Data Analytics: Demystifying the Buzz and

Exploring Practical Applications in Private Equity

DATA IS THE

NEW OIL

THE DATA ENGINES OF AI AND ANALYTICS

One example is artificial intelligence, or AI - technology that’s designed to mimic human thinking and behavior (and improve upon what

humans do). AI has come a long way since 1997, when IBM’s Deep Blue supercomputer beat Gary Kasparov at chess. Deep Blue

accomplished that without processing power for a lot of data. Since then, computing capabilities have changed dramatically, creating

striking examples of what happens when AI analyzes enormous data sets in milliseconds.

2

Google programmed its AI,

AlphaZero, with only the rules

of chess and no game

strategies. Four hours later,

Google’s AI was able to beat

the highest-rated chess-

playing program available.

Consider the recent advances in one form of artificial intelligence, called machine

learning - essentially, getting a computer to use data to teach itself concepts, without

being explicitly programmed. In 2017, Google programmed its AI, AlphaZero, with

only the rules of chess and no game strategies. Four hours later, Google’s AI was able

to beat the highest-rated chess-playing program available.

A related but slightly more nuanced concept of data analytics has already begun

popping up with applications across industries. In the traditional sense, analyzing data

was done primarily through the tedium of manually examining spreadsheets and

easily quantifiable data outputs. Fortunately, technology has overhauled analytics by

allowing companies to examine much more information, faster than ever. The days of

limited sample sizes - and the limited insights they yielded – are over. Companies are

already analyzing large swaths of data to optimize supply chains, improve how they

serve customers, and make existing capital investments more productive.

Technology designed to mimic human thinking and behavior, and improve upon what humans do

BIG DATAInformation that can be analyzed to yield business insights, but exists in large volumes too complicated for traditional data-processing techniques

AI

MACHINE LEARNING

DATA SCIENCE

DATA ANALYTICS

Algorithms that use data sets to automatically learn which actions to take, without being guided by programmers

A field that encompasses all aspects of data cleansing, preparation, and analysis

Involves the collection and structuring of data using specialized software to process the data and interpret the results

Data analytics encompasses

collecting and preparing data,

using specialized software to

process it, and interpreting the

results. Although data analytics

can incorporate elements of

machine learning (for data

extraction, as an example) and

other forms of artificial

intelligence, there are

important distinctions. Its

robust algorithms are not

putting businesses on autopilot,

i.e., the goal of data analytics is

not to learn the parameters of

investing strategies and then

run the core functions of the

firm (although after a rough

day, that might sound like a

great idea).

Data analytics helps organizations make decisions. The channels of output facilitated by data analytics in this sense changes the nature of

decision making by transforming the type of data firms can now have access to. It is designed to allow executives to learn new,

meaningful things – quickly - and make tactical and strategic decisions with increased confidence.

That phrase has repeatedly proclaimed data’s game-changing potential. In many ways, it’s a clunky

comparison. Unlike oil, data is not scarce. It’s much more than a renewable resource because

volume keeps increasing exponentially. That’s led to big data - amounts that prove too much for

traditional data-processing techniques. Here’s what oil and data do have in common: It takes

significant effort to unearth the valuable stuff. And data is the fuel powering vital engines that are

now transforming business.

YOUR COMPETITORS PROBABLY USE IT (OR WILL SOON)

One sign of data analytics’ widespread use is the new C-suite role that’s emerged in recent years: The Chief Data Officer, or CDO, whose

responsibilities include overseeing data quality and strategy. A Gartner survey last year underscored how organizations view data as an

integral part of success. When more than 3,000 CIOs in 98 countries named the top differentiating technologies, business intelligence

and analytics held the top spot on the list.

The private equity industry has yet to fully embrace data analytics as a critical way to ensure success and provide a differentiating factor

in an increasingly competitive environment that firms must navigate. Average valuation multiples now exceed 10x, and firms are facing

more pressure to generate higher returns for their investors. With only limited bandwidth to grow through financial engineering, firms

have started to focus more on improving the operating performance of their assets. Historically, the operating value-add playbook often

was limited to investment and implementation of systems and software, primarily focused on FP&A and sales and marketing. Going

forward, with the right data analytics expertise, PE firms can uncover opportunities to strengthen their portfolio companies’

performance in a more meaningful and differentiated way.

