Here's What's Coming: Why Data Integration Companies Will Dominate M&A in 2025 & 2026

Kade Brewster • June 3, 2025

AI is Going to Drive the Acquisition Market, but it Might Look Different Than You Think

There is an underlying trend in the AI revolution that I feel many didn't expect. While everyone was watching ChatGPT and Claude duke it out for conversational supremacy, real money has been quietly flowing into a completely different category of companies. The next wave of billion-dollar acquisitions won't be primarily flashy AI model creators—it'll be companies solving AI's most fundamental problem: making sense of messy enterprise data.

If you want to predict where the next AI unicorns will emerge, don't just follow the hype. Follow the money. And right now, that money is flooding into data integration and management companies at unprecedented levels.

The $8 Billion Notice

Salesforce just announced an $8 billion acquisition of Informatica. This wasn't just another big tech deal—it was a signal flare illuminating the future of AI M&A. Here's a company that already has sophisticated AI capabilities in the enterprise space, not to mention they already own Tableau, and have acquired other data/analytics companies like MuleSoft and Datorama in the last decade. Yet they just spent more money than many countries' GDP on a top-of-the-line data management platform.

Why? Because Salesforce discovered what every enterprise grappling with AI implementation already knows: the technology works in controlled environments, but real-world deployment is a nightmare when your data architecture isn't in line.

Informatica isn't sexy. Despite being one of the top data management platforms, it's a backend focused platform that's always been geared towards integrations, data quality, master data management, and proper governance. Its selling point isn't reporting or modeling, it helps companies clean, organize, and govern their data. And Salesforce paid premium prices for that capability because they understand something crucial: in the AI era, data infrastructure isn't a nice-to-have—it's the foundation that determines whether your AI strategy succeeds or fails spectacularly.

The Market Numbers Don't Lie

The data tells a compelling story about where this market is heading. AI M&A deals surged 20% year-over-year in 2024, hitting 326 deals. But more telling is what types of companies are being acquired. While pure AI model companies grab headlines, the real acquisition frenzy is happening in data infrastructure:

Databricks went on a buying spree, acquiring Tabular for over $1 billion, plus Einblick and Lilac—all companies focused on prepping data for AI
Cisco's $28 billion Splunk acquisition was explicitly about "redefining data utilization" for AI
IBM announced plans to acquire DataStax to enhance their watsonx portfolio
HPE's $14 billion bid for Juniper Networks was driven by AI-powered networking capabilities

The AI data management market is projected to explode from $34.7 billion in 2024 to $260.3 billion by 2033. That's a 25% compound annual growth rate in a market that barely existed five years ago.

Why Data Integration and Governance Companies Are the New Gold Rush

Here's the uncomfortable truth about AI adoption: the technology has largely solved the hard problems. Large language models can write, reason, and create with stunning capability. Computer vision can identify objects better than humans. Machine learning algorithms can spot patterns in data that would take analysts years to discover.

So why aren't enterprises deploying AI at scale? Because most companies' data looks like a digital junkyard.

The average enterprise uses 106+ different software applications. Customer data lives in Salesforce, financial data sits in NetSuite, operational data hides in custom databases, and marketing data sprawls across six different platforms. Getting all this information to talk to each other—cleanly, accurately, and in real-time—is where AI projects go to die.

This is why data integration companies are becoming acquisition targets. They're not just selling software; they're selling the bridge between AI's promise and reality. Companies that can solve the "how do we actually use AI with our messy data" problem are worth their weight in gold because they're the difference between a successful AI transformation and an expensive science experiment.

The Characteristics of Tomorrow's Unicorns

Based on current market dynamics and acquisition patterns, the next AI unicorns will likely share several key characteristics:

Real-time Data Processing at Scale: Companies that can handle massive data volumes while maintaining quality and governance standards. The winners won't just move data—they'll ensure it's clean, compliant, and immediately usable for AI applications.

Multi-platform Integration Capabilities: Solutions that can seamlessly connect legacy systems with modern AI platforms. The companies that figure out how to make 20-year-old ERP systems play nicely with cutting-edge AI models will command premium valuations.

Built-in AI Governance: As enterprises deploy AI at scale, they need systems that can track data lineage, ensure compliance, and provide audit trails. Companies building these capabilities into their core platforms are positioning themselves as essential infrastructure.

SME-Focused Solutions: While everyone chases enterprise deals, there's a massive opportunity in the small and medium business market. Companies that can package enterprise-grade data integration into affordable, easy-to-deploy solutions for smaller businesses are sitting on potential goldmines.

Industry-Specific Expertise: Generic solutions are becoming commoditized. The real value lies in companies that understand the specific data challenges of healthcare, financial services, manufacturing, or retail and build tailored solutions.

The Acquisition Logic

From an acquirer's perspective, buying data integration companies makes perfect strategic sense. Tech giants are in an arms race to become the definitive AI platform for enterprises. But having the best AI models means nothing if companies can't actually deploy them against their real data.

This creates a "build vs. buy" decision for every major tech company. Building world-class data integration capabilities in-house takes years and requires specialized expertise that's in short supply. Acquiring proven companies with existing customer bases and battle-tested technology is often the faster, more reliable path.

The acquirers also understand something crucial: data integration companies often have deeper, stickier customer relationships than pure AI vendors. Once a company builds its data architecture around your platform, switching costs become astronomical. That's the kind of defensive moat that justifies billion-dollar valuations.

