Automotive Data Conditioning via SSIS for Secure OEM Analytics
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Solution Overview
Problem
Automotive Original Equipment Manufacturers (OEMs) face challenges in accessing and effectively utilizing data from dealer management systems due to inefficiencies in data processing, security concerns, and diverse data management practices across dealerships, leading to strained system resources and poor quality search results.
Innovation Solution
A data conditioning and visualization system that uses SQL Server Integration Services (SSIS) to standardize and secure data from various sources, providing aggregate access while maintaining privacy, through customized data extraction applications, data transformation, and secure data transmission protocols.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If OEMs access dealer management system data, then data utilization and decision-making improve, but data security and privacy are compromised
Solution Approach 1:
A data conditioning system acts as an intermediary between dealer management systems and OEM analytics platforms. The system receives raw data from multiple dealer sources, applies standardized transformation rules, masks sensitive information, and delivers processed data to OEMs. This mediator architecture enables data utilization while preserving security boundaries.
Solution Approach 2:
The system creates anonymized copies of dealer data for OEM analysis. Instead of accessing original sensitive data, the OEM receives transformed copies where personally identifiable information is masked or removed. These copies retain analytical value for sales forecasting and inventory planning without compromising privacy.
2Quantity of substance
If data from multiple dealer sources is aggregated, then data comprehensiveness improves, but data quality and consistency deteriorate
Solution Approach 1:
The data conditioning system applies parameter transformation rules to standardize data from different dealer sources. It converts various data formats, units, and structures into a common standardized format. This parameter harmonization enables aggregation of diverse data while maintaining consistency across the consolidated dataset.
Solution Approach 2:
The system segments the data processing workflow into distinct transformation stages: extraction from different sources, individual data cleaning and validation, standardization transformation, and final aggregation. This segmented approach allows each data source to be processed according to its specific characteristics before unification.
3Ease of operation
If custom data extraction applications are developed for each dealer, then data accessibility improves, but system complexity increases
Solution Approach 1:
The patent implements a universal data conditioning platform that serves multiple dealer management systems through a single standardized interface. Rather than developing separate extraction applications for each dealer, the system uses configurable transformation rules that can be adapted to different data sources without requiring custom application development. This multi-functional approach reduces system complexity while maintaining broad accessibility.
Data Source
AI summary
A visual discovery tool for automotive manufacturing with network encryption, data conditioning, and prediction can include an extraction device configured to receive data records from application-specific file source databases. The tool can further include a vehicle alert database that receives the vehicle records from the plurality of extraction databases. The visual discovery tool can include at least one hardware processor in communication with the extraction device and the vehicle alert database. The tool can be configured to selectively restrict access to an interactive display based on whether a client device receives authorization credentials.