Companies value corporate data in mergers and acquisitions (M&A) according to its ability to generate future revenue, strengthen competitive advantages, and reduce operating costs, rather than its volume alone. Buyers assess data quality, exclusivity, usability, ownership rights, and privacy risks as part of due diligence.
For years, businesses have described data as the “oil of the XXI century”. The comparison reflects its growing importance to digital operations, AI, customer intelligence, and strategic decision-making. However, unlike oil, data can be reused across multiple applications without being depleted. Its economic value therefore depends less on how much information a company possesses than on what it can legitimately extract from it.
When evaluating a potential target, buyers are not simply acquiring its revenue, employees, infrastructure, or intellectual property. Companies may also be acquiring years of customer interactions, purchasing histories, financial records, operational information, and proprietary datasets that competitors cannot easily reproduce.
Consequently, corporate data can influence an acquisition’s strategic rationale and purchase price, although it rarely receives an independently disclosed valuation. Its contribution is generally incorporated into the expected future cash flows, intangible assets, and competitive advantages of the business being acquired.
The scale of Mexico’s acquisition market provides relevant context. According to PwC México’s 2026 midyear M&A analysis, 69 transactions were completed in the country during the first half of 2026, representing approximately US$22.6 billion. Technology accounted for 19% of transactions, recovering from 15% in 2025. Although these figures do not isolate data-driven acquisitions, they demonstrate the importance of examining technological capabilities when evaluating corporate transactions.
Measuring the Economic Value of Data
There is no universally accepted price per record, customer profile, or terabyte that determines what corporate data is worth. Valuation specialists instead examine the economic benefits associated with an information asset, its replacement cost, and comparable transactions when reliable benchmarks exist.
Deloitte Insights’ research on data asset valuation identifies three principal approaches.
The income approach estimates the future financial benefits generated by a dataset, including additional revenue, improved customer retention, or operational savings. Analysts isolate the contribution attributable to the information itself from the returns generated by other resources, then calculate its present value.
The cost approach estimates how much it would cost to reproduce the dataset. This includes collection, infrastructure, data engineering, enrichment, and maintenance, adjusted for obsolescence and quality limitations.
Lastly, the market approach compares the asset with similar datasets traded in other transactions. However, reliable comparisons remain difficult because information is rarely sold independently, and transaction disclosures seldom reveal the precise characteristics of the datasets involved.
Consider two companies with databases containing one million customer records each. The first maintains outdated contact information collected without sufficiently documented permissions. The second holds regularly updated purchasing histories, verified customer profiles, and information that can legally support personalized financial services.
Despite having identical record counts, the second dataset could generate greater economic benefits because it supports more accurate segmentation, customer acquisition, and product development.
The distinction also explains why data volume is an inadequate valuation metric. Accuracy, recency, uniqueness, interoperability, provenance, and permitted uses determine whether information can actually generate returns.
When Data Becomes an Acquisition Driver
The importance of information becomes clearer when companies acquire businesses specifically to access capabilities they would otherwise take years to develop.
One example is Mastercard’s acquisition of Finicity, announced in June 2020 for US$825 million, with an additional potential earn-out of up to US$160 million. Finicity provided technology enabling real-time access to financial information and insights.
The acquisition strengthened Mastercard’s open banking capabilities through connections to financial institutions and technologies supporting the authorized exchange of financial information. Rather than purchasing an isolated database, Mastercard acquired an established infrastructure for accessing and using financial data across banking, lending, and wealth management.
When the transaction closed in Nov. 2020, Mastercard highlighted the opportunity to expand financial services while maintaining consumer control over how account information was used. The strategic benefit was access to an existing ecosystem, its technology, and the capabilities required to develop additional services.
Another example is Intuit’s acquisition of Credit Karma, announced in February 2020 for approximately US$7.1 billion in cash and stock. Credit Karma brought more than 100 million members across the United States, Canada, and the United Kingdom, together with a platform built around consumer financial information.
The acquisition gave Intuit access to a substantial consumer platform that complemented its existing financial software and services. Its subsequent performance provides measurable evidence of the business developed around that asset base.
According to Four Week MBA, Credit Karma’s revenue increased from US$1.6 billion in fiscal 2024 to US$2.6 billion in fiscal 2026. Its membership also expanded from 143 million to 149 million over the same period.
These results demonstrate the commercial scale achieved by the acquired business. They do not establish that customer data alone generated the growth, since product development, distribution, marketing, and integration also contributed. Nevertheless, the case illustrates how customer information can support a larger financial-services platform when combined with complementary capabilities.
However, acquiring data can also create liabilities that directly affect transaction economics.
Verizon’s acquisition of Yahoo provides a particularly clear example. In February 2017, the companies reduced the purchase price of Yahoo’s operating business by US$350 million, from approximately US$4.83 billion to US$4.48 billion, following the disclosure of significant data breaches.
The revised agreement also established arrangements for sharing certain post-acquisition legal and regulatory liabilities. The transaction demonstrated that the information accumulated by a digital business can represent both a strategic asset and a financial exposure when its security and governance are inadequate.
For Mexican businesses, these considerations have an additional regulatory dimension. The Federal Law for the Protection of Personal Data Held by Private Parties, published in the Diario Oficial de la Federación on Mar. 20, 2025, establishes requirements concerning lawful processing, consent, privacy notices, and transfers of personal information.
Articles 35 and 36 address transfers to third parties and the circumstances under which they may occur without individual consent. Consequently, an acquisition agreement cannot be treated as unrestricted authorization to repurpose every customer record for the buyer’s commercial objectives. The transaction structure, applicable exceptions, original processing purposes, and contractual obligations must be examined.
What Will Determine Data’s Weight in M&A by 2027?
Research published in 2026 suggests that information will become increasingly important throughout the acquisition lifecycle, although it does not establish a universal premium for companies possessing proprietary datasets.
KPMG International’s 2026 Global M&A Outlook, based on a survey of 700 dealmakers across 20 countries and jurisdictions, identifies technological capabilities, AI adoption, and execution discipline as increasingly important considerations. Half of respondents anticipate moderate or significant growth in carve-out activity over the following 12–24 months. These transactions frequently require separating interconnected IT environments, information systems, and operational resources, making data governance and portability relevant to transaction feasibility.
Meanwhile, DealRoom and M&A Science’s State of AI in M&A 2026 report, based on 237 responses from 233 practitioners, found that 82% of deal teams were already using or piloting AI. Due diligence represented 25% of reported applications, while respondents identified it as the area expected to experience the greatest impact over the next 12–24 months, at 26%.
The research also identified data privacy and security concerns as obstacles to wider adoption. This highlights an important distinction: information is becoming more valuable not only as something acquired but also as the foundation for analyzing, validating, and integrating acquisitions.
PwC’s Sept. 2026 M&A Integration Survey adds another dimension. Based on 530 senior executives and dealmakers, it found that only approximately one-third of acquirers fully achieved their original transaction objectives. The results emphasize the importance of defining operating-model decisions before closing, rather than assuming that anticipated synergies will materialize automatically.