If you believe better data is one of the top drivers of growth, you're not alone.
According to a 2019 survey, "Fortune 1000 companies are now recognizing that they must become more adept at leveraging their data assets if they are to compete successfully against highly agile data-driven competitors." - MIT Sloan Management Review
The terms Big Data, Machine Learning, Factory of the Future, Industry 4.0, and Artificial Intelligence are bandied about at workshops and industry events as though they’re well-understood and commonly applied.
They’re buzzwords - more a means of staying in the conversation than technologies that actually apply to common use cases.
In reality, employing complex algorithms and neural-networks to identify correlations beyond the reach of common sense requires an enormous quantity of transactional data, as well as a high tolerance for trial and error.
How Private Equity Uses Data
When the buzz around buzzwords diminishes in the face of real management issues and real market challenges, hopes of finding data’s philosopher’s stone gives way to the perpetual churning out of more spreadsheets.
Excel is the tried and true way to “pan for business gold,” so we keep swirling the numbers and looking for gems that will get us through another day. But PE firms don’t succeed by turning to a trained eye and old-timer wisdom to find more fertile streams.
This is where business intelligence (BI) comes in. Those serious about outsmarting the competition don’t rely on the status quo, nor are they lured by magic formulas. They put their data to work to support smarter, faster decision-making, not just for executives, but throughout the organization to make sure everyone is working the most profitable veins with the best tools.
Business Intelligence Challenges Private Equity Firms Face
The proliferation of Excel spreadsheets can bring as much confusion as clarity. They’re a snapshot and they’re difficult to distribute and maintain, leaving a company working in isolated camps rather than as a single force.
Data intelligence is the tools and processes that enable a company to mobilize its performance data, giving everyone in the organization the feedback they need to excel at their work. It’s the most underused asset in most companies, and the one that can deliver the greatest transformation in the shortest time, making tight exit windows infinitely more manageable.
For private equity, effective, scalable business intelligence should:
- Take the form of a cohesive, transferable asset, governed and documented, not a one-and-done project that loses its impact and gets lost in the maelstrom of fast growth.
- Transform a company for greater focus and accountability, harnessing data from every significant system into an intelligible, trustworthy database that automatically distributes actionable dashboards to every member of an organization, every day.
- Be capable of answering any questions an interested party may have about the company’s performance in order to substantiate valuation.
Business intelligence, though much more accessible than just a few years ago, is a science unto itself with unique nomenclature, systems, and best practices.
Below are the essential elements of a stable business intelligence system. You don’t need technical mastery of the following, but a basic knowledge will help you start harnessing one of your most valuable (and un-mined) assets--your data.
How Successful Private Equity Companies Create Business Intelligence Strategies
Before we jump in, consider the following statistics:
Nearly eight in ten executives agree or strongly agree with the following statement: If we could harness all of our data, we would be a much stronger business.
The fact that businesses are blind without accurate data instruments from which they can make confident decisions leads 67 percent of North American businesses to be interested in using advanced analytics to improve business operations.
The PE industry, though sometimes slow to change, is starting to embrace the tools that convert data into action and intelligence. Without needing a team of data gurus, and with minimal investment, companies now have the ability to quickly zero in on the metrics that most influence progress, and make those metrics easily consumable for those who can most affect outcomes.
For the first time in history, virtually every business has the means to leverage the power of data analytics and dashboards. It is no wonder Harvard Business Review recently labeled Data Scientist “the sexiest job of the twenty-first century.”
Let’s go under the hood and layout the core components of BI for private equity firms.
How Master Data Management Helps Private Equity
Despite the name, MDM is not the oversight process by which a company manages its data strategy. Rather, it’s a technical toolset and process used to maintain data quality and integrity.
This includes removing duplicates, standardizing data, and incorporating rules to eliminate incorrect data from entering or persisting in the system.
MDM is the domain of IT professionals and likely doesn’t need to be on the radar of management teams (let alone their boards), though it’s useful to know its meaning to avoid confusion.
How To Create A Master Data Roadmap For Your Private Equity Firm
From a strategic perspective, a Master Data Roadmap is the tool private equity firms use to establish a business intelligence system, and keep that system on course.
The roadmap starts with a document (or deck) outlining the business case for deploying a business intelligence system, including:
- Business outcomes (their current and desired state)
- The functional areas of the business to be addressed
- Key personas or roles that will consume the BI dashboards
- A catalog of data sources
- Key metrics and KPIs essential to the company’s success
- A governance plan
The roadmap should also define a BI steering committee, which reviews the efficacy of the company’s BI initiative and adjusts tactics and strategy over time.
The Role Of Data Governance In Private Equity
Most companies view business intelligence initiatives like typical IT projects, with a start and end date. But, companies (and their underlying systems and data) constantly evolve.
A data governance team can provide the change-management oversight needed to make sure that data is effectively leveraged on an ongoing basis. Through process and security management, they empower users of the system while maintaining control.
How Private Equity Uses Scorecards, Dashboards, and Reports
Depending on who you talk to, these terms are often used interchangeably. While the semantics are debatable, a scorecard generally refers to a summary of key metrics and KPIs that show the highest-level view of any focus area (whether the whole company, a department, a client, or an employee).
Dashboards and reports go deeper, showing trends, correlations and other performance insights at a more atomic level. Generally, the term “dashboards” refers to more graphical representations of data (i.e., the proverbial gas-gauge and pie charts), whereas reports tend to take a tabular form (rows and columns of data).
