Create and Manage Enterprise Data Models
Keep track of your data with purpose-fit models.
Onsite Workshop
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.
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: | |
|---|---|---|
| 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: | |
|---|---|---|
| 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: | |
|---|---|---|
| 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: | |
|---|---|---|
| 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: | |
|---|---|---|
| 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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