The data industry is on the verge of a drastic transformation.
The market is consolidating. And if the deal flow in the past two months is any indicator — with Databricks buying Neon for $1 billion and Salesforce snapping up cloud management firm Informatica for $8 billion — momentum is building for more.
The acquired companies may range in size, age, and focus area within the data stack, but they all have one thing in common. These companies are being bought in hopes the acquired technology will be the missing piece needed to get enterprises to adopt AI.
On the surface level, this strategy makes sense.
The success of AI companies, and AI applications, is determined by access to quality underlying data. Without it, there simply isn’t value — a belief shared by enterprise VCs. In a TechCrunch survey conducted in December 2024, enterprise VCs said data quality was a key factor to make AI startups stand out and succeed. And while some of these companies involved in these deals aren’t startups, the sentiment still stands.
Gaurav Dhillon — the co-founder and former CEO of Informatica and current chairman and CEO at data integration company SnapLogic — echoed this in a recent interview with 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.”
But is this strategy of snapping up companies built before a post-ChatGPT world the way to increase enterprise AI adoption in today’s rapidly innovating market? That’s unclear. Dhillon has doubts too.
“Nobody was born in AI; that’s only three years old,” Dhillon said, referring to the current post-ChatGPT AI market. “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 industry has grown into a sprawling and fragmented web over the past decade — which makes it ripe for consolidation. All it needed was a catalyst. From 2020 through 2024 alone, more than $300 billion was invested into data startups across more than 24,000 deals, according to PitchBook data.
The data industry wasn’t immune to the trends seen in other industries like SaaS where the venture swell of the last decade resulted in numerous startups getting funded by venture capitalists that only targeted one specific area or were in some cases built around a single feature.
The current industry standard of bundling together a bunch of different data management solutions, each with its own specific focus, doesn’t work when you want AI to crawl around your data to find answers or build applications.
It makes sense that larger companies are looking to snap up startups that can plug into and fill existing gaps in their data stack. A perfect example of this trend is Fivetran’s recent acquisition of Census in May — which yes, was done in the name of AI.
Fivetran helps companies move their data from a variety of sources into cloud databases. For the first 13 years of its business, it didn’t allow customers to move this data back out of said databases, which is exactly what Census offers. This means prior to this acquisition, Fivetran customers needed to work with a second company to create an end-to-end solution.
To be clear, this isn’t meant to cast shade on Fivetran. At the time of the deal, George Fraser, the co-founder and CEO of Fivetran, told TechCrunch that while moving data in and out of these warehouses seems like two sides of the same coin, it’s not that simple; the company even tried and abandoned an in-house solution to this problem.
“Technically speaking, if you look at the code underneath [these] services, they’re actually pretty different,” Fraser said at the time. “You have to solve a pretty different set of problems in order to do this.”
This situation helps illustrate how the data market has transformed in the last decade. For Sanjeev Mohan, a former Gartner analyst who now runs SanjMo, his own data trend advisory firm, these types of scenarios are a big driver of the current wave 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.”
The broader market plays a role here, too, Mohan said. Data startups are struggling to raise capital, Mohan said, and an exit is better than having to wind down or load up on debt. For the acquirers, adding features gives them better pricing leverage and an edge against their peers.
“If Salesforce or Google isn’t acquiring these companies, then their competitors likely are,” Derek Hernandez, a senior emerging tech analyst at PitchBook, told TechCrunch. “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].”
This trend brings big benefits to the startups getting acquired. The venture market is starving for exits and the current quiet period for IPOs doesn’t leave them a lot of opportunities. Getting acquired not only provides that exit, but in many cases it also gives these founding teams room to keep building.
Mohan agreed and added that many data startups are feeling the pains of the current market regarding exits and the slow recovery of venture funding.
“At this point in time, acquisition has been a much more favorable exit strategy for them,” Hernandez said. “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 the doubt still remains if this acquisition strategy will achieve the buyers’ goals.
As Dhillon pointed out, the database companies being acquired weren’t necessarily built to easily work with the rapidly changing AI market. Plus, if the company with the best data wins the AI world, will it make sense for data and AI companies to be separate entities?
“I think a lot of the value is in merging the major AI players with the data management companies,” Hernandez said. “I don’t know that a stand-alone data management company is particularly incentivized to remain so and, kind of like, play a third party between enterprises and AI solutions.”
