The data landscape is rapidly evolving – reshaping how we understand and value companies. With China's recent move to recognise data resources as assets in financial statements, the implications for mergers and acquisitions (M&A) and private equity (PE) transactions are far reaching. This development emphasises the necessity of viewing data as a critical commercial asset, impacting valuation, deal structuring, and strategic investment decisions.
Below are takeaways from a longer article, in which we discuss how these changes may impact private equity investments and processes more widely.
Data as a Commercial Asset: TAKEAWAYS
New Chinese regulations allow data resources to be categorised as assets.
China's changes may influence global standards for data asset valuation, impacting M&A activities.
Recognising data assets introduces broader deal structuring methods, which could include more asset-based transactions and earn-outs based on data performance.
Compliance with new standards will incur costs but improves data transparency and integration post-acquisition.
PE firms will focus more on data-rich companies.
Data and AI due diligence will become increasingly crucial for valuation and investment planning.
For PE firms, this shift necessitates a deeper understanding of how data assets contribute to a target company's overall strategy, operations, and – vitally – valuation.
Coupled with advances and adoption of AI and generative AI in particular, we believe that over time, enhanced valuation metrics will become essential, and the complexity of due diligence processes will increase.
Accordingly, PE firms must incorporate data and AI due diligence into their standard pre-investment assessments, alongside more traditional diligence (such as financial, legal, tax, and commercial).
Moreover, investors will need to verify data assets and the ecosystem(s) in which they operate, plus interrogate how data and analytics contribute to a target company’s business model. Also, investors must test the accuracy of data valuations and the methodologies used, ensuring a robust and realistic appraisal of these intangible assets.
What is more, PE firms will need to equally apply this thinking during exit processes – to optimally position data and AI assets within broader commercial considerations and valuation parameters.
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