Data Analytics Demystifying the Buzz and Exploring ... · Data Analytics: Demystifying the Buzz and...
Transcript of Data Analytics Demystifying the Buzz and Exploring ... · Data Analytics: Demystifying the Buzz and...
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.
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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.
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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
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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
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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.
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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
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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