70 % to 90 % of M&A deals fail to deliver expected value—and data integration is a leading culprit1
KPMG reports that over 40 % of M&A data-migration projects encounter serious issues—and the average budget overrun is $0.3 million per dataset.2
Oracle found that 83 % of data-migration projects either fail or exceed budgets and timelines—on average, overruns are 30 % in cost and 41 % in time.3
Source 7
Why Does Data Migration Matters?
Data is at the heart of an organization in today's digital world. When organizations are engaged in M&A, they are charged with the task of integrating data systems. In 2016, Hewlett Packard Enterprise suffered a $180 million writedown due to issues stemming from a flawed data migration during its IT services merger with Computer Sciences Corp. 4
According to Gartner, 83% of data migration projects either fail or exceed their budgets and schedules. Yet in M&A transactions—where timing, accuracy, and business continuity are everything—data migration remains one of the most underestimated threats.3
Common Data Migration Mistakes
Data migration is not simply a copy and paste procedure. Rather, you are transferring data across multiple programs, systems and formats, each of which has its own difficulties.
- Data Quality Defects: Transferring materials from different systems will result in stranded inconsistency, duplicates, or errors. If it doesn’t get cleaned properly before it migrates, those mistakes will be brought into your new system; this will lead to erroneous analytics and reporting.
- Data standardization issues: Different data definitions or data standards between merging entities can lead to confusion and misalignment that disrupt seamless integration.
- Security and compliance concerns: Data integration that involves sensitive data without preventative security measures can expose the organization to security breaches and potential regulatory sanctions.
Strategies for Effective Data Migration
In a McKinsey study, companies that conducted test migrations before full-scale implementation reported 30–40% fewer post-migration issues and a 20% faster time-to-productivity5
Comprehensive Review: Initiate by having a thorough inventory of your existing data assets. It is not enough to just "know" what database formats, types, and sources, etc. are being migrated, but also the quality of the data.
2. Develop a Detailed Project Plan
Document the full migration life cycle - timelines, people, and resourcing, as well as the risk strategy. AI can help use predictive modelling to identify potential choke points or failure points in the migration based on historical data of a similar data migration project.
3. Data Cleansing, Standardisation (with AI)8
Use AI and machine learning tools for:
- Identifying duplicates using fuzzy matching
- Automatically correcting formatting errors
- Classifying data types and labelling
- Suggesting naming standards
This will improve accuracy and can lead to significant reductions in the time taken to clean the data compared to doing it manually.
4. Pilot Testing
Pilot migration on some of the data. AI can run up to hundreds of migration processes and benchmark the performance of each of them, so that you can determine which placeholders appear to be the most performative while identifying risks that were unintended for the project and hidden incompatibility items in the planning process.
5. Security Procedures
Plan to create encrypted transfer methods, with strictly enforced access controls. Just as important is your ability to provide real time visibility of anomalous access criteria or unusual behaviours during and after the migration. AI can also be applied in supporting compliance with data protection regulations.
6. AI as an Incessant and Immediate Improvement Engine8
Aside from the migration, AI can be used to:
- Monitor post-migration performance
- Continuously identify areas for optimization
- Predict future migration needs or risks based on usage trends
Conclusion
There is an identifiable process around data migration as a component of successful M&A integration. Data migration is almost always underestimated as an important component of a successfully managed integration. With over 80% of mergers failing to deliver expected value, and data integration cited as one of the top 3 reasons6, organizations cannot afford to ignore this risk. Ensuring a secure, clean, and aligned data migration process isn’t a backend task—it’s a front-line determinant of M&A success. In the area of M&A focus, organizations cannot afford to ignore data migration - since not incorporating it can lead to negatively severe events as a risk.
References:
https://hbr.org/2020/03/dont-make-this-common-ma-mistake
https://assets.kpmg.com/content/dam/kpmg/ca/pdf/2025/03/ca-white-paper-on-data-migration-en.pdf
https://www.oracle.com/a/ocom/docs/middleware/data-integration/data-migration-wp.pdf
https://www.winsavvy.com/top-reasons-behind-tech-ma-failures-with-supporting-data/
https://www.altexsoft.com/blog/data-migration/
https://www.pwc.ch/en/insights/digital/ai-in-data-migration.html
