What is the Role of Business Intelligence in Private Equity?
In response to the strongest levels of competition that the private equity industry has ever experienced, digital transformation is a necessary differentiator. Like traditional operating and financial strategies, digital transformation is an additional value creation vector for private equity firms.
In today’s data-focused world, funds need to look beyond the conventional playbook consisting of strategies such as market consolidation, geographic expansion, capitalizing on operating synergies, and financial engineering to optimize returns. As more capital is deployed in buyout strategies that engage in conventional value creation approaches, returns will naturally begin to decline.
As such, PE funds must continue to look for other ways to produce alpha, making digital transformation a natural focal point. Despite this requirement, and according to Sajjad Jaffer of GrowthCurve Capital in his interview with Wharton, the private equity industry hasn’t experienced much innovation since the creation of the Excel spreadsheet.
In today’s highly competitive deal making environment, buoyed by historically low interest rates, and ample dry powder, PE firms can turn to data intelligence to speed up the value creation process. Being data-driven will also meaningfully contribute to better buying decisions when executing strategies such as buy-and-build. By using data intelligence in combination with experience and intuition, funds can confidently determine where the best value creation opportunities are and quickly identify potential issues and risks.
What is the Role of Data in Private Equity and Portfolio Growth?
As a result of the 4th Industrial Revolution, private equity funds have access to the analytical tools and technological infrastructure to digitize fund management operations, drive portfolio company value creation, and narrow acquisition diligence gaps presented by data.
By embracing and implementing data transformation and business intelligence, private equity portfolio companies can empower executive teams with greater visibility into the metrics that most drive value creation. This positions investments for increased profitability, enhanced asset utilization, higher customer satisfaction, better operating efficiency, and the cultivation of data as a strategic, managed asset.
Data Creates Differentiation in Commoditized Markets by
- Driving Productivity
- Fostering a Culture of Shared Accountability
- Creating Alignment Between the Board, Management Teams, and Employees
- Helping Portfolio Company Executives Delegate and Scale
- Acting as a Lever to Improve EBITDA and Cash Flow
- CAPEX Rather than OPEX
- Providing a Prerequisite for Advanced Analytics
- Fueling Cross-Portfolio Value Creation
Read more here: How Can PE Firms Differentiate Through Digital Transformation?
Data Intelligence Drives Organic Growth
Data intelligence is a powerful tool to operationalize revenue growth. It offers actionable insights into opportunities for market and product expansion. Since organic growth initiatives are central to PE value creation, the right data-driven insights enable a portfolio company to:
- Focus efforts on optimizing sales and marketing performance in the highest value markets. (Read more on that here: One Rock Capital: Top Line Growth Strategies)
- Target its most loyal customers through upsell and cross-sell efforts without having to rely on experience or institutional knowledge.
- Gain a higher degree of control over how they can develop relationships with existing clients, target highest value prospects, and surface at-risk accounts.
- Bring clarity to an organization and remove the “fog of war” dynamic that portfolio companies operating in traditional industries so often experience.
However, most middle-market companies fail to use their data as a strategic asset. As a result, they are impeded by a lack of clarity into key growth drivers such as market demand patterns and the impact of marketing campaigns.
Data-driven companies can keep a pulse on these areas, providing significantly more value compared to organizations that are not data-driven. It is much easier for a data-driven company to offer customers a new product when the initiative is based on concrete data and patterns, as opposed to hunches or biasing toward whatever is top-of-mind.
Labor Demand Forecasting
Labor forecasting is an important way for companies operating in commonly targeted private equity sectors such as commercial services to manage capacity utilization. Resources (e.g., service technicians) need to continually track with client demand, so having a pulse on projected and available labor levels is critical.
The following sample dashboard offers labor managers visibility into workforce capacity versus demand. It empowers managers to project labor needs based on past trends, backlog, and forecast. Users can further toggle their view by branch to fine-tune labor planning.
Customer Satisfaction Analysis
A customer satisfaction dashboard is another tool to help portfolio companies boost revenue from their existing customer base. A business can remain more competitive by maintaining continual insight into customer’s ratings around on-time delivery, service quality, and the like. Data-driven analysis takes the guesswork and reactivity out of customer dissatisfaction and strengthens proactive engagement.
The following sample dashboard includes key metrics commercial services operations would use to manage customer experience. The dashboard allows managers to filter by region and branch to see important metrics such as service call aging, fix rates vs. target, and install red flags to identify points of influence that enhance customer satisfaction.
Data Intelligence Drives Inorganic Growth
In addition to organic growth initiatives, data intelligence also supports the “buy” component of the buy-and-build strategy followed by most mid-market PE firms (How the Modern Data Warehouse Supports Buy-and-Build in Private Equity).
Integration of bolt-on acquisitions can more quickly impact value creation by integrating at the data layer rather than waiting for the long process of systems and operational integration. When well-executed, integrating data sources from disparate business units minimizes operational disruption and provides management teams with an integrated view of operations and key metrics.
Since buy-and-build is a commonly used value creation approach and an increasing amount of capital is chasing a limited number of targets, incorporating data intelligence in today’s data-driven world offers an attractive differentiation strategy.
