Data disasters and how to avoid them: a MOPs guide to post-M&A hygiene

Introduction: When data becomes collateral damage

Mergers and acquisitions are high-stakes moves designed to unlock growth, expand market share, or streamline operations. But buried under the headlines and handshake photos is the unglamorous reality: data chaos.

Marketing Operations (MOPs) professionals often inherit this mess. Disparate systems, duplicate records, mismatched models, missing consent flags - it all creates a perfect storm of bad decisions and broken customer experiences if not handled right. While strategy, branding, and integration get the boardroom attention, it’s the unseen data layer that makes or breaks post-merger success.

This article is a deep dive into post-M&A data hygiene: What it is, why it matters, and how to do it right. It covers the four pillars of MOPs-led data recovery and hygiene:

Governance
Mapping
Validation
Cleansing

And yes, we’ll also walk through the disasters that happen when these are ignored.

Data governance: laying down the law

Why it matters

Post-M&A, data governance isn’t just a compliance checkbox - it’s a risk mitigator, a trust builder, and a sanity saver. Without strong governance, your unified MarTech stack becomes a Frankenstein of conflicting standards, undefined ownership, and liability minefields.

What good governance looks like

Ownership clarity: Every dataset needs an owner, especially when multiple teams are involved.
Documentation: Define data types, standards, usage rules, access rights, and lifecycle policies.
Policy alignment: Harmonize policies from both companies to ensure legal compliance across geographies and industries.
Steering committees: Form a cross-functional team to oversee ongoing data decisions and resolve conflicts.

Pitfalls to avoid

Assuming one company’s policies will automatically take precedence
Letting IT own governance without marketing input
Neglecting governance for shared systems (CRM, CDP, MAP)

Governance isn’t sexy, but it’s the backbone of sustainable integration.

Data mapping: Connecting the chaos

Why it matters

You can’t unify what you don’t understand. Mapping is about creating a clear inventory of fields, formats, relationships, and flows across your systems. It’s the first step toward integrating meaningfully - not just technically.

What mapping entails

Field matching: Align first name to first name, job title to job title—and know where they don’t match.
Field definition: Understand how each field is used, not just what it’s called. ("Lead Source" may mean wildly different things across systems.)
Data lineage: Document where data originates, how it flows, and where it’s stored.
System roles: Know which systems are sources of truth, which are derivative, and which are deprecated.

Tools and tactics

Schema comparison tools (e.g. Talend, Informatica)
Field-level audits
Data flow diagrams
Workshops with key users to uncover tribal knowledge

Mapping is tedious—but skipping it guarantees bad integrations.

Validation: Trust but verify

Why it matters

Post-merge systems may look unified on the surface, but they can hide deep inconsistencies. Validation ensures your mapped and migrated data is not just present, but accurate, relevant, and usable.

Validation best practices

Sample-based QA: Run audits on subsets of records across systems to confirm consistency.
Business rule checks: Validate that scoring models, segmentation logic, and lifecycle stages still function correctly.
User testing: Bring in marketers and sales reps to test real workflows.
Volume monitoring: Watch for spikes or dips in activity that could indicate pipeline blockages.

Key questions to answer

Is the right data arriving in the right system at the right time?
Are marketing workflows firing as expected?
Is sales seeing the same customer truth as marketing?

If not validated, even clean data is useless.

Cleansing: Removing rot at the root

Why it matters

Cleansing is where the real hygiene happens. All the governance, mapping, and validation in the world won’t help if you’re sitting on outdated, duplicate, or irrelevant data.

Core cleansing activities

Deduplication: Use AI or rules-based tools to consolidate records
Normalization: Standardize field formats (job titles, phone numbers, countries, etc.)
Obsolete data removal: Delete or archive records that no longer meet quality thresholds
Consent flag alignment: Update or remove contacts without compliant opt-in
Enrichment: Add missing firmographic or behavioral data to enhance records

Tools and tech

Data cleansing software (e.g. Openprise, Ringlead)
CRM/MAP dedupe tools
Custom scripts for batch cleansing
Data brokers for enrichment

Cleansing is an ongoing discipline, not a one-time event. Build it into your post-merger roadmap.

Real-world data disasters

The misaligned country field: One company used "US" while the other used "United States." Result: segments broke, emails went out to the wrong regions, and privacy violations occurred.
Duplicate CRM records: Sales teams unknowingly worked the same accounts twice. Customers received multiple outreach emails and churned.
Consent chaos: Half a million records lacked GDPR flags after a merger. Emails went out. Fines followed.

Final thought: Integration is a hygiene test, not a tech test

Technology won’t save you if your data hygiene sucks. What matters is the discipline behind how data is governed, mapped, validated, and cleansed.

For MOPs teams, post-M&A is your moment to lead - not just to clean up, but to shape how the new marketing engine runs. Because when the data is clean, compliant, and aligned, everything else becomes easier: campaigns, segmentation, lead flow, reporting, sales handoffs, and customer experience.

The tech stack might merge in a few months. But the data battle? That’s yours to win every day.

Need help navigating a messy M&A integration? Get in touch for a MarTech assessment built for the chaos.