Here's the first question to consider: What tools are needed to conduct deep data analysis? The gold standard application of financial

industry diligence, Microsoft Excel, is in fact what most firms use today to conduct high-level analysis. However, even the all-

encompassing Excel has its own limitations, mainly in analyzing large swaths of data. This is where an analytics engine is required that

operates at peak performance and is capable of analyzing and computing a myriad of data points, instead of sputtering along with Excel

as the main tool. The intent is to make more informed decisions and make them faster. Rapid shifts in technology can feel daunting, but

with right approach, data analytics can drive value. Although we have seen firms starting to embrace more complex data analysis

solutions, we expect this will be a mainstream process when looking at growing (and even pitching for) portfolio assets.

3

HOW DATA ANALYTICS CAN HELP PRIVATE EQUITY FIRMS MAKE INFORMED DECISIONS ACROSS THE VALUE CHAIN

PE firms can benefit from better data insights throughout the investment lifecycle starting with Deal Sourcing. Data analytics allows

firms to identify potential targets that usually fly under the radar. For example, analytics can assess customer sentiment about a brand,

or a specific product or service. Large volumes of social media posts, customer reviews, and other unstructured data are analyzed to

achieve that. By looking at relevant data patterns and trends, firms can conduct early detection of the buzz surrounding brands and

determine whether it’s an opportunity worth pursuing. Though there is a proliferation of firms that will outsource list building and cold

email marketing, differentiated and intelligent sourcing can only be done on a bespoke basis driven by the private equity firm. Since

there is nearly no limit to the type of data that can be analyzed, each firm can create their own recipe for differentiated success.

DATA ANALYTICS

BRINGS VALUE

THROUGHOUT THE

INVESTMENT

LIFECYCLE

01

03

04 02

Deal SourcingAllows firms to identify potential targets for

deal sourcing that usually fly under the radar

Portfolio Company PerformanceHelps monitor and improve portfolio companies’

performance by uncovering business insightswithin ERP (Enterprise Resource Planning)

systems

ExitIdentifies assets that need

fixing for a successful exit

Due Diligence Aids due diligence by creating a clearer

picture of a target’s performance and

potential

4

Data analytics also aids Due Diligence by creating a clearer picture of a target’s performance

and potential. This technology can allow firms to dig deeper into information surrounding

customer demographics and potential new customers, and more carefully explore how a brand

stacks up to competitors. Involving data analytics during the diligence phase rather than post-

close allows PE firms to identify areas for operational improvement that can be priced into

offers. Analytics let firms conduct due diligence faster and deeper, and keep pace with tight

deadlines during the diligence period.

The biggest advantage rests with data analytics capabilities for monitoring and improving

portfolio companies’ performance. This usually starts with a firm’s ERP (Enterprise Resource

Planning) system — software that integrates the processes and data throughout an

organization’s functional areas (including financial planning, human resources, and supply

chain) to enhance collaboration and provide more detailed, timely information for the

management team. The ERP system tracks useful operational level data (such as warehouse

inventory, payroll, and product traceability) for high level scrutiny, but rarely is there a specific

person or team dedicated to dissecting operational level data to gain deeper operational

awareness. This is where data analytics can prove to be a valuable asset, combing through the

data to streamline operations and create a strategic plan for the business. The ability to

provide these insights and improvements can not only help create a strong performance record

for existing assets but also become the partner of choice for future portfolio assets as well.

WHERE SHOULD YOU START?

PE firms have no shortage of information. Among TresVista’s client base, we’ve calculated that roughly 80% of portfolio companies

already have robust ERP systems that collect operational level data. However, since portfolio companies have historically placed a low

priority on IT budgets, this also means that these data sets are sometimes housed in disparate systems. More often than not, PE firms as

part of their ‘100 day plan’ will try to migrate systems under one roof but are often left to tackle unstructured data in large volumes, and

there’s still the gap of how to analyze the information.

By taking a disparate approach with a mountain of data, they can get buried in an avalanche that obscures insights instead of revealing

them. This is where Data Scientists come in. The overwhelming majority of firms (especially those focused on the middle market) do not

have typically have a data scientist resource to manipulate this data and turn it into something useful.

