With more data than ever before, the question becomes: Where does true competitive advantage in private equity come from?
For decades, competitive advantage in private equity was built on relationships and proprietary networks. Today, nearly every firm — regardless of size or AUM — licenses third-party datasets and tracks opportunities in CRMs.
While these tools are powerful, their widespread adoption has created both abundance and sameness. With more data than ever before, the question becomes: Where does true competitive advantage come from?
The new rules of deal sourcing in private equity
The days of manual spreadsheets and lists are over. As deal teams drown in high volumes of mixed-quality information, the pressure to scale, compete, and meet increasingly demanding limited partnership expectations has never been higher. The industry is on the verge of graduating beyond experimentation into mainstream adoption of AI, machine learning, and next-generation data engineering.
Enter the data engine for private equity
A proprietary data engine is more than just another CRM or data lake. It’s the connective tissue of a firm’s dealmaking strategy — pulling together external and internal data, enriching it, and applying scoring logic and learning models to elevate high-quality opportunities. This technology transforms a firm’s data ecosystem into a treasure trove of firm-specific insights and operationalizes AI in a critical function of the fund.
The benefits of using a data engine
- Integrates and structures data from across your firm
- Applies AI and machine learning to surface actionable insights
- Enables market coverage, origination, and value creation at scale
- Provides a single source of truth for decision-making
Should private equity firms build or buy a data engine?
Firms face three main options for deploying a proprietary engine:
- Build in-house — Offers full control and customization but is expensive, resource-intensive, and slow.
- Buy off-the-shelf SaaS — Fast deployment, a good option for mid-market firms without extensive technical staff but lacks customization.
- Build with a consultant — Combines proprietary data with a productionized backbone and technical experience, enabling tailored innovation and future-proofing for rapid generative AI evolution.
Recommendations for building a data engine with a consultant
To increase ROI and reduce risk, firms should:
- Appoint a dedicated product owner to drive the vision and manage stakeholders
- Design an integrated view of data across all systems
- Establish a forward-thinking beta team for early feedback
- Nail the business case and track KPIs for success
- Prepare for pitfalls between the firm and technical enabler
How to deploy your data engine
Adoption is a journey, not a one-time event. Successful firms deploy intelligence subtly into normal workflows, gather requirements from early users, and codify AI use cases to move from experimentation to execution. Interoperability with CRMs, portfolio monitoring, and fund accounting provides intelligence flows across the firm, not into another silo.
The future is now
Within two years, most leading private equity firms will leverage proprietary engines for predictive insights, faster execution, and greater visibility. Simply licensing more data will no longer be enough; firms must build structured, auditable systems leveraging their specifications. Those who act now can help identify better deals, build conviction faster, strengthen LP relationships, and create more value across their portfolios.
How CLA can help private equity firms with data engines
At CLA, we believe the future of private equity lies in intentional data strategy and AI-enabled origination. Our private equity team is helping firms design and deploy proprietary data engines aligning with their investment thesis, market coverage goals, and portfolio value creation strategies. Contact us to explore your options.