Data June 24, 2024
16 min read
How to Simplify Post-M&A Data Migration Projects
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M. Çınar Büyükakça
M. Çınar Büyükakça Peaka / Prolific Polemicist

How to Simplify Post-M&A Data Migration Projects

Mergers and acquisitions (M&As) result in a situation where many of the participating companies' assets must be combined: Workforce, tech stack, physical inventory, and data. Data integration and migration are needed to combine the two companies' data. In this blog post, we will take a look at how this process works and the steps that can be taken to ensure that it achieves the intended goals.

Mergers and acquisitions (M&As) tend to have a cyclical character that is closely related to macroeconomic changes. While periods of reduced economic activity, like the early 2000s recession and the 2007-2008 financial crisis, caused a sudden drop in M&A deals, times of economic growth and optimism, like the mid-2000s and 2010s, saw an uptick in M&As.

A survey by Deloitte revealed that three main reasons drive most M&A deals:

  • Technology acquisition

  • Growing the market share

  • Diversifying the products/ services being offered.

Besides these reasons, dealing with uncertainty can be another one leading to more M&A deals.

2021 saw the peak in global M&A activity, with 58,308 deals amounting to more than $5.2 trillion. These figures denote a 23 percent increase from the number of deals in 2020 and a whopping 60 percent increase from the total amount of deals in the same year.

This surge was a result of the pandemic's impact on the global economy. Faced with unprecedented levels of uncertainty, companies chose to join forces and combine capabilities to weather the storms. For many firms, though, being acquired was simply the only option as they lacked the resources to overcome the challenges imposed by the pandemic.

In addition to the Covid-19 pandemic, another factor in the increase in the number of M&A deals was the recent proliferation of startups. Investors poured billions of dollars into tech startups over the last decade as these companies offered high returns at a time when interest rates were low. The ones that survived among these startups became attractive acquisition targets for bigger companies and tech giants, which were looking for convenient ways to plug holes in their product ranges and acquire new capabilities as quickly as possible. Apple was particularly ambitious in its acquisition strategy in this period, acquiring close to 100 startups in the second half of the 2010s.

The M&A activity subsided to a large extent after the peak in 2021. The number of deals in 2023 was just above two-thirds of what it was two years ago. The total value of deals was also less than half of what it was in 2021.

However, things are looking better in 2024. The boom in the number of AI startups has been driving this last upward trend. Determined to stay competitive in the AI race, tech giants are acquiring startups leveraging artificial intelligence in ingenious ways to solve problems: Cisco has made more than 20 AI-focused acquisitions over the last few years. Apple made 32 acquisitions in 2023 alone, followed by Google with 21 deals and Meta with 18, signaling a consolidation of the market as the technology matures.

Check out this video on the possible trajectory of m&As in 2024:

Key takeaways

  • M&As are cyclical in nature and follow the ebb and flow of the economy.

  • Taking stock of the data in two distinct companies, understanding the relationships between different data sets, breaking down data silos, dealing with legacy systems, and reconciling inconsistent data formats headline the major challenges in post-M&A data migration challenges.

  • By virtualizing legacy data for later reference, companies can significantly reduce the amount of data to be physically moved from one storage location to another.

Mergers & acquisitions in numbers

  • According to Harvard Business Review, between 70 and 90 percent of acquisitions fail.

  • The total value of mergers and acquisitions in China has declined by 75 percent since 2015, when it was $1.038 trillion, dropping to $260.123 billion in 2023.

  • From 1985 to 2016, banking was the leading industry for mergers and acquisitions, with a total value of $5072.24 billion in deals.

Types of post-M&A data migration

There are six main types of data migration and any post-M&A data migration project will involve a combination of one of these main types:

Storage migration

Storage migration refers to the process of moving data from one physical storage medium to another. Most of the time, this is done to replace outdated storage equipment with something better performing. Examples of storage migration include moving from paper to digital documents or from on-premises systems to the cloud.

Database migration

Database migration describes the process of changing the database management system (DBMS) altogether or adding a new database to the ones already in use.

Application migration

Application migration occurs when an organization changes its software vendor. Due to possible compatibility issues, companies try to avoid this as much as possible, preferring to upgrade their current software instead of switching vendors until a vastly superior option surfaces.

Cloud migration

Cloud migration involves moving data from on-premises systems to the cloud or from one cloud environment to another. While the first transition is driven by a desire to achieve scalability, flexibility, and increased accessibility, moving from one cloud vendor to another is usually motivated by cost concerns.

