I've been stepping through some of the "Principles of Data Value" shared by Dr. Jim Short of UC San Diego during his recent visit to an internal EMC Technology Summit. In recent posts I've shared that
- Some data value can be ephemeral. Instead of holding on to data and attempting to mine it over time (e.g. a "refinery" model), some corporations extract value from data that is temporal or fleeting (e.g. Snapchat data).
- Some data value can be eternal. Examples of data having eternal value would be genealogical records, or documents related to the history of a nation (or a presidency, as in the case of the John F Kennedy LIbrary and Museum).
A third principle of data value is that it is contextual.
In other words, data value might be variable based on who is asking. After hearing Dr. Short describe this principle I likened it to a set of business units within a larger corporation that assign different amounts of value to the same data set. In addition, these business units are often grouped into two levels: the business level (the "demand" side of requesting IT resources) and the infrastructure level (the "supply side" of providing IT resources).
For example, consider a data set that contains customer order information. This data set represents different amounts of value depending on the dependencies that each organization has with the data set.
Listed below are several organizations that have a different "value" view, such as:
- Manufacturing: this organization has a critical reliance on these purchase orders, and the growth in size of this data set is of particular interest.
- Chief Security Officer: the names of these customers, should they be compromised, represent a potential risk in the form of damages and brand value.
- IT infrastructure team: this data set will have high availability and reliability characteristics. Smooth operation of the infrastructure increases revenue, failures decrease revenue.
- The Line of Business for the product being sold has strong affinity to the data set.
- etc.
Given this reality, it is difficult to go to the IT team and ask "what's the business value of the data set"? Specific value is defined differently by key stakeholders in each organization; creating an overall enterprise value of the data set must take each of these stakeholders' views into account.
This is one of the key areas of the research that Dr. Short is conducting. Specific questions of concern are:
- Is data value being calculated at all by individual business units?
- What is the process whereby this value is calculated?
- How can an aggregate value be calculated? Should a value be calculated on the supply side as well as the demand side?
- How does the value get communicated across organizations?
- How are fluctuations handled over time?
The principle of contextual data value represents one of the meatier areas of the research being conducted this year. I plan on posting several follow-up posts on this topic going forward.
Steve
EMC Fellow
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