The data industry is at the end of drastic transformation.
The market consolidates. And if the agreement flows in the past two months is some kind of indicator – with Databricks buying Neon for $ 1 billion And Salesforce taking out Cloud Management Company Informatica for $ 8 billion – A moment is built for more.
The acquired companies can be comprehensive, age and focus areas within the data stack, but they all have one thing in common. These companies buy hoping that the acquired technology will be the missing piece needed to get businesses to adopt AI.
At the surface level, this strategy makes sense.
The success of AI companies, and AI applications, is determined by accessing quality below data. Without it, there is simply no 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 do AI -starts stand out and succeed. And while some of these companies involved in these agreements are not startups, the feeling still stands.
Gaurav Dhillon, the former co -founder and general manager of Informatica, and current president and general manager at Data Integration Company Snaplogic, Echois this in a recent interview with Techcrunch.
“There is a complete restoration of how data is managed and flows around the company,” Dhillon said. “If people want to catch the AI -Importive, they have to redo their data platforms a very big way. And here I think you see all these acquisitions, because this is the foundation to have a unique AI strategy.”
But is this strategy to capture companies built before Post-ChatGPT world the way to increase corporate AI adoption in today’s rapidly innovative market? That’s unclear. Dhillon also has doubts.
“No one was born in AI; this is only three years old,” Dhillon said, referring to the current Post-Chatgpt AI market. “For a larger company, provide AI innovations to re-imagine the business, especially the active enterprise, it will need a lot of retrieval to realize it.”
Fragmented data landscape
The data industry has grown on a widespread and fragmented website over the past decade – which makes it mature for consolidation. Only it needed a catalyst. From 2020 to 2024 alone, more than $ 300 billion were invested in data starts through more than 24,000 deals, according to Pitchbook Data.
The data industry was not resistant to the trends seen in other industries like SaaS, where the company from the last decade resulted in Many startups fund of corporate capitalists, who have only targeted one specific area or were in some cases built around a single feature.
The current industrial standard to combine a multitude of various data management solutions, each with its own specific focus, does not work when you want AI to crawl around your data to find answers or build applications.
There is a sense that larger companies are looking to grab startups that can insert and fill in existing shortcomings in their data stack. A perfect example of this trend is Fivetran’s recent acquisition of census In May – who yes, was made in the name of AI.
Fivetra helps companies move their data from various sources into cloud databases. During the first 13 years of its business, it did not allow customers to move these data from relevant databases, exactly what Census offers. This means prior to this acquisition, five-tank customers needed to work with a second company to create an end-to-end solution.
To be clear, this does not intend to throw a shadow on a fiivetral. At the time of the agreement, George Fraser, Fivetran’s co -founder and general manager, told Techcrunch that while moving data in and out of these warehouses seem like two sides of the same currency, it’s not that simple; The company even tried and left an internal solution to this problem.
“Technically speaking if you look at the code below [these] Services, they are actually quite different, “Fraser said at the time.” You have to solve a different set of problems to do this. ”
This situation helps illustrate how the data market has been transformed in the last decade. For Sanjeev Mohan, a former Gartner analyst, who now manages Sanjmo, his own advice on data trends, these types of scenes are a great driver of the current wave of consolidation.
“This consolidation is driven by customers who are full of a multitude of products that are incompatible,” Mohan said. “We live in a very interesting world, where there are many different data storage solutions, you can make an open source, they can go to Kafka, but the one area where we have failed, there are metadata. Dozens of these products capture some metadata, but to do their job, it is an overlap.”
Good for triggers
The wider market also plays a role, Mohan said. Data startups struggle to earn capital, Mohan said, and exit is better than having to prevent or charge debt. For buyers, adding features give them a better price of lever and edge against their peers.
“If Salesforce or Google doesn’t get these companies, then their competitors are probably,” Derek Hernandez, a senior emerging tech analyst at Pitchbook, told Techcrunch. “The best solutions are Akira [acquirer]. ”
This trend brings great benefits to the starts obtained. The corporate market is hungry for exits and the current quiet period for IPOs do not leave them many opportunities. Getting not only provides that exit, but in many cases gives these foundation teams a room to continue building.
Mohan agreed and added that many data startups feel the pains of the current market in relation to outputs and a slow recovery of corporate funding.
“At this time, acquisition was a much more favorable exit strategy for them,” Hernandez said. “So I think both sides are very motivated to reach the end line on these. And I think Informatica is a good example of where even with some hair from where Salesforce spoke to them last year, it is still, you know, was the best solution, according to their board.”
What’s going on next
But the doubt still remains if this acquisition strategy attains the goals of the buyers.
As Dhillon pointed out, the database companies acquired were not necessarily built to easily work with the rapidly changing AI market. Moreover, 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 much of the value merges the main AI players with data management companies,” Hernandez said. “I don’t know that a standalone data management company is particularly encouraged to stay like that and, to play third among businesses and AI solutions.”
