This post describes the final insight in a series of five data valuation insights shared at Evanta's Global CIO Executive Summit at the Skytop Lodge in Pennsylvania last week. The insights have their roots in a research project that EMC launched with the San Diego Supercomputer Center nearly two years ago. Over the course of the past two years Dr. Jim Short and myself, along with various research partners, have spent a good deal of time interviewing CxOs, surveying the industry, and interacting with other academic partners. The insights generated during these two years was shared during an Evanta keynote entitled Data's Economic Value in the Age of Digital Business. The blog posts represent our intentions to socialize these insights more broadly. Before diving into the fifth and final post, I'd like to review the first four insights.
First Key Insight: CIOs Need to Insert Themselves Into the Data Value Conversation
There are plenty of resources that allow a CIO to insert themselves into a corporate discussion about the value of data and analytic models. Certainly Dr. Short's research is ongoing (e.g. the upcoming Data West summit in December). The second example is Bill Schmarzo, who has proposed a method for associating the value of business decisions to data, and a second method for associating the value of business decisions to analytic models. Doug Laney has developed an Infonomics approach to valuing data. Chief Data Governance Officer Barbara Latulippe has built a framework for valuing data as an asset. These are some of the many available resources that CIOs can use to educate themselves on strategies to assign value to data and analytic assets.
Second Key Insight: Data Workflow and Ingest are the IT Touch Points for Measuring Data's Value
As part of the research into data value, Dell EMC's product teams had some brainstorming sessions to enumerate possible locations to run valuation algorithms. We determined that there were 5 candidates worthy of discussion (listed in the picture below), with 2 of them qualifying as the most likely to succeed (content ingest and content workflow).
Third Key Insight: Combine Business & Technical Metadata
This third insight represents conclusions reached based on our own implementation of a framework that treats data and analytic models as capital assets. Our Chief Data Governance Officer partnered with the IT organization to build a data catalog that combined business and technical metadata. As the teams focused on the full lifecycle of data and analytic models, they used a metadata enrichment approach to increase the overall value of the data. This focus on combining business and technical metadata allowed the company to release its first data service: MyService360.
Fourth Key Insight: Annotate Data & Models with Valuation Metadata'
Once a framework has been put in place to combine business metadata with technical metadata, all of the ingredients are in place to run algorithms that calculate value based on the business context. Once the calculated value (CV) has been established, whether it be for data, analytic models, or both, a mechanism must be in place to permanently record the CV against the asset. This requires that all assets exist in a catalog, and that each catalog entry can be annotated with the CV. The diagram below highlights a product that has these capabilities (the Analytic Insights Module or AIM).
Fifth Key Insight: Build Valuation Business Processes on Top of IT Valuation Services
The fifth and final step is for the IT organization to surface the annotation method to new corporate business processes that have been created for the purpose of treating data and models as capital assets with value. These new business processes can be thought of as the algorithms that the CIO helped to create in step 1. The annotation can be surfaced in two ways:
- Manual APIs that associate value via a function call.
- Automated approaches that are implemented as a running process that sits alongside the catalog and calculates value.
The diagram below depicts these two methods as they could be integrated with an Analytic Insights Module.
This fifth insight completes a vision of an end-to-end data valuation system.
In an upcoming post I will step through a real-world use case that highlights this system in action.
Fellow, Dell Technologies