Vertical data platforms are being shaped by AI demand, data governance, regulatory complexity, and buyer interest in proprietary datasets that are embedded in customer workflows.
If you have built a business around proprietary data, whether in healthcare, financial services, logistics, or another vertical, buyers will focus on one question first: does the data create a durable workflow advantage that cannot be easily replicated?
The M&A market for vertical data platforms is being shaped by AI demand, cloud infrastructure, data governance, and regulatory complexity. Large technology and information-services buyers have continued to acquire data infrastructure and data management assets. IBM completed its $6.4 billion acquisition of HashiCorp, Salesforce agreed to acquire Own for $1.9 billion in cash, and S&P Global completed its $1.8 billion acquisition of With Intelligence.
For founders, the useful lesson is not that every data company will command a premium. It is that buyers are paying attention to datasets, infrastructure, and workflow products that are difficult to replace, deeply embedded, and relevant to AI, analytics, compliance, or customer decision-making.
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 and why will help you make more informed decisions about the future of your company.
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 is 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 reported fiscal Q2 2026 revenue growth and continues to be a major life sciences data and CRM platform. IQVIA, formed from the merger of IMS Health and Quintiles, is another large reference point in healthcare data and analytics.
Financial data is dominated by established information-services companies. S&P Global reported $3.9 billion in Q3 2025 revenue and completed the $1.8 billion acquisition of With Intelligence to strengthen private markets data. FactSet reported $2.3 billion in fiscal 2025 revenue and has acquired Irwin and LiquidityBook to deepen its workflow and data coverage.
Supply chain and logistics data has emerged as a growth segment, with companies providing visibility into global trade flows, shipping routes, inventory levels, and supplier risk. Alternative data providers serving hedge funds and institutional investors are another part of the market, though founders should be careful with headline market-size forecasts because definitions vary widely across research providers.
The key characteristic uniting these businesses is the proprietary data moat. Once a company has become an authoritative source for a particular dataset and that data is embedded in customer workflows, switching costs can become meaningful.
The past eighteen months have produced a series of landmark transactions that define the current M&A environment for data platforms.
IBM has been an active strategic acquirer in data and infrastructure. Its agreement to acquire Confluent, announced in 2025, was positioned around data streaming and enterprise generative AI. This followed IBM's $6.4 billion purchase of HashiCorp, which strengthened its hybrid cloud platform.
Salesforce's $1.9 billion cash acquisition of Own in September 2024 highlighted the importance of data management, backup, and archiving capabilities inside large software ecosystems.
S&P Global continued its acquisition-driven expansion with the $1.8 billion purchase of With Intelligence, strengthening its private markets data and analytics. S&P's earlier $44 billion all-stock acquisition of IHS Markit remains a defining transaction in financial data consolidation.
Snowflake's acquisition of Datavolo targeted data integration, while Cloudera's purchase of Octopai addressed data lineage and governance. Both deals reflect the growing importance of data quality, provenance, and control in AI-driven workflows.
Financial sponsors remain active in data platform transactions, especially where a business has recurring revenue, strong retention, and room for operational improvement. Broader software M&A reports are useful context for buyer activity, but vertical data companies still need to be evaluated on their own data rights, workflow depth, and customer concentration.
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. Niche data providers in areas such as clinical trial data, pharmaceutical pricing, and medical device intelligence have attracted sponsor interest.
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 workflow capabilities.
Vertical data platforms are not valued on a single category multiple. Buyers underwrite the quality of the dataset, the rights attached to it, the workflows it supports, and the durability of the revenue model.
Important diligence factors include:
Broader SaaS valuation resources can provide context, but a vertical data platform needs a more specific analysis. A commodity aggregator and a proprietary workflow dataset may both be "data businesses" while underwriting very differently.
For founders, the practical implication is clear: demonstrate the uniqueness and defensibility of your data, the way it is embedded in customer workflows, and the degree to which it supports AI, analytics, compliance, or mission-critical decisions.
