Case Study: Investor Due Diligence — Separating Data Reality from the Investment Narrative

Context: A Transaction Where Data Became a Risk Question

The transaction was progressing smoothly on the surface.

A mid-market company was entering late-stage discussions with a private equity investor. Commercial performance was strong, management was credible, and the equity story was coherent. As part of confirmatory due diligence, the investor requested a review of the company’s “data capability” — specifically whether it was robust enough to support the growth, integration, and reporting expectations implied in the investment thesis.

Management presented confidently:

  • A documented data strategy
  • A multi-year roadmap
  • Examples of executive reporting
  • References to analytics maturity and automation

From an investment perspective, however, the question was narrower and more consequential:

“Is this data strategy operationally real, governable, and aligned to how the business actually runs — or does it exist primarily at presentation level?”

An independent assessment was requested to answer that question without promoting further implementation.


Why the Investor Required an Independent View

The investor was not seeking validation of tools, platforms, or architecture.

Their concerns were more fundamental:

  • Would post-acquisition reporting withstand scrutiny?
  • Could performance be measured consistently across business units?
  • Was management relying on data that could not be defended under pressure?
  • Would growth or integration amplify hidden data risk?

Crucially, the investor wanted assurance that future value creation plans were not dependent on fragile or ambiguous data foundations.


Scope of the Independent Assessment

The review was explicitly positioned as non-delivery, non-implementation work.

It focused on four investor-relevant questions:

  1. Is the data strategy grounded in how the business actually operates?
  2. Are decision rights and ownership clearly defined?
  3. Is governance proportional to financial and regulatory risk?
  4. Does reported performance align with operational reality?

The goal was not to score maturity, but to identify exposure.


Key Findings

1. Strategy Existed — But Decision Alignment Did Not

The company had a well-articulated data strategy, but it was largely framed around capability aspirations rather than executive decisions.

Findings included:

  • No explicit mapping between strategic KPIs and leadership decision-making
  • Metrics described as “critical” without clarity on who approved definitions
  • Different executives using the same metrics for different purposes

From an investor perspective, this meant:

  • Reported performance could be challenged post-close
  • Management alignment relied on trust rather than authority
  • Integration assumptions were fragile

The strategy looked credible on slides, but thin under pressure.


2. Operating Model and Data Model Were Misaligned

The business operated with significant decentralisation:

  • Autonomous business units
  • Localised operational decision-making
  • Variation in commercial models

However, the data strategy assumed:

  • Standardised definitions
  • Centralised control
  • Uniform reporting expectations

This mismatch created hidden risk:

  • Local performance views differed materially from group reporting
  • Adjustments were made manually to “align” results
  • Executives accepted reconciliation as normal

For the investor, this signalled future friction during scale or integration.


3. Governance Was Implicit, Not Defensible

Governance responsibilities were described informally:

  • “Finance owns the numbers”
  • “Operations owns the drivers”
  • “Technology supports the data”

In practice:

  • No executive had clear accountability for end-to-end metric integrity
  • Escalation paths were unclear
  • Disputes were resolved through negotiation rather than authority

This raised a red flag:

Governance worked only while relationships held.

Under acquisition pressure, reporting scrutiny, or leadership change, this would not scale.


4. Value Creation Assumptions Depended on Data That Was Not Yet Stable

The investment thesis included:

  • Margin optimisation
  • Cross-unit performance comparison
  • Faster, more granular reporting

The assessment found that:

  • Margin definitions varied across units
  • Cost allocation logic was inconsistently applied
  • Historical data required adjustment before comparison

This did not invalidate the thesis — but it changed its risk profile.

The investor concluded that some value levers were conditional, not immediate.


What the Investor Took From the Review

The outcome was not a deal breaker.

Instead, the review allowed the investor to:

  • Adjust valuation assumptions
  • Re-sequence post-acquisition priorities
  • Separate data stabilisation from data enhancement
  • Protect downside risk without derailing the transaction

Most importantly, it replaced confidence based on presentation with confidence based on reality.


What Changed Before Close

Before signing, several non-technical decisions were made:

  • Executive ownership was assigned to a small set of investor-critical metrics
  • Reporting definitions were clarified and documented at leadership level
  • Known data risks were explicitly acknowledged in the integration plan
  • Expectations around reporting speed and precision were recalibrated

No systems were changed. No tools were selected. No transformation programme was launched.

What changed was investment certainty.


Why Independence Was Essential

The credibility of the assessment depended entirely on its independence.

It did not:

  • Sell remediation
  • Recommend platforms
  • Justify prior investments
  • Optimise for future spend

This allowed both management and the investor to engage honestly, without defensiveness or signalling risk.


Case Study Takeaway

For investors and private equity firms, data strategy due diligence is not about maturity or sophistication.

It is about:

  • Whether performance can be trusted
  • Whether governance will hold under pressure
  • Whether value creation assumptions rest on stable foundations

In this case, the independent assessment did not change the deal.

It changed how the deal was understood.

That difference mattered.


This case study illustrates how independent advisory can help investors distinguish between presentation-level narratives and operational data reality. If you are evaluating data risk, governance, or value creation assumptions in a transaction, these resources may be helpful:

  • Enterprise Data Strategy — Executive guidance on data governance, ownership, and operating models, with a focus on leadership decisions and risk that directly affect investment certainty
  • How It Works — The evaluation framework used to assess data, governance, and automation decisions before capital is committed
  • Data Strategy Advisory — Strategic advisory for investors, boards, and executive teams navigating data risk, governance, and complex operating models
  • Failed Data Automation Initiative — A complementary case study on restoring decision confidence after a data platform investment failed to deliver outcomes

If you are in the middle of a transaction, or planning one and want to understand how data risk could affect value and governance, get in touch to discuss how independent advisory can support your due diligence process.