Business process migration

Business process migration usually results from mergers and acquisitions or reorganization activities. These initiatives require moving applications and business-critical data for optimized performance.

Data center migration

A data center is the physical infrastructure comprising servers, computers, network routers, and other types of hardware. Data center migration involves the relocation of this infrastructure to a new facility or moving all digital assets to new servers and storage.

Post-M&A data migration challenges

An M&A deal is one of the most complex transactions one can encounter in the business world. It involves integrating the various business functions of two distinct entities, in addition to the standard legal, financial, and personnel-related challenges found in any significant transaction.

One of the more challenging aspects of an M&A deal is the data migration needed to unify the data repositories of the companies involved in the deal. Post-M&A data migration refers to the process of transferring data from one environment to another in order to unify and harmonize data from the companies that are party to an M&A deal. It is a complicated process that involves taking inventory of the data the companies possess, transforming that data into the required format, and unifying it so the post-M&A entity enjoys a single view of truth.

Taking inventory

The first challenge in post-M&A data migration is to find out where all the data is. Considering hundreds of different apps, software programs, and databases used by an enterprise nowadays, this task can turn into a massive project that can keep engineers occupied for months.

IT people should analyze the various data formats and decide how much of the data will be used by the post-M&A entity. Sifting through the different naming conventions practiced by organizations can really test patience as the data is being prepped for integration.

Data consolidation

In 2023, an enterprise company used 473 SaaS apps on average. Add to that number all the spreadsheets, databases, and legacy systems in use, you are looking at more than a thousand data sources for most companies. The problem is these data sources will act as data silos unless you can make them talk to each other.

Data silos are a scourge on organizations and have to be dealt with properly after an M&A because:

  • They damage data integrity, resulting in fragmented data. Unless synced, this fragmented data causes multiple views of the same phenomenon, which reduces the searchability and discoverability of the data.

  • Data silos come with individual storage, maintenance, and management requirements, which drive up the costs. The redundant effort different departments expend to work with the same data brings about inefficiencies in allocating human resources.

  • Data silos limit collaboration between teams. Working with different versions of the same data creates inconsistencies in analyses and makes it difficult to pursue organizational goals. Productivity takes a plunge when precious time is spent searching for data across different systems.

  • Decision-makers lack a single view of truth in organizations where data silos are prevalent. Without deeper insight into daily operations, they cannot make data-informed decisions.

Handling legacy systems

Companies tend to have various software programs that they have been using for decades. Despite being state-of-the-art at the time of their launch, these legacy systems become obsolete over time, making it difficult for companies to operate and maintain them.

Managing legacy systems becomes particularly problematic after an M&A deal as companies find it hard to tap into the data stored in different on-premises systems. Integrating these systems can be quite challenging. Decision-makers would be well-advised to first pick which systems to integrate and which to sunset as the benefits of data migration, in some cases, may not justify the efforts.

Reconciling inconsistent data formats

Enterprise data tends to be structured in widely different ways depending on the departments and what the data is for. This variety has significantly increased over the last decade with the boom in the amount of data being produced and newly-introduced formats aiming to make the storage and transfer of data more efficient.

This sea change has made post-M&A data integration and migration even more complicated than they were before. Cleaning and prepping the data in various formats and changing the data format to ensure that it maps to the target schema used by the destination platform requires hundreds of hours of engineering work. This is no easy thing to do, considering that IT departments are already stretched thin by having to oversee daily operations and maintenance work.

Post M&A data migration best practices

New technologies have revamped the way data migration is implemented. Cloud, AI, and machine learning are here to help engineers with labor-intensive tasks in the process. AI-powered automation tools can fast-track data mapping between the source and the destination, data transformation, and data validation processes.

Another trend that helps with data migration projects is containerization. With software packed in containers, migrating applications from one environment to another becomes a breeze. A container holds an application with all its dependencies in a self-contained unit, making the application more portable and eliminating the concerns that it may not perform in another environment.

Here are seven best practices you should follow for successful post- M&A data migration:

Run an audit of your data, applications, and systems

Any data migration project should start with an audit of systems, software licenses, and hardware at hand. A data migration project can serve as a good cut-off point to get rid of underperforming legacy systems, unused applications, and obsolete hardware. Simplifying the tech stack prior to data migration can reduce complexity, minimize the risk of errors, and accelerate the project.

See data migration as a company-wide process

Data is a shared asset for an organization. Therefore, it is critical to talk to the stakeholders across different departments about the scope of the changes they can expect and their required deliverables while giving them the chance to provide feedback. Talking to the actual users of departmental data can be a good source of insight for IT people as they work to decide which systems, applications, and data sets to migrate and which to sunset.

