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Data Architecture Inventory

Duration

Four Days

This workshop consists of four full days. The overall schedule is as follows:

  • Prior Workshop Homework
    • Review of preceding workshop's “Homework” of Resource Life. Cycle networks that are prerequisite to this workshop.
    • Revise Resource Life Cycle networks as appropriate.
  • Operational Data Model Importing
    • Identify Operational Data Model DBMS Schemas
    • Employ DBMS process for exporting SQL DDL.
    • Employ if available process to export table and column definitions.
    • Formulate a prototype data loading.
    • Add or modify as appropriate any DBMS data types.
    • Import the SQL DDL scripts.
    • Formulate table and column definition data loading.
    • Import table and column definitions.
    • Develop and deliver presentation of accomplished work products.
    • Revise as necessary.
  • NonSQL Data model Importing into Operational Data Models.
    • Identify Non-SQL Data model to be imported.
    • Formulate a Schema, Tables, and Columns strategy to define the non-SQL data model as if it were SQL based.
    • Add or modify as appropriate any DBMS data types.
    • Import the NonSQL data models.
    • Develop and deliver presentation of accomplished work products.
    • Revise as necessary.
  • Implemented Data Model formulation
    • Discover fundamental set of subject areas addressed by the collection of Operational Data models.
    • Create a named Schema for each subject area.
    • Selectively “promote” tables from the Operational Data Models to become member tables in the Implemented data model database schemas.
    • Develop and deliver presentation of accomplished work products
    • Revise as necessary
  • Map Implemented Data Models to Database Objects
    • Identify database object classes that correspond to one or more implemented data model tables.
    • Select appropriate SQL data types
    • Relate and describe database object class relationship to implemented data model tables.
  • Specified Data Model formulation
    • Discover fundamental set of concepts represented as collections of data structures in the various implemented data model tables.
    • Create a named subject for each concept
    • Selectively “promote” tables from the Implemented Data Models to become member entities within a given specified data model subject area.
    • Develop and deliver presentation of accomplished work products
    • Revise as necessary
  • Data Element Model formulation
    • Discover semantically common columns from within Implemented Data Model tables and attributes from within Specified Data Model entities.
    • Promote these columns and attributes to be business data elements.
    • Select appropriate data types
    • Develop and deliver presentation of accomplished work products
    • Revise as necessary
  • Build Semantic and Data Use Modifiers
    • Semantic Modifier Management
      • Review Implemented Data Model table columns, Specified Data Model entity attributes, and business data elements to identify semantic modifiers for creation of prefix meta category values.
      • Create prefix meta category value classes for common collections of uncovered semantic modifiers.
      • Create meta category values for the uncovered semantic modifiers within their appropriate meta category value classes.
      • Remove semantic modifiers first from data elements, then from entity attributes and finally from from column names and associate newly created meta category values in a hierarchical fashion from the least semantically restrictive (data elements) to the most restrictive (columns).
      • Modify meta category value association sequences as appropriate.
    • Data User Modifier Management
      • Review Implemented Data Model table columns, Specified Data Model entity attributes, and business data elements to identify data use modifiers for creation of suffix meta category values.
      • Create suffix meta category value classes for common collections of uncovered data use modifiers.
      • Create meta category values for the uncovered data use modifiers within their appropriate meta category value classes.
      • Remove data use modifiers from column names and associate newly created meta category values.
      • Remove data use modifiers first from data elements, then from entity attributes and finally from from column names and associate newly created meta category values in a hierarchical fashion from the most general (data elements) to the most specialized (columns).
      • Modify meta category value association sequences as appropriate.
    • Generate revised database table columns.
    • Revise definitions
      • Starting with meta category values, create local, context independent definitions as these definitions will be “grabbed” an then used in the construction of data element, attribute, and column definitions.
      • Review “local” definitions of attributes within entities and create entity specific local definitions for the attributes. These too will be “grabbed” and employed in the full definitions of columns.
      • Review “local” definitions of columns within tables and create table specific local definitions for the columns. These will form the basis for column definitions and will be supplemented by attribute and data element local definitions.
      • Regenerate all definitions
  • Develop and deliver presentation of accomplished work products
  • Revise as necessary

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