In a hierarchy, scorecards show the highest-level summary of performance, dashboards provide greater analytical insight into that performance, and reports deliver the transactional detail that are the base units of performance.
Does Your Private Equity Firm Need A Data Warehouse?
A data warehouse is the single, structured repository where all the data you need to produce reports and dashboards is organized. It’s the sole source of truth for a company and the engine behind business intelligence.
The term “warehouse” may sound daunting, but it’s really quite simple. Every time you load data into Excel and organize it into pivot tables, you’ve created the fundamentals of a data warehouse. However, while virtually indispensable, Excel files are rarely governed and can therefore easily proliferate, having the ironic effect of exacerbating a company’s data entropy. Raw data pulled straight from transactional systems is generally not report-friendly, but that can be overcome.
Think of your data as a jigsaw puzzle. Each piece is virtually meaningless on its own. Gathering all the pieces into a pile doesn’t improve the situation much. But by organizing them by type and fitting them together, patterns emerge to eventually reveal the big picture. This is the role of the data warehouse.
Building a data warehouse can seem ominous. If you don’t have a data architect on staff, you may worry that data warehouses are the exclusive domain of other, uber-technical companies.
Ten years ago, you would have been right, but today virtually any business can join the data revolution. Data warehouses are much easier and more affordable to deploy than just a few years ago.
In Microsoft’s ecosystem, an Azure SQL Database can be spun up in a matter of minutes. Connect it via an ETL (explained below) tool to your CRM or ERP, and you can pull in the fact and dimension tables you need to start producing reports and dashboards. Voila, a data warehouse is born.
Every Portfolio Company Has Fact Tables
Fact tables contain the output data from transactions or events. For example, a construction company in your portfolio will capture transaction data for a project, including hours worked and dollars spent on labor and materials. This data is typically numerical and, therefore, suitable for calculating measures (e.g., total cost).
Dimension Tables Add Necessary Context To Your PE Portfolio Data
Dimensions describe, filter, and/or group the facts. In other words, they apply dimension or perspective to your facts.
An example of a dimension might be a list of your clients (whereas facts might include the number and cost of items sold to those clients).
Generally, anything in a fact table can be aggregated, whereas anything in a dimension table is what you reference to provide descriptive context to the fact data.
How To Relate Facts and Dimensions To Manage Your PE Portfolio
Tables can be related in various schemas, depending on your objective (or your preference). The two most common schemas are star and snowflake. Do a search and you’ll find a hotly contested debate over which is better for reporting data models (in most cases, we favor the star schema, per Ralph Kimball). To get started, you only need to know what they are.
In a star schema, fact tables are connected directly to dimensions. Simple as that. This model is centered around facts and, therefore, is good for producing metrics, such as the total revenue for a given customer and/or given time period.
In a snowflake schema, the data is organized to minimize redundancy and keep data volume to a minimum. Rather than connecting only to facts, dimensions can have their own dimensions. A snowflake schema is better for some types of analysis. However, it has a higher number of joins between tables, which can potentially slow performance and make writing measures more complex.
Setting Up Your Private Equity Firm’s Entity Relationship Diagram (or ERD)
Like the OLAP Cube, this one sounds impressive, but it’s simply the term for a diagram that illustrates the relationships between fact and dimension tables in a database (like the star and snowflake examples above). Here’s an example:
In order to move data from its source to a data warehouse, you’re going to need an ETL tool. ETL stands for Extract, Transform, and Load.
Examples of ETL tools include Informatica, SQL Server Integration Services (SSIS), Scribe, and Azure Data Factory.
These tools take data from one or more sources, modify that data in some manner (e.g., converting times to a local time zone, euros to dollars, transactions to totals), and deliver it to the appropriate destination. In other words, ETL tools enable you to extract data from a source, transform it to make it more report-ready, and load it into your data warehouse.
For your data initiative to succeed, your ETL processes must be efficient and accurate. Your business can’t pause while your data loads, and you can’t afford for your data to be untrustworthy. If you base critical decisions on your data, it must paint a reliable, timely picture.
Private Equity Data Marts and How They Differ from Data Warehouses
Unlike the all-encompassing data warehouse, data marts in the private equity world typically have a specific focus (e.g., sales, finance, production, warranty). They are also simpler to build, including only a slice of the overall data.
Depending on your goals and data environment (or your data expert’s preferred best-practice), your strategy may call for building either the warehouse or the marts first.
Some experts would argue that the warehouse should come first and that mart should be developed as a by-product. Others prefer to build marts first, then assemble the warehouse by aggregating the marts.
How PE Firms Use An Operational Data Store (or ODS)
An ODS is simply a copy of one or more transactional databases. It stops short of offering report-ready dimensions and measures.
For some businesses, an ODS is as far as they care to take their data-management strategy and frankly, it’s better than nothing. It may lack the power of a reporting cube, but it’s simple to create, removes reporting overhead from transactional systems, and provides a basic foundation for reporting.
BI For Private Equity Takeaway
How Private Equity Firms Can Overcome The Status Quo
In their 2019 Global Private Equity Report, Bain argues in favor of buy-and-build as a clear path to value. However, the report also warns that deal multiples are at record levels and GPs are under heavy pressure to find strategies that don’t rely on traditional tailwinds like falling interest rates and stable GDP growth.
Armed with a conversational understanding of the above business intelligence concepts and the right data partners, data-driven PE firms and their portfolio companies can mobilize performance data, give everyone in the organization the feedback they need to excel, and complete the quest for differentiation in an increasingly competitive landscape.