Orbit Data Models for Oracle Fusion Cloud Applications
A curated, governed layer of analytics-ready data models that makes it easy to answer questions consistently across teams, dashboards, and decisions. Oracle Fusion data models turn raw sources into trusted definitions, clear business logic, and reusable datasets so analysts and stakeholders can move faster with fewer “what does this metric mean?” loops.
Highlights
Consistent Metrics
Analytics Ready Data
Governed & Documented
Clear owners, SLAs, lineage, and data quality checks
Built for Scale
How Oracle Fusion Data Models Are Structured
Fusion models follow a layered approach that stays easy to understand and maintain:
Bronze (Raw / Staged)
Silver (Cleaned / Conformed)
Standardized fields, de-duplication, type casting, unified IDs, and conformed dimensions
Gold (Business-Ready)
Why this matters: You can move quickly using Gold models, dive deeper with Silver for nuance, and always trace metrics back to Bronze when validation is needed.
- Data Quality and Freshness
- Deployment and Warehouse Support
- End-to-End Data Lineage
- AI-Ready Fusion Data Models
Quality Checks You Can Rely OnFusion models are monitored for:
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| Orbit Analytics works with both cloud and on-premises data warehouse environments, so you can run analytics where your data lives. Supported cloud platforms include: Oracle Autonomous Data Warehouse (ADW), Databricks, Snowflake, Amazon Redshift, and Microsoft Fabric. On-premises support: Orbit Analytics also supports on-prem data warehouses, with connectivity options designed for enterprise security and governance. |  |
| Fusion data models include end-to-end data lineage so you can trace any metric or field from its Gold business model back through Silver transformations to the original Bronze/source systems. Lineage helps teams quickly understand where data comes from, what transformations were applied, and which downstream reports or dashboards are impacted when something changes—improving trust, troubleshooting, and change management. |  |
| AI-ready Fusion data models provide clean, well-defined, point-in-time accurate datasets with stable keys and quality signals—so teams can build reliable AI features, forecasts, and copilots without data leakage or metric drift. |  |
Quality Checks You Can Rely On
Quality checks you can rely on
Fusion models are monitored for:
- Row-count anomalies
- Null and uniqueness checks on key fields
- Referential integrity across joins
- Schema changes and drift
- Business-rule validations (for example, preventing negative revenue)
Deployment and Warehouse Support
Orbit Analytics works with both cloud and on-premises data warehouse environments, so you can run analytics where your data lives.
Supported cloud platforms include: Oracle Autonomous Data Warehouse (ADW), Databricks, Snowflake, Amazon Redshift, and Microsoft Fabric.
On-premises support: Orbit Analytics also supports on-prem data warehouses, with connectivity options designed for enterprise security and governance.
End-to-End Data Lineage
Fusion data models include end-to-end data lineage so you can trace any metric or field from its Gold business model back through Silver transformations to the original Bronze/source systems.
Lineage helps teams quickly understand where data comes from, what transformations were applied, and which downstream reports or dashboards are impacted when something changes—improving trust, troubleshooting, and change management.
AI-Ready Fusion Data Models
AI-ready Fusion data models provide clean, well-defined, point-in-time accurate datasets with stable keys and quality signals—so teams can build reliable AI features, forecasts, and copilots without data leakage or metric drift.
HCM Analytics
Improve visibility into workforce trends
Gain a consolidated view of your employee population across departments, locations, and job families.
Analyze trends in hiring, attrition, and internal mobility over recent months.
Ensure operational continuity and employee availability
Monitor expiring contracts, vacancies, probation completions, and planned leaves.
New Hire and Vacancy analysis
Track workforce size, vacancies, and labor spend to stay within budget.
Gender Pay gap analysis
Analyze pay gaps based on gender across departments and locations.
Key KPIs and Metrics Available
- Headcount
- Open Positions and Vacancy
- Hires (MTD/QTD/YTD)
- Attrition (Voluntary / Involuntary) and Attrition Rate
- Net Headcount Change
- Internal Mobility Rate (Transfers/Promotions)
- Internal Mobility Rate (Locations)
- Internal Mobility Rate (Supervisor)
- Absence Rate / Leave Utilization
- Average Compensation and Compensation Growth
Key Areas Covered
- Workforce overview
- Headcounts by department/location/job family
- Hiring and recruiting pipeline
- Open requisitions Analysis
- Gender pay gap analysis
- Attrition trends by department/location/job family
- Attendance and utilization
- Absences and leave patterns by period
- Employees Mobility by department/location/job family
Best Practices We Follow
Define the Grain Up Front
Use Canonical, Conformed Dimensions
Keep Modeling Layered and Intentional (Bronze → Silver → Gold)
Handle Schema Drift Deliberately
Use Surrogate Keys and SCDs for Historical Accuracy
We generate surrogate keys and apply Slowly Changing Dimension (SCD) patterns where attributes evolve over time (for example, plan, segment, or account ownership). This preserves historical accuracy, prevents identifier churn from source systems, and enables reliable point-in-time reporting and cohort analysis.
