In private equity, value creation is often framed around cost control, operational improvement and strategic growth. Yet one of the most powerful assets inside most portfolios is still routinely overlooked: data.

Across many PE-backed businesses, data is treated as a reporting byproduct rather than a strategic driver. It exists to satisfy board packs, lender requirements and regulatory returns. Rarely is it viewed as an asset that can actively shape decisions, accelerate integration or unlock new sources of value.

That gap is becoming harder to justify.

As portfolios grow more complex, markets more volatile and AI more accessible, the firms that understand and invest in their data foundations will create a material advantage by 2026.

The due diligence blind spot

Technology and data are rarely absent from due diligence. Most transactions include some form of technical assessment, platform review or cyber evaluation.

The issue is misalignment.

Due diligence often focuses on identifying risk rather than potential. Data is assessed for compliance, security and basic availability, but not for its ability to support growth, integration or automation post-deal. As a result, investment committees may sign off on a deal without a clear view of whether the data estate can support the value creation plan.

Key questions are left unanswered. Can data be integrated quickly across acquisitions? Is it structured well enough to support advanced analytics? Can it be trusted at scale? These gaps surface later, when timelines are tighter and expectations higher.

Siloed systems, fragmented truth

This challenge is magnified in buy-and-build strategies.

Portfolio companies often operate with different ERP systems, CRM platforms, operational tools and reporting standards. Data lives in silos, shaped by local processes rather than group-wide needs. Even when reporting is centralised, it is often stitched together manually, brittle by design and slow to evolve.

The consequence is familiar. Leadership teams struggle to get a single, reliable view of performance. Integration programmes take longer than expected. Synergies are harder to prove. Decision-making relies more on experience than evidence.

Data exists in volume, but not in a form that compounds value.

From reporting artefact to value engine

The firms that break this pattern treat data engineering as an early value creation lever, not a back-office clean-up exercise.

By integrating fragmented systems, standardising key data sets and establishing a scalable architecture, data becomes a shared asset rather than a local constraint. This enables faster integration post-acquisition, more confident operational decisions and a clearer line of sight from activity to outcome.

This foundation is also what makes AI viable at portfolio scale.

AI value depends on data readiness

AI is increasingly referenced in PE value creation plans, from operational optimisation to pricing, forecasting and automation. But AI does not create value in isolation.

Without connected, governed and accessible data, AI tools remain shallow. Productivity gains are limited to individuals rather than embedded across operations. Insights are inconsistent. Automation stalls at the edges.

When data foundations are in place, the picture changes. AI can be grounded in enterprise data, connected to workflows and used to coordinate action rather than just generate output. This is where measurable performance improvement emerges.

By 2026, the differentiator will not be who has experimented with AI, but who has engineered their data estate to support it reliably at scale.

Data engineering as a PE growth capability

In our work with investor-backed businesses, we see the strongest outcomes when data is treated as a strategic asset across the full investment lifecycle.

Early on, it supports technical due diligence and de-risks integration. During ownership, it enables operational insight, automation and faster decision-making. Approaching exit, it strengthens the equity story with credible, consistent evidence of performance and scalability.

This requires more than dashboards. It depends on integrating disparate systems, improving data quality, designing architectures that can evolve, and embedding governance that supports growth rather than slowing it down.

These are engineering challenges with commercial consequences.

Turning undervalued assets into advantage

Data already exists inside most portfolios. The opportunity lies in how it is connected, structured and activated.

As AI becomes a practical lever for value creation in 2026, firms that have invested in data engineering and governance will move faster, integrate more effectively and operate with greater confidence. Those that have not will find ambition constrained by foundations.

At Answer Digital, we work with private equity firms and their portfolio companies to build data and AI capability that supports value creation from day one through to exit. From technical due diligence and integration, through to scalable data assets and AI-enabled operations, we help turn fragmented data into a platform for growth.

If data is one of the most undervalued assets in your portfolio, it may also be one of the most powerful.

Talk to our expert PE team today for more.