Data in M&A: due diligence, valuation and post-merger integration

Deals fail because of data more than they succeed because of it. This is one of the most under-discussed dimensions of M&A, and one where CDOs can add enormous value.

The three phases of M&A data work

Phase 1: Pre-Deal Data Due Diligence

Before the deal closes, the acquiring team needs to understand what data assets they're actually buying, and what data liabilities they're inheriting.

A thorough Data due diligenceData due diligenceAudit systématique de la qualité, la gouvernance, la sécurité et la valeur des assets de données d'une entreprise dans le cadre d'une acquisition ou d'un partenariat. covers:

• Data qualityData qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète →: Is the target's customer data accurate, complete, and up to date? Poor Data qualityData qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète → in a CRMCRMCustomer Relationship Management: software and strategy to manage and analyse customer interactions throughout their lifecycle.Voir la définition complète → can mean 30-40% of "customers" are duplicates, inactive, or inaccurate, directly impacting revenue projections.
• Data architecture: What systems exist? What's the integration complexity? Can data be migrated to the acquirer's stack, and at what cost?
• Governance and compliance: Is the target GDPR-compliant? Are there pending regulatory actions related to data? Do Data processing agreements (DPAs) exist with all key partners?
• Data lineageData lineageData lineage maps how data moves and transforms across systems, from origin to consumption, showing where it came from, what changed it, and where it goes.Voir la définition complète →: Can the target demonstrate where its financial data comes from? For regulated industries, this is often a deal-breaker.
• Intellectual property: Are data models, algorithms, and datasets clearly owned by the company? Or are there third-party datathird-party dataData purchased from external aggregators, collected from audiences you don't own. It is bought or licensed rather than gathered through your own direct relationships.Voir la définition complète → dependencies that could be revoked post-acquisition?

The standard M&A due diligence process often gives data 10% of the attention it deserves. For data-rich businesses, it should be 40-50%.

Phase 2: Data as a Valuation Driver

Data assets can dramatically affect acquisition price. The Microsoft-LinkedIn deal is canonical: LinkedIn's 2016 revenue was approximately $3B. Microsoft paid $26.2B, a 9x revenue multiple considered extraordinary at the time.

The justification: LinkedIn's data on 430M+ professionals was worth significantly more than the revenue line suggested. Microsoft was acquiring the largest professional behavioral dataset in the world, along with the network effects that made it self-reinforcing. The premium over book value was largely payment for data.

When Google acquired Fitbit for $2.1B in 2019, the strategic logic was primarily about health data, 100M+ users' biometric data over years, not Fitbit's declining hardware revenue.

Phase 3: Post-Merger Data Integration

This is where deals most often go wrong.

McKinsey research shows 70% of M&A deals fail to deliver promised value. Data integration failures are among the most common causes. Two organizations with different data architectures, different definitions of "customer," and different Data qualityData qualityThe degree to which data is fit for purpose: accurate, complete, consistent, timely, valid and unique. Poor quality data undermines analytics, reporting and AI.Voir la définition complète → standards are told to share data on Day 1.

What typically happens:

• Customer data from both organizations can't be merged due to duplicate records, different schemas, and incompatible formats
• Financial reporting becomes a nightmare because the same metric means different things in each system
• Sales teams lose access to key customer data during the transition period
• Data governanceData governanceData governance is the set of policies, roles, and processes that ensure data is accurate, secure, well-defined, and used responsibly across an organization. policies clash, creating compliance gaps