Data Model Adaptability via Configuration Layer Segmentation
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Solution Overview
Problem
Current enterprise data models face challenges in handling source field changes and key performance indicator (KPI) calculations, leading to high costs, long sales cycles, and limited flexibility, as they require significant changes to the data model structure, which can result in performance regressions and downtime.
Innovation Solution
A system and method that allow for source field changes and KPI calculation modifications without altering the data model structure, using a single fact table design and a graphical user interface (GUI) to manage new source fields and KPI calculations through a generic algorithm and relational bridge table, enabling users to create and modify algorithms without affecting the underlying data model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the data model structure is altered to accommodate source field changes and new KPI calculations, then the system can support new requirements, but the complexity of the data model increases and performance may regress
Solution Approach 1:
The patent segments the data model into distinct layers: a stable core data model and flexible configuration layers (component tables, KPI definition tables, calculation rule tables). This allows changes to be isolated in configuration layers without affecting the core data model structure, thereby maintaining simplicity while enabling adaptability.
Solution Approach 2:
The patent introduces a new dimension of configuration management by adding metadata tables (component tables, KPI definition tables, calculation rule tables) that operate alongside the traditional data model. This dimensional addition allows flexibility to be injected without modifying the existing data model structure.
2Adaptability or versatility
If the data model structure is modified to support new source fields and KPI calculations, then new requirements can be met, but implementation time and costs increase
Solution Approach 1:
The patent performs preliminary action by pre-defining calculation rules, component relationships, and KPI templates in configuration tables before actual KPI calculations are needed. This allows new KPIs to be rapidly deployed by simply configuring parameters rather than implementing new calculation logic from scratch, significantly reducing technical turnaround time.
Solution Approach 2:
The patent enables copying of existing KPI definitions, calculation rules, and component configurations. When new KPIs are needed, users can copy and modify existing templates rather than creating new calculations from scratch, reducing implementation time and costs while maintaining adaptability.
3Ease of operation
If users can directly modify KPI calculations in the data model, then user self-service capability improves, but the risk of errors and performance issues increases
Solution Approach 1:
The patent implements self-service by enabling users to directly configure and modify KPI definitions, select components, and define calculation parameters through a user interface that interacts with configuration tables. Users can perform these modifications without requiring data model changes or technical intervention, improving ease of operation while maintaining system stability through the isolated configuration layer architecture.
Data Source
AI summary
Most of the business intelligence and analytics applications uses a data model. Any change in the source field or in the key performance indicator (KPI) calculation changes result in long turn-around time and complex changes in the background coding of the data model. A method and system for handling the source field change and the key performance indicator (KPI) calculation change in the data model has been provided. The disclosure provides a data modelling design, in particular, for handling source field changes or additions and target KPI calculation changes without any impact on the data model. The solution section is divided in two areas so as to tackle the technical problem statement points. First part is the data ingestion and second is data reporting.