AI is forcing the data industry to consolidate — but that’s not the whole story

The data industry is undergoing a monumental shift, largely driven by the pervasive influence of artificial intelligence. A wave of consolidation is sweeping across the market, signaling a drastic transformation ahead. Recent acquisitions, such as Databricks’ notable $1 billion purchase of open-source database startup Neon and Salesforce’s strategic $8 billion acquisition of cloud management firm Informatica, highlight a growing momentum that suggests more such deals are on the horizon. These moves underscore a critical belief: the pathway to widespread enterprise AI adoption hinges on access to robust, high-quality data.

At first glance, this acquisition strategy appears logical. The very success of AI companies and their applications is fundamentally determined by the quality of the underlying data they access. Without it, the value proposition of AI diminishes significantly – a sentiment echoed by enterprise venture capitalists. A December 2024 TechCrunch survey revealed that VCs consider data quality a crucial factor for AI startups to distinguish themselves and thrive. While some of the companies involved in these recent deals are not nascent startups, this underlying principle remains firmly in place.

Gaurav Dhillon, co-founder and former CEO of Informatica, and currently chairman and CEO at data integration company SnapLogic, articulated this transformation in a recent interview with TechCrunch. “There is a complete reset in how data is managed and flows around the enterprise,” Dhillon stated. He further emphasized, “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.”

However, questions linger about whether acquiring companies built before the advent of the post-ChatGPT AI era is the most effective way to accelerate enterprise AI adoption in today’s rapidly evolving market. Dhillon himself expressed doubts, noting, “Nobody was born in AI; that’s only three years old,” referring to the current landscape. He stressed that for larger companies aiming to deliver AI innovations and reimagine the enterprise, particularly the ‘agentic enterprise,’ significant retooling will be necessary.

Fragmented Data Landscape: A Catalyst for Consolidation

Over the past decade, the data industry has evolved into a vast, often fragmented web, making it inherently ripe for consolidation. All it required was a powerful catalyst – which AI has now provided. Between 2020 and 2024 alone, PitchBook data indicates that over $300 billion was invested into data startups across more than 24,000 deals. The data industry mirrored trends seen in other sectors, such as SaaS, where a decade of venture funding resulted in numerous startups focusing on niche areas or even single features.

The prevailing industry standard of integrating numerous disparate data management solutions, each with its own specialized focus, proves inefficient when attempting to enable AI to seamlessly crawl and analyze data for insights or application development. This reality naturally compels larger enterprises to acquire startups that can effectively plug into and address existing gaps within their complex data stacks. A prime illustration of this trend is Fivetran’s May acquisition of Census, a move explicitly made “in the name of AI.”

Fivetran specializes in moving data from various sources into cloud databases. For 13 years, it lacked the functionality for customers to move this data back out – a core offering of Census. This meant Fivetran customers previously required a second company to achieve an end-to-end data solution. George Fraser, co-founder and CEO of Fivetran, explained to TechCrunch that while moving data in and out might seem like two sides of the same coin, the technical challenges are distinct, noting that even an internal attempt at a solution was abandoned. This scenario perfectly encapsulates how the data market has shifted. Sanjeev Mohan, a former Gartner analyst and head of SanjMo, his data trend advisory firm, identifies such incompatibilities as a major driver behind the current wave of consolidation. “This consolidation is being driven by customers being fed up with a multitude of products that are incompatible,” Mohan asserted, highlighting the widespread failure in metadata management across dozens of overlapping products.

A Silver Lining for Startups Amidst Market Pressures

The broader market dynamics also play a significant role in this trend, according to Mohan. Data startups, like many others, are grappling with challenges in securing capital, making an exit via acquisition a more favorable outcome than winding down or accumulating debt. For the acquiring companies, integrating new features provides enhanced pricing leverage and a competitive edge. Derek Hernandez, a senior emerging tech analyst at PitchBook, told TechCrunch, “If Salesforce or Google isn’t acquiring these companies, then their competitors likely are. 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 offers substantial benefits to the acquired startups. The venture market is currently starved for exits, and the subdued IPO landscape leaves limited opportunities. An acquisition not only provides a crucial exit but often allows founding teams the continued autonomy to build and innovate. Mohan concurred, emphasizing the market pressures on data startups regarding exits and the slow recovery of venture funding. Hernandez added, “At this point in time, acquisition has been a much more favorable exit strategy for them. 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.”

The Road Ahead: Unanswered Questions Remain

Despite the strategic benefits for both sides of these deals, the ultimate effectiveness of this acquisition strategy in achieving buyers’ ambitious AI adoption goals remains uncertain. As Dhillon highlighted, many acquired database companies were not originally designed to seamlessly integrate with the rapidly evolving and dynamic AI market. Furthermore, if the company with the superior data truly wins the AI race, it begs the fundamental question: will it make sense for data and AI companies to remain as separate entities?

“I think a lot of the value is in merging the major AI players with the data management companies,” Hernandez mused. “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.” The unfolding narrative of AI-driven consolidation in the data industry is indeed complex, with its full story yet to be written.