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Data Interoperability Strategy

The Data Interoperability Strategy seminar introduces the key topics and scenarios involved in creating interoperable data environments. When the seminar is followed up with the Data Interoperability Workshop, attendees apply the strategy by proceeding through formal workshop based processes that discover shareable data across a collection of legacy schemas, store these  in a metadata repository, and, at the end, using these shared data specifications to build a shared data system.

The key topics of the Data Interoperability Strategy seminar include the characteristics of data interoperability and the two classes of data interoperability errors that commonly occur. Identified as well are the problems that commonly occur such as complexity and latency. Each of these are defined and illustrated.

Also presented in this seminar are the levels of data interoperability than can be achieved. These levels closely parallel those of a capability maturity model, and these levels can be assessed within an organization in generally the same manner as can software or data maturity.

The seminar then presents an overall framework for data interoperability and shows how common frameworks such as Zachman, Enterprise Architecture, and the Knowledge Worker affect the achievement of data interoperability.

The seminar then describes the key technological components of any really first-class interoperable data environment and provides examples of each including why these technological components are so important.

The seminar presents the overall contract and construction of a metadata repository environment critical component to a success strategy. Described as well is the necessary data interoperability environment including governance, infrastructure, communities of interest, key processes, success measures and the necessary training and tools.

The seminar then details the various scenarios that must occur to achieve a data interoperability environment including enterprise architectures, information systems plans, data model engineering, and then both reverse and forward engineering. Collectively these scenarios cause the creation of and enable the maintenance and evolution of data interoperability environments.

The seminar then concludes with the key measures and returns on investment, and an overall summary and "way ahead."

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