Vertical Data Platforms M&A: A Founder's Guide to the 2025-26 Market
Introduction
If you have built a business around proprietary data, whether in healthcare, financial services, logistics, or any other vertical, you are sitting on one of the most valuable asset classes in enterprise technology. The market for vertical data platforms has entered a period of intense consolidation, driven by the convergence of artificial intelligence, cloud infrastructure, and an insatiable corporate appetite for unique, defensible datasets.
The numbers tell a compelling story. Global software M&A hit a record 2,897 announced transactions in 2025, a 35% year-on-year increase, with an aggregate deal value of $291 billion according to Kroll's Winter 2026 sector update. Within this surge, data-centric businesses have commanded some of the highest valuations: IBM's $11 billion acquisition of Confluent and $6.4 billion purchase of HashiCorp underscored how aggressively large technology companies are paying for data infrastructure. Salesforce paid $1.9 billion in cash for data management firm Own, its largest deal since the Slack acquisition.
For founders of vertical data platforms, these headline deals represent more than market enthusiasm. They reflect a structural shift in how enterprises value proprietary data assets. The rise of generative AI has made high-quality, domain-specific datasets exponentially more valuable: every large language model, every predictive analytics engine, and every AI-driven workflow depends on data that cannot be easily replicated.
This guide examines the M&A landscape for vertical data platforms in 2025 and 2026. Whether you operate a healthcare data business, a financial information service, a supply chain intelligence platform, or an alternative data provider, understanding who is buying, at what valuations, and why will help you make informed decisions about the future of your company. The window of opportunity is wide open, but it will not remain so indefinitely.
Market Overview
Defining the Vertical Data Platform Landscape
Vertical data platforms encompass businesses that collect, curate, and distribute proprietary datasets within a specific industry. Unlike horizontal data infrastructure companies (which provide tools for managing any data), these businesses derive their value from the data itself: its exclusivity, depth, accuracy, and the difficulty of replicating it.
The landscape spans several major verticals:
Healthcare data represents one of the largest segments. Definitive Healthcare, which went public in 2021 and reported $252 million in revenue for fiscal 2024, provides commercial intelligence to life sciences and healthcare technology firms. Veeva Systems, with fiscal Q2 2026 revenue growing 17% year-on-year, dominates life sciences data and CRM. IQVIA, formed from the merger of IMS Health and Quintiles, generates over $15 billion annually from healthcare data and analytics.
Financial data is dominated by established giants. S&P Global reported $3.9 billion in Q3 2025 revenue alone, a 9% increase year-on-year, and announced its $1.8 billion acquisition of With Intelligence to strengthen private market data capabilities. FactSet achieved record revenues of $2.3 billion in fiscal 2025, expanding its client base to 9,000 firms and acquiring Irwin and LiquidityBook to deepen its data moat. Bloomberg and Refinitiv (now part of the London Stock Exchange Group) round out the top tier.
Supply chain and logistics data has emerged as a growth segment, with companies providing real-time visibility into global trade flows, shipping routes, and inventory levels. Alternative data providers, serving hedge funds and institutional investors, represent a market projected to grow by $60.3 billion from 2024 to 2029, reflecting a compound annual growth rate of 52.5% according to Technavio.
The overall data-as-a-service market is estimated to exceed $50 billion globally, with vertical-specific platforms growing at rates well above the broader software market. The key characteristic uniting these businesses is the proprietary data moat: once a company has established itself as the authoritative source for a particular dataset, switching costs become extraordinarily high.
M&A Activity and Deal Flow
Headline Transactions Reshaping the Market
The past eighteen months have produced a series of landmark transactions that define the current M&A environment for data platforms.
IBM has been the most aggressive strategic acquirer. Its $11 billion acquisition of Confluent, announced in 2025, creates an end-to-end data streaming and processing platform for enterprise AI. This followed IBM's $6.4 billion purchase of HashiCorp in 2024, valued at $35 per share (a 42.6% premium over the prior closing price). IBM's thesis is clear: enterprises need integrated data infrastructure to deploy AI at scale, and the company is willing to pay substantial premiums to assemble these capabilities.
Salesforce's $1.9 billion cash acquisition of Own in September 2024 marked the company's largest deal since Slack. Own provides data management, backup, and archiving services for the Salesforce ecosystem, highlighting how even platform companies recognise the value of dedicated data management capabilities.
S&P Global continued its acquisition-driven expansion with the $1.8 billion purchase of With Intelligence, strengthening its private markets data and analytics. The company also divested non-core assets, selling its Enterprise Data Management and thinkFolio businesses, demonstrating a disciplined portfolio optimisation approach. S&P's earlier $44 billion all-stock acquisition of IHS Markit in 2022 remains the defining transaction in financial data consolidation.
