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Relational Data Architecture for Automotive Service Operations
Project Overview
I engineered a custom relational data model to integrate and streamline the operational and financial data of a high-volume automotive service center. By transitioning from flat files to a structured Star Schema, I enabled complex time-intelligence analysis and improved the accuracy of financial reporting for the entire organization.
Key Features & Technical Implementation:
Relational Schema Design: Established a "One-to-Many" (*) relationship between operational tasks (Fact Table) and service categorizations (Dimension Table), ensuring data integrity and reducing redundancy.
Financial Data Governance: Mapped critical date fields—including Invoice, Payment, Work, and Appointment dates—to allow for precise tracking of the collection cycle and cash flow timing.
Advanced DAX Calculations: Implemented Time-Intelligence measures, such as "Previous Month Revenue" (Ingresos Mes Anterior), to provide real-time month-over-month growth comparisons.
Operational Categorization: Structured a hierarchical service classification system (Category vs. Job Type) to identify the most profitable labor segments and resource allocation.
Tools Used:
Power BI Data Modeling: For architecting the relational schema and managing cross-filter directions.
DAX (Data Analysis Expressions): For creating custom financial metrics and period-over-period comparisons.
Data Normalization: Transforming raw service logs into structured, relatable tables.
The Result:
This architecture serves as the "back-end" engine for all business intelligence at the facility. It allows the management team to drill down from high-level monthly revenue to specific job-type performance, drastically reducing the time spent on manual data reconciliation and enabling a more agile response to market demand.
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