Create and Manage Enterprise Data Models
Keep track of your data with purpose-fit models.
RETIRED CONTENT
Please note that the content on this page is retired. This content is not maintained and may contain information or links that are out of date.Conceptual data models are often neglected:
- Because of limitations of modeling tools, the conceptual model is a standalone artifact and is not part of the data modeling environment.
- Data Architecture and Data Modeling are seen as one and the same process.
- The SDLC is delivery focused and only uses the physical data model. Conceptual modeling is normally not a part of the SDLC.
- The long-term perceived value of the conceptual model is low. The conceptual model only has value during the initial stages of a large program.
A conceptual data model is an important business artifact:
- A conceptual data model is an important business artifact:
- It represents business concepts and relationships to business.
- It verifies IT understanding of business concepts as used by business processes.
- It acts as a business taxonomy and eases communication both within the organization and to the outside world.
- It defines the business from a data-centric perspective and assists in the definition of data domains.
- The conceptual model plays a significant role in domain modeling to identify and create bounded contexts.
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Module 1: Establish the Data Architecture Practice
The Purpose
- Understand the context and goals of data architecture in your organization.
Key Benefits Achieved
- A foundation for your data architecture practice.
Activities: | Outputs: | |
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1.1 | Review the business context. |
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1.2 | Obtain business commitment and expectations for data architecture. |
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1.3 | Define data architecture as a discipline, its role, and the deliverables. |
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1.4 | Revisit your SDLC to embed data architecture. |
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1.5 | Modeling tool acquisition if required. |
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Module 2: Business Architecture and Domain Modeling
The Purpose
- Identify the concepts and domains that will inform your data models.
Key Benefits Achieved
- Defined concepts for your data models.
Activities: | Outputs: | |
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2.1 | Revisit business architecture output. |
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2.2 | Business domain selection. |
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2.3 | Identify business concepts. |
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2.4 | Organize and group of business concepts. |
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2.5 | Build the Business Data Glossary. |
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Module 3: Harvesting Reference Models
The Purpose
- Harvest reference models for your data architecture.
Key Benefits Achieved
- Reference models selected.
Activities: | Outputs: | |
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3.1 | Reference model selection. |
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3.2 | Exploring and searching the reference model. |
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3.3 | Harvesting strategies and maintaining linkage. |
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3.4 | Extending the conceptual and logical models. |
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Module 4: Harvesting Existing Data Artifacts
The Purpose
- Gather more information to create your data models.
Key Benefits Achieved
- Remaining steps and materials to build your data models.
Activities: | Outputs: | |
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4.1 | Use your data inventory to select source models. |
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4.2 | Match semantics. |
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4.3 | Maintain lineage between BDG and existing sources. |
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4.4 | Select and harvest attributes. |
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4.5 | Define modeling standards. |
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Module 5: Next Steps and Wrap-Up (offsite)
The Purpose
- Wrap up the workshop and set your data models up for future success.
Key Benefits Achieved
- Understanding of functions and processes that will use the data models.
Activities: | Outputs: | |
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5.1 | Institutionalize data architecture practices, standards, and procedures. |
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5.2 | Exploit and extend the use of the Conceptual model in the organization. |
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