The data industry is at a decisive turning point. In just two months, we’ve seen major moves such as Databricks’ $1 billion acquisition of Neon and Salesforce’s $8 billion purchase of Informatica. And everything suggests this is only the beginning.
Consolidation or Transformation?
AI Redefines the Rules of the Data Industry
Last updated: diciembre 2025
Beyond the numbers, these deals share a common goal: integrating technology that enables companies to make the definitive leap into artificial intelligence. Because without high-quality data, AI simply doesn’t work.
According to a TechCrunch survey of venture capital investors, data quality is one of the main factors when evaluating AI startups. Gaurav Dhillon, co-founder of Informatica and CEO of SnapLogic, sums it up clearly: “If we want to seize the opportunity that AI represents, we need to rebuild our data platforms from scratch.”
A Fragmented and Hard-to-Scale Ecosystem
Over the past decade, the data industry has grown explosively—but also in a highly fragmented way. Thousands of startups have been funded, many focused on very specific functions or even a single feature. The result is an ecosystem full of solutions that don’t always integrate well with one another.
This is a serious obstacle for AI adoption in enterprise environments, which require fast, comprehensive, and cross-functional access to all their data. Hence the growing interest in acquiring startups that help close the loop. A recent example is Fivetran’s acquisition of Census, completing its offering to move data both in and out of cloud warehouses.
Market Pressure and the Need for Consolidation
Major tech players know that if they don’t acquire these companies, their competitors will. Adding to this is pressure on the startup side: funding is scarce, IPO markets are frozen, and many see acquisition as the most viable path to continue developing their technology within larger structures.
“The best product on the market probably can’t stay independent for long,” PitchBook notes. And that is precisely the logic behind moves like Informatica’s acquisition: even though Salesforce didn’t pay last year’s valuation, it remains the best solution to integrate and scale.
What Comes Next?
The big question is whether these acquisitions will truly achieve their goal: making AI adoption easier for companies. Many of the acquired technologies were conceived in a pre-ChatGPT world, and adapting them to today’s rapid pace of AI development will be far from straightforward.
As Gaurav Dhillon puts it: “No one was born in AI; it’s a market that’s only three years old. Big companies will need a lot of retooling to adapt.”
What is clear is that value no longer lies solely in data or in models, but in the combination of both. And that balance—between robust data infrastructure and agile AI capabilities—is the next major challenge for the digital enterprise.