The Investment Thesis

For investors looking to identify the next AI related unicorns, focus on companies solving these fundamental problems:

Data Pipeline Automation: Companies that can discover, map, and transform data across enterprise systems without requiring armies of data engineers.

AI-Ready Data Preparation: Platforms that don't just move data but prepare it specifically for AI consumption—handling formats, ensuring quality, and maintaining the context AI models need to function effectively.

Compliance-First Architecture: Solutions built from the ground up to handle regulatory requirements around data privacy, security, and governance while maintaining AI accessibility.

Edge-to-Cloud Integration: Companies that can seamlessly move and process data across on-premises, cloud, and edge environments as AI deployments become more distributed.

What This Means for the Market

I think we're witnessing a shift in how the market values AI companies. Pure technology plays are giving way to practical infrastructure solutions. The companies that will dominate the next phase of AI aren't necessarily the ones with the most sophisticated models—they're the ones that make sophisticated models actually usable in the real world.

This creates enormous opportunities for entrepreneurs and investors willing to look beyond the glamorous AI applications to the unglamorous but essential plumbing that makes everything work. The next time you see a headline about a data company getting acquired for billions, remember in the AI economy, the pickaxe sellers often get richer than the gold miners.

The AI revolution is real, but it's not being won exclusively by the companies making the flashiest demos. It's being won by the companies solving the hardest, most mundane problems that stand between AI's potential and its practical deployment. And those companies are about to become very, very valuable.

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Turning Data into Direction: Why Small Businesses Need Fractional Analytics

By Ranae Peterson • October 9, 2025

Many small businesses are laser-focused on growth, sales, and staying afloat. In that constant hustle, one of the most valuable assets, data , often gets overlooked. Even when businesses “look at the numbers,” that doesn’t always mean they’re collecting the right data, tracking the right KPIs, or translating those insights into actionable strategies. Fractional analytics bridges that gap. It allows small businesses to access experienced data analysts or analytics teams on a part-time or project basis, essentially bringing enterprise-level data expertise without the full-time cost. Think of it as analytics-as-a-service for small business owners who need clarity and insight but can’t justify a full analytics department. The Reality: Why Data Gets Overlooked While most business leaders know data is “important,” it can be hard to see its impact until it’s visualized through dashboards, real-time reporting, or guided by a professional who knows how to turn information into action. Without these tools and expertise, business decisions often rely on gut instinct instead of insight, leading to inefficiencies, missed opportunities, and in some cases, failure. Statistics show that: About 20% of small businesses don’t survive their first year. Nearly 50% fail within five years. Around 65% close within ten years. While the reasons vary, from financial mismanagement to poor planning, many of these challenges stem from a lack of data visibility or poor data management . In other words, the root cause often isn’t just financial; it’s analytical . Real World Analogy Imagine running a restaurant without ever checking what inventory levels, most frequent orders, or best margin products. You might think you’re doing well because the restaurant is busy, but when you do the books, you’re not actually making any money. Now, imagine having a fractional analytics consultant helping you organize and visualize your data. They don’t need to be in the kitchen every day, but when they are, they bring order to chaos, tracking inventory and margin by menu item, predicting staffing needs, and saving costs by cutting waste. That’s what fractional analytics does for your business data. It helps you understand what’s really happening across operations, finance, and customer service so you can make smarter, faster decisions. Common Operational Challenges Solved by Fractional Analytics Here are a few common pain points small businesses face that fractional analytics services can address: Inefficient reporting and manual data entry. Lack of clear KPIs or performance tracking. Inconsistent financial forecasting. Missed revenue opportunities due to incomplete data. Poor inventory or resource management. Lack of clarity around customer behavior and profitability. Benefits of Fractional Analytics for Small Businesses Engaging a fractional analytics provider offers far more than just numbers and dashboards. It’s about empowering smarter decisions without breaking the bank. Cost efficiency: Avoid the high salary and benefits of full-time analytics hire. Speed to impact: Gain immediate access to expertise, no lengthy onboarding or training required. Scalability: Bring in analytics support when you need it and scale it up as you grow. Better decision-making: Replace guesswork with data-driven insights and actionable KPIs. Time savings: Spend less time in spreadsheets and more time growing your business. Long-term cost reduction: Identify duplicate payments, missed invoices, and inefficiencies that quietly drain profit. Fractional analytics also enhances cash flow management and enables business leaders to spot opportunities for optimization, often resulting in measurable growth and sustainability. Why It’s a Make-or-Break Decision Choosing whether to work with a fractional analytics consultant could be the difference between surviving and thriving. Many business owners don’t realize that outsourced analytics is even an option, one that offers flexibility, affordability, and strategic insight without high overhead. The truth is, small business leaders don’t have to do it all alone, nor do they need to rush into hiring a full-time team. Fractional analytics gives them the best of both worlds: the expertise of a senior data strategist and the flexibility of a part-time engagement. Final Thoughts In today’s competitive market, data-driven decision making isn’t optional; it’s essential. Small businesses that embrace fractional analytics gain clarity, confidence, and control over their operations. By investing in the right insights today, they set up the foundation for sustainable growth tomorrow. If your business is ready to move from guessing to growing, now’s the time to explore what fractional analytics services can do for you.

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