Wider aperture: data quality in dealmaking

November 2026  |  FEATURE | MERGERS & ACQUISITIONS

Financier Worldwide Magazine

November 2026 Issue

Data is often regarded as the most valuable commodity of the digital age. All-pervasive and infinitely reusable, it can be transformed into a valuable asset that fuels innovation and insight.

However, while accurate, complete and consistent data underpins business success, many organisations still treat data quality and integrity as an afterthought. By assuming data is inherently reliable, businesses can overlook the potentially serious consequences of poor-quality information.

Actian’s report ‘The Consequences of Poor Data Quality: Uncovering the Hidden Risks’ defines poor-quality data as information that is inaccurate, incomplete, duplicated or inconsistently formatted. Such issues can arise from a range of sources, including data integration challenges, data capture inconsistencies, migration issues, data decay and duplication.

According to the report, poor data quality quietly drains millions in revenue, productivity and trust. In the US, for example, the average business loses $15m annually due to poor data quality, with the wider economic impact estimated at $3.1 trillion.

“The value of data has historically been underestimated, often sitting knowingly unused or certainly undervalued by companies,” says Simon Heath, chief operating officer and corporate finance partner at the Heligan Group. “The exponential growth of data generated by companies provides a lake of valuable data points that have an intrinsic value if suitably analysed or extrapolated into commercial applications.

“All companies generate data, whether a manufacturing company or a software as a service. Understanding the provenance of the information, and importantly the ownership of that data which is often a grey area, is integral to being able to convert into monetisation,” he adds.

Data quality in M&A

In an M&A context, the consequences of poor data quality can be particularly costly. Dealmakers rely on extensive quantitative and qualitative information to assess value, identify risks and shape future integration strategies.

“It is not that digital assets are new, but they are now the layer acquirers can no longer verify from the outside,” attests Liz Henderson, non-executive director and board adviser in digital, data and artificial intelligence (AI). “Financial statements are audited. Data estates, in most cases, are not.

“A target’s data, its accuracy, its ownership and its accumulated technical shortcuts increasingly determine what the acquired business can actually do post-completion,” she continues. “Yet it is still the part of a deal least likely to have been independently tested before signing.”

Without adequate testing, poor-quality data can distort valuations, obscure critical liabilities and undermine post-merger integration. These factors contribute to the widely cited high failure rate of M&A transactions, often estimated at between 70 and 90 percent depending on the methodology used.

Conversely, trusted data provides a foundation for informed decision making, efficient integration and long-term value creation. When organisations merge operations or transfer assets, accurate and consistent information is essential to maintaining business continuity and realising anticipated benefits.

Another increasingly important consideration is the speed at which buyers can gain confidence in a target’s underlying information. Competitive auction processes often place significant pressure on bidders to complete diligence activities within compressed timeframes. Where data is fragmented, poorly governed or spread across multiple systems, management teams may struggle to provide timely responses to requests, delaying analysis and potentially raising concerns about operational maturity.

By contrast, organisations with well-structured data environments are often better positioned to demonstrate transparency, respond quickly to diligence enquiries and provide clear evidence supporting key performance indicators. This can help build buyer confidence, reduce uncertainty and strengthen a target’s negotiating position during the transaction process.

Astera’s 2025 report ‘The Impact of Data Quality on M&A Success’ highlights several ways in which well-governed data supports transaction success.

Accurate valuation and deal structuring is the first. Reliable financial information provides the basis for robust analysis and forecasting, helping buyers avoid overpaying and enabling more effective structuring of purchase price, payment mechanisms and earn-out arrangements.

The second is effective due diligence. Comprehensive and accurate data helps uncover hidden risks, liabilities and opportunities. It enables buyers to detect inconsistencies, anomalies and operational weaknesses that might otherwise remain hidden until after completion.

In the experience of Ms Henderson, dealmakers should check what has been assumed, not only what has been disclosed. “In one case I am aware of, an entire operating location was not identified during due diligence because no one had asked the direct question, it simply had not been considered,” she affirms. “Data-driven due diligence works best paired with structured questioning designed to surface what a data room alone will not show.

“None of the above replaces the financial and legal diligence it sits alongside,” she continues. “But a data-literate due diligence process turns ‘we think this is a good business’ into ‘we know what this business costs to run properly’, which is a materially different basis for pricing a deal.”

A third benefit is smoother integration. Combining systems, processes and datasets from two organisations requires a consistent and accurate information base. Clean data reduces disruption, accelerates integration and minimises operational errors.

High-quality data also plays a critical role in identifying synergies and cost-saving opportunities. By providing clearer insight into operations, customers and market trends, it allows businesses to optimise combined operations, improve efficiency and drive revenue growth.

Risk mitigation is another important consideration. Reliable data helps identify regulatory, legal and operational risks early in the process, enabling organisations to strengthen due diligence, develop mitigation strategies and undertake more effective contingency planning.

