The great migration: data challenges in the year of M&A

In a world where we can often associate business news with bad news, it’s exciting to see that many of the world’s biggest financial organisations expect 2026 to be the biggest year in quite some time for mergers and acquisitions.

 

Tim Ingrassia, co-chairman of global M&A at Goldman Sachs, expects “a record year for deal flow.” Legal firm CMS Law has reported that half of dealmakers expect European M&A activity to increase over the next 12 months. Deloitte’s M&A Trends Survey for 2026 found that barely one in six executives expect the value of deals to stall or decrease. The list goes on.

 

This level of optimism is important as M&A can be an inherently risky game. Fortune’s study of 40,000 M&A deals over the last 40 years found that 70-75% of them fail.

 

One of the biggest reasons companies miss cost-saving goals, post-acquisition growth, or share price expectations is simple: they focus on closing the deal, not integrating for long-term success.

 

For that long-term success, effective M&A integration starts with data. But an organisation’s data is more sophisticated, better protected, and more diverse than any time in history.

 

Not only that, but the type of data and treatment of that data is incredibly nuanced. It can be siloed into too many different containers, providers, or platforms to be able to offer any value. It rarely presents a clear or consistent picture.”

 

Meanwhile, the regulations that protect and shape data change every year, which is only accelerating with the advances of AI. It must be treated differently in every single country in which it resides, always secure and optimised for high levels of performance and end use satisfaction.

 

Any successful M&A is therefore dependent on navigating this minefield. To blend the best of businesses together without error, interruption or rule violation is an incredible undertaking.

 

 

Tackling data inconsistencies head-on

An effective M&A process demands that you take meaningful agency over your data — whether it’s the systems you use, the speed at which you work, or the specific insights that your business depends on. Failing to do so risks delays across the business, serious challenges to data integrity, and higher cost for the project — all of which threaten to undermine it entirely.

 

Firstly, your organisation needs to untangle the deeply complex systems that manage data. Different systems have different requirements; aligning the inconsistencies in structure, format, taxonomy and so on is incredibly important.

 

As you align these systems and migrate the data to where it should be, maintaining the lowest possible downtime is also vital. The market that a business operates within will not stop while your project goes on. The quicker you can return to operations, the less risk the business is exposed to.

 

The volume of data that you are transforming is also a challenge in and of itself. It has a direct impact on costs, processing times, and system performance. This process needs to be as lean and as efficient as possible to maximise performance and minimise costs.

 

Whether you’re migrating a new company into your existing systems, adopting theirs instead, or a mix of the two, all these challenges threaten to be your undoing.

 

The solution will always need to tackle data inconsistencies head on, standardising formats and harmonising structures in order to align the disparate systems across businesses into one cohesive flow. This greatly simplifies a transform journey and can accelerate it — reducing both manual effort and the risk of errors.

 

Picking a partner, provider and infrastructure that has built-in capabilities for data validation and automation without compromising control, leads to confident data migration, ensuring its integrity and compliance throughout the process.

 

 

Diminishing downtime

Traditionally, companies will take one of two approaches to minimising downtime.

 

The first is a wave-based approach, breaking down the migration into multiple phases or waves to minimise the downtime of several departments or systems at once. This minimises disruption while also allowing for more robust testing and validation of each phase.

 

The trade-off is that it does typically take longer than the alternative: the ‘big bang’ approach. Just as it sounds, this is a complete and simultaneous migration of all functionalities at once. Any minimised downtime therefore also happens at once, which makes it easier to make large-scale infrastructural changes — but the preparation for this can take time.

 

However, there is a better way that can mean a transformation project is both delivered within the expected timeline for all parties involved and delivers business continuity. Near-zero downtime (NZD) and minimized downtime on target (MDT) are the way forward.

 

For NZD, businesses are kept operational with a three-phrase data transfer process. Firstly, an initial data transfer is performed while the business remains operational, then a sequence of delta migrations captures and transfers any new or modified data during that initial migration phase. Lastly, a brief downtime window is then agreed to apply any final data changes to ensure the system is fully updated with minimal disruption.

 

MDT reduces downtime for target systems during wave-based migrations. Key components include staging and environment before integration into a production system, identification of duplicate data records in existing operational data, managing data renumbering between the first two stages, and then the final data move.

 

Taking these approaches can mean reduced transformation project timelines, sometimes shaving off four months, and achieving downtime of less than 24 hours.

 

 

Verifying volume

Another challenge with M&A is volume of data that can slow down system performance and decision-making and increase storage costs. The good news is, there is likely a lot of historical or less relevant data that the newly combined system will not need to regularly draw upon.

 

When it comes to managing all this data, the key is distinguishing between what needs to be kept active within your database, and what can be archived until it’s needed to inform decisions and processes. Typically, inactive transactional data is archived, e.g. completed orders, deliveries, or accounting documents, while master data and open transactions remain in the live system.

 

In offloading historical and less frequently used data to an external archive, the main system and corresponding databases are kept as lean as possible. Operations are streamlined and processing can take place as quickly as required, but the information within that archived data can still be called on when needed.

 

 

Year of the merger

These data challenges are complex, and every business will require a unique answer to its circumstances. But they’re not insurmountable — far from it.

 

With careful planning, the right tools, and the right support, the size of the task can be reduced to something that your organisation feels in control of from start to finish. Minimal disruptions to business operations, accuracy and completeness of migrated data, and robust system performance are all achievable. It’s the year of the merger for a reason.

 

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Ibrahim Kanalici is Head of Solutions NEMEA at SNP Group

 

Main image courtesy of iStockPhoto.com and mikdam