Data is transforming virtually every industry, and private equity is no exception. Private equity firms are beginning to realize the power of data analytics to drive deal flows, inform investment decisions, improve portfolio company operations, and maximize value creation.
But leveraging data effectively—and harnessing its analysis—requires significant effort. Firms must embrace a data-driven culture, invest in technology, develop or source data analytics professionals, and implement data analysis strategies that align with overall business objectives.
This guide explores the benefits of data analytics for private equity firms, the challenges shared by today's PE firms, and the best practices discovered by firms with a handle on data analytics struggles.
How data analytics has changed private equity
Private equity firms no longer solely rely on gut feelings, detailed experience, or financial reports to source deals and manage assets. Today, data analytics helps firms more easily:
Identify high-potential investment targets
Conduct better-informed due diligence
Uncover value creation opportunities post-acquisition
Continuously monitor clients' portfolio performance
Remain competitive in a constantly evolving landscape
Today's PE firms aggregate data from an ever-growing list of alternative data sources, apply advanced analytics techniques, and build centralized data platforms. According to a 2023 survey from Ernst Young , assets under management increased from just over $2 trillion in 2013 to over $4.4 trillion in 2022. Of the PE firms surveyed, 87% expect moderate to accelerated asset growth over the next three years. This growth is expected thanks to improved adoption of data analysis.
A data-heavy focus can help private equity firms achieve tangible results such as:
Faster deal sourcing: PE firms leveraging external data and analytics source more deals and identify candidates faster than those relying purely on internal market knowledge.
Enhanced due diligence: Advanced analytics improves accuracy in due diligence compared to traditional methods, thereby reducing risk and bolstering profits.
Better value creation planning: Firms applying data-driven insight into integration planning have higher average portfolio company EBITDA growth versus those relying on experience alone.
Data and analytics applications span the entire PE value chain—from initial deal sourcing through to eventual exit. The potential for private equity data analytics continues expanding rapidly.
Data and analytics have clear applications across the entire private equity value chain, from initial deal sourcing to exit.
Key benefits for private equity firms
Leveraging data analytics strategically generates a range of commercial and operational benefits, including:
More effective deal sourcing
Analyzing data from across the market allows firms to more easily identify potential targets meeting their investment criteria. Reducing time spent searching manually for deals gives them a competitive edge.
Enhanced due diligence
Assessing cash flows, company culture, supply chain issues, customer churn risks, executive incentives, and more during due diligence allows for better-informed investment decisions aligned to the firm's goals.
Improved value creation
Applying analytics post-acquisition provides tangible opportunities to cut costs, boost revenues, and improve operational efficiencies—thus maximizing portfolio company value.
Ongoing portfolio monitoring
Continuous data analysis enables firms to track KPIs, compare performance benchmarks, and stay on top of market/sector trends. This allows them to rapidly identify and respond to any issues.
Informed decision making
Data analytics equips firms with the timely, relevant, and reliable insights needed to make smart investment choices, governance decisions, and operational adjustments.
Challenges of data analytics in private equity
Despite the clear potential, private equity firms still face hurdles in leveraging data analytics to its full potential. Some of the greatest challenges facing PE firms when embracing data analytics adoption include:
Building a data-focused culture
Implementing a robust data strategy requires significant cultural change. Some teams may simply lack the willingness to move from an intuition-based method to data-driven decision making.
Cost concerns
Quality data, analytics tools, and data talent don't come cheap. The ROI on a data analytics expert or team of experts isn't always immediately evident, making it hard for partners to justify the increased upfront spending.
Data strategy alignment
For private equity firms used to dealing in gut feelings, it's tough to keep data analytics initiatives targeted to clear business goals, rather than pursuing generic analytics activities with unclear outcomes.
Legacy systems limitations
Integrating modern data analytics tools with legacy systems or highly customized platforms poses significant technical barriers for many established PE firms.
Talent shortages
Data analytics introduces a pressing need to fill technical roles such as data scientists and data engineers. Private equity firms haven't traditionally hired for these types of positions, and competition over proven talent is understandably fierce.
Best practices for data analytics adoption success
For most PE firms, these hurdles seem insurmountable. However, firms can overcome these obstacles by focusing on data analytics best practices, such as:
Securing buy-in
Get the firm's partners aligned on data strategy and convince them of the commercial potential. Engagement can be incentivized by tying data objectives to performance evaluation.
Starting small
Run targeted data analysis pilot programs aimed at delivering a rapid ROI, rather than trying to overhaul entire departments and revolutionize operations overnight. Small wins help build confidence in the value of enhanced data.
Upskilling teams
Focus skills development around data literacy and analytics. Make these skills part of everyone's annual training and emphasize their practical application over theoretical applications.
Outsourcing strategically
Working with specialist data partners can help supplement missing skills and data analysis tools as you scale your internal capabilities. No matter what, though, maintaining internal ownership of data initiatives is important.
Centralizing data
Break down data silos via pipelines that feed into a central, cloud-based analytics platform. Housing data in a centralized platform enables and encourages collaboration.
Monitoring impact
Continuously track the impact of your analytics activities through agreed-upon key performance indicators (KPIs). Continuously evaluate your approaches, and discontinue or adjust failing efforts while doubling down on what's working.
A practical partnership for PE firms adopting data analytics
While data analytics adoption remains uneven across private equity, its potential to enhance decisions and performance is compelling.
Firms able to build robust data strategies will gain informational and operational advantages that translate into superior returns over the long term. But realizing this requires addressing cultural and technical barriers.
By securing leadership backing, maintaining a business focus, and emphasizing small, incremental improvements, PE firms can start benefiting from analytics without getting overwhelmed.
What data analytics capabilities does your firm currently leverage? What are the next practical steps to advance your data agenda? Partner with Codal to accelerate and optimize your digital transformation and data analytics adoption efforts.
Our experts are experienced in data-driven, AI-enabled strategies and solutions that will modernize data analysis throughout your business.
