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Is AI finding its way back to Data?
Is AI finding its way back to Data?

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As AI continues to mature, large-scale investments are increasingly aimed at expanding the scope of data-driven offerings for customers. Recently, there have been several M&A focusing on AI-enabling data capabilities. Deals such as Salesforce’s $8 Bn acquisition of Informatica or OpenAI’s acquisition of Rockset reflect the inclination towards enterprise-grade data infrastructure and services. This isn’t simply the pursuit of expanded market share but a shift back to the foundation AI was built on - Data.

As a part of their accelerated growth strategy, specialist data companies are creating their own M&A niche, alongside hyperscalers and incumbents, such as SAP and Oracle, ensuring that they offer end-to-end solutions for managing AI data lifecycle.

Data lifecycle management typically comprises of four key stages, requiring further focus and differentiated action to support an AI lifecycle:

  1. Data Acquisition: Gathering and obtaining relevant data for AI models
  2. Data Pipelining: Streamlining the flow of data for efficient processing
  3. Data Security: Protecting data from unauthorized access and breaches
  4. Data Governance: Ensuring data is managed and used responsibly with AI

A diagram of data processing

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Figure 1: The key reasons why data companies are in AI spotlight

While data has always been the enterprise’s real oil, the demands of AI, particularly around lifecycle management have made it urgent for enterprises to scale quickly through M&A. The table below highlights a few prominent data M&As that have happened between 2024-25.

Select Data M&As During 2024-25

Nature of Acquiring Company

Acquiring Company

Acquired Company/

Startup

Primary Acquisition Objective

Data Companies

Databricks

Blade Bridge

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Accelerate and simplify data warehouse migrations by automating and streamlining the process from legacy data warehouses to Databricks’ cloud platform.

Neon

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Strengthen Databricks’ analytics platform with technology that can help businesses develop and use AI agents easily.

Tabular

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Strengthen Databricks’ offering in the data Lakehouse market and accelerate the adoption of open table format.

Lilac

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Enhance Databricks’ platform capability to handle unstructured data by building scalable and user-friendly tools for analysing text datasets and simplify the evaluation process for LLMs.

Snowflake

Snowflake logo in transparent PNG and vectorized SVG formats

Crunchy Data

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Improve Snowflake’s AI capabilities and offer an enterprise-ready, fully integrated PostgreSQL solution within its AI Data Cloud.

Datavolo

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Enhance Snowflake’s data integration capabilities, particularly for handling complex, multimodal data pipelines, simplifying data engineering workflows and reducing time to value for customers.

Samooha

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Bolster Snowflake’s data security posture by providing secure and governed data collaboration. Allow users to combine and analyse data with partners while maintaining privacy and security.

Product Company

Salesforce

 

Informatica

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Enhance trusted data foundation for agentic AI; build unified architecture for safe, responsible, scalable AI.

Own

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Bolster data protection and security capabilities, particularly in backup, recovery, and compliance. 

IBM

IBM Logo Evolution: Symbolizing Progress and Reliability

Datastax

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Enhance Watsonx AI and data platform for handling unstructured and real-time data, crucial for GenAI applications. Providing businesses with the tools to efficiently manage and utilize their data for AI workloads. 

LLM Makers

Meta

Meta company logo | Premium Vector

Scale AI

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Enhance Meta's AI capabilities across advertising, messaging, wearables, and the metaverse—unlocking significant new revenue opportunities

OpenAI

OpenAI Logo & Brand Assets (SVG, PNG and vector) - Brandfetch

Rockset

A black background with a black square

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Strengthen retrieval infrastructure for enterprise products and strategically enhance data processing, analytics and retrieval capabilities.

AI Infrastructure

NVIDIA

Nvidia (ChatWithRTX) Logo Free Downl... · LobeHub

Gretel

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Enhance NVIDIA’s GenAI services by leveraging Gretel's expertise in synthetic data generation

HPE

hpe" Icon - Download for free – Iconduck

Morpheus Data

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Expand Green Lake platform offerings with unified hybrid and multi-cloud management for further simplifying operations and optimizing cloud costs.

Cisco

Cisco - Wikipedia

Splunk

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Strengthen security and observability by improving cybersecurity and IT operations, thereby enabling customers to better address cybersecurity threats, improve ITOps and accelerate digital transformation.

 

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AI-generated content may be incorrect. - Depicts Companies

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AI-generated content may be incorrect.- Depicts Startups

A closer look at the acquisitions (refer the table) reveals several emerging patterns that highlight how companies are approaching data readiness in an AI-first world.

  1. Startups make nearly 50% of the acquired companies, often offering specialized solutions across the data lifecycle, like BladeBridge supports Databricks in moving from legacy to cloud-based data warehouses or Splunk adds observability and security analytics on top of Cisco’s data infrastructure.
  2. Data companies are leading the charge in refining and deepening lifecycle capabilities through focused acquisitions, primarily startups.
  3. Product companies are outpacing service providers in the race to become data-ready like Meta invested in Scale AI for labelling and training data or Salesforce acquired informatica and Own to strengthen data infrastructure.
  4. Data labelling and annotation is seeing renewed interest, driven by AI model needs. For e.g., Meta’s $ 15 Bn acquisition of Scale AI and the criticality of data as a moat with Google and OpenAI now trying to look for other data annotation players.
  5. Most M&A activity is happening outside India, with limited participation from Indian startups in the data lifecycle

Outlook

Data was, is, and will always be the real enterprise oil. It took the era of GenAI and now agentic AI to drive the learning home. Yet, the action, we believe is just starting and will accelerate as more niche companies having mastered the art and science of data management for an industry, function, process, or for sovereign usage, get the opportunity to scale and be an attractive acquisition opportunity. Particularly the specialized data annotation space offers massive growth opportunity for smaller sized companies such as like Dataloop or Labelbox, and that niche data labelling opportunities, such as diagnostic or radiology data, traffic patterns, weather-related agricultural produce impact patterns, visual and gestural patterns (wherever allowed by law), etc. will create new business opportunities for the upcoming data companies.

 

Sources: Company websites, Time magazine, Tech Crunch


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AI Analyst and Learner with a background in AIML and Data science

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