1️⃣Overview
The Data Dictionary serves as the backbone for structured data definitions across financials, operations, projects, compliance, and workforce domains. It ensures standardised terminology, clear metadata definitions, and structured governance for all key data entities.
Key Functions:
✅ Standardised Definitions – Ensures clarity and uniformity across datasets.
✅ Metadata-Driven Organisation – Supports schema validation and query efficiency.
✅ Data Governance Alignment – Ensures compliance with naming conventions and security policies.
✅ Facilitates API & Data Exchange – Supports integrations by providing a structured reference.
2️⃣ Data Dictionary Structure
Each entry consists of:
- DataMart Domain (Customers, Employees, Projects, etc.)
- ViewName (CDS Table or View where the attribute exists like
.core,.codes,.value,.status) - UtilityView (simplifies data consumption by cross-combining relevant details into a single, user-friendly dataset)
- Attribute (Specific field name)
- Description (Definition and purpose of the field)
3️⃣ Data Dictionary Entries (Examples)
📌 Customers
| DataMart | ViewName | UtilityView | Attribute | Description |
|---|---|---|---|---|
| Customers | Audit | Audit | CreatedBy | User or system that created the customer record. |
| Customers | Codes | Contract | CustomerPriority | Priority level (e.g., High, Medium, Low). |
| Customers | Codes | Organisation | Industry | Industry or sector of the customer. |
| Customers | entity/core | Core (Detail) | CustomerID | Unique identifier for the customer (primary key). |
| Customers | Status | Financial | LifetimeValue | Total revenue generated by the customer. |
📌 Employees
| DataMart | ViewName | UtilityView | Attribute | Description |
|---|---|---|---|---|
| Employees | Audit | Audit | CreatedBy | User or system that created the employee record. |
| Employees | Codes | Organisation | EmploymentType | Type of employment (e.g., Full-Time, Part-Time). |
| Employees | entity/core | Core (Detail) | EmployeeID | Unique identifier for the employee (primary key). |
| Employees | Status | Quality | FTE | Full-time equivalent percentage (e.g., 1.0 = FT). |
| Employees | Status | Employment | WorkHours | Standard work hours per week. |
📌 Projects
| DataMart | ViewName | UtilityView | Attribute | Description |
|---|---|---|---|---|
| Projects | Audit | Audit | CreatedBy | User or system that created the project record. |
| Projects | Codes | Operations | CurrencyCode | Currency associated with project costs. |
| Projects | Dates | Operations | StartDate | When the project starts. |
| Projects | entity/core | Core (Detail) | ProjectID | Unique identifier for the project. |
| Projects | Status | Activity | Status | Current status (e.g., In Progress, Completed). |
4️⃣ Metadata Classification & Hierarchy
📌 The Data Dictionary follows a structured classification system to ensure clarity across datasets:
- Audit – Tracks creation, updates, and data quality.
- Codes – Stores reference data (classifications, lookups).
- Core (entity/core) – Unique identifiers and key attributes.
- Details – Additional metadata extending
core. - Dates – Tracks time-related attributes (e.g., StartDate, HireDate).
- Status – Tracks dynamic attributes like project state or employee status.
- Values – Stores financial or quantitative metrics.
- Links – Establishes relationships between entities.
5️⃣ Future Development & Integration
📌 The Data Dictionary will be regularly updated to reflect new business rules and reporting requirements.
📌 It will be used in parallel with the Common Data Standard (CDS) for structured DataMart views.
6️⃣ Next Steps & Considerations
- Data Governance – Ensure all new datasets align with the Data Dictionary.
- Integration with CDS – Link the Data Dictionary entries with Common Data Standard views for seamless reporting.
- Documentation Update – Maintain an up-to-date version of the Data Dictionary within myBMT.
🌟 Final Thoughts
📌 The Data Dictionary is a living document that supports structured metadata management, API integrations, and query efficiency.
📌 It complements the Common Data Standard (CDS) by providing granular definitions of each attribute.
📌 This ensures a clear, consistent, and scalable approach to Data Engineering & Reporting.
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