Data Architecture and Modeling

The Entity shall develop and document comprehensive data modeling guidelines and methodologies to ensure a consistent and standardized approach to data modeling across the organization. These guidelines and methodologies shall include, at a minimum:

• Establish and document principles and best practices for developing conceptual, logical, and physical data models
• Define standards for creating and maintaining data models, including naming conventions, data types, and relationships
• Provide guidelines on various modeling techniques, such as entity-relationship diagrams and other tools to represent data structures
• Ensure that data models are integrated with the overall data architecture to support business objectives
• Establish procedures for version control and detailed documentation of data models
• Integrate data modeling guidelines with data governance and quality management processes to ensure accuracy and consistency
• Implement a regular review and update process to keep the guidelines and methodologies current and relevant
• Reference international standards and best practices for data modeling to ensure global alignment

The Entity shall establish its current baseline Data Architecture. The current state Data and Technical architecture should include, at a minimum:

• Data Model: The Entity's existing Enterprise Data Model, detailed at the Conceptual, Logical, and Physical levels. Each level should cover, at a minimum:
  • Conceptual Model: Identification of key business entities and their relationships, organized into business subject areas.
  • Logical Model: An extension of the conceptual model, incorporating attributes for business entities and adding less significant entities and relationships.
  • Physical Model: A physical representation of the logical data models within key system components, including physical table names, attribute names, data types, and primary and foreign keys.
• Key Processes: The crucial current processes involved in the Entity's ongoing business operations and decision-making.
• Key System Components: The essential current applications, data storage solutions, data processing platforms, and data analytics solutions involved in key processes.
• Data Flows and Lineage: A visualization of the current data movement across key processes and system components.

The Entity shall define and develop a comprehensive target data architecture which shall be reviewed and approved at the appropriate governance checkpoints to ensure alignment with the Entity’s overall data strategy and business objectives. The target Data Architecture shall include, at a minimum:

• Data Model: The Entity's target Enterprise Data Model at Conceptual, Logical, and Physical levels. Each data model level shall include, at minimum:
  • Conceptual Model: Identification of key business entities and their relationships, organized into business subject areas
  • Logical Model: Extension of the conceptual model with attributes for business entities and the inclusion of less significant entities and relationships
  • Physical Model: Physical representation of logical data models within key system components, including physical table names, attribute names, data types, and primary and foreign keys
• Key Processes: Identification of essential target processes involved in ongoing business operations and decision-making
• Key System Components: Identification of essential target applications, data storage solutions, data processing platforms, and data analytics solutions involved in key processes
• Data Flows and Lineage: Visualization of target data movement across key processes and system components, ensuring a clear understanding of data flow and lineage

The Entity shall conduct a thorough gap analysis between the baseline and target data architectures. This analysis shall, at a minimum:

• Identify business data requirements that are currently not being fulfilled
• Highlight technical data components that are absent between the baseline and target architectures
• Assess gaps in roles, skills, tools, and training required to support the target data architecture

Based on the results of the gap analysis, the Entity shall develop a detailed roadmap for guiding the Entity in transitioning from the baseline data architecture to the target data architecture. The roadmap shall, at a minimum:

• Include timelines for implementation, budgetary estimates, and required capabilities.
• Specify the priority and sequence for changing, upgrading, replacing, or retiring components and systems, while maintaining flexibility to adapt to business priorities.
• Undergo periodic review by the Data Committee to ensure alignment with both the baseline and target data architectures, and include an evaluation and justification of changes in priority or exceptions to the roadmap as reviewed by the Data Committee (if applicable).

The Entity shall define and maintain comprehensive metadata for all data models to ensure proper management, traceability, and version control. The Entity shall, at a minimum:

    
• Ensure that each data model has a unique identifier in the format: [Entity Initials]-[Reference Number]-[Version]. For example, NPC-123-V1.0 represents Version 1.0 of model 123 for NPC.
    
• Clearly define roles using the RACI matrix (Responsible, Accountable, Consulted, Informed)
    
• Indicate whether the model is in Draft or Published status
    
• Document the history of changes, including dates, authors, and descriptions of changes made
    
• Store data models in a repository with robust version control capabilities. The recommended options for repositories, in order of preference, include:         
              
  • Integrated version control within data modeling tools
              
  • External version control repositories or document management systems supporting version control
     
  • Version control through organized file system structures (as an interim or last resort solution)
          
      

The Entity shall monitor and report on the established data architecture and modeling KPIs. This shall include, at a minimum:

• Implementing continuous monitoring mechanisms to track KPIs. The frequency of monitoring shall, at a minimum, be on a quarterly basis
• Defining clear escalation protocols for performance deviations and non-compliance. The Data Committee shall be responsible for overseeing and managing the escalation process to address issues promptly

The Entity shall establish and implement a structured change management process for reviewing, approving, and implementing changes to both the current and target state data architectures, to ensure that all modifications are systematically evaluated and aligned with the Entity’s strategic objectives. This process shall include, at a minimum, the following components:

• Handling requests for new data architecture and data modeling projects
• Managing changes to the Statement of Architecture Work documents for ongoing initiatives