Template-Based Cross View Generation for Data Consistency
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Solution Overview
Problem
Current cross-system analytics solutions face high total cost of ownership due to the need for multiple data models and lack of flexibility in defining cross views when the system landscape is unknown, leading to difficulties in extending or updating views without sacrificing bug-fixes or new releases.
Innovation Solution
The system defines cross views based on a minimum number of involved system types using templates, with configuration data from explicit customer system landscapes to generate landscape-specific views, allowing for extensibility and flexibility without cutting off bug-fixes or new releases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If cross views are defined for each specific customer system landscape, then the views can be customized to match explicit customer requirements, but the total cost of ownership increases and flexibility is lost when bug-fixes or new releases are needed
Solution Approach 1:
The system segments the cross-view definition into two independent parts: a template component that defines the generic structure and logic, and a configuration component that stores customer-specific system landscape parameters. This separation allows the template to remain reusable and maintainable while accommodating different customer requirements through configuration alone, thereby reducing total cost of ownership while maintaining adaptability.
Solution Approach 2:
The system performs preliminary action by pre-defining cross-view templates with generic system landscape assumptions during the product development phase. These templates encapsulate common analytical scenarios and can be directly applied to multiple customers without requiring custom development for each specific landscape, thus reducing implementation costs and maintaining flexibility for future updates.
2Ease of manufacture
If cross views are defined with assumptions about system landscape, then development is simplified, but the views cannot be extended to accommodate different customer environments
Solution Approach 1:
The system uses parameter changes by introducing configurable parameters that represent system landscape characteristics (such as number of source systems, system types, data models). The cross-view templates are defined with these parameters as variables, allowing the same template to adapt to different customer environments by simply changing the parameter values during configuration, thus maintaining both development simplicity and extensibility.
Solution Approach 2:
The system applies universality by designing cross-view templates that can serve multiple customer environments and system landscapes. The templates are constructed with generic assumptions and configurable parameters that allow them to function universally across different organizational contexts, eliminating the need for separate custom developments for each customer while maintaining adaptability through configuration.
3Reliability
If multiple data models are created for different purposes (integration, user interface, analytics, transactions), then each functionality gets its own optimized model, but the total cost of ownership increases due to repeated content provision
Solution Approach 1:
The system applies universality by creating a single core data model that serves multiple functionalities (integration, user interface, analytics, transactions). This core model is designed with sufficient generality and configurability to support all these purposes without requiring separate specialized models, thereby reducing the total cost of ownership while maintaining functionality optimization through proper model design and configuration.
Data Source
AI summary
The present disclosure relates to systems and methods for generating a virtual data model of data from one or more source systems, where the exact number of each type of source system is not known. Templates for each source system type may be defined and then explicit views may be generated for each source system based on configuration data provided by a customer.