Transparent Data Access Interface for Heterogeneous Sources
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Solution Overview
Problem
Business process outsourcing often results in logistical and operational issues due to incompatible architectures and the need for manual processes to integrate and access heterogeneous data sources, leading to increased costs and reduced transparency for companies outsourcing their business processes.
Innovation Solution
A transparent data access interface/layer that provides real-time, customer-specific access to heterogeneous data sources by using a data source identification processor to determine the location and method of access, generating queries in parallel across multiple data sources, and creating virtual tables for seamless access, thereby eliminating the need for manual intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a BPO uses multiple proprietary or incompatible architectures to implement business processes, then the BPO can serve multiple customers with customized access, but the system complexity increases and manual compensation processes are required
Solution Approach 1:
The patent introduces a data virtualization layer as an intermediary between the BPO's heterogeneous data sources and the customers. This virtualization layer abstracts the complexity of multiple proprietary architectures and provides a unified, standardized interface for data access, eliminating the need for manual compensation processes while maintaining customized access for each customer.
Solution Approach 2:
The system segments the data access architecture into distinct layers: the heterogeneous data sources at the bottom, the data virtualization layer in the middle, and the customer-specific access interfaces at the top. This segmentation allows each layer to be optimized independently, with the virtualization layer handling the complexity of integrating multiple architectures while providing simple, customized access points for customers.
2Reliability
If manual processes are used to access and aggregate data from disparate sources, then data coherency can be maintained, but administrative overhead and errors increase
Solution Approach 1:
The data virtualization layer performs self-service by automatically querying, retrieving, and aggregating data from disparate sources without requiring manual intervention. The system maintains data coherency through automated synchronization mechanisms while eliminating the administrative overhead and errors associated with manual data aggregation processes.
3Productivity
If real-time data access is provided across heterogeneous sources, then transparency and productivity improve, but system complexity and resource requirements increase
Solution Approach 1:
The data virtualization layer acts as an intermediary that enables real-time data access across heterogeneous sources without requiring direct connections between all systems. It manages the complexity of real-time queries by abstracting the underlying data sources and providing a simplified, standardized interface that maintains productivity while reducing system complexity.
Data Source
AI summary
A transparent data access interface/layer for repackageable virtualized transparent access to heterogeneous business process data sources, internally maintained or outsourced, is disclosed. This data access interface provides substantially real time customer/client specific, i.e. transparent, access to a customer/client generic enterprise storage and data processing architecture, such as an architecture operated by a business process outsourcing organization (“BPO”), which includes multiple disparate/heterogeneous data sources, having disparate formats and access methodologies, storing and processing customer/client specific data for multiple customers, while also permitting similarly transparent access across the enterprise storage architecture, e.g. across multiple customers/clients, such as for BPO-internal processing and reporting requirements. The data stored in the data sources may include data collected/received from the customer of the BPO, such as data identifying the BPO's customer's customers/clients and/or business processing rules or algorithms, data received/collected from the customers/clients of the BPO's customer, such as transactional data, e.g. insurance claims, etc., data calculated or computed by the BPO based on stored or collected data, data representative of business processing rules developed by the BPO, such as rules for maintaining customer specific service level agreements, or combinations thereof.