The Data Capital Convergence: A Unified Valuation Schema for the 2025 Global Regulatory Shift

The global economic architecture in 2025 reached an inflection point where the customary boundaries between macroeconomic statistics and microeconomic financial reporting began dissolving under the weight of the digital economy. For decades, data has been an invisible production factor—vital for value creation but largely absent from the formal ledgers of nations and corporations alike.

The simultaneous activation of the United Nations System of National Accounts 2025 (SNA 2025), the Financial Accounting Standards Board (FASB) Accounting Standards Update (ASU) 2025-06, and the OECD Handbook on Compiling Digital Supply and Use Tables (SUTs) represents a coordinated effort to rectify this invisibility. 

Together, these moves mean CFOs and CDOs now have both permission and obligation to treat data as capital—not just cost.

This paper provides an analysis of the joint interaction of these valuation regimes, examining how they redefine data as a produced asset, modernise software capitalisation and establish a framework for fair value measurement under IFRS 13 and ASC 820. By utilising the Infonomics metrics framework as a translation mechanism, this analysis presents a schema that bridges the gap between cost-based historical recording and the future-oriented valuation of data as capital.

(Refer to the previous paper in this series to see how to use the Infonomics framework as a grammar and vocabulary connecting the economics, accounting and management domains when describing data as an asset).

The Macroeconomic Reconstitution: SNA 2025 and the Capitalization of Data

The formal adoption of the 2025 System of National Accounts marks the most significant evolution in global statistical standards since the 2008 revision, specifically designed to capture the structural shifts brought about by digitalization, artificial intelligence and the intangible economy. [1, 2] The core innovation of SNA 2025 lies in its decision to record data and databases as standalone produced assets within the national accounts production and asset boundaries. [3, 4] This transition acknowledges that data is not an ephemeral byproduct of digital transactions but is an explicit output of production that provides sustained economic benefits over time. [5, 6]

The conceptual boundary and definition of data

The SNA 2025 defines data as information content produced by accessing and observing phenomena, and subsequently recording, organising and storing these information elements in a digital format that provides an economic benefit when used in productive activities. [4, 5, 7] This definition is distinct from the classical understanding of information, as it emphasises the production aspect—the labour and capital required to transform raw phenomena into a structured, electronically accessible resource. [5, 6, 8]

The boundary established by the Eurostat-IMF Task Team on Measuring Data as an Asset in National Accounts restricts the asset class to digital data, explicitly excluding non-digital records. [5] Furthermore, the framework distinguishes between Produced Data and Ancillary Data. If data is not intended to provide a direct economic benefit to the entity—such as temporary logs that are not utilised for analytics or secondary production—it remains outside the asset boundary. [5] This creates a high bar for capitalisation, requiring entities to demonstrate that the data will be used in production for at least one year. [3, 8]

The Sum-of-Cost (SoC) methodology for own-account data

Because the majority of data assets are generated internally by firms for their own use, a pervasive lack of observable market prices exists. To resolve this, SNA 2025 mandates the Sum-of-Cost (SoC) approach for valuing own-account data production. [5, 7, 8]

This methodology assumes that the value of the asset is at least equal to the total cost of the inputs required for its creation. The formula for Gross Output (GO) of data is defined as:

GO=∑(RE+IC+CFC+T+R)

Where:

• RE (Remuneration of Employees) accounts for the wages and benefits of workers in data-intensive occupations. [5]
• IC (Intermediate Consumption) includes the cost of services and materials consumed during the data production process. [5]
• CFC (Consumption of Fixed Capital) represents the depreciation of the hardware and software used to generate the data. [5, 7]
• T (Other Taxes less subsidies) reflects taxes on production. [5]
• R (Net Return to Capital) is a markup representing the opportunity cost of the capital invested. [5, 8]

The estimation of the labour component (RE) relies on identifying specific occupations and their involvement rates. The 2025 SNA recommendations provide a standard list of occupations—such as data analysts, database designers, and AI specialists—along with default involvement rates to ensure international comparability. [5, 7]

