Project: Data Dictionary

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& Common Data Standards – MVP Development

Project Description

The Data Dictionary & Common Data Standards initiative aims to create a structured, standardised approach for defining and managing the key DataMarts, including:

  • Projects
  • Customers
  • Employees
  • Suppliers

This initiative will form the Minimum Viable Product (MVP) for describing the data landscape, improving data discoverability, governance, and quality assessment across the organisation.

The Data Dictionary will serve as a reference framework, enabling:

  • Consistent DataMart descriptions – including codes, dates, and values.
  • Field lookup capabilities – mapping source fields to standard definitions.
  • Classification of fields – distinguishing essential vs. nice-to-have attributes.
  • Data quality assessment – measuring completeness, accuracy, and integrity.

This structured approach will support data lineage, governance, and usability, allowing teams to trust and effectively use data across different reporting and analytics functions.

Key Benefits

✅ Standardisation – Ensures a consistent structure for DataMarts.
✅ Improved Discoverability – Enables users to easily understand field definitions and relationships.
✅ Enhanced Data Quality – Provides a framework for assessing and improving data integrity.
✅ Governance & Compliance – Establishes a foundation for data stewardship and policy enforcement.
✅ Supports Automation – Can be extended to power metadata-driven processes.

Risks & Issues

Key Risks

  1. Incomplete Source Field Mapping
    • Some fields may not be clearly defined or mapped across different data sources.
    • Mitigation: Conduct a detailed data profiling exercise to identify gaps early.
  2. Inconsistencies in Data Definitions
    • Different teams may use conflicting definitions for similar fields.
    • Mitigation: Align definitions with business and technical stakeholders.
  3. Scalability & Maintenance
    • The dictionary may require continuous updates as new data fields emerge.
    • Mitigation: Establish a governance process for versioning and updates.
  4. Limited Engagement from Data Consumers
    • If not adopted, the dictionary may remain unused.
    • Mitigation: Provide training, documentation, and incentives for teams to use the dictionary.

Main Tasks & Deliverables

1. Define Data Dictionary Structure

  • Establish core metadata fields:
    • Field Name
    • Data Type
    • Description
    • Source System
    • Business Use Case
    • Example Values
    • Essential vs. Nice-to-Have Classification
  • Standardise common field groups (e.g., Codes, Dates, Values).

2. Populate Initial Dictionary for MVP

  • Document DataMart definitions for:
    • Projects
    • Customers
    • Employees
    • Suppliers
  • Align with existing DataMarts to ensure consistency.

3. Implement Lookup & Search Features

  • Enable field search across data sources.
  • Map data source fields to dictionary definitions.

4. Extend Dictionary for Data Quality Assessment

  • Define Quality Dimensions:
    • Completeness
    • Accuracy
    • Integrity
  • Implement basic quality scoring where applicable.

5. Validate & Iterate

  • Engage stakeholders (Data Engineers, Business Analysts).
  • Refine based on feedback before expanding the scope.

Next Steps

  1. Finalise core metadata model for the dictionary.
  2. Map key fields across Projects, Customers, Employees, and Suppliers.
  3. Develop lookup functionality to support field discovery.
  4. Introduce quality assessment attributes for key fields.
  5. Engage users & iterate the MVP for broader adoption.

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