Enterprise Data, Analytics and AI Services
Growth and Consolidation: What's Driving M&A in Data, Analytics & AI Services
The richest multiples we track, and they're not going to the firms you'd expect
If you've read our other pieces in this series, you'll know the pattern by now: platform-specific ecosystems like Salesforce, Google Cloud and ServiceNow are consolidating, and multiples vary depending on how scarce the good partners are. Enterprise Data, Analytics & AI Services is the same story, just turned up. It's currently commanding the richest valuations of any category we track, and the reasons why say a lot about what buyers actually want right now.
The market didn't just recover, it kept growing
Deal activity in this space dipped after the post-Covid rebound, in line with the rest of IT services. But since 2023 it's been climbing steadily: transaction volumes went from 717 deals that year to 829 in 2025, a roughly 7.5% CAGR. That's not a bounce-back to a previous peak. It's genuine, sustained expansion, driven by enterprises moving generative AI and advanced analytics out of pilot programmes and into scaled, budgeted deployment.
Not every data firm is valued the same way
"Data, analytics and AI services" is a wide category, and the businesses inside it look quite different from each other. We think about firms along three axes:
Horizontal or vertical. Do you serve every industry the same way, or have you built deep expertise in one (financial services, healthcare, manufacturing)?
Builder or operator. Is your revenue mostly project-based implementation work, or do you run platforms on an ongoing, recurring basis?
Ecosystem-aligned or platform-neutral. Are you a specialist in one ecosystem (Snowflake-only, Power BI Elite), or do you work across multiple platforms?
None of these is inherently better, but they change who buys you and why. A vertical specialist with deep healthcare data governance expertise gets bought for domain IP. A horizontal BI generalist gets bought for scale and headcount, if it gets bought at a premium at all.
The shift buyers are paying for
The single biggest driver of premium valuations right now is the move from build to operate. Clients increasingly want ongoing managed data services, not one-off implementation projects, and recurring revenue models are commanding real premiums as a result. At the same time, horizontal BI firms without a specific angle are facing margin compression, while firms with genuine vertical specialisation or proprietary IP are pulling away from the pack.
There's also a talent story underneath all of this. Senior data engineers are scarce, and that scarcity is translating directly into pricing power for firms that have already built and retained scaled, certified teams. Buyers would rather pay a premium for an assembled team than try to hire one themselves in this market.
Recent deals worth watching
Three transactions from the past year show where the demand is concentrated:
- SAP agreed to acquire Reltio, a cloud-native master data management platform, specifically to make SAP and non-SAP enterprise data "AI-ready" for its Business Data Cloud. This is a software giant buying a data specialist to unlock its own AI roadmap, a pattern worth noting if you sit anywhere near master data management or data unification.
- ServiceNow acquired Pyramid Analytics, a decision intelligence platform unifying data prep, BI and data science, for a price estimated in the hundreds of millions. ServiceNow's stated goal was closing the gap between analytical insight and automated action, which is precisely the "operate" behaviour buyers are now rewarding.
- Straive acquired SG Analytics, a Pune-headquartered provider of AI-powered insights and contextual analytics with deep expertise in financial services and technology verticals. This is the roll-up side of the market: a data and AI operationalisation platform acquiring vertical specialisation to serve clients with more precision.
Two of these three are platform vendors buying their way into AI readiness. The third is a services platform buying vertical depth. Different buyers, same underlying logic: acquire what would take too long to build.
What the multiples tell you
Global comparables in this sector show an EV/Gross Revenue range of 2.0x to 4.4x at closing, averaging around 3.2x, the highest average in this whitepaper series (ahead of ServiceNow at roughly 2.8x, Google Cloud at roughly 2.3x, and Salesforce at roughly 1.2x to 1.5x). Publicly listed data and AI firms currently trade at a median EV/EBITDA of 14x to 15x, with platform-led outliers pulling the average higher still. Public market pricing is anchoring private valuations upward, particularly for businesses with genuine platform exposure and scalable delivery.
What this means if you're building a data or AI services business
Move your revenue mix toward operate, not just build. Recurring managed services and embedded analytics engagements are what's commanding premium multiples right now. If most of your revenue is still one-off implementation projects, that's the highest-leverage thing to change before a sale.
Pick a lane, and go deep in it. Whether that's a specific vertical, a specific ecosystem (Snowflake, Databricks), or both, specialisation is what separates the firms getting premium multiples from the horizontal generalists facing margin pressure.
Show production-grade AI delivery, not pilots. Acquirers are explicitly looking for firms that can integrate models into real business workflows, not consultancies with a portfolio of GenAI proofs of concept. The gap between "we ran a pilot" and "we operate this in production" is where a lot of valuation gets made or lost.
Your team is an asset buyers can't easily replicate. With senior data engineering talent scarce, a stable, certified, scaled team is itself a source of value, not just a cost line acquirers will look to cut.
If you want to talk through where your business sits across these dimensions, or what a realistic valuation range looks like given your mix of build versus operate revenue, we're happy to help. Our full breakdown of the Data, Analytics & AI Services landscape, including the six provider archetypes, deal multiples, and our READY+ framework for exit preparation, is available in the whitepaper below.
