The data industry is on the verge of a drastic transformation.<\/p>\n
The market is consolidating. And if the deal flow in the past two months is any indicator \u2014 with Databricks buying Neon for $1 billion<\/a> and Salesforce snapping up cloud management firm Informatica for $8 billion<\/a> \u2014 momentum is building for more.<\/p>\n 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.<\/p>\n On the surface level, this strategy makes sense.<\/p>\n The success of AI companies, and AI applications, is determined by access to quality underlying data. Without it, there simply isn\u2019t value \u2014 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<\/a> and succeed. And while some of these companies involved in these deals aren\u2019t startups, the sentiment still stands.<\/p>\n Gaurav Dhillon \u2014 the co-founder and former CEO of Informatica and current chairman and CEO at data integration company SnapLogic \u2014 echoed this in a recent interview with TechCrunch.<\/p>\n \u201cThere is a complete reset in how data is managed and flows around the enterprise,\u201d Dhillon said. \u201cIf 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\u2019re seeing all these data acquisitions, because this is the foundation to have a sound AI strategy.\u201d<\/p>\n But is this strategy of snapping up companies built before a post-ChatGPT world the way to increase enterprise AI adoption in today\u2019s rapidly innovating market? That\u2019s unclear. Dhillon has doubts too.<\/p>\n \u201cNobody was born in AI; that\u2019s only three years old,\u201d Dhillon said, referring to the current post-ChatGPT AI market. \u201cFor a larger company, to provide AI innovations to re-imagine the enterprise, the agentic enterprise in particular, it\u2019s going to need a lot of retooling to make it happen.\u201d<\/p>\n Fragmented data landscape<\/p>\n The data industry has grown into a sprawling and fragmented web over the past decade \u2014\u00a0which 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.<\/p>\n The data industry wasn\u2019t immune to the trends seen in other industries like SaaS where the venture swell of the last decade resulted in numerous startups getting funded<\/a> by venture capitalists that only targeted one specific area or were in some cases built around a single feature.<\/p>\n The current industry standard of bundling together a bunch of different data management solutions, each with its own specific focus, doesn\u2019t work when you want AI to crawl around your data to find answers or build applications.<\/p>\n 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\u2019s recent acquisition of Census<\/a> in May \u2014\u00a0which yes, was done in the name of AI<\/a>.<\/p>\n Fivetran helps companies move their data from a variety of sources into cloud databases. For the first 13 years of its business, it didn\u2019t 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.<\/p>\n To be clear, this isn\u2019t 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\u2019s not that simple; the company even tried and abandoned an in-house solution to this problem.<\/p>\n \u201cTechnically speaking, if you look at the code underneath [these] services, they\u2019re actually pretty different,\u201d Fraser said at the time. \u201cYou have to solve a pretty different set of problems in order to do this.\u201d<\/p>\n 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.<\/p>\n \u201cThis consolidation is being driven by customers being fed up with a multitude of products that are incompatible,\u201d Mohan said. \u201cWe 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\u2019s an overlap.\u201d<\/p>\n Good for startups<\/p>\n 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.<\/p>\n \u201cIf Salesforce or Google isn\u2019t acquiring these companies, then their competitors likely are,\u201d Derek Hernandez, a senior emerging tech analyst at PitchBook, told TechCrunch. \u201cThe best solutions are being acquired currently. Even if you have an award-winning solution, I don\u2019t know that the outlook for staying private ultimately wins over going to a larger [acquirer].\u201d<\/p>\n 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\u2019t 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.<\/p>\n 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.<\/p>\n \u201cAt this point in time, acquisition has been a much more favorable exit strategy for them,\u201d Hernandez said. \u201cSo 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\u2019s still, you know, was the best solution, according to their board.\u201d<\/p>\n What happens next<\/p>\n But the doubt still remains if this acquisition strategy will achieve the buyers\u2019 goals.<\/p>\n As Dhillon pointed out, the database companies being acquired weren\u2019t 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?<\/p>\n \u201cI think a lot of the value is in merging the major AI players with the data management companies,\u201d Hernandez said. \u201cI don\u2019t 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.\u201d<\/p>\n","protected":false},"excerpt":{"rendered":"The data industry is on the verge of a drastic transformation. The market is consolidating. 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