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Transform data into information using data valuation
As data stored in isolation cannot generate any value or insights, enterprises are increasingly classifying them as assets of economic value and institutionalizing them in financial statements. It is crucial to understand the relationship between value and repeatable usage, time, divisibility, and quantity in order to identify whether and how the value of data should be reported in the balance sheet.
Like assets, the value curve of data decreases when they are not upgraded to include up-to-date records. Viewing data as the precursor to information and adapting the data, information, knowledge and wisdom pyramid can help generate information that translates to knowledge, resulting in overall wisdom.
Three methodologies can be established to value data based on its potential for usage:
- Incremental income approach, which values data on their net present value of the future value generated
- Replacement cost concept, used in cases where enterprises build data assets whose value isn’t immediately known
- Market equivalents approach, where the value of assets through cost and incremental income may be inconsequential
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