IBM has been assembling data infrastructure, automation, and AI capabilities through acquisitions including HashiCorp and the announced Confluent transaction. Its strategy centres on building an enterprise AI platform that can support data ingestion, infrastructure automation, and analytics.
S&P Global pursues acquisitions that deepen its data moats across financial services verticals. Its acquisition of With Intelligence exemplifies its focus on private markets, alternatives, and wealth management data.
Salesforce remains a relevant buyer for data management capabilities inside its ecosystem. The Own acquisition showed that backup, governance, and data resilience can be strategic inside a large application platform.
Snowflake and Databricks compete for data platform capabilities, with both companies using product development and targeted acquisitions to strengthen data engineering, governance, and AI offerings.
Thoma Bravo, Vista Equity Partners, and Francisco Partners are relevant names in data-centric software. These firms typically look for recurring revenue, retention, pricing power, and opportunities for operational improvement.
Leeds Equity Partners and STG Partners have also been active in niche data verticals, including education and social services data platforms.
Within individual verticals, domain-specific acquirers play an important role. In healthcare data, companies like IQVIA, Veeva Systems, and Optum have strategic reasons to evaluate niche data providers. In financial data, major exchanges and information providers such as London Stock Exchange Group, Intercontinental Exchange, and Nasdaq have acquired data and analytics assets. In supply chain and logistics, companies like Descartes Systems, project44, and FourKites are relevant names for 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.
Several structural forces are accelerating M&A activity in vertical data platforms:
The AI imperative is a major driver. Enterprise AI initiatives require high-quality, domain-specific data, but buyers will still ask whether the dataset is licensed, clean, permissioned, and useful in real workflows.
Data regulation and compliance create barriers to entry that can benefit incumbents. Regulations such as GDPR, HIPAA, and emerging AI governance frameworks make it more expensive for new entrants to collect and distribute sensitive data.
Platform consolidation is driving horizontal expansion. Large software companies seek to offer broader data solutions within their ecosystems, acquiring vertical data providers or workflow products to fill gaps.
The maturation of alternative data is creating exit opportunities for some providers. Niche datasets can be attractive when they are differentiated and already embedded in investment, risk, or operating workflows.
Cloud migration of legacy data businesses is generating both acquisition targets and acquirer interest. Some established data businesses still operate on-premises or in hybrid environments, creating modernisation opportunities for strategic buyers and sponsors.
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.
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 stronger negotiating position.
AI integration matters, but it needs to be real. Even if your platform was not originally designed for AI use cases, show how your data is used in analytics, model workflows, governance, or automation. Avoid positioning AI as a wrapper if the underlying dataset is the real asset.
Retention metrics are under scrutiny. Acquirers will closely examine net revenue retention, customer churn, expansion dynamics, and whether the data is embedded deeply enough to survive budget cycles.
Timing depends on readiness. AI-driven demand and active strategic buyers can help, but a founder still needs clean data rights, credible metrics, and a clear buyer map before going to market.
Consider your exit path carefully. Strategic buyers and PE-backed transactions can have very different implications for product direction, team structure, rollover equity, and founder role. The right path depends on personal objectives, company stage, and growth trajectory.
The M&A market for vertical data platforms in 2025-26 is active, but it is selective. AI demand, regulatory barriers, and buyer interest in proprietary workflows can all help, but only when the business has defensible data, clear rights, and customer usage that supports the story.
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 key is to understand your position in the market and prepare your business to demonstrate the qualities buyers value most.
The companies that attract the strongest buyer interest 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 AI, analytics, compliance, or operating decisions.
Founders who want broader context on software buyer behaviour can also read our guides to vertical SaaS M&A and AI's implications for SaaS in 2026.
Levera Partners advises technology founders on mergers and acquisitions. If you are exploring a sale or strategic partnership, we would welcome a confidential conversation.
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