companies or roll up a bunch
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of them, build a strong customer
acquisition model, and and factor that into
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your valuation and and the multiples you're
planning on achieving, and what are you
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what are you seeing in terms of
the balance of technology solutions maintained by the
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parent company versus the portfolio company,
because I imagine you've got quite a bit
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of a mix. Some portfolio companies
are probably more advanced than others, and
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some parents are probably some more more
advanced than their port cas. Yeah,
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size has a lot to do with
that. I mean, if you're really
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small portfolio company, uh, you
know, a hundred million or less or
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even up to a couple hundred million, you may not have the deep talent
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needed to support some advanced analytics kinds
of solutions for example, and so uh
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and then depends on the resources of
the of the parent portfolio a parent private
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equity company how many how many of
their team are really data scientists or data
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engineers that are needed to build these
kinds of solutions. We see it.
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The biggest private equity companies threat that
they have the resources and the talent to
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go uh firm that out to their
portfo leo companies and help them build solutions.
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But at the mid size that it
really really can be a challenge to
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get that kind of talent on board
and delivering the value that you want.
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So, if you're a mid sized
private equity firm and you've got varying degrees
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of I guess data saving us within
your portas, and you want to do
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more with analytics, you're you want
to start to measure your operational KPIs,
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you want to do a better job
of reducing the amount of human effort involved
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in doing some of these financial roll
ups. What's the best way to get
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started? Where should you start?
Well, we strongly advocate starting with a
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little bit of a data strategy so
you can figure out what's the right architecture.
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This is a rapidly moving industry.
There's new tools come out every day,
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every week, every month, and
so it's important to pick the right
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ones. Ultimately, you know you're
gonna buy and hold a portfolio company for
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five years and then sell it,
and part of the evaluation would be debased
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on did you pick the right product? Did you pick the right cloud data
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warehouses? There's still a strong,
viable product in the marketplace, and so
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that strategy phase get you started fred
with a good architecture. The right products
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and then quickly build something quickly in
just some day to build a couple of
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dashboards, a couple of models and
start to use them and apply them.
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And then the other thing that quickly
drive value is to have aggressive adoption program.
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And what are the dangers of just
building? And I guess what are
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the most common reasons that some of
these projects don't go well. Talk to
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the CEO of a private equity firm
that we built a solution for and asked
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them after the fact, kind of
how happy it would the work that that
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appitive did for you? And he
said, loved everything we built so valuable
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for the company. The only thing
is I wish we the private equity parent
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had worried more about adoption. And
so the rate of adoption drives the value,
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and the faster you can get your
management team you're engaging these new analytics
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and making decisions in a different way
the fact you're going to drive value.
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So it is one of the biggest
challenges we face. You take a really
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small company, you probably just barely
had decent reporting. Let's take a hundred
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million dollar manufacturing company. The reporting
is probably not that great at that company.
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You probably have difficult to get in
your hands on the right data to
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drill into problems and solve problems.
And so having a different set of analytics,
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a different set of data, and
being able to use that and leverage
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it two more quickly resolve problems.
That's just a different way of thinking and
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acting. And so you've got to
kind of make that transformation, make that
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change in your management team and your
use in adoption of analytics. So last
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question for you. You know,
as a private equity company starts to embark
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upon an acquisition and thinking about that
data strategy they want to bring in.
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What is some of that advice at
top level advice you would give that private
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equity company, I think twofold Michael
one would be there's a ton of value
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you can drive and your investments with
better analytics and data. And two that
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there are some even higher returns and
better investments with some of these advanced analytic
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solutions. Even your smallest company can
do a lot better, be more effective,
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more efficient, higher cat flows,
higher investment returns with some of these
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advanced analytic solutions, and they are
coming down in size and scale so that
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the smallest company could start to apply
them. And I think that's a key
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of what data insights have really brought
to those smaller companies, giving them the
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power that they never had before.
Beyond just an Excel spreadsheet from a large
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enterprise, they had all this power
and technology and capability. This really does
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bring the power to those smaller firms. Yeah, we had one again,
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a different private equity company was rolling
up smaller you know, million dollar companies,
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but rolling them up into a billion
dollar enterprise. But they introduced customer
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attribution model to tell them why their
customer came to them via a website,
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online ad, print ad, direct
call call campaign, so they could do
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a better job of targeting customer acquisition
and getting new customers and driving up revenue.
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And once you're a little company and
you could do that more effectively,
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you can also spot invest in that
you know, you wait until you have
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service technicians available to do a job
and then drive some instantaneous kind of marketing.
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And you could not do that as
a little, tiny, million dollar
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company in the past, but now
these tools will let us do this.
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And the great thing about the cloud
is that you're paying only for consumption.
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You're not buying the giant analytic licensees
You're just paying when you run them,
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so you only paying for what you
use, which is awesome. And that
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is the power of the cloud,
of that pay as you go type of
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capability that it brings. So I
want to thank the audience for tuning in
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to cloud Crunch, covering cloud analytics
for the private equity industry with special guests
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Paul Corny, private equity practice director
for second Watch. Paul has been an
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absolute pleasure. I appreciate you joining
us today. Thanks Michael, glad to
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be here. You've been listening to
cloud Crunch with Michael Elliott and Fred Bliss.
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00:20:38.319 --> 00:20:44.160
For more information, check out the
blog second watch dot com, forward
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00:20:44.200 --> 00:20:49.519
slash cloud dash blog, or reach
out to second Watch on Twitter and LinkedIn.
