In developing a DGPSI-Data Valuation framework in support of DGPSI-Full version, let us now explore some globally prevailing thoughts on Data Valuation.
In 1999, two Australian researchers Daniel L Moody and Peter Walsh published a paper “Measuring the value of Information An asset Valuation Approach”. In this paper they discussed what are referred to as the “Seven Laws of Data Valuation”. This concept covered the following nature of data that could contribute to its valuation
- Data Is infinitely shareable
- Value increases with Use
- Information is perishable
- Value increases with Accuracy
- Value increases with synergy
- Value of data increases with more data upto an overload point but may trip later.
- Information is self generating.
Moody and Walsh also described how Data Can be “A raw material” which when used with Software and hardware as plant and equipment produces an information as the end product.
In this concept, Software itself is an asset which like a catalyst works with input data to produce an output data while it remains unchanged. The self learning AI algorithm is a distinct category of data which cannibalizes the output data to transform itself as it evolves as a tool.
“Data” as software represents an asset like a “Fixed Asset” in the user organization while it could be a “Finished Data Product” in the software company.
When “Data” is used in 3D printing, data as input (3D scan of the object) combines with data as software for 3D printing and physical raw material (The component that is printed as an object) to result in the Physical Object as an end project. The software remains as a re-usable element for the next production of a different product. The data used for the design also remains as a by product which can be re-used if a similar object has to be printed once again.
There is another kind of Data which is also used as a “Fixed Asset”. It is the “Content” that is used to generate value by subscription or carrying advertisements. The advertisement revenue or the subscription revenue depends on how good is the content and how it is itself promoted. An advertising “of the content” advertises “the content” where other “advertisements” are embedded. The valuation of such assets need to take this into account. Such assets are also amenable for depreciation and sensitive to time and accuracy of the content.
Valuing such content needs to take into account these complex web of revenue generation possibilities with time value. They are better suited for the Discounted Cash Flow (DCF) or Net Present Value (NPV) method of valuation than the Cost of Acquisition method.
Hence some data assets are more amenable to Cost of Acquisition (COA) method while some are more amenable to DCF/NPV method. Some may require frequent adjustments of time value of content to the extent that a “Revaluation Method” is more acceptable.
Naavi has earlier propounded a “Theory of Data” from which many of the above 7 laws can be implied. Naavi’s theory had been built on three hypothesis namely “Data Is in the beholder’s eyes”, “Data has a reversible life cycle” and “Changes in the Value of data during the life cycle belongs to different contributors).
In the Puttawamy Judgement, Justice Chandrachud remarked that “Data is non-rivolrous” to mean that it can be duplicated. It can change hands without depriving its use to the earlier person. However, when we look at the valuation of data we are confronted with two conflicting valuation dilemmas.
Firstly in case of “Confidential” information, the sharing of data dilutes the value. Some times it destroys the value of the data completely. For example “Password” is one data which when shared will destroy its value completely.
On the other hand, some data such as “News” or “Education” increases in value when it is available for access by many. Hence a Data Valuer needs to classify the data properly before assigning the value to the data under this law.
The second law “Value increases with use” is reflected in the type of content mentioned above.
For example if no body knows that there exists a certain data, it cannot have a value. A Classic example is the DGPSI framework which is today known only to a few in India and its value is limited to the recognition of this set of people. If it is known to more number of people, its value would correspondingly increase. This is because it is a “Data Asset” which is meant to be used by other data users like a software.
The third law that “Information is perishable” is relevant for Personal Data valuation and has been used in the DVSI model because the permission to use the data by the Data Fiduciary is dependent on the Consent or legitimate use. The utility value of the data vanishes once the consent expires. In the data category of “News” the data may become stale for the news reader while for an investigative researcher, there may be a premium value in the “Forgotten Data”. A classic example is some of the articles on naavi.org which may be 25+ years old but for some body who wants to track the legislative history of Cyber Laws in India, it is a treasure.
This principle that the value of data may depend on the context and the audience is part of the first hypothesis of Naavi’s theory of data that “Data Itself is in the beholder’s eyes” and therefore the “Value of Data also in the beholder’s eyes”.
This means that the valuation of data has to be tagged with a “Context” weightage.
The fourth law that value of information increases with accuracy is from the context of its usage. There can be some instances such as the “Anonymised Data” where accurate data is masked for a specific purpose and though the accuracy of the data is deliberately reduced, the value may be preserved or even enhanced because the data can be used for purposes other than to which the accurate data could have been used.
The fifth law that the value of information increases when combined with other information is well noted not only because data from one division may be useful for another division in an organization but also because the entire Data Engineering and Data Analytics industry revolves around the synthesis of data and generating new insights.
However in a personal data context, where permissions are “Purpose limited”, use of data collected for one purpose may not be automatically usable for another purpose and this may conflict with this observation of Moody and Marsh. It is however fine with non personal data.
The sixth law that there is a “Overload Pont” after which “more data is not necessarily better” since “Data Fatigue” may set in. Where laws are different for different scales of operation (eg: “Significant” social media intermediary or “Significant Data Fiduciary””, beyond a overload point, new obligations may come in changing the usage pattern of the data.
The seventh law “Information is not depletable” is an interesting observations since the more we use a certain data, the usage pattern itself becomes additional meta data that enriches the core data. Again this has to be seen along with the “Data usage license” such as a Digital Library license which is number of use based (from the user perspective) or expiry of permission or “limitation of use under law”.
Thus the seven laws of data valuation indicated by Moody and Walsh is an interesting study which can be compared with the implications of Naavi’s theory of data and the DVSI model.
Request academicians to study this relationship further.
Naavi
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