“This is at the core of digital and data due diligence, particularly if a company is multijurisdictional and subject to differing regulatory and legislative frameworks,” says Mr Heath. “Additionally, understanding the intellectual property (IP) ownership of self-generated digital solutions is increasingly important, and using patent attorneys to protect digital assets will become a fast-growing focus to preserve inherent value in a transaction.

“Beyond the digital and data landscape, protecting data and digital assets is critical,” he continues. “Cyber security and protecting the digital footprint is of increasing importance given the global trend of increasing cyber attacks. Cyber due diligence is about protecting value, ensuring that a company has the appropriate defences to maintain and retain its operations and assets, and is a critical diligence path for any transaction.”

Customer and operational data are also becoming increasingly important sources of value during transaction assessments. Beyond financial performance, acquirers are seeking deeper insight into customer retention, purchasing behaviour, service performance and supply chain resilience.

Reliable datasets can reveal trends that may not be immediately visible through conventional financial reporting alone, allowing buyers to better understand recurring revenue streams, customer concentration risks and operational dependencies.

In sectors undergoing rapid digital transformation, these insights can have a material impact on valuation assumptions and strategic planning. As a result, organisations should strive to effectively demonstrate the quality and reliability of their operational data, to justify value expectations and support growth projections.

Best practices

From the earliest stages of a transaction through to completion, strong data governance helps dealmakers build reliable information pipelines and establish a clearer understanding of operational realities.

“A target’s digital footprint – its technology stack, platforms, integration architecture and digital channels – tells us things financials cannot,” says Ms Henderson. “How much it will actually cost to run the business the day after completion, and how much of its reported performance depends on infrastructure that will not survive migration and integration intact.”

Prometheus Group’s ‘8 Master Data Best Practices for a Successful M&A’ highlights several approaches that can improve data integrity and support successful system consolidation.

Data considerations should be integrated into deal timelines from the outset, supported by dedicated teams responsible for coordinating integration efforts. Technology leaders should also become involved early in the transaction process, helping ensure critical systems, dependencies and integration requirements are identified before key decisions are made.

Equally important is strong business sponsorship. Employees across functions should understand the strategic value of maintaining well-governed information assets and the benefits they bring to operational efficiency and decision making.

Organisations should establish a common set of business rules, processes and data standards, supported by recognised champions within each business unit who can communicate decisions, explain methodologies and encourage adoption. Existing governance platforms and technology tools can then be leveraged to accelerate migration and integration activities.

Dealmakers should also focus on identifying early opportunities within data assets by understanding where information resides, how it is used and how it aligns with the future operating model. Finally, long-term success requires a clearly defined post-integration governance strategy to ensure improvements are sustained rather than eroded over time.

Successful acquirers are increasingly treating data integration as a strategic initiative. While systems migration remains an important component, the broader objective is to establish a common foundation for reporting, decision making and future innovation across the combined organisation. This often requires the harmonisation of data definitions, governance frameworks and ownership responsibilities, alongside the integration of technology platforms.

Failure to address these issues early can result in inconsistent reporting, duplicated processes and difficulties measuring post-deal performance. By establishing clear accountability and governance mechanisms from the outset, organisations can improve the likelihood that anticipated synergies are achieved and sustainable value is created over the longer term.

“The gap I see most often is between the digital capability a company presents and what is actually running underneath,” notes Ms Henderson. “Layers of legacy platforms, duplicated systems from prior acquisitions never fully retired, and customer-facing digital channels held together by manual workarounds rather than the infrastructure the organisational chart implies.”

“A digital footprint assessment is not about counting systems,” she continues. “It is about testing whether the technology can carry the deal’s growth assumptions, or whether it is already at capacity holding up the business as it stands today.”

Wider aperture

As data continues to proliferate, dealmakers will need to broaden their understanding of the wider data ecosystem and place greater emphasis on data quality, governance and digital resilience.

Increasingly, acquirers are also assessing whether a target’s data is suitable for AI-driven applications. As organisations accelerate adoption of generative AI technologies, the quality, governance and provenance of corporate data are becoming critical diligence considerations. Buyers are seeking assurance that information assets can support future AI initiatives without creating undue regulatory, operational or reputational risk.

“The organisations best placed for the next few years are the ones treating data governance as core infrastructure now, not as a fix to apply post-acquisition,” says Ms Henderson. “As AI accelerates what can be built on top of a data estate, the gap between well-governed and poorly-governed businesses will widen faster than most boards currently expect – which makes this less a future consideration for dealmakers and more an urgent one.”

By recognising the hidden costs of poor-quality information, quantifying its impact and implementing effective governance strategies, organisations can unlock the full value of their data and position themselves for sustained success.

© Financier Worldwide

BY

Fraser Tennant