Ensuring Structured, Reusable, and Scalable Data Architecture
1️⃣ Purpose of the Common Data Standard
The Common Data Standard (CDS) establishes a structured framework for defining DataMart views in a consistent and scalable manner. This standard:
✅ Promotes reusability across different datasets.
✅ Ensures alignment between business logic and reporting needs.
✅ Enables efficient data joins and minimises redundancy.
✅ Supports data governance and quality control.
2️⃣ Meta-Structured Views
The CDS model follows a consistent naming convention, ensuring interoperability across datasets.
| View Type | Purpose | Example Views |
|---|---|---|
Core (.core) | Unique identifiers and key attributes. | project.core, employee.core |
Codes (.codes) | Standard reference codes and classifications. | project.codes, customer.codes |
Details (.details) | Additional attributes extending core data. | project.details, employee.details |
Value (.value) | Financial or metric-based attributes. | project.value, order.value |
Status (.status) | Tracks the current and past state of an entity. | project.status, employee.status |
History (.history) | Tracks changes and historical values. | project.history, customer.history |
3️⃣ Naming Convention Guidelines
To maintain consistency across views, all DataMart structures follow a clear and predictable naming convention.
📌 Format:<Datamart>.<ViewName>
| Component | Description | Example |
|---|---|---|
| DataMart | The subject of the dataset. | project, employee, customer |
| ViewName | The type of data view. | .core, .codes, .details |
📌 Example Views Using Naming Conventions:
project.core→ Contains essential identifiers (Project ID, Name, Status).project.codes→ Stores classification codes (Cost Centre, Business Type).project.value→ Holds financial metrics (Total Cost, Revenue).project.status→ Monitors project states (Active, Completed).project.history→ Retains historical records (Previous Status, Manager Changes).
🔹 Benefits of Standardised Naming:
✅ Improves Discoverability – Easier for analysts and engineers to locate relevant data.
✅ Enhances Data Governance – Reduces ambiguity in schema definitions.
✅ Facilitates Query Optimisation – Simplifies SQL joins across structured datasets.
4️⃣ First Common Data Standard Views
Core Details (.core)
Core attributes that uniquely define each entity, providing fundamental identifying details.
✅ Standardised Naming Convention: <entity>.core
✅ Ensures Reusability Across Systems
✅ Immutable Identifiers with Core Metadata
| Entry Code | Description |
|---|---|
project.core | Project ID, Name, Type, Business Unit |
employee.core | Employee ID, Name, Job Title, Department |
Dates & Timestamps (.dates)
🔹 Definition: All key date fields relevant to an entity, including start, end, and status update timestamps.
🔹 Examples:
| Entry Code | Description |
|---|---|
project.dates | Start Date, End Date, Estimated Completion Date, Milestone Dates |
employee.dates | Hire Date, Probation End Date, Promotion Dates |
Extended Attributes (.details)
Definition:
Additional attributes that extend the core entity’s information but are not required for identification.
✅ Standardised Naming Convention: <entity>.details
✅ Enriches Core Data with Additional Context
✅ Allows Flexible Schema Expansion Without Impacting .core
| Entry Code | Description |
|---|---|
project.details | Project Start Date, End Date, Budget, Risk Level |
employee.details | Employment Type, Contract Status, Benefits Tier |
customer.details | Preferred Contact Method, Account Manager |
order.details | Fulfilment Status, Expected Delivery Date, Carrier |
Financial Values & Measures (.values)
Quantitative or financial attributes related to the entity, supporting monetary, volume, or percentage-based analytics.
✅ Standardised Naming Convention: <entity>.value
✅ Financial & Metric-Based Representation
✅ Optimised for Aggregation & Reporting
| Entry Code | Description |
|---|---|
project.values | Project Budget, Actual Spend, Variance, Forecasted Cost |
employee.values | Salary, Bonus, Hours Worked, Utilisation Rate |
Standard Codes & Identifiers (.codes)
Stores reference codes, classifications, and standard identifiers used across multiple datasets to maintain consistency.
✅ Standardised Naming Convention: <entity>.codes
✅ Predefined Lookups for Normalisation
✅ Enhances Data Quality & Consistency
| Entry Code | Description |
|---|---|
project.codes | Project Classification, Category Codes, Cost Centre |
employee.codes | Job Classification, Payroll Codes, HR Band |
Historical Tracking (.activity)
Definition:
Tracks time-based changes to an entity, enabling auditability, trend analysis, and versioning.
✅ Standardised Naming Convention: <entity>.activity
✅ Supports Slowly Changing Dimensions (SCD)
✅ Ensures Full Data Lineage for Compliance
Includes Status & Progress
Maintains the current and previous status of an entity, often linked to operational workflows.
✅ Standardised Naming Convention: <entity>.status
✅ Provides Real-Time State Information
✅ Essential for Business Process Monitoring
| Entry Code | Description |
|---|---|
project.activity | Status Changes, Budget Revisions, Milestone Updates |
employee.activity | Job Title Changes, Department Transfers, Promotions |
customer.activity | Address Changes, Loyalty Tier Updates |
order.activity | Status Transitions, Refunds, Modifications |
Roles & Responsibilities (.roles)
🔹 Definition: Defines people and positions associated with an entity.
🔹 Examples:
| Entry Code | Description |
|---|---|
project.roles | Project Manager, Sponsor, Stakeholders, Assigned Team Members |
employee.roles | Direct Manager, HR Partner, Reporting Chain |
🌟 Key Benefits of This Approach
✅ Modular Structure: Easily extendable as business needs evolve.
✅ Consistent Naming Conventions: Promotes readability and standardisation.
✅ Scalability & Reusability: Reduces redundancy and improves data interoperability.
✅ Optimised Querying: Enables efficient joins and filtering without excessive duplication.
🔍 Why This Structure Works
✅ Metadata-Driven – Supports scalable standardisation across all DataMart views.
✅ Optimised for Querying – Encourages modular structures in APIs & DataWarehouses.
✅ Reusability – Field groupings are consistent across multiple domains.
✅ Cross-Domain Insights – Easily maps financial, project, and workforce data together.