The inclusion of a net return to capital for non-market producers (such as governments and non-profits) is a notable development, ensuring that the contribution of public-sector data to national wealth is not underestimated. [8, 9] This interaction between labour inputs and capital markups transforms data expenditure from a cost of doing business into a national capital formation event, directly increasing reported Gross Domestic Product (GDP) and Net Domestic Product (NDP).[2]

What SoC does in SNA 2025 terms

Under SNA 2025, own‑account data moves from intermediate consumption (expense) into gross fixed capital formation (GFCF) via the Sum‑of‑Cost (SoC) method, reclassifying part of staff, cloud and software spend as investment in a produced fixed asset (data/databases) rather than current production cost. That investment flows into both GFCF (raising GDP on the expenditure side) and the capital stock and net worth of sectors such as general government and corporations, effectively creating a macro‑CVI—a cost‑based floor value for data accumulated over time.

Because GDP on the expenditure side includes investment in produced assets, moving 0.5–1% of GDP worth of digital spend into GFCF directly lifts reported GDP by the same order of magnitude, even though underlying activity is unchanged. What changes is the distribution of measured value: less intermediate consumption, more capital formation, which in turn alters observed capital deepening and productivity indicators that use capital stock in the denominator, narrowing the apparent productivity paradox and strengthening the case that digital and AI programmes are genuine capital deepening, not just consumption.

This reclassification is not neutral across the economy: data‑intensive sectors (IT, finance, platforms, advanced manufacturing, large retailers, telcos, banks, insurers) will show disproportionate increases in capital stock and value added once their SoC‑valued data is capitalised, raising their share of GFCF and GDP relative to low‑data sectors.

For Treasuries, that implies sectoral weights in growth and productivity diagnostics shift towards digital industries, justifying more targeted policy support (digital infrastructure, AI skills, cloud‑compatible regulation), reframing competition policy around data moats vs physical capital and treating public data production (health, geospatial, statistics) as government investment that warrants multi‑year capital budgeting and supports meaningful cross‑country comparisons of public data capital (e.g. UK vs Canada vs Australia).

The OECD Digital SUTs: Making Digitalisation Visible in Value Chains

While SNA 2025 provides the aggregate rules for capitalisation, the OECD Handbook on Compiling Digital Supply and Use Tables (SUTs) provides the granular mechanism for tracking the flow of digital value through the economy. [10, 11, 12] Traditional SUTs often aggregate digital products and industries within broader categories, masking the true impact of the data-informed economy. The Digital SUT framework introduces three distinct dimensions—the how, the what, and the who—to isolate digital activity. [11, 13]

Dimensions of the digital economy framework

The How dimension focuses on the nature of the transaction, distinguishing between digitally ordered and non-digitally ordered goods and services. [11, 14] Digitally ordered transactions are those conducted over computer networks by methods specifically designed for the purpose of receiving or placing orders.[13, 14]

The What dimension identifies specific digital products. In the Digital SUTs, these are separated into high-policy-interest rows:

1. ICT Goods and Digital Services: Aggregated to show their share of total supply and use. [11]
2. Cloud Computing Services (CCS): Specifically isolated to track the shift from on-premise hardware to remote infrastructure. [11, 12, 13]
3. Digital Intermediation Services (DIS): The fees charged by platforms to facilitate transactions between third parties. [11, 13]

The Who dimension introduces seven new digital industries into the SUT columns, allowing for the isolation of gross value added (GVA) by actors whose business models are fundamentally digital. [12, 13]

The integration of these Digital SUTs into the SNA 2025 framework as extended or supplementary accounts enables a feedback loop between micro-level business data and macro-level economic indicators.[13, 16] This is particularly relevant for the valuation of data assets, as it allows statisticians to observe the intermediate consumption of data by different industries, thereby calculating the Return on Data at a sectoral level.[11, 13]

Corporate Financial Modernization: FASB ASU 2025-06 and the Principles-Based Shift

As macroeconomic standards shift toward the capitalisation of data, US GAAP has undergone a parallel modernisation of its rules for the digital infrastructure that generates data. FASB ASU 2025-06, Intangibles—Goodwill and Other—Internal-Use Software (Subtopic 350-40), issued on September 18, 2025, replaces the stage-based accounting model with a principles-based framework. [17, 18, 19]

