Data infrastructure companies have become the hottest targets in tech M&A as legacy firms like Meta, Salesforce, and ServiceNow scramble to compete in the AI race. AI software deals now account for 75% of tech M&A value this year, with generative AI spending expected to reach $644 billion in 2025—up 76.4% from 2024.
The big picture: Tech deals represent one of the few bright spots in an otherwise sluggish M&A market, accounting for $421 billion of the $1.67 trillion in global deals announced in the first five months of the year.
- This represents about 25% of total M&A activity, up from 20% last year and 17% in 2023.
- Of tech deals, those involving AI software makers accounted for almost three-quarters of the overall value.
Why this matters: Companies are realizing that high-quality data management is essential for AI success, making specialized data infrastructure firms increasingly valuable targets.
- “AI without data is like life without oxygen, it doesn’t exist,” said Brian Marshall, global co-head of software investment banking at Citi.
- Goldman Sachs Managing Director Matthew Lucas called enterprise data “the most dynamic area in software M&A right now.”
Recent major deals: Several multibillion-dollar acquisitions have closed or been announced in recent weeks, highlighting the urgency around data infrastructure.
- Meta announced a $14.8 billion deal for a 49% stake in Scale AI, a data-labeling company.
- Salesforce plans to acquire Informatica, a data integration company, for $8 billion to better feed data into its Einstein AI system.
- IBM closed its acquisition of DataStax, a data management provider, to help process unstructured data for its AI platform.
- ServiceNow bought Data.world, a data catalogue platform, to better understand business context behind data.
Potential targets: Investment bankers identify several enterprise data companies as likely acquisition candidates in the near term.
- Companies mentioned include Confluent, Collibra, Sigma Computing, Matillion, Dataiku, Fivetran, Boomi, and Qlik.
- These firms help businesses integrate, analyze, and store information more effectively.
- The pool of potential targets is rapidly shrinking as deals accelerate.
What they’re saying: Industry executives emphasize the existential nature of solving data challenges in the AI era.
- “Messy, siloed data has long undermined the attempts of enterprises to deliver on the transformative potential of analytics. Now, with the urgency to deploy effective AI, fixing it isn’t just essential — it’s existential,” said Florian Douetteau, co-founder and CEO of Dataiku.
- “There’s a very strong perception that speed matters a lot, getting there first matters a lot, and that tends to lend itself to doing M&A,” Lucas explained.
The risks: Companies can’t simply dump any data into AI systems and expect good results, as demonstrated by real-world failures.
- Air Canada was found liable in small claims court and forced to refund airfare after one of its AI chatbots gave a customer bad advice due to poor data management.
- “A lot of companies have a huge amount of data, but I think they’re learning that you can’t just funnel every piece of data you have into an AI engine with no organization, and hope that it spits out the right answer,” said Brian Mangino, partner at Latham & Watkins.
Role reversal: Some data companies are becoming acquirers themselves, as seen when Databricks, recently valued at $62 billion, announced plans to buy serverless database manager Neon for $1 billion.
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