A solid data architecture built on a modern data warehouse enables sponsored companies to remain adaptive and scalable, easing the process of adding new data sources and dashboards, as well as operating centers and functional areas without accruing unnecessary technical debt and creating more noise than signal.
On the other hand, when data integration is mismanaged, companies can end up with more complexity which defeats the economies of scale they are seeking through acquisition.
The Criticality of Assessing Data-Readiness in Diligence Phase
Data is an integral part of the acquisition diligence process for private equity firms and platform companies. With a buy-and-build strategy, data continues to emerge as a primary factor in assessing the viability of bolt-on deals.
Data diligence, however, is still a largely overlooked component of IT diligence. This is surprising as digital transformation is increasingly noted as a value creation vector. Nevertheless, and as noted by EY, many organizations in the private equity arena do not prioritize data and business intelligence in practice or at scale.
In today’s data-driven and digitally focused world, data diligence should be on the same footing as the traditional pillars of diligence.
Benefits of Conducting Data Diligence Through an Expert Partner
Partnering with an expert consultant minimizes disruption to private equity firms and their portfolio companies by incorporating targeted working sessions and independent data discovery. Furthermore, the consultant partner can maximize actionable business recommendations from technical findings and help establish stakeholder buy-in to data transformation strategies.
An expert partner can work as a fully operational extension of deal and management teams. The following illustrates a best practice methodology for assessing a target’s data readiness with minimal disruption to the team.
Upon completion of data diligence, a strategic partner should provide a thorough assessment of the target’s data environment and existing resources, identify gaps, and provide a roadmap to bring the acquisition’s data environment up to standards.
The Benefits of Data Intelligence Post-Acquisition
The output from the diligence phase, including the creation of a company scorecard, helps set the stage for immediate integration and for the development of a data-driven company. Once the company is onboard, data should play a central role in their 100-day plan (see image below) as a means to operationalize strategy and the value creation plan. In other words, while process reengineering and systems integration are important, they are long-term endeavors.
A company can immediately focus its entire team on its highest priorities for greater organizational alignment and accountability. Furthermore, solid data intelligence will become the foundation upon which process reengineering and systems integration can be developed and monitored.
A 100-day data roadmap aligns a portfolio company’s board, executive team, and employees to the highest strategic priorities. The following is an example of 100-day data plan.
The Importance of a Strategic Data Roadmap
Successful data transformation initiatives begin with a north star, and that north star is the Data Roadmap.
Critical elements that make a roadmap effective include:
- Definition of the company’s top operating metrics (less is more: limiting to 8 allows them to be displayed reasonably on a single scorecard).
- Definition of baseline and future state of the company’s key operating metrics. Quantify the gap.
- Definition of key operating metrics for each functional area of the business.
- Definition of the most critical roles for each functional area of the business, and the performance metrics they are accountable to deliver.
- Mapping of the company’s data sources to the key metrics for each functional area. In other words, where do we get the data to surface these metrics?
- Designation of a BI oversight committee, their roles, and meeting cadence (we recommend quarterly).
- A data governance framework. This should be kept simple but allow for ad-hoc data exploration from analysts and power users while limiting the proliferation of extraneous reports, overlapping information, and variable definitions of key metrics.
- A mapping of the sequence of roll out, starting with the lightest lift for the highest impact.
One of the essential goals of the 100-day data roadmap should be to convert what is typically a disparate data ecosystem into a consolidated, managed asset. Equally important, establishing transparency through easy access visibility into the company’s data will shift the culture toward an ownership mentality in every role, a sense of teamwork and mission, and an ethic of accountability and performance.
The Importance of Data Intelligence at Liquidity
It’s no secret that technology companies command the highest valuation multiples. However, a company doesn’t need to be operating in a high-tech sector such as software or social media to benefit from digital transformation.
Data assets are often a collection of spreadsheets, piecemeal databases, and ERPs rather than a strategic, managed asset. As such, portfolio companies are not able to present their data as an asset or even as a differentiator to potential acquirers. Conversely, companies that “productize” their data can increase their valuation and create buyer confidence.
Create Buyer Confidence
The adage “a confused mind always says no” holds true during acquisitions. A buyer may lower an offer, or even walk away from an acquisition if they do not receive ready answers backed by data. On the other hand, a prospective buyer will more easily commit to investing in an enterprise with a strong data culture because of the clarity it provides.
Operate within the Constraints of a 5–7-year Liquidity Window
Private equity firms usually have a limited window to buy, improve, and liquidate an investment before returning capital to investors. Digital transformation is a highly accessible force to create differentiation and increase valuation.
For example, if operational improvements are the primary value creation thesis for an investment, and the fund is anticipating a four-year holding period, dashboards can spotlight and keep and top-of-mind operational inefficiencies and excess costs. This compares favorably to general expense reductions based on subjectivity or guesswork.
By contrast, business intelligence supports portfolio companies’ goal of increasing cash flow earlier in the holding period through data-driven execution, translating into a meaningful boost in IRR at exit.