Snowflake's acquisition of Datavolo in November 2024 targeted the data integration layer, aiming to simplify multimodal data pipelines for enterprise AI. Cloudera's purchase of Octopai addressed data lineage and governance, critical capabilities for regulated industries. Both deals reflect the growing importance of data quality and provenance in AI-driven workflows.
Private Equity Activity
Financial sponsors have been increasingly active in data platform transactions. The Kroll Winter 2026 report noted that financial sponsors paid an average EV/LTM revenue multiple of 4.4x in 2025 software deals, while strategic buyers averaged 5.6x, representing the largest strategic premium in a decade.
Private equity firms are particularly drawn to data businesses with high recurring revenue, strong retention metrics, and defensible market positions. Healthcare data has been a favoured sector: Definitive Healthcare, taken private by its PE backers after going public, continues to operate as a standalone platform. Niche data providers in areas such as clinical trial data, pharmaceutical pricing, and medical device intelligence have attracted significant sponsor interest.
Mid-Market and Emerging Deals
Below the headline transactions, a steady flow of mid-market deals continues. FactSet's acquisitions of Irwin and LiquidityBook expanded its wealth management and trading data capabilities respectively. Datasite completed its acquisition of Sealk, strengthening its position in deal management data. Across the alternative data sector, smaller acquisitions of web scraping platforms, satellite imagery analytics providers, and social sentiment analysis tools have become routine.
Valuation Benchmarks
What Data Platforms Command
Vertical data platforms consistently trade at premium valuations relative to the broader software market, reflecting the defensibility and stickiness of proprietary datasets.
| Metric | Data Platforms (Median) | Broader Software (Median) |
|---|---|---|
| EV/Revenue (Strategic) | 7x-12x | 5.6x |
| EV/Revenue (Financial Sponsor) | 5x-8x | 4.4x |
| EV/EBITDA | 20x-30x | 15x-22x |
| Revenue Growth | 10-20% | 8-15% |
| Net Revenue Retention | 110-130% | 100-115% |
Sources: Kroll Winter 2026 Software Sector Update; Aventis Advisors; SaaS Capital. Ranges represent estimates for data-centric vertical platforms.
Several factors drive premium valuations in this sector:
Proprietary data moats are the single most important value driver. Acquirers will pay significantly more for data that cannot be replicated through publicly available sources. A healthcare data platform with exclusive hospital purchasing data or a financial data provider with proprietary credit risk models commands multiples well above commodity data aggregators.
High switching costs and retention rates are closely related. Once an enterprise has integrated a data feed into its workflows, analytical models, and compliance processes, the cost of switching becomes prohibitive. Net revenue retention rates above 120% are common among best-in-class data platforms.
AI readiness has emerged as a critical factor in 2025-26 valuations. Data platforms that can demonstrate their datasets are being used to train or fine-tune AI models, or that offer AI-native analytics capabilities, are commanding significant premiums. The Kroll report noted a 14% increase in strategic buyer multiples from 2024 to 2025, driven substantially by AI-related demand.
Regulatory barriers to entry further enhance valuations in sectors such as healthcare and financial services, where data collection, storage, and distribution are subject to stringent compliance requirements.
For founders, the practical implication is clear: demonstrating the uniqueness and defensibility of your data, along with strong retention metrics and AI relevance, will position your company at the upper end of valuation ranges.
Key Acquirer Profiles
Strategic Buyers
IBM has committed over $17 billion to data infrastructure acquisitions in the past two years. Its strategy centres on building an end-to-end enterprise AI platform spanning data ingestion (Confluent), infrastructure automation (HashiCorp), and analytics (watsonx). IBM targets companies with strong open-source communities and enterprise adoption, and is willing to pay premiums of 30-40% for strategic assets.
S&P Global pursues acquisitions that deepen its data moats across financial services verticals. With annual revenues approaching $15 billion, S&P has both the financial capacity and strategic motivation to acquire niche data providers. Its acquisition of With Intelligence for $1.8 billion exemplifies its focus on private markets, alternatives, and wealth management data.
Salesforce, despite a stated focus on organic growth, remains an opportunistic acquirer of data management capabilities within its ecosystem. The $1.9 billion Own acquisition demonstrated willingness to deploy cash for strategic data assets.
Snowflake and Databricks compete aggressively for data platform capabilities, with both companies making targeted acquisitions to strengthen their data engineering, governance, and AI offerings.
Financial Sponsors
Thoma Bravo, Vista Equity Partners, and Francisco Partners are the most active PE firms in data-centric software. These firms typically target companies with $20-200 million in ARR, strong net retention, and opportunities for operational improvement. Hold periods of 4-6 years are typical, with exits via sale to strategic buyers or secondary PE transactions.
Leeds Equity Partners and STG Partners have been active in niche data verticals, including education and social services data platforms.
