For example, a workforce data model might connect employees, departments, job assignments, and payroll transactions. These relationships help teams analyze compensation, staffing, and workforce costs without rebuilding the connections for every report.
Key Components of a Data Model
- Entities: The subjects represented in the data, such as employees, customers, suppliers, or invoices.
- Attributes: Details about each entity, such as an employee’s start date or an invoice’s amount.
- Relationships: Connections between entities, such as a customer placing multiple orders.
- Keys: Identifiers that distinguish records and link related information.
- Rules and constraints: Requirements that govern valid data, such as mandatory fields or unique identifiers.
Together, these components describe what the data represents and how its parts fit together.
Types of Data Models
Data models commonly exist at three levels of detail:
| Type | What it defines | Example |
|---|---|---|
| Conceptual | Business entities and their main relationships | Employees belong to departments |
| Logical | Attributes, identifiers, relationships, and rules | Each employee has an ID and department assignment |
| Physical | Implementation in a specific database | Tables, columns, data types, and keys |
These levels take a design from business requirements to technical implementation. Learn more about data modeling.
Why Data Models Matter for Analytics
A well-designed data model helps teams:
- Build reliable reports: Defined relationships reduce incorrect joins and double counting.
- Combine information: Shared identifiers help connect records from different systems.
- Reduce repeated work: Analysts can reuse established structures across reports and dashboards.
- Maintain historical context: Effective dates and historical records support analysis of changes over time.
A model provides structure, but source data still needs validation. Missing identifiers or inaccurate records can affect results even when the design is sound.
Example of a Data Model in Workforce Analytics
Suppose HR wants to compare monthly payroll costs by department. The model connects payroll transactions to employee records and department assignments.
If an employee changes departments, dated assignments help allocate costs to the correct department for each period. Without that history, a report could assign earlier payroll costs to the employee’s current team.
How SplashBI Supports Data Models
SplashBI’s enterprise intelligence platform provides pre-built data models for Finance, HR, Operations, and Sales, alongside options to build custom models. Teams can use these structures to support reporting and analysis across connected enterprise systems.
A data model is the resulting structure. Data modeling is the process of designing, implementing, and maintaining that structure.
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