The growing adoption of artificial intelligence, cloud-based services, and Internet-of-Things devices is driving a massive increase in connectivity and data generation. As data becomes a key value driver in technology and software M&A, business and financial due diligence must include a robust assessment of how companies are collecting and monetizing data assets and the impact on financial performance.

Why now?

The global data monetization market is forecast to grow from $3.47 billion in 2024 to $12.62 billion by 2032.[1] The trend is expected to reap significant benefits in the U.S. in particular, driven by higher data generation.

Moreover, tech and software investors are keen to generate new pockets of value from their investments. Those who bought at peak valuations in the aftermath of the pandemic – when the Nasdaq stock index as well as global startup funding hit all-time highs[2] – are particularly under pressure as time draws closer to attract buyers or the next round of funding.

Companies must therefore consider data as a monetizable asset class with commercialization potential and financial statement implications that directly affect valuation.

Based on our expertise and on-the-ground experiences with clients, we outline some key data monetization considerations and action-oriented insights from a valuation and financial due diligence (FDD) perspective.

Data monetization: A due diligence checklist

When viewing data monetization with an investor’s lens, it is essential to consider the potential impact of data from the outset.

Platform expertise

A buyer or investor must develop a clear understanding of the engine that enables data ingestion, processing and deployment. Platform expertise is vital to determine feasibility of data monetization projects and scalability.

Actionable insights:

• Conduct a review of the systems and data flow to determine bottlenecks
• Evaluate data ingestion models for monetization potential
• Assess scalability and level of investment required to execute the model(s)
• Identify risks around data ownership and rights

Customer base

Understanding the customer base, customer retention profile, and sell-through is essential to quantifying both short-term monetization opportunities and long-term potential.

Actionable insights:

• Assess the customer base by industry, size, usage frequency and willingness to pay for enhanced data products.
• Analyze industry and in-house usage data (if available) to identify potential targets for premium data tiers.
• Quantify ongoing support costs that take into account data update costs, number of people supporting the product and reliance on third-party vendors.
• Review SLAs and vendor dependencies for impact on margins.

Product stage

Companies may be in different stages of product rollout – pre-commercial (yet to hit the product rollout stage), early commercial (figuring out how to roll out a nascent product) or mature. Investors must assess the valuation risks and capital requirements of each stage.

Actionable insights:

• Classify products by readiness level.
• Conduct customer interviews to determine viability and product-market fit of early-stage products.
• Model rollout timelines and capital needs.

Revenue streams

A critical aspect of FDD is exploring various revenue streams and assessing how the company is monetizing data – through one-time fees, subscriptions or usage-based pricing. FDD must identify the levers impacting scalability and margin for each revenue type to assess each product’s ability to generate cash flow.

Actionable insights

• Identify current and potential data-driven revenue streams.
• Evaluate each revenue model and their impact on margins (for example, subscription-based vs upfront data purchase).
• Investors may use scenario modelling and pricing simulations to analyze various monetization strategies.

Financial statement impact

Data monetization calls for specific accounting and cashflow considerations that must be assessed to determine the sustainability of earnings.

Actionable insights

• Review capitalization policy to ensure data cleansing and platform development costs are accounted for appropriately.
• Isolate revenue and cost components directly attributable to data products to ensure data revenue and margin claims hold up under scrutiny.
• Assess working capital and free cash flow impact (for example, large one-time data purchases may cause lumpy cash flow versus subscription models).

How A&M can help

Our software and technology (S&T) team within Alvarez & Marsal’s Global Transaction Advisory Group (TAG) delivers comprehensive financial due diligence services to clients. Whether the target business is an early-stage startup or a mature enterprise, we provide deep insights into its quality of earnings, quality of revenue / ARR and customer retention. Our S&T team leverages its extensive industry knowledge and experience to help clients identify risks and opportunities and execute successful transactions.

[1] https://www.fortunebusinessinsights.com/data-monetization-market-106480# 

[2] https://news.crunchbase.com/venture/2021-2025-market-comparison-valuations-ai-startups/