The alternative to linear stage-based accounting

The previous guidance under ASC 350-40 was built on a waterfall development model, where costs were expensed during the preliminary project stage, capitalised during the application development stage, and expensed again during the post-implementation stage. [18, 19, 20] This model was increasingly incompatible with modern agile and DevOps workflows, where development, testing and deployment occur in continuous overlapping loops. [19, 20]

ASU 2025-06 removes all references to project stages, focusing instead on whether a project has reached a probable-to-complete threshold. [17, 20] Capitalisation must now commence when:

1. Management Authorization: Documented evidence of support and funding for the project is obtained. [18, 19]
2. Probability of Completion: It is probable that the project will be completed and the software will be used to perform its intended function. [17, 18]

The uncertainty threshold for innovation

A critical provision of ASU 2025-06 is the significant development uncertainty clause. If the software or its features are deemed novel, unique, or untested, or if performance requirements are unclear, the entity must defer capitalization until these uncertainties are resolved through coding and testing. [19, 20] This creates a high hurdle for the capitalization of cutting-edge AI models and complex data analytics layers.

In an illustrative example from the Deloitte Heads Up report, a company implementing a five-year on-premise license for a base layer software while simultaneously developing a novel analytical layer must distinguish between the two for accounting purposes. [20] The base layer, being a proven solution, meets the probable-to-complete threshold at inception and is capitalized as an intangible asset. [20]

However, the analytical layer, having unproven functions that the provider has not successfully delivered in the past, faces significant development uncertainty. [20] Consequently, the professional service costs for the analytical layer must be expensed as incurred until uncertainties are resolved through technical testing. [20]

ASU 2025-06 also changes the reporting of capitalised internal-use software from general intangible asset disclosures to the rules governing Property, Plant, and Equipment (PP&E) under ASC 360-10. [18, 19] This requires firms to provide detailed roll-forwards of software costs, including additions, disposals and impairments, providing a level of transparency previously reserved for physical machinery. [19] This alignment with PP&E suggests that regulators now view digital infrastructure as the modern factory floor where data assets are manufactured.

How ASU 2025‑06 and IAS 38 may shift the capex/opex balance, EBITDA and valuation stories

ASU 2025‑06 shifts US GAAP for internal‑use software from a rigid, stage‑based model to a principles‑based “probable‑to‑complete” threshold for capitalisation, while IAS 38 is under review to clarify treatment of internally generated intangibles such as data, algorithms and platforms. For CFOs, that means a larger share of software/data build costs (core data platforms, productionised AI models and analytics layers) can be capitalised once projects are authorised, funded and likely to complete, while genuinely experimental AI work remains opex.

The net effect is higher capex and lower opex for qualifying projects, improving EBITDA in the build phase (because more spend sits below EBITDA as capex), with higher depreciation/amortisation over time affecting EBIT and net income profiles.

For a given data/AI programme, the previous regime pushed large upfront opex through the P&L, depressing EBITDA and obscuring the payback story; under ASU 2025‑06, a portion shifts into capitalised software, smoothing the expense profile over 3–5+ years via depreciation and aligning reported numbers more closely with how investors already think about data/AI capex.

This makes cross‑company comparisons more meaningful as similar digital investments move onto a PP&E‑style footing (ASU 2025‑06) or clarified intangibles (IAS 38), and it gives management room to articulate an investment narrative. Rather than “IT costs went up” we can explain that “we're building digital factories whose output is reusable models and data products.”

ASU 2025‑06 also moves capitalised software into PP&E‑style roll‑forwards (ASC 360‑10)—additions, disposals, impairments—while IAS 38 considers enhanced disclosures for intangibles, giving investors a much clearer asset‑base view of digital infrastructure. Data‑intensive firms can then underpin valuation stories with hard numbers: capitalised software/data (CVI), demonstrated performance lift (PVI), net economic contribution (EVI) and, where relevant, potential market value (MVI) in licensing or M&A.

Well‑run organisations will use this to justify higher valuation multiples and demonstrate capital discipline by pruning Tech Debt Spirals and Proven but Unprofitable projects (high CVI, low EVI) from the capital stock, while the symmetry with SNA 2025’s SoC treatment at macro level anchors the language of data-as-capital in both national statistics and corporate accounts.

Fair Value Discrepancies: Navigating IFRS 13, ASC 820, and the Asset Hierarchy

While SNA 2025 and ASU 2025-06 focus primarily on the cost of data and software, the actual value of these assets for investment and M&A purposes is governed by the fair value regimes of IFRS 13 and ASC 820. These standards define fair value as the exit price—the price that would be received to sell an asset in an orderly transaction between market participants at the measurement date. [21, 22, 23]

The fair value hierarchy for data assets

The fair value hierarchy categorises valuation inputs into three levels based on their observability and liquidity. [21, 24, 25] For data assets, this hierarchy presents significant challenges:

1. Level 1 (Highest): Quoted prices in active markets for identical assets. This is virtually non-existent for data, which is typically unique and non-fungible.[21, 23, 25]
2. Level 2: Observable market data for similar assets, such as prices from recent mergers of comparable data-rich companies (Guideline Transaction Method).[21, 23, 25]
3. Level 3 (Lowest): Unobservable inputs reflecting the entity's own assumptions. This is the default for most data valuations, utilizing Income Approach models like Discounted Cash Flow (DCF) or Multi-Period Excess Earnings Method. [21, 23, 24]

The tension between these regimes is significant. Under SNA 2025, a government might record a data asset at its Sum-of-Cost (e.g., $10 million). However, under IFRS 13, a market participant might value the same data at its highest and best use—which could be $100 million if used to train a generative AI model, or $0 if the data has no external marketability. [22, 25]

IAS 38: closing the intangible gap

The International Accounting Standards Board (IASB) and the European Financial Reporting Advisory Group (EFRAG) are currently engaged in a comprehensive review of IAS 38 Intangible Assets. [26, 27, 28] Investors have increasingly called for clearer reporting on data and data governance, noting a large gap between the book value and market value of digital firms. [27, 29, 30]

The IASB project, as of June 2025, is exploring several key areas:

1. User Information Needs: Identifying what granular data investors need to distinguish between maintenance spending and growth investment. [26, 30]
2. Recognition of Newer Items: Testing whether assets like data resources, algorithms, and carbon credits should be formally recognised on the balance sheet. [26, 28, 31]
3. Comparability: Addressing the disparity between acquired intangibles (which are capitalized at fair value in a business combination) and internally generated intangibles (which are currently expensed). [26, 28, 30]

Workshops held by EFRAG in November 2025 specifically focused on software, AI, data resources, algorithms and digital platforms, highlighting that the lack of standardised disclosure for these items prevents accurate risk assessment and rate-of-return estimates by analysts. [27, 28, 30]

Geopolitical Vanguard: China's Data Assetisation Prototype

China has emerged as the global leader in operationalising the data-as-capital concept, providing a prototype for the rest of the world. In 2019, China officially listed data as a production factor alongside land, labour, capital and technology. [32] This was followed by the Ministry of Finance's Interim Provisions on Accounting Treatment of Enterprise Data Resources, effective January 1, 2024. [32, 33, 34]

The dual recognition framework

The Chinese framework allows for the recognition of data assets under two categories:

1. Intangible Assets: For data resources used internally to improve efficiency or create long-term strategic value. [33]
2. Inventory: For data resources intended for sale or circulation in the digital economy. [33]

This Dual Recognition approach allows for the activation of dormant data assets. For example, the 2024 annual reports showed that 100 Chinese listed companies recorded a total of 2.245 billion RMB in data assets on their balance sheets. [32] One case study, Haitian Ruisheng, saw its net profit increase by 105.24% in 2024 after successfully recording its data assets. [34]

Furthermore, the Implementation Measures for Corporate Registration Management (effective February 10, 2025) allow shareholders to contribute capital in the form of valuated data or virtual cyber properties. [35] This marks a landmark shift where data is legally treated as equivalent to cash or physical property for the purpose of equity formation. [32, 35]

The Regulatory Counterforce: The EU Data Act and Asset Exclusivity

While China and the SNA 2025 focus on enabling valuation, the European Union's regulatory landscape has introduced a significant counterforce: the EU Data Act, which took effect on September 12, 2025. [36, 37] The Act reshapes the Control and Exclusivity requirements that are prerequisites for an item to be considered an Asset under GAAP and IFRS.

The erosion of exclusive control

Under Article 4 and Article 5 of the EU Data Act, users (both consumers and business customers) are granted extensive rights to access, control and share the data generated by their use of connected products. [36] Businesses can no longer treat usage data as their exclusive asset. [36, 38] This creates a significant revaluation requirement:

1. Impairment Risks: If a company’s valuation was based on its exclusive access to high-value aftermarket data, the Data Act’s mandate to share that data with competitors (e.g., repair shops) may lead to immediate impairment of the capitalised data asset. [36, 38]
2. Revenue Recognition (IFRS 15 / ASC 606): The Data Act allows customers to terminate contracts with short notice and switch providers without vendor lock-in fees. [36, 39, 40] Traditional multi-year revenue recognition models for SaaS and cloud services must now be reassessed, as the enforceability of long-term contracts is amended by law. [39, 40]

The Data Act moves data from a Proprietary Asset to a Common-Use Resource, necessitating a shift in valuation techniques from the Market Approach (which assumes exclusivity) to the Income Approach (which must now model the risk of competitive data sharing). [36, 39, 40]

The Infonomics Metrics: A Unified Schema for Data Valuation

The disparate requirements of SNA 2025 (cost-focused), IFRS 13 (fair-value-focused), and the EU Data Act (legal-rights-focused) can be unified using the Infonomics metrics framework. [41, 42] Infonomics provides the connective tissue that allows an entity to translate technical data quality into macroeconomic wealth and microeconomic profit.

Foundational measures: the intrinsic and business Utility

Foundational measures act as quality filters for the Sum-of-Cost (SoC) calculations in SNA 2025.

1. Intrinsic Value of Information (IVI): Measures accuracy, completeness and scarcity. In the context of SNA 2025, IVI determines the Quality-Adjusted Price Index used for deflation. [6, 41]
2. Business Value of Information (BVI): Measures relevance to specific processes. This aligns with the OECD SUT columns, identifying which Digital Industry the data asset primarily supports. [12, 41]
3. Performance Value of Information (PVI): Measures the impact on KPIs. PVI provides the objective evidence needed to meet the probable-to-complete threshold for ASU 2025-06 capitalisation. [18, 41]

Financial measures: the cost, market and economic worth

Financial measures reconcile historical cost with future potential and current market conditions.

1. Cost Value of Information (CVI): The expense of collection and management. This is the equivalent of the SoC model in SNA 2025 and the Capitalized Cost under ASU 2025-06. [5, 41]
2. Market Value of Information (MVI): What the data would fetch in a sale. This corresponds to Level 1 and Level 2 inputs in the IFRS 13 hierarchy. [21, 41]
3. Economic Value of Information (EVI): The contribution to revenue growth or expense savings. This provides the Unobservable Inputs for Level 3 fair value measurements. [21, 41]

The unified valuation schema (2025 Integration)

By mapping the 2025 regulatory updates to the Infonomics thread, we arrive at a unified schema for data valuation:

Conclusion: The Strategic Path Forward

The joint interaction of these regimes in 2025 signifies the end of the informal economy for data. Regulators have collectively acknowledged that data is the new capital, but its valuation requires a hybrid approach that historical accounting cannot provide alone. The SNA 2025 provides the baseline, while the Infonomics framework provides the strategic delta (EVI - CVI) that justifies investment.

For professionals in finance and statistics, the implications are clear: the valuation of data assets should move from an ad-hoc exercise to a continuous-monitoring function. As software capitalisation moves toward PP&E standards under ASU 2025-06 and national accounts record data as fixed capital, the Digital Balance Sheet can become the primary instrument for assessing corporate and national competitiveness. 

However, as the EU Data Act demonstrates, the value of data is no longer tied purely to its ownership, but rather to its orchestration—the ability to generate economic value (EVI) in an increasingly open and interoperable global market. Organisations that adopt this unified schema will not only meet the new compliance demands but will be the architects of the data-capital era.