This is part of the reason why many firms that have adopted data analytics are still working to maximize its potential. In another Gartner

survey released in February 2018, respondents were asked to rate their organizations according to Gartner's five levels of maturity for

data and analytics. Worldwide, about 60 percent of respondents rated themselves in the lowest three levels.

With effective software, real-time analytics can identify portfolio company problems faster, which means solutions can be implemented

sooner. And instead of taking hours — or days — to run reports and uncover patterns, effective analysis can create timely, useful and

accurate reports to keep advisors informed.

By analyzing financial and operating metrics with speed and clarity, data analytics can guide PE firms toward opportunities that create

additional value at the portfolio company level. This is particularly relevant for the heightened expectations of limited partners, and a

much-needed edge in the midst of a highly competitive deal environment.

For a successful Exit, analytics can help identify assets that need fixing. This technology can also provide guidance for the timing of an

exit — evaluating the potential of a portfolio company’s particular market, along with the company’s strengths and weaknesses. If the

data conveys a compelling story about areas for revenue growth or creating cost efficiencies, it can help boost value by sharing that story

with buyers during the diligence process.

Data Analytics let

firms conduct due

diligence faster and

deeper, and keep

pace with tight

deadlines during the

diligence period.

DATA ANALYTICS VALUE CHAIN

Below are the typical steps involved in analyzing data at the portfolio company level, although this might differ from firm to firm

depending on data availability and structure.

5

To create reliable insights, you will need accurate and consistent data. Ensure that the data is properly cleansed and normalized - meaning, unrecognizable information is removed, and data is converted to a single format. Without proper data preparation, you risk ending up with error-filled “insights”.

Lay the foundation for your analysis by defining a realistic objective. Know what you want to achieve, and also consider whether it’s feasible that the available data will support that objective.

IDENTIFICATION

Determine what information you need to achieve your objective, and whether you will need to combine data from multiple sources. Who will pull that data?

AGGREGATION AND EXTRACTION

PREPARATION

With the right tools, you will be able to analyze an array of variables, ranging from balance sheets to social media posts. Do you need to gather additional information for a more complete analysis? And do you have someone to help you interpret the results?

ANALYZE

To make the analysis worthwhile for business users, you’ll need a dashboard that’s customizable, with visuals that show metrics and analytical insights at a glance, and also allows users to drill down and view data at a granular level.

VISUALIZATION

Case 1 Case 2

Conduct sales and price-volume analyses on a large volume of transaction data through the creation of dashboards for a portfolio company of a private equity client.

Objective

Geo-plot potential customers on a map around a primary location and enable the filtering of customer locations based on custom distance parameters for a private equity client.

Objective

TresVista created a comprehensive dashboard in ‘Qlik Sense’ by loading the sales data in the DBMS (SQL Server), simplifying the querying and analysis of the data. TresVista then conducted the price-volume analysis using the programming language ‘R’, which was also to be showcased in the dashboard.

Approach

TresVista extracted the coordinates of the customer locations through Google API services using the ‘R’ tool. With the help of ‘Tableau’, these locations were plotted on a comprehensive map and the distance was calculated from the pre-specified source using the ‘Haversine Formula’.

Approach

TresVista was able to simplify the price-volume analysis on approximately one million transactions, and also provided sales KPI tracking and analysis through a data visualization solution. The ease of visualization within the dashboard allowed for quick aggregation and summarization of information while providing the ability to seamlessly drill down into the information for quicker identification of potential problem areas (e.g., decreased sales across a category). The task was completed in 30 minutes compared to the initially expected duration of 1 day.

Outcome

TresVista provided the client with a distance filter that allowed them to locate customers within 25, 50, 100, 150, 200 and 300 mile radii from the source.

Outcome

6

CONCLUSION

When PE firms extract meaning from the data mountain, they find analytics provides a crucial competitive edge. The need today

is to move beyond the traditional methods of value creation and embrace the technological changes that are already driving

businesses to build and improve. To deliver on its promise, analytics must provide new information that’s relevant and

meaningful, at a speed that makes organizations more agile.

Done right, it helps companies focus on objective decision-making. To borrow from a popular statistics quote, businesses should

avoid using data the way a drunk uses a lamp post - for support rather than for illumination. With the right implementation and

organizational mindset, data analytics can be the benchmark for continuous improvement.

PRACTICAL APPLICATION - CASE STUDIES

Below we showcase a few examples of how we used Data Analytics with our clients