The data industry is undergoing a fundamental transformation driven by a strong wave of acquisitions, propelled largely by the rapid rise of artificial intelligence. Recent high-profile deals, such as Databricks’ $1 billion acquisition of Neon and Salesforce’s $8 billion purchase of Informatica, illustrate this consolidation trend clearly. Although these acquired firms vary greatly in size and technological specialization, they share a common motive: each represents a critical piece of the puzzle necessary to encourage enterprises to fully embrace AI.
This new acquisition climate arises partly from a widely shared belief—echoed by industry insiders and venture capitalists alike—that robust, high-quality data underpins successful AI implementation. Without solid foundational data, AI applications lose their core value.
Gaurav Dhillon, SnapLogic’s CEO and former co-founder of Informatica, argued that the widespread adoption of AI “demands a complete reset in how data is managed within organizations. Companies hoping to capitalize on AI must significantly update their existing data infrastructures, hence the flurry of data-related mergers and acquisitions.”
Yet, despite widespread enthusiasm, questions linger about whether integrating established data-focused companies built before the recent wave of generative AI technology can accelerate enterprise AI adoption effectively. Dhillon expressed doubts about this strategy’s efficacy, noting the inherently rapid pace of AI-driven developments. “Nobody was born in AI. The technology as we now understand it is barely three years old. Companies looking to leverage AI for significant enterprise reinvention will need thorough retooling,” he explained.
This surge in consolidation also reflects the historical state of the data industry, which over the past decade has become increasingly fragmented. According to figures from PitchBook, data startups received more than $300 billion in venture capital financing across 24,000 deals between 2020 and 2024, many of them niche companies built around highly specialized software solutions. However, such fragmentation is increasingly incompatible with current AI demands, which require seamlessly accessible data within well-integrated platforms and ecosystems.
One clear example is Fivetran’s acquisition of Census earlier this year. While Fivetran historically specialized in moving customer data into cloud warehouses, the company relied upon Census’ technology to move data out again—something Fivetran itself could not easily develop internally. The complexities involved illustrated the industry’s broader integration challenges. The deal highlighted the pressing need among enterprises to create streamlined, comprehensive data infrastructures capable of supporting sophisticated AI applications.
Industry analyst Sanjeev Mohan, formerly of Gartner, argued that the core driver behind this consolidation is customer fatigue with incompatible, redundant data products. Enterprises, Mohan says, have reached their breaking point and now demand unified data management solutions to prevent overlapping functionality and unnecessary complexity in their tech stacks.
For startups targeted by this acquisition spree, there are positive outcomes as well. Given intense investor pressure and an IPO market that remains largely dormant, acquisitions provide much-needed exit opportunities. These deals allow founders to continue growing their teams and products under more stable financial circumstances. Analysts believe this dynamic maintains competitive advantages for buyers, particularly tech giants and large enterprises, who can fill gaps in their offerings by securing innovative solutions before rivals do.
However, doubts remain about the overall effectiveness of an acquisition-heavy strategy for fully leveraging AI advantage. As Dhillon pointed out, acquired legacy technologies often face difficulties keeping pace with the rapidly evolving AI landscape. Moreover, if controlling quality data increasingly determines AI dominance, the longer-term relevance of standalone data management firms may be called into question. Analyst Derek Hernandez believes that deep integration between established data providers and cutting-edge AI firms may soon become crucial for long-term viability.
In short, while the current wave of mergers and acquisitions reshapes the data-management landscape—driven primarily by AI adoption—persistent uncertainties linger around whether these strategies will achieve their intended goal of making businesses AI-ready. The next few years will clarify whether purchasing established data technologies will help enterprises accelerate their AI journey or whether more fundamental rethinking of industry relationships will be necessary.
