By Ibrahim Kanalici, Head of Solutions NEMEA, SNP Group
After years of economic uncertainty, global dealmakers are finally signalling confidence. Many of the world’s largest financial institutions now see 2026 as one of the most active years for mergers and acquisitions in over a decade.
Legal firm CMS Law reports that half of European M&A will experience rising activity; Deloitte’s M&A Trends Survey for 2026 found that fewer than one in ten executives expect deal values to stall or contract. There’s similar optimism in 2025’s success stories, with Electronic Arts, Chevron, and Nippon Steel amongst the global names engaging in eleven-figure deals over the year.
While on the surface this is good news, executives need to be aware of the risks. Fortune’s study of 40,000 M&A deals over the last four decades reveals that on average somewhere between 70-75% fail. They are often undoubtedly a high-risk growth strategy.
The data debacle
One of the constant themes in these failures is the struggle to effectively consolidate the data estates of each business.
In today’s digital economy, the first and most consequential battleground defining success or failure is dealing with proprietary business data. These companies have often spent decades refining themselves based on demand, performance, and market competition.
This creates unique challenges when it comes to bringing the operational, cultural, and technological qualities of each business together. Businesses entering an M&A dealrarely have simple data estates that are ready to “plug-and-play” into the newly mergedcompany.
There are three critical areas that cannot be ignored when it comes to data migration in M&A. Companies should watch out for wasted value due to delayed integration;regulatory data exposure across jurisdictions; and fragmented data that limits scalability.
Tiptoeing through the data minefield
The first step to help address these issues is to start by untangling data across complicated systems. Systems will have varying requirements, as will the teams that rely on them. Aligning on format, structure, taxonomy and so on is fundamental to success.
Failing to untangle complex data or to understand its nuances can cause risks to the business. These include compromising data integrity, raising costs, and potentially undermining the purpose of the undertaking of the merger in the first place.
The best solution to prevent migration issues as datasets are merged within a new system is to pre-empt them, tackling inconsistencies at the outset.
Establish a clear strategy and a defined, robust data architecture that can completely standardise formats, harmonise structures, and align siloed data into one cohesive system. This is not easy, but you don’t have to do it alone — finding a partner or provider to assist with the infrastructure alignment is highly recommended.
Look for solutions with built-in capabilities for data validation and automation that gives you the most control. This will help with the confidence of the data management during migration, helping to ensure compliance and integrity by minimising both manual effort and errors.
Alleviating business downtime headaches
Once data is standardised and flowing to its rightful home, minimising system downtime becomes vital to the transition process.
Maybe prolonged downtime during a data transformation project sees key business processes grind to a halt. Perhaps a key customer or stakeholder can’t access crucial information at the right time. There’s a potential avalanche of problems negatively impacting employees, customers, and suppliers to avoid. And, while they’re being solved, costs can balloon.
Fortunately, there are several approaches businesses can take to reduce the downtime required for data migration.
One approach is the wave or phased based method, breaking down the process into defined groups of departments or systems. This option is often better for data testing and validation during each of the phases, as it allows natural pauses to assess and improve the process as you go.
The other is more of a ‘big bang’ approach, in which a meticulously detailed plan is executed in one go. This takes less time when done correctly but leaves less margin for error — if something is even fractionally off systems could go down completely.
Fortunately, a compromise exists here. Businesses can take a two-pronged approach to meet expected timelines and maintain business continuity, combining near-zero downtime (NZD) and minimised downtime on target (MDT).
NZD leverages a three-phase data transfer process: an initial data transfer, followed by a swift sequence of delta migrations to move any new or modified data missed in the initial phase, before an agreed and brief downtime window to make any final data system to ensure integrity before finalising.
MDT meanwhile is intended to reduce downtime for all target systems during wave migrations. Businesses can stage the new environment before integration, set up an appropriate production system, identify and deal with duplicate records, and manage the numbering od data points between migration data sets — before a final migration of all operational data completes the process.
In tandem, this allows businesses to reduce the overall timelines for their project by as much as four months. Downtime can be less than 24 hours.
Discerning the businesses data needs
This does, of course, depend on how much data you are migrating.
The mass of business data involved in these projects can reach eyewatering volumes, which can slow down even the most high-performance systems — much to the detriment of the bottom-line.
The key to success here is discerning between the crucial data that the business needs access to at all times, and the pieces that can be deprioritised.
Old transactional data, like completed orders or delivery details, aren’t typically as important as yesterday’s financial reports, for example. In minimising the data that the business ‘actively’ maintains in this way, the main system and linked databases are kept running lean.
This reduces the costs of maintaining and running databases, as well as making use of them more cost-effective in terms of time per query.
2026: the year for M&A
Managing business data will always be a unique challenge for businesses engaging in M&A. Each distinct combination of businesses will have its own journey. But that doesn’t mean there aren’t key principles to follow.
If your team can untangle the data strings, get everything organised, plan carefully and thoughtfully, using the right tools and support — your organisation will be primed for success post-merger. The sooner a merger is completed, the sooner you can get back to your customers.
It’s achievable on your timescale, budget, and needs, no matter how complex. 2026 will be the biggest year for M&A in quite some time, with good reason!