Industry-Specific Acquirers
Within individual verticals, domain-specific acquirers play an important role. In healthcare data, companies like IQVIA, Veeva Systems, and Optum (part of UnitedHealth Group) have both the financial resources and strategic motivation to acquire niche data providers. In financial data, the major exchanges and information providers (London Stock Exchange Group, Intercontinental Exchange, Nasdaq) have been active acquirers, seeking to expand their data and analytics offerings. In supply chain and logistics, companies like Descartes Systems, project44, and FourKites are potential acquirers of specialised data assets. Understanding which industry-specific buyers would derive the most strategic value from your dataset is crucial for positioning a sale process effectively.
Consolidation Drivers
Several structural forces are accelerating M&A activity in vertical data platforms:
The AI imperative is the dominant driver. Every enterprise AI initiative requires high-quality, domain-specific training data. As organisations move beyond proof-of-concept deployments to production AI systems, the demand for proprietary datasets is intensifying. Acquirers are paying premiums today to secure data assets that will become even more valuable as AI adoption scales.
Data regulation and compliance are creating barriers to entry that benefit incumbents. Regulations such as GDPR, HIPAA, and emerging AI governance frameworks make it increasingly difficult and expensive for new entrants to collect and distribute sensitive data. Established data platforms with robust compliance infrastructure become acquisition targets precisely because building these capabilities from scratch is prohibitively costly.
Platform consolidation is driving horizontal expansion. Large software companies seek to offer comprehensive data solutions within their ecosystems, acquiring vertical data providers to fill gaps. This was evident in FactSet's targeted acquisitions to expand beyond its core financial data into wealth management and trading.
The maturation of alternative data is creating exit opportunities for early-stage providers. As the alternative data market scales towards $60 billion, niche providers are being acquired by larger platforms seeking to broaden their coverage.
Cloud migration of legacy data businesses is generating both acquisition targets and acquirer interest. Many established data businesses still operate on-premises or in hybrid environments. Cloud-native competitors and PE firms see opportunities to acquire these businesses, migrate them to modern infrastructure, and unlock higher margins and growth rates.
Cross-industry data convergence is driving strategic acquisitions across traditional vertical boundaries. Healthcare data intersects with insurance, employment, and social services; financial data intersects with alternative data sources and supply chain intelligence. Acquirers seeking to build cross-vertical data platforms are pursuing companies at these intersection points.
What This Means for Founders
If you are a founder or CEO of a vertical data platform considering a sale, several practical insights emerge from the current market:
Your data moat is your most valuable asset. Before engaging in any M&A process, invest in documenting the uniqueness, exclusivity, and defensibility of your dataset. Can your data be replicated from public sources? If not, you have a strong negotiating position.
AI integration matters more than ever. Even if your platform was not originally designed for AI use cases, demonstrating how your data is being used to train models or power AI-driven analytics will meaningfully improve your valuation. Consider developing AI-native features or partnerships before going to market.
Retention metrics are under intense scrutiny. Acquirers, both strategic and financial, will closely examine your net revenue retention, customer churn, and expansion dynamics. NRR above 120% will place you in the premium valuation tier.
Timing favours sellers in 2025-26. The combination of aggressive strategic buyer activity, well-capitalised PE funds, and AI-driven demand is creating a favourable environment for data platform exits. The Kroll data showing a 35% increase in software deal volume and a 14% increase in strategic buyer multiples suggests the window is currently wide open.
Consider your exit path carefully. Strategic buyers are paying significant premiums (27% above financial sponsors on average), but PE-backed transactions may offer opportunities for partial liquidity while retaining upside. The right path depends on your personal objectives, company stage, and growth trajectory.
A Generational Opportunity for Data Founders
The M&A market for vertical data platforms in 2025-26 represents a generational opportunity for founders. The convergence of AI demand, regulatory moats, and aggressive buyer activity has created an environment where high-quality data businesses command premium valuations and attract intense acquirer interest.
Whether you operate a healthcare data platform serving life sciences companies, a financial data service powering institutional workflows, or a supply chain intelligence tool enabling global logistics, the fundamental value of your proprietary data has never been higher. The key is to understand your position in the market, prepare your business to demonstrate the qualities buyers value most, and engage the process with clear strategic objectives.
The companies that will command the highest valuations are those that can demonstrate not just valuable data, but defensible data: datasets that are difficult to replicate, deeply embedded in customer workflows, and increasingly relevant in an AI-driven world.
The strategic and financial buyer universe for data platforms has never been broader or more well-capitalised. From IBM's multi-billion-dollar acquisition programme to PE firms seeking stable, high-retention businesses, the demand for quality data assets far exceeds supply. Founders who have spent years building proprietary datasets are uniquely positioned to benefit from this dynamic, provided they approach the M&A process with the preparation and strategic clarity it demands.