Tailored data diligence is critical to the success of private equity investments.
Data analytics and IT diligence facilitate data accuracy, completeness and reliability.
A well-structured data strategy enhances value at every stage of the PE lifecycle.
Private equity (PE) firms operate within tight timelines to execute business strategies and transform assets during the holding period. In a dynamic mergers and acquisitions market, influenced by various internal and external factors, targeted data diligence can provide firms with a competitive edge.
PE investors should aim to add a data analytics component into their IT due diligence process to evaluate data quality within the target company, assess growth feasibility and identify gaps in revenue strategies. By understanding how a target company governs and leverages its data, firms can gauge organizational maturity and capital requirements to realize their investment thesis faster.
Achieving the full potential of an investment thesis requires the seamless integration of strategy, technology and data. Poor data quality can undermine informed decision making, misdirecting efforts in critical areas like customer acquisition, pricing optimization and cost reduction. In contrast, robust data capabilities drive growth, innovation and higher valuation upon exit.
A well-structured data strategy enhances value at every stage of the PE lifecycle. The following chart summarizes the lifecycle stages and how data diligence drives success:
| Lifecycle stage | Role of data | Outcome |
| Sourcing and diligence | Evaluate data quality, gaps and governance | Identify growth potential and risks |
| Pre-close | Model investments and prepare for integration | Set the stage for transformative initiatives |
| Post-close transformation | Implement data-driven strategies, with a focus on the first 100 days post-close to define a roadmap and accelerate value creation | Accelerate growth and optimize performance |
| Exit planning | Standardize and enhance data for valuation | Maximize value and ensure smooth transitions |
Middle market companies often face challenges in data governance and analytics due to limited resources and experience. After being acquired, these portfolio companies may prioritize operational efficiency over data integrity, leading to suboptimal insights and misaligned business strategies. However, a portfolio company’s ability to gather, process, analyze and derive insights from data directly affects its potential to achieve the investment thesis.
RSM’s experience shows that many portfolio companies struggle with poor-quality customer master data, such as incomplete, inaccurate or inconsistent records. These gaps not only hinder strategic decision making but also mean lost opportunities, revenue and client retention. Investing in tailored data diligence can help address these challenges, enabling firms to:
Incorporating a data analytics component with IT due diligence helps ensure that data assets are accurate, complete and reliable. A systematic process for evaluating and enhancing data assets might include:
For ongoing investments, a low-effort data profiling exercise can help identify variations, inconsistencies and gaps within the data. Leveraging advanced artificial intelligence tools to cleanse and standardize the data helps ensure it meets high-quality benchmarks; this ultimately saves time and minimizes errors.
Tailored data diligence is the backbone of decision making and the success factor in private equity investments. By leveraging strategy, technology and data in harmony, firms can confidently realize their investment thesis faster, drive sustainable growth and achieve higher returns. In today’s fast-evolving M&A landscape, data diligence isn’t just an asset, it’s a competitive necessity.
