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Create and Manage Enterprise Data Models

Keep track of your data with purpose-fit models.

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Contributors

  • Eric Carroll, Strategy, Architecture & Development Execution, Bank of Nova Scotia
  • Keith Enhagen, Manager Enterprise Architect, Bank of Montreal
  • Mike Lapenna, Senior Enterprise Architect Consultant
  • Mehrdad Novin, Senior Data Architect and Data Governance Consultant
  • Inna Pronina, Senior Data Architect Consultant
  • Raul Vomisescu, Senior Architect Consultant, Felmer Consulting
  • Business executives don’t understand the value of Conceptual and Logical Data Models and how they define their data assets.
  • Data, like mercury, is difficult to manage and contain.
  • IT needs to justify the time and cost of developing and maintaining Data Models.
  • Data as an asset is only perceived from a physical point of view, and the metadata that provides context and definition is often ignored.

Our Advice

Critical Insight

  • Data Models tell the story of the organization and its data in pictures to be used by a business as a tool to evolve the business capabilities and processes.
  • Data Architecture and Data Modeling have different purposes and should be represented as two distinct processes within the software development lifecycle (SDLC).
  • The Conceptual Model provides a quick win for both business and IT because it can convey abstract business concepts and thereby compartmentalize the problem space.

Impact and Result

  • A Conceptual Model can be used to define the semantics and relationships for your analytical layer.
    • It provides a visual representation of your data in the semantics of business.
    • It acts as the anchor point for all data lineages.
    • It can be used by business users and IT for data warehouse and analytical planning.
    • It provides the taxonomies for data access profiles.
    • It acts as the basis for your Enterprise Logical and Message Models.

Research & Tools

Start here – read the Executive Brief

Read our concise Executive Brief to find out why you should create enterprise data models, review Info-Tech’s methodology, and understand the four ways we can support you in completing this project.

1. Setting the stage

Prepare your environment for data architecture.

2. Revisit your SDLC

Revisit your SDLC to embed data architecture.

3. Develop a Conceptual Model

Create and maintain your Conceptual Data Model via an iterative process.

4. Data Modeling Playbook

View the main deliverable with sample models.

Guided Implementations

This guided implementation is a nine call advisory process.

Guided Implementation #1 - Establish the data architecture practice

Call #1 - Scope requirements, objectives, and your specific challenges.

Guided Implementation #2 - Business architecture and domain modeling

Call #1 - Set the playing field.
Call #2 - Identify business context and domain selection.

Guided Implementation #3 - Harvesting Reference models

Call #1 - Identify business entities within selected domain.
Call #2 - Harvest Reference models.

Guided Implementation #4 - Harvesting existing data artifacts

Call #1 - Harvest application models.
Call #2 - The use of the Data Glossary.

Guided Implementation #5 - Build the Conceptual model

Call #1 - Build the Conceptual model.
Call #2 - Verify model with business for correctness.

Onsite Workshop

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Onsite workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost onsite delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.

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.

  • Data Architecture vision and mission and governance.
1.2

Obtain business commitment and expectations for data architecture.

  • Revised SDLC to include data architecture.
1.3

Define data architecture as a discipline, its role, and the deliverables.

  • Staffing strategy.
1.4

Revisit your SDLC to embed data architecture.

  • Data Architecture engagement protocol.
1.5

Modeling tool acquisition if required.

  • Installed modeling tool.

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.

  • List of defined and documented entities for the selected.
2.2

Business domain selection.

  • Practice in the use of capability and business process models to identify key data concepts.
2.3

Identify business concepts.

  • Practice the domain modeling process of grouping and defining your bounded contexts.
2.4

Organize and group of business concepts.

2.5

Build the Business Data Glossary.

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.

  • Established and practiced steps to extend the conceptual or logical model from the reference model while maintaining lineage.
3.2

Exploring and searching the reference model.

3.3

Harvesting strategies and maintaining linkage.

3.4

Extending the conceptual and logical models.

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.

  • List of different methods to reverse engineer existing models.
4.2

Match semantics.

  • Practiced steps to extend the logical model from existing models.
4.3

Maintain lineage between BDG and existing sources.

  • Report examples.
4.4

Select and harvest attributes.

4.5

Define modeling standards.

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.

  • Data governance policies, standards, and procedures for data architecture.
5.2

Exploit and extend the use of the Conceptual model in the organization.

  • List of business function and processes that will utilize the Conceptual model.

Search Code: 96013
Published: January 27, 2021
Last Revised: January 27, 2021

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