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Migration to the Architected Environment

This plan outlines the needs, relationships, and modeling of major subjects, keys, and attributes in migration to an architected environment. It addresses the impact analysis, resources estimate, and a data-driven development methodology for successful migration.

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Migration to the Architected Environment

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  1. Migration to the Architected Environment

  2. Migration Plan • Corporate data model • Needs to identify the following • Major subjects of the corporation • Definition of the major subjects of the corporation • Relationships between the major subjects • Grouping of keys and attributes that more fully represent the major subjects, including the following : • Attributes of the major subjects • Keys of the major subjects • Repeating groups of keys and attributes • Connectors between major subject areas • Subtyping relationships • Example

  3. The Feedback Loop Data Warehouse Existing systems environment DSS Analyst Data Architect

  4. Strategic Considerations • A better ploy is to coordinate the effort to rebuild operational systems with what are termed the “agents of change” • The aging of systems • The radical changing of technology • Organizational upheaval • Massive business changes

  5. Methodology and Migration • Delta list: how the data model differs from existing systems • Impact analysis : how each delta item makes a difference • Resources estimate : how much will it cost to “fix” the delta item • Report to management : • What needs to be fixed • The estimate of resources required • The order of work • The disruption analysis

  6. A Data-Driven Development Methodology • Why have methodologies been disappointing ? The reasons are many : • Methodologies generally show a flat, linear flow of activities. • Methodologies usually show activities as occurring once and only once. • Methodologies usually describe a prescribed set of activities to be done. • Methodologies often tell how to do something, not what needs to be done. • Methodologies often do not distinguish between the sizes of the systems being developed under the methodology. • Methodologies often mix project management concerns with design.development activities to be done. • Methodologies often do not make the distinction between operational and DSS processing, • Methodologies often do not include checkpoints and stopping places in the case of failure. • Methodologies are often sold as solutions, not tools • Methodologies often generate a lot of paper and very little design.

  7. Data-Driven Methodology • What makes a methodology data driven ? • How is a data-driven methodology and different from any other methodology ? • Data-driven methodology does not take an application–by-application approach to the development of systems.

  8. System Development Life Cycles • What we must do in SDLC ? • What a different between SDLC and data-driven methodology ?

  9. A Philosophical Observation • Example

  10. Operational Development/DSS Development • The data-driven methodology will be presented in three parts • METH 1 : is for operational systems and processing. • METH 2 : is for DSS systems and processing-the data warehouse. • METH 3 : describes what occurs in the heuristic component of the development process.

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