The data industry is on the verge of dramatic transformation.
The market is integrated. And if you’ve had transactions in the last two months, momentum is gaining if Databricks buys Neon for $1 billion and Salesforce snaps cloud management company Informatica for $8 billion.
The acquired companies range in size, age and focus area within the data stack, but they all have one thing in common. These companies are being purchased in the hope that the acquired technology will become the missing pieces needed to employ AI in companies.
At the surface level, this strategy makes sense.
The success of an AI company and its AI applications are determined by access to quality underlying data. Without it, it simply isn’t worth it. A belief shared by Enterprise VCS. In a TechCrunch survey conducted in December 2024, Enterprise VCS stated that data quality is a key factor in making AI startups stand out and succeed. And while some of these companies involved in these transactions are not startups, the sentiment is still standing.
Gaurav Dhillon, former co-founder and CEO of Informatica, is the current chairman and CEO of Data Integration Company Snaplogic, and has repeated this in a recent interview with TechCrunch.
“There’s a complete reset in how you manage your data and it flows around the company,” Dillon said. “If people want to seize AI orders, they need to redo the data platform in a very big way, and I believe this is looking at all this data collection.
But is this strategy of snapping companies built before the post-chat grit world a way to increase the adoption of corporate AI in today’s rapidly innovative markets? That’s unknown. Dillon has doubts too.
“No one was born with AI. It was only three years ago,” Dhillon said, referring to the current post-chat AI market. “For large companies, it will need a lot of retools to make it happen, especially to provide AI innovations to reimagin those that are agents.”
Fragmented Data Landscape
The data industry has grown into a vast, fragmented web over the past decade. This makes integration ripe. All I needed was a catalyst. According to Pitchbook Data, over $300 billion was invested in data startups across more than 24,000 transactions in 2020-2024 alone.
The data industry has not been immune to the trends seen in other industries, such as SaaS, where ventures have swelled over the past decade.
Current industry standards combining different data management solutions are each with their own focus, so it won’t work if AI wants to craze through data and find answers or build applications.
It makes sense that large companies are trying to snap startups that can connect and fill existing gaps in their data stack. A perfect example of this trend is Fivetran’s recent census acquisition in May. This was done in the name of AI.
Fivetran helps businesses move data from a variety of sources to cloud databases. For the first 13 years of that business, customers were unable to move this data from the above database. This is exactly what Census offers. This means that prior to this acquisition, Fivetran customers will need to work with the second company to create an end-to-end solution.
To be clear, this is not intended to chant the shade on Fivetlan. At the time of the agreement, Fivetran co-founder and CEO George Fraser told TechCrunch that data coming and going to these warehouses appears to be two aspects of the same coin, but it’s not so simple. The company has abandoned its attempts and internal solutions to this problem.
“Technically speaking, if you look at the code below, [these] The service is actually quite different,” Fraser said at the time. “To do this, you have to solve a fairly different problem.”
This situation will help explain how the data market has changed over the past decade. For former Gartner analyst Sanjeev Mohan, who currently runs his own data trending advisory company, Sanjmo, these types of scenarios are a major driving force behind the current wave of integration.
“This integration is driven by customers getting tired of a large number of incompatible products,” Mohan said. “We live in a very interesting world where there are a variety of data storage solutions. You can do open source. You can go to Kafka, but the area we failed is metadata. Many of these products have acquired some metadata, but to do the job, it’s overlapping.”
Suitable for startups
Again, a wider market plays a role, Mohan said. Data startups are struggling to raise capital, Mohan said, and the exit is better than finishing or loading debt. For acquirers, adding features will improve price leverage and give them an advantage over their peers.
“If Salesforce or Google hasn’t acquired these companies, then your competitors are probably,” Derek Hernandez, senior emerging technology analyst at Pitchbook, told TechCrunch. “We have the best solutions now. Even if there is an award-winning solution, we don’t know that the outlook for maintaining our private life will ultimately win on a massive scale. [acquirer]. ”
This trend will have a great benefit to startups being acquired. The venture market is hungry for exits, and the current quiet period of the IPO leaves them with much opportunity. Getting not only provides that outlet, but often gives these founding teams room to maintain the building.
Mohan agreed, adding that many data startups are feeling a slow recovery in current market pain regarding the exit and a slow recovery in venture capital.
“At this point, the acquisition was a much more advantageous exit strategy for them,” Hernandez said. “So I think both sides are very incentivized to get to these finish lines, and I think Informatica is a good example of that. Even though there was a bit of a haircut from where Salesforce was talking last year, according to the board, it was still the best solution.”
What will happen next
However, doubt remains if this acquisition strategy meets buyer’s goals.
As Dhillon pointed out, the acquired database companies were not necessarily built to easily work together in the rapidly changing AI market. Furthermore, if the best data company wins the AI world, does it make sense for the data and AI companies to become separate entities?
“I think a lot of the values are blending with major AI players and data management companies,” Hernandez said. “I don’t know that standalone data management companies are specifically encouraging them to do so.