Using Data Intelligence to Drive Deal Sourcing and Outreach
In addition to portfolio value creation, digital transformation is integral to fund management and operations. Business intelligence can promote a culture of proactive acquisition outreaches by providing an objective view into outreach activity and creating a stronger sense of ownership among deal teams for building relationships with investment bankers and prospective sellers.
In fact, given how digital transformation has moved to the forefront of business, maintaining a data-driven deal sourcing culture can increase the attractiveness of a PE partner by demonstrating control of method and process.
In other words, if a PE firm demonstrates that it values the importance of digital transformation at the fund level, they will be perceived as likely to incorporate the same culture into its investments, helping platforms outpace the competition.
The Importance of Data Intelligence for Portfolio Management and Oversight
Finance and investment banking professionals are traditionally trained to use Excel as a primary analysis tool. Heavy reliance on Excel quickly creates disconnected data sources and static insights. Excel is also limited in its options for advanced analysis. Additionally, mistakes easily proliferate in Excel spreadsheets as users update, alter, hide, and delete values in spreadsheets without change control. When updates are overlooked, mistakes quickly compound.
Finance professionals are realizing the importance of data literacy at an increasing rate, and many are learning the basics of programming, business intelligence, and data visualization.
Business intelligence provides fund management and operations teams with the ability to manage data using filters, KPIs, intra-portfolio company comparisons, and much more. By deploying data intelligence with a tool such as Power BI, a PE firm can obtain consolidated reporting through a single pane of glass for both fund and portfolio company performance.
Data Intelligence for Managing at the Fund Level
Business intelligence provides on-time oversight of consolidated fund performance. Because private equity firms tend to manage more than one fund at a time, it is common for senior partners to spend time searching through disparate reports to gain insight. By contrast, a dashboard allows partners and portfolio managers to view and filter KPIs and visuals by performance across relevant dimensions such as time, fund vintage, and strategy in a single screen.
As seen in the sample dashboard below, users can view the latest performance of four different funds on an aggregated level, or drill down as needed. As a result, actionable insights can be gleaned for key metrics such as total EBITDA, gross margin, and revenue generated across each portfolio for specific time intervals.
Data Intelligence for Managing PE Portfolios
Oversight of portfolio level performance using data intelligence creates transparency and encourages collaboration between the fund, boards, and executive management. Since a lack of visibility often causes increased scrutiny, using business intelligence to inform stakeholders is imperative for reducing the burden of ad-hoc reports and meetings, freeing management teams to focus on improving operations rather than answering myriad questions from their boards.
In the example dashboard below, data can provide comprehensive oversight and otherwise difficult to gain insights such as internal benchmarks between portfolio companies. Sector-focused portfolios will find such insights helpful.
Data Intelligence to Drive Fundraising
Business intelligence is a powerful tool to help PE funds drive and track the fundraising process. PE firms often manage fundraising stages and performance through Excel spreadsheets.
As the firm adds new funds over time and looks for commitments from previous and new investors, managing and maintaining outreach activity and the investor pool can become inordinately cumbersome. A lack of information on the status of prior, existing, or potential investors is a well-known and frustrating problem during outreach.
This issue can be alleviated through up-to-date dashboards that help fund personnel easily track existing investors, previous investors, and new investors by important dimensions such as stage, class, amount of intended investment, and investment history. Just as data intelligence aids the management of deal sourcing and origination, providing fundraising personnel with data intelligence promotes accountability and collaboration while keeping investors from falling through the cracks.
Data Intelligence to Improve Investor Relations
Investor relations is another area that PE firms can benefit from data insights. Performance data can take hours and even days to become available to investors. This problem is compounded by disparate data sources, reports, and inconsistent metrics. Data intelligence helps fund operations respond quickly to limited partner inquiries.
With the right dashboards, IR team members can overcome the burden of inconsistent information and variable methodologies for calculating common metrics such as IRR or MOIC. If their questions are answered quickly and accurately, limited partners will gain confidence, increasing the odds they will participate in future deals.
Additionally, business intelligence provides an automated way for PE firms to produce impactful, intuitive, and standardized quarterly reporting.
When to Use In-house Resources vs. Third-Party Consultants
For data transformation efforts, engaging an expert consultant with experience in middle-market, PE-backed companies provides many advantages. Benefits include industry experience helping companies in traditional industries like healthcare, manufacturing, and commercial services, become data-driven.
Private equity firms can leverage a data intelligence partner throughout their investment lifecycle including target acquisition data diligence, portfolio company value creation, liquidity event support, and fund and portfolio reporting.
Why Power BI?
Power BI is the market leader in business intelligence according to Gartner, Forrester, and other credible sources. Other platforms such as Tableau and Qlik are also fully capable of delivering data insights. However, one of the primary advantages of Power BI is its integrations with the Office 365 suite and all things Microsoft.
Additionally, because Power BI was built on the shoulders of Excel and SQL (IP that had previously been monetized by Microsoft) it is the most cost competitive BI platform among the market leaders. Because of this, PE firms are increasingly incorporating Power BI into their data strategy.
If you’re considering Power BI, please feel free to reach out. Here are some additional resources: