Articulating Value from Data

The document examines the challenges of valuing data, noting that while intangible assets now comprise up to 90% of company valuations, traditional financial methods (cost, income, and market approaches) are limited. It proposes a new framework where data value is viewed as a 'staircase,' increasing as it moves from generation and collection to analysis and sharing. The authors argue that data valuation should move beyond monetization to include socio-economic impacts and stakeholder well-being to foster trust and innovation.

• There is a significant gap between the recognized importance of data and the ability of organizations to measure its value. While tangible assets accounted for up to 83% of company valuations in 1975, today up to 90% of valuation is found in intangible assets, including data, brand, and reputation.
• Common economic metaphors, such as describing data as 'the new oil,' are insufficient because data possesses unique attributes: it can be reused without depletion, is easily replicated, can produce externalities affecting non-transactional parties, and its value fluctuates based on complementary technology and context.
• Traditional financial valuation methods have specific limitations: the 'cost' approach fails to capture ROI or liability costs; the 'income' approach struggles to isolate the value of data from other product performance factors; and the 'market' approach ignores data held for competitive advantage and fails to reflect societal value.
• Data value is not static but increases as it progresses through a value chain—from generation and collection to analysis and sharing. The greatest potential for value creation occurs at the analysis and sharing stages, where data can be combined with other sets to create richer value propositions.
• A stakeholder-based approach to valuation is necessary because different actors experience different costs and benefits. For example, while businesses may monetize data, employees may bear costs of surveillance, and individuals may suffer privacy violations or algorithmic bias.
• Data collaboration and sharing lead to superior economic outcomes. Organizations that share data externally are reported to generate three times more measurable economic value than those that do not, and public sector data sharing is estimated to create socio-economic benefits between 0.1% and 1.5% of GDP.
• Digital trust is a critical prerequisite for data sharing. Many consumers feel a lack of benefit in return for their data, with 85% of global consumers wishing there were more companies they could trust with their data.

APA

World Economic Forum (2021). Articulating Value from Data. https://www3.weforum.org/docs/WEF_Articulating_Value_from_Data_2021.pdf

Chicago

World Economic Forum. Articulating Value from Data. 2021. https://www3.weforum.org/docs/WEF_Articulating_Value_from_Data_2021.pdf.

Wikipedia

{{cite report |author=World Economic Forum |title=Articulating Value from Data |date=November 2021 |url=https://www3.weforum.org/docs/WEF_Articulating_Value_from_Data_2021.pdf |access-date=23 October 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{worldeconomicforum2021articulating, author = {{World Economic Forum}}, title = {{Articulating Value from Data}}, institution = {World Economic Forum}, year = {2021}, month = nov, url = {https://www3.weforum.org/docs/WEF_Articulating_Value_from_Data_2021.pdf}, urldate = {2026-10-23}, note = {Indexed by Climate Insights Directory} }