--------------------------------------------------------------------------------

1. Cartography Between Worlds – The 2025 update to the system of national accounts re-opens debates on key economic measures - LSE Blogs, https://blogs.lse.ac.uk/impactofsocialsciences/2025/08/26/cartography-between-worlds-the-2025-update-to-the-system-of-national-accounts-re-opens-debates-on-key-economic-measures/
2. New Standards for Economic Data Aim to Sharpen View of Global Economy, https://www.imf.org/en/blogs/articles/2025/07/31/new-standards-for-economic-data-aim-to-sharpen-view-of-global-economy
3. System of National Accounts 2025 - UN Statistics Division, https://unstats.un.org/unsd/nationalaccount/snaupdate/2025/2025_SNA_Combined.pdf
4. Economic and Social Council Distr.: General - UNECE, https://unece.org/sites/default/files/2024-04/12_Handbook%20on%20the%20capitalisation%20of%20Data%20in%20the%20System%20National%20Accounts%20_outline_0.pdf
5. Handbook on measuring Data in the System of National Accounts - UNECE, https://unece.org/sites/default/files/2025-09/3_5_Eurostat%20_Data%20handbook%20-%20Massarelli.pdf
6. SNA/M3.24/03 - Handbook on Measuring Data in the System of National Accounts - UN Statistics Division, https://unstats.un.org/unsd/nationalaccount/aeg/2024/M27/M27_03_Data_Handbook_Cover.pdf
7. Handbook on measuring data in the System of National Accounts - UN Statistics Division, https://unstats.un.org/unsd/nationalaccount/aeg/2024/M27/M27_03_Data_Handbook.pdf
8. 2025 SNA: Latest developments and potential impact on ICP (November 2024) - The World Bank, https://thedocs.worldbank.org/en/doc/22def71d03cb96fe29a4022bc1ed85ea-0050022024/original/2025-SNA-Latest-developments-ICP-November-2024-FINAL.pdf
9. Measuring Natural Capital in SNA 2025 - CEPAL, https://www.cepal.org/sites/default/files/presentations/measuring_natural_capital_sna-2025_oecd_sep2024.pdf
10. National Accounts - OECD statistics blog, https://oecdstatistics.blog/category/national-accounts/
11. OECD Handbook on Compiling Digital Supply and Use Tables, https://www.oecd.org/en/publications/oecd-handbook-on-compiling-digital-supply-and-use-tables_11a0db02-en.html
12. Measuring the digital economy (Part 1): Why is it important?, https://oecdstatistics.blog/2023/11/22/measuring-the-digital-economy-part-1-why-is-it-important/
13. OECD Handbook on Digital Supply and Use Tables - Insee, https://www.insee.fr/fr/statistiques/fichier/8191500/n15.Jorrit.pdf
14. Applying digital supply-use tables in developing economies - UNCTAD, https://unctad.org/system/files/official-document/tcsdtlinf2024d1_en.pdf
15. Digital Supply and Use Tables: a useful tool for digital transformation, https://www.amf.org.ae/sites/default/files/publications/2025-01/First%20Session%20-%206.%20AMF-Digital%20Supply%20and%20Use%20tables%20and%20digital%20transformation.pdf
16. Handbook on Extended Supply and Use Tables and Extended Input-Output Tables | OECD, https://www.oecd.org/en/publications/handbook-on-extended-supply-and-use-tables-and-extended-input-output-tables_e9fdcf85-en.html
17. Accounting for and Disclosure of Software Costs - FASB, https://www.fasb.org/page/PageContent?pageId=/projects/recently-completed-projects/accounting-for-and-disclosure-of-software-costs.html
18. New ASU on Internal-Use Software Costs Guidance | BDO's Accounting, Reporting, and Compliance Hub, https://arch.bdo.com/new-asu-on-internal-use-software-costs-guidance
19. Capitalization Requirement Internal Use Software | Atlanta CPA, https://windhambrannon.com/blog/updated-capitalization-requirements-for-internal-software-development-projects-under-asu-2025-06/
