Over the last few months I've introduced different aspects of the Data Value research that EMC is conducting in partnership with Dr. Jim Short of the University of California San Diego. In particular I have:
- written an introductory post on the topic
- reflected on the positives and negatives of data valuation
- introduced data insurance and data audits as valuation use cases
- considered new IT architectures in support of data valuation
- proposed a 3-phase journey to move to this new IT architecture
- stepped through Dr. Short's Four Principles of Data Value
Clearly the topic of data value is wide-ranging and it can be difficult to know where to begin.
Out of all the research conducted by Dr. Short to date, I found the following statement to be the most valuable:
Surveys conducted and our own interviews / surveys show that currently in over two thirds of companies surveyed there is no systematic method for accurately measuring data value over time. And our research is showing that “architecting for value” is a critical future requirement in IT and business strategy planning and investment (to successfully compete and meet strategy and performance goals).
In other words, there are a large number of companies interested in positioning themselves to "compete on value".
The good news is that Dr. Short's early stage research has reached a juncture where business executives can come together and (a) share where they are currently at, and (b) learn from surveys of their peers. His work can serve as the impetus for "architecting for value" at the corporate level, including:
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Making data valuation explicit (versus implicit data policies).
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Making valuation part of the business strategy: tools and processes, data products and services, acquiring and combining data assets, and selling data.
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Managing data value: assessing value/data risk and evaluating risk/data insurance just as other valuable corporate assets (brand, patents, IP, customer relationships / knowledge, partnerships, etc.).
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Making data value part of the technology strategy: instrumentation of systems for both technical information and business information that is understandable to the business manager, not just the technical organization.
- Considering the impact of data mining of your own infrastructure to understand and communicate to the business how your data is (actually) organized / aligned with business activities and functions.
The beauty of the research is that it has a strong focus on the business side of data value while still giving strong consideration to the underlying technologies required to support valuation. For example:
- If the business were to invest to make changes supporting valuation, what investments are required? By whom, when and where? What would be the benefits (financial, organizational, etc.)?
- If the infrastructure team were to invest to make changes supporting valuation, the research will discuss classifying data according to value to start managing data at a lower level of granularity and higher degree of accuracy that organizations do today.
For information about upcoming papers, conferences, and surveys on the topic, I recommend reaching out to Dr. Short and/or myself to register your interest.
Dr. Short is planning a survey during the summer of 2015; follow this blog for more details on the specifics.
Steve
EMC Fellow
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