Data Integration Hub for Client Valuation
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Solution Overview
Problem
Traditional methods for calculating a client's average daily theoretical (ADT) value struggle due to difficulties in sharing data across different data platforms, especially in industries like casinos and hotels, where disparate inventory management and customer resource management systems are used, making it challenging to integrate data for accurate client valuation.
Innovation Solution
A data integration hub system that translates external datasets from different database schemas into a compatible format for a centralized database, allowing for the generation of client valuation profiles based on comprehensive data from various sources, including brick-and-mortar institutions, online platforms, and social networking sites, and communicates these profiles to marketing platforms for targeted marketing decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is shared across multiple data platforms with different schemas, then client valuation accuracy is improved, but data integration complexity increases
Solution Approach 1:
The patent implements a data integration hub that serves as an intermediary layer between multiple external data sources with different schemas and the internal centralized database. This hub translates and adapts data from various external platforms (casino management systems, hotel reservation systems, retail POS systems) into a unified format that can be stored and processed in the centralized database, thereby enabling accurate client valuation without requiring direct complex integrations between all data sources.
Solution Approach 2:
The patent employs schema translation mechanisms that transform data parameters and structures from external sources with different database schemas into a standardized internal schema. This involves mapping different data formats, data types, and structural organizations into a common framework, allowing the system to handle diverse data sources while maintaining data consistency and accuracy for client valuation calculations.
2Productivity
If comprehensive data from multiple sources is integrated, then marketing effectiveness is improved, but data processing time increases
Solution Approach 1:
The patent implements preliminary data translation and validation processes within the data integration hub that prepare data from external sources before it is loaded into the centralized database. By performing schema translation, data validation, and initial processing in advance, the system reduces the time required for subsequent data analysis and client valuation calculations, thereby improving overall marketing effectiveness without excessive processing delays.
3Adaptability or versatility
If data translation is performed for all external sources, then data compatibility is improved, but processing overhead increases
Solution Approach 1:
The patent implements a universal translation framework within the data integration hub that handles multiple external data sources with different schemas through a common translation mechanism. This multi-functional approach allows the system to adapt to various data sources (casinos, hotels, retail stores) using a unified translation process, improving data compatibility while avoiding the need for separate custom translation routines for each source, thereby reducing overall processing overhead.
Data Source
AI summary
A data integration hub system includes a centralized database storing data according to a dimensional modeling schema. The data integration hub system accepts an external dataset from an external data source that stores data according to a database schema that differs from the dimensional modeling schema of the central database. The data integration hub translates the external data set into a first importable format compatible with the dimensional modeling scheme of the centralized database, and imports the translated external dataset into the centralized database. The data integration hub also accepts online platform data from an online platform and imports the online platform data into the centralized database. In some cases, the data integration hub translated the online platform data prior to importation. The data integration hub generates client valuation profiles based on the data imported from the external data source and the online platform.