20. Heads Up — FASB Amends Guidance on the Accounting for and Disclosure of Software Costs (September 18, 2025) | DART, https://dart.deloitte.com/USDART/home/publications/deloitte/heads-up/2025/fasb-asu-amends-software-costs-guidance
21. What is ASC 820? A Guide to Fair Value Measurements - Carta, https://carta.com/learn/private-funds/management/asc-820/
22. IFRS 13 Fair Value Measurement - Australian Accounting Standards Board, https://aasb.gov.au/admin/file/content105/c9/IFRS13_BC_1-22.pdf
23. IFRS 13 — Fair Value Measurement - IAS Plus, https://www.iasplus.com/en/standards/ifrs/ifrs13
24. Fair value hierarchy - KPMG International, https://kpmg.com/ie/en/insights/asset-management/fair-value-hierarchy.html
25. Fair Value Measurements - GAAP Dynamics, https://www.gaapdynamics.com/insights/accounting-topics/fair-value-measurements-accounting-resources-for-asc-820-and-ifrs-13/
26. IASB Technical Update June 2025 - EFRAG, https://www.efrag.org/sites/default/files/media/document/2025-08/03-01_iasb_technical_update_june_-_efrag_fr_teg-cfss_2025-07-01_1.pdf
27. view report - EFRAG, https://www.efrag.org/sites/default/files/media/document/2025-12/Summary%20of%20workshops%20on%20user%20information%20needs%20on%20intangibles%20%284%29.pdf
28. Intangible Assets - EFRAG, https://www.efrag.org/system/files/sites/webpublishing/Meeting%20Documents/2510221509509409/06-01%20Intangibles%20-%20Users%27%20needs%20of%20information%20-%20EFRAG%20FR%20TEG-CFSS%2025-11-26.pdf
29. Data as Capital—Rethinking Intangibles in a Digital World, https://aasb.gov.au/news/data-as-capital-rethinking-intangibles-in-a-digital-world/
30. 09-02 - Summary of intangible workshops - EFRAG, https://www.efrag.org/system/files/sites/webpublishing/Meeting%20Documents/2511061015554396/09-02%20-%20Summary%20of%20intangible%20workshops%20-%20EFRAG%20FRB%20and%20FR%20TEG%202025-12-17.pdf
31. Financial Reporting and IFRS 18: What to Expect in 2025? - 3Bee, https://blog.3bee.com/en/financial-reporting-and-ifrs-18-what-to-expect-in-2025/
32. The Chinese Experience of Using Data as a New Production Factor to Realize Economic Value - SciDataCon, https://scidatacon.org/event/9/contributions/165/
33. Can data assetization drive high-quality enterprise development ..., https://pmc.ncbi.nlm.nih.gov/articles/PMC12594403/
34. Research on the Practice of Data Assets Entering into the Table of Haitian Ruisheng Company, https://ojs.apspublisher.com/index.php/apemr/article/download/527/424/1264
35. LPA Asia Smart News - Business Lawyers | LPA Law Avocats, https://www.lpalaw.com/en/lpa-asia-smart-news-2025-02/
36. EU Data Act — What Businesses Need to Know - Latham & Watkins LLP, https://www.lw.com/en/insights/eu-data-act-what-businesses-need-to-know
37. What Salesforce Customers Need to Know About the EU Data Act, https://www.salesforce.com/news/stories/eu-data-act/
38. The Data Act: A new era for the data economy | EY - Belgium, https://www.ey.com/en_be/insights/risk/the-data-act-a-new-era-for-the-data-economy
39. EU Data Act 2025, SaaS Switching and Revenue Recognition - RightRev, https://www.rightrev.com/the-eu-data-act-just-changed-saas-pricing-strategy/
40. EU Data Act: SaaS Accounting Awareness Brief - BDO USA, https://www.bdo.com/insights/industries/technology/eu-data-act-saas-accounting-awareness-brief
41. Infonomics: The New Economics of Information, https://www.energy.gov/sites/prod/files/2020/08/f78/07-beto-leveraging-bioenergy-data-july2020-laney.pdf
42. INFONOMICS - 1105 Media, https://download.1105media.com/tdwi/Remote-assets/Events/2020/LasVegas/TDWI-LV2020-Keynote-Infonomics.pdf