Intangible Asset Masterclass — Lesson 6 of 10
Data has been called "the new oil" so frequently that the phrase has become a cliche. But the comparison is misleading. Oil is consumed when used. Data is not. Oil is fungible. Data is not. Oil's value decreases with extraction. Data's value often increases with accumulation and combination. The economics of data assets are fundamentally different from any physical resource — and understanding these economics is essential for identifying and valuing the technology capital that increasingly drives enterprise value.
This lesson examines four categories of technology capital: proprietary data and databases, algorithms and AI models, platform technology and network effects, and digital infrastructure. For each, we cover the identification criteria, protection strategies, valuation approaches, and the strategic implications for businesses that own them and investors who evaluate them.
Data assets and technology capital are the fastest-growing category of intangible value, yet they are among the hardest to identify and measure using traditional frameworks. A database's value depends not on how much data it contains, but on the uniqueness of that data, the insights it enables, and the cost a competitor would incur to replicate it. Companies that treat data as a strategic asset — with formal governance, quality standards, and monetisation strategies — create significantly more value than those that treat it as a byproduct of operations.
The Data Asset Landscape
The volume of data generated globally is staggering, but volume alone does not create intangible asset value. Forrester Research estimates that 68% of enterprise data goes unused — collected and stored but never analysed, monetised, or even accessed after creation. The intangible value of data depends entirely on whether it can be converted into economic benefits.
Proprietary Data and Databases
Under IFRS 3, databases are classified as technology-based intangible assets. They are identifiable because they can be separated from the entity and sold or licensed independently. But the value of a database varies enormously depending on four characteristics.
Data Asset Value Framework
| Characteristic | Higher Value | Lower Value |
|---|---|---|
| Uniqueness | Data that cannot be replicated or purchased from third parties | Commodity data available from multiple sources |
| Completeness | Comprehensive coverage of the target domain | Partial, fragmented, or inconsistent records |
| Freshness | Continuously updated with real-time or near-real-time data | Static or infrequently updated |
| Actionability | Directly enables revenue-generating decisions or products | Raw data requiring extensive processing before use |
Bloomberg Terminal's value rests on a proprietary database of financial data covering 35 million instruments, fed by 5,000 data providers, updated in real-time. This dataset cannot be replicated — the relationships with data providers, the cleaning and normalisation processes, and the 40 years of historical depth represent a formidable intangible asset. Bloomberg generates approximately $12 billion in annual revenue, with the Terminal's data asset being the foundational value driver. An acquirer would classify this as a technology-based identifiable intangible asset worth billions.
Customer Databases as Intangible Assets
Customer databases sit at the intersection of customer-related and technology-based intangible assets under IFRS 3. A customer database typically includes contact information, purchase history, behavioural data, preferences, and interaction records. Its value as an intangible asset depends on the database's size, quality, recency, and the revenue that can be attributed to the relationships it represents.
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