Databricks and Salesforce are doubling down on data acquisitions to fuel enterprise AI. The latest deals: Databricks dropped $1 billion for Neon, and Salesforce shelled out $8 billion for cloud management firm Informatica. More deals are on the horizon.
These buyouts aim to patch holes in the data stacks that companies need to boost AI adoption. The value of AI hinges on data quality — enterprise VCs told TechCrunch in a December 2024 survey that data quality is key to AI startup success.
Gaurav Dhillon, former Informatica CEO and current SnapLogic head, told TechCrunch: 
“There is a complete reset in how data is managed and flows around the enterprise,” Dhillon said. “If people want to seize the AI imperative, they have to redo their data platforms in a very big way. And this is where I believe you’re seeing all these data acquisitions, because this is the foundation to have a sound AI strategy.”
Dhillon warns that none of these companies were born post-ChatGPT. The AI market is only three years old and enterprise retooling remains a big challenge.
“Nobody was born in AI; that’s only three years old,” Dhillon said. “For a larger company, to provide AI innovations to re-imagine the enterprise, the agentic enterprise in particular, it’s going to need a lot of retooling to make it happen.”
The data sector is wildly fragmented, making it ripe for consolidation. From 2020 to 2024, over $300 billion poured into data startups across 24,000 deals (PitchBook). The patchwork of narrowly focused tools doesn’t cut it for AI that needs to scan vast data webs.
Fivetran’s recent pick-up of Census overcomes one glaring gap. Fivetran moved data into cloud warehouses but couldn’t move it back out — Census fixes that. CEO George Fraser explained:
“Technically speaking, if you look at the code underneath [these] services, they’re actually pretty different,” Fraser said. “You have to solve a pretty different set of problems in order to do this.”
Sanjeev Mohan, ex-Gartner analyst, calls this patchwork a key driver of consolidation.
“This consolidation is being driven by customers being fed up with a multitude of products that are incompatible,” Mohan said. “We live in a very interesting world where there are a lot of different data storage solutions, you can do open source, they can go to Kafka, but the one area where we have failed is metadata. Dozens of these products are capturing some metadata but to do their job, it’s an overlap.”
Startups are also feeling the squeeze. Funding is tight so acquisition offers a lifeline. PitchBook analyst Derek Hernandez told TechCrunch:
“If Salesforce or Google isn’t acquiring these companies, then their competitors likely are,” Hernandez said. “The best solutions are being acquired currently. Even if you have an award-winning solution, I don’t know that the outlook for staying private ultimately wins over going to a larger [acquirer].”
“So I think, kind of both sides are very incentivized to get to the finish line on these. And I think Informatica is a good example of that, where even with a bit of a haircut from where Salesforce was talking to them last year, it’s still, you know, was the best solution, according to their board.”
But will this acquisition spree actually deliver AI breakthroughs? Dhillon remains skeptical.
“I think a lot of the value is in merging the major AI players with the data management companies,” Hernandez added. “I don’t know that a standalone data management company is particularly incentivized to remain so and, kind of like, play a third party between enterprises and AI solutions.”
The race is on. Data giants are being stitched together to power enterprise AI, but the stitching might need a lot more work.