Clean the data

Corporate data gets corrupted over time as people work on it, convert it from one format to another, and generate copies of it. Incorrect and corrupted pieces of data should be removed before the start of a data migration project. Make sure that the data you are going to migrate is of high quality, that is, correctly formatted, accurate, complete, and relevant.

Back up your data

Companies should ensure that reliable copies of their data are ready before the migration starts. These copies will be used to restore the systems in case something goes wrong. Extra care should be taken while taking backups of business-critical and sensitive data.

Do not upgrade your systems during data migration

Any updates to the systems and applications should be done before the data migration starts. Updating the systems during the migration can cause compatibility issues and data loss.

Follow a phased approach

A one-and-done type of data migration can disrupt daily operations. A phased approach with goals set for the end of each phase makes data migration more manageable. Migrating the critical applications first, like IBM did after it acquired Red Hat in 2019, performing the migration in stages, and running tests at the end of each stage help contain possible problems and prevent compounding effects.

Establish a framework for data governance

Providing governance and guidelines before, during, and after a data migration is key for resolving problems. Data governance frameworks enable organizations to determine the rules of data ownership, lay out how data is to be created and consumed, and describe the data management practices to be followed. By ensuring that data is complete, consistent, accurate, relevant, valid, timely, and unique, data governance upholds data quality throughout the data lifecycle.

Using data virtualization to simplify post-M&A data migration

Today, most data migration projects involve moving databases, data warehouses, and analytical workloads to the cloud. Companies prefer migration to the cloud because they want to achieve scalability, reduce costs associated with operating on-premises and legacy systems, or start using cloud-optimized platforms.

Data virtualization is an interesting option that can be used in combination with data migration or replace the latter completely in some cases. Data virtualization establishes a virtual layer over diverse data sources and facilitates data integration without having to copy or move the data. It makes it possible for organizations to unify their fractured data without any back-end processes to restructure the data and helps keep complexity low without burdening the IT people with ever-growing maintenance responsibilities. Thanks to data virtualization, users can query the data in different formats regardless of where it resides. This capability enhances accessibility and usability, making it easier to run an audit of the data assets, catalog them, and analyze the relationships between them before starting a data migration project.

Most organizations follow the well-trodden path of establishing point-to-point integrations between different data sources after a post-M&A data migration. However, this decision is usually wrong, as each point-to-point integration entails custom coding, which is hardly practical in an enterprise with hundreds of different applications. Building those integrations one by one is a challenge for any enterprise, regardless of the size of the IT department. Maintaining them for years to come is a whole other story.

Like technical debt swept under the rug until it becomes too difficult to ignore, there comes a point where point-to-point integrations start to break, and IT teams become overwhelmed with maintenance tasks. Each new integration added to the system increases complexity exponentially and requires refactoring of existing components for smooth operation. Things can easily spiral out of control under these conditions, leaving the IT department paralyzed within a few months.

Data virtualization eliminates the need to build point-to-point integrations between systems, applications, and databases that are so commonly used to stitch systems together in a post-M&A data migration. It offers the flexibility needed in an enterprise environment where new components can be added to or removed from a system without disrupting other integrations.

Post-M&A data migration content to consume


The increasing pace of digital transformation has made data migration inevitable for healthcare organizations. However, medical data migration projects can pose a significant challenge due to the complexity of the data landscape in such organizations and the rules and regulations at play. Adopting a systematic approach to medical data migration, leveraging specialized data migration tools, prioritizing data security and compliance, and following the rules and regulation go a long way toward successfully migrating data.

Peaka uses data virtualization to bring data together from a wide range of databases, data warehouses, SaaS tools, and APIs. Peaka’s innovative zero-ETL approach eliminates brittle ETL pipelines, allowing users to access and query data wherever it resides without any maintenance or data teams involved.

With Peaka, companies can virtualize their legacy data for later reference and migrate just a minimal amount of data, simplifying the gigantic task of data migration by multiple orders of magnitude.

Book a free demo and discover how Peaka can simplify your data migration project!


Frequently Asked Questions

<p>AI, machine learning, AI-powered automation, containerization, and data virtualization are the recent technologies that can help simplify different aspects of post-M&A data migration.</p>
<p>Data virtualization establishes a semantic layer over different data sources, making data searchable and accessible. This simplifies the process of taking stock of data to be migrated. Thanks to data virtualization, organizations can virtualize the bulk of their legacy data to be accessed when needed and minimize the amount of data to be physically moved.</p>
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