5. How mergers and acquisitions overlook data examples of deals where data value was ignored or misjudged
Many deals close without ever putting a number on the target's data. This post looks at examples where data value was ignored or misjudged during M&A, and what those oversights cost the parties involved.
When companies merge or change hands, dealmakers typically focus on tangible assets, revenue streams, intellectual property, and customer contracts. Yet one of the most valuable assets often slips through the cracks: the data itself. Whether it is customer behavior patterns, operational insights, or proprietary datasets accumulated over years, the financial worth of data frequently goes unrecognized during mergers and acquisitions. This oversight can cost both buyers and sellers millions, and in some cases, it reshapes the entire logic of the transaction.
Consider the case of a retail chain acquired primarily for its real estate footprint and brand recognition. The buyer focused on store locations and supplier relationships, while the seller emphasized market share. What neither party formally valued was a decade of point-of-sale data capturing shopping trends, seasonal demand fluctuations, and customer preferences across demographics. A competitor later used similar data to optimize pricing and inventory strategies, gaining a significant market advantage. The acquirer, meanwhile, had failed to recognize the untapped potential sitting in its own transaction logs. Had that data been properly valued and leveraged, the post-merger integration could have unlocked entirely new revenue opportunities.
Another example comes from the technology sector, where a software company was purchased largely for its user base and subscription model. The deal was structured around recurring revenue projections, but little attention was paid to the behavioral data generated by millions of users interacting with the platform daily. That data, if analyzed and applied strategically, could have informed product development, personalized marketing, and even new service offerings. Instead, it remained siloed and underutilized, representing a missed opportunity to enhance customer lifetime value and competitive positioning.
These examples illustrate a broader problem: the M&A process is not yet designed to systematically surface and quantify data value. Due diligence checklists rarely include questions about data quality, governance, or strategic applicability. Valuation models seldom account for the income potential or cost savings that proprietary datasets might generate. As a result, data becomes an afterthought rather than a line item in the negotiation. For businesses preparing to enter a transaction, whether as buyer or seller, conducting a formal data valuation can reveal hidden value, support better deal terms, and ensure that one of the most critical assets of the modern economy is neither ignored nor misjudged.