What is a data strategy and why does it matter at due diligence?

Private equity firms are getting better at spotting technical debt in software. They are getting worse at spotting data debt - and the two are not the same problem.

A business can run on creaking infrastructure and still be fixable. A business whose data is fragmented, ungoverned or structurally dependent on people rather than systems is a different kind of risk. The fixes are slower, the costs are harder to scope and the value creation thesis often rests on assumptions that the data cannot actually support.

Most operating partners review a data room. Fewer review a data strategy. The gap between those two activities is where deal value quietly disappears.

This article explains what a data strategy actually is, why it matters specifically in the context of M&A due diligence and what good assessment looks like at the pre-close stage. If you are evaluating a business where data, analytics or AI capability is part of the investment thesis, this is the framework you need before you sign.

What a data strategy actually is

A data strategy is not a technology roadmap. It is not a list of tools the business uses or a diagram showing how data flows between systems. Those things may form part of it, but they are not it.

A data strategy is a set of deliberate decisions about how an organisation collects, governs, stores and exploits data to deliver commercial outcomes. It answers four questions:

1. What data do we have, and what data do we need?
2. Who owns it, and who is accountable for its quality?
3. How does it move through the business and at what latency?
4. How does it create value - in decisions, in products, in operations?

Most businesses at the £500m–£1.5bn revenue range have answered parts of these questions, usually through accumulated habit rather than deliberate design. They have a data warehouse that grew organically. They have a BI tool that three people in finance use. They have a head of data who reports into IT and has no seat at the commercial table.

That is not a data strategy. That is a data accident that has not caused a serious incident yet.

The distinction matters at due diligence because you are not just buying the current state. You are buying the cost and the time required to move from where they are to where your thesis needs them to be.

Why data strategy is a due diligence issue, not a post-close one

The conventional view is that data capability is an operational improvement - something the operating team addresses in the first hundred days. That view is expensive.

Consider a mid-market B2B distribution business being acquired on a thesis of pricing optimisation and customer lifetime value improvement. Both initiatives require clean, consistent transaction-level data linked to customer records. At close, the acquirer discovers that customer data lives in three systems, two of which are managed by external providers with restrictive data access clauses. The transaction data has an 18-month gap caused by a platform migration. Rebuilding the data foundation takes 14 months and delays the pricing programme by nearly two years.

That is not a post-close operational problem. That is a pre-close valuation problem that was not caught.

The data strategy assessment belongs in the same workstream as commercial, financial and legal due diligence. Not because data is abstract or technical, but because it is increasingly the mechanism through which the value creation plan operates. If the mechanism is broken or absent, the plan is not executable on the assumed timeline.

At minimum, a pre-close data assessment should surface:

1. Whether the business has a defined data ownership structure or whether data quality is person-dependent
2. Whether key commercial data - revenue, margin, customer, product - can be extracted cleanly and independently of specific staff
3. Whether there are contractual, regulatory or technical constraints on data access or portability
4. The maturity gap between the current state and the state required to execute the value creation thesis
5. A credible cost and time estimate to close that gap

The three patterns that destroy deal value

Across deals where data capability is material to the thesis, three failure patterns appear repeatedly.

The key-person data problem. The business has good data, but it lives in spreadsheets maintained by two analysts who understand the logic. There is no documentation, no reproducibility and no governance. One person leaves post-close - which happens frequently after a transaction - and the analytical capability goes with them. This is common in businesses that have grown quickly without building data infrastructure to match.

The vendor lock-in problem. The business runs its analytics through a platform that either owns the data contractually or makes export prohibitively difficult. This surfaces frequently in retail and ecommerce businesses using third-party marketplace or fulfilment platforms, and in SaaS businesses where the product data lives inside a vendor's infrastructure. The commercial data the buyer needs to run the business may not be portably theirs.

The integration illusion. The business reports confidently on its KPIs, but those KPIs are assembled manually each month from multiple source systems by a finance team who know which adjustments to make. The underlying systems are not integrated. The reported metrics are accurate, but they are not automated or scalable. When the business grows or when you try to build BI capability on top, the seams show immediately.

None of these problems are fatal. All of them change the cost structure and timeline of the value creation plan if discovered late.

What good data due diligence looks like in practice

Good data due diligence is not an IT audit. It is a commercial capability assessment with a technical lens.

The right question is not "what systems do they use?" It is "can this business generate the insight it needs to execute the plan you are paying for?"

A structured assessment covers five areas:

1. Data assets - what data exists, at what granularity, over what time horizon, and with what known quality issues
2. Data governance - who owns definitions, who is accountable for quality, and whether that accountability is structural or informal
3. Data infrastructure - how data is stored, moved and accessed, and whether the architecture can support the analytical use cases in the thesis
4. Analytical capability - what the business currently produces from its data, how decisions are actually made and whether there is genuine self-service capability or a bottleneck through a small team
5. Regulatory and contractual exposure - GDPR obligations, data sharing agreements, platform terms that affect portability

This assessment takes two to three days of structured interviews and technical review. It produces a gap analysis tied directly to the value creation workstreams, not a generic maturity score. At Rodan, we run this as a fixed-scope diagnostic that feeds directly into the operating plan.

The output is not a technology recommendation. It is a commercial risk quantification: here is what the thesis requires, here is what the business can currently deliver and here is what it will cost and take to close the gap.

The cost of treating this as an afterthought

Businesses are increasingly valued on their data and AI capability, not just their historical earnings. That means the gap between what a business claims it can do with data and what it can actually do is growing - and that gap is costing acquirers real money.

An operating partner who catches a fundamental data architecture problem pre-close can reprice, restructure or walk away. The same person who catches it twelve months after close is managing a remediation programme that was never budgeted, against a timeline the board did not expect.

The businesses that get this right treat data strategy assessment as a standard line item in the diligence workplan. Not because it always surfaces a problem, but because the ones where it does are the deals that define a fund's track record.

If you are running diligence on a business where data capability is material to the thesis, the right time to assess it is now - not during the first hundred-day review.

Rodan runs focused data due diligence diagnostics for private equity investors and operating partners. Scoped to the deal, priced to the stage. Book a conversation with our team at rodan.io.