Mergers and acquisitions (M&A) promise rapid growth, expanded market reach, and improved operational efficiencies. But they also introduce enormous complexity—especially when organizations attempt to combine systems, processes, and data from two previously independent businesses.

While leadership teams often focus on financial modeling, legal frameworks, and operational synergies, one factor consistently determines whether an M&A deal succeeds or fails: the quality of the underlying data.

Organizations that prioritize clean, governed data during due diligence, integration, and post-merger operations are far more likely to realize the full value of their deals.

Why Up to 70% of M&A Integrations Fail

Despite the strategic promise of mergers and acquisitions, studies consistently show that between 60% and 70% of M&A initiatives fail to achieve their expected value. While leadership teams often attribute these failures to cultural differences or operational challenges, a less visible issue frequently sits at the center of the problem: poor data quality and fragmented systems.

When two companies combine, they bring together:

• Multiple ERP systems
• Inconsistent data definitions
• Duplicate customer and vendor records
• Conflicting financial reporting structures
• Different governance processes

Without a clear data strategy, these issues create delays in integration projects, reduce operational visibility, and make it difficult for leadership teams to measure the true performance of the combined organization.

In many cases, companies attempt to address data challenges after the deal closes—often during large-scale ERP migrations or system consolidation initiatives. By that point, inconsistent or incomplete data can significantly slow integration efforts and increase project risk.

For example, when companies integrate newly acquired systems into a unified ERP environment, they must ensure that all business-critical data—from customers and suppliers to financial transactions—is accurate and standardized. Organizations that prioritize data quality early are able to integrate systems faster, maintain operational continuity, and unlock the value of the deal sooner.

This is why leading organizations are shifting toward a "Data First" approach, ensuring that data quality, data governance, and alignment are addressed before and during M&A integration rather than after problems emerge.

Why do most mergers and acquisitions fail?

Most M&A deals fail to meet expectations because companies underestimate the complexity of integrating systems, processes, and data. Poor data quality, inconsistent reporting structures, and fragmented ERP environments can delay integration and prevent organizations from realizing expected synergies.

Key Takeaways: Why Data Determines M&A Success

• Clean data reduces M&A risk by improving due diligence accuracy and revealing hidden operational issues.
• Data quality accelerates integration, allowing companies to combine ERP systems and business processes faster.
• Trusted data enables faster synergy realization, helping organizations identify cross-sell opportunities, supplier consolidation, and operational efficiencies.
• A Data First strategy prevents costly surprises during post-merger system integration and migration.
• Companies that treat data as a strategic asset during M&A achieve faster time-to-value and lower integration costs.

The Role of Clean Data in M&A Due Diligence

Accurate, trusted data is essential during due diligence, when buyers assess the true value and risks of a target organization.

Clean financial, operational, and customer data allows organizations to:

• Accurately value the business and support a stronger sale price
• Avoid hidden risks caused by incomplete or impaired data
• Identify red flags early, including compliance gaps, reporting inconsistencies, or operational inefficiencies

Without reliable data, due diligence becomes guesswork—turning unknowns into costly surprises after the deal closes.

American Airlines: Data Integration After a Major Merger

Following the merger of American Airlines and US Airways, the combined airline faced the challenge of consolidating HR and payroll data across more than 130,000 active employees and 500,000 historical employee records. The organization migrated multiple legacy systems into a unified SAP SuccessFactors platform while implementing automated data quality rules and governance processes to ensure accuracy and compliance.

Read more about the American Airlines case study

Where Data Determines M&A Success

1. Due Diligence

Clean financial, operational, and customer data helps organizations accurately assess the real value of a selling company.

Conversely, incomplete or poorly governed data can introduce major liabilities after acquisition.

For example, Phillips 66 needed to integrate data from a recently acquired organization into its SAP S/4HANA environment. Before integration could begin, the company had to consolidate legacy ERP systems and ensure that acquired data met strict quality standards. By applying a Data First approach, the organization achieved 100% data load accuracy at go-live, enabling a smooth transition and reducing operational risk.

Read the full Phillips 66 case study

This level of visibility allows acquiring companies to identify risks early and make informed investment decisions.

2. Integration

After a deal closes, integration becomes the most complex phase of the transaction.

Organizations must merge systems, standardize processes, and harmonize data from multiple environments. Poor data quality often leads to project delays, budget overruns, and operational disruption.

Consider the example of a global life sciences organization expanding through acquisitions, which needed to consolidate ERP data from newly acquired companies into a centralized SAP environment. Using Syniti’s data migration methodology, the organization successfully processed more than 35 million records across over 100 data objects, enabling unified reporting and consistent operations across the combined enterprise.

Similarly, British American Tobacco leveraged a data-driven SAP transformation to accelerate value from acquisitions. By cleansing and harmonizing data before migration, the company reduced downtime and ensured a seamless transition across its global systems landscape.

Read the full case study with British American Tobacco 

These examples demonstrate an important lesson: M&A integrations succeed when organizations prioritize data quality before technology transformation.

3. Post-Transaction Value Realization

The long-term value of an M&A deal depends on how effectively the combined organization can operate as a unified business.

Clean, well-governed data helps companies identify operational synergies such as:

• Cross-selling opportunities across shared customers
• Supplier consolidation and procurement optimization
• Market expansion opportunities
• Standardized operational processes
• Improved financial reporting and liquidity

For instance, Bio-Rad Laboratories relied on a Data First approach during a global SAP transformation to ensure 100% data accuracy, a critical requirement for maintaining compliance in a highly regulated life sciences environment.

Read Bio-Rad's full case study

By ensuring data accuracy and traceability across systems, organizations gain the insights needed to unlock the full strategic value of their acquisitions.

A Data First Approach to M&A Success

Successful M&A transactions require more than financial alignment—they require data alignment.

Syniti’s Data First approach helps organizations prepare, govern, and integrate data throughout the entire M&A lifecycle.

By ensuring that both buyer and seller data is accurate, standardized, and business-ready from valuation through integration, organizations can:

• Reduce operational and financial risk
• Accelerate ERP consolidation and migration
• Improve transparency across combined operations
• Identify synergies faster and capture deal value sooner

When organizations treat data as a strategic asset, M&A integration becomes significantly faster, more predictable, and more successful.

Frequently Asked Questions About Data in M&A

Why is data quality important in mergers and acquisitions?

Data quality is critical during M&A because buyers rely on accurate financial, operational, and customer data to assess the value and risks of the target company. Poor data quality can hide compliance issues, reporting inconsistencies, or operational inefficiencies that create costly problems after the deal closes.

What are the biggest data challenges during M&A integration?

The most common data challenges include:

• Inconsistent data structures between companies
• Duplicate or incomplete customer and vendor records
• Legacy systems that cannot easily integrate
• Lack of data governance or ownership
• Limited visibility into historical transactions

Without addressing these challenges early, M&A integration projects often experience delays and increased costs.

How can companies reduce risk during M&A data integration?

Organizations can reduce M&A risk by adopting a Data First approach, which focuses on:

• Assessing and cleansing data during due diligence
• Establishing governance and ownership of critical data objects
• Standardizing data structures before system migration
• Implementing automation for data validation and integration

This approach ensures data is trusted and usable before systems are combined.

What role does ERP migration play in M&A?

ERP migration often becomes necessary when organizations consolidate systems after a merger or acquisition. Clean, harmonized data is essential for successful ERP migration because inaccurate or incomplete data can disrupt operations, financial reporting, and supply chain processes.

Ready to Improve M&A Outcomes With Data First?

Learn how putting Data First can drive better M&A outcomes and pave the way to efficiency and growth across your enterprise.