Parameterized Statistical Model for Context-Aware Analytics
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Solution Overview
Problem
Current self-service analytics products are limited by hardcoded user interfaces and workflows, preventing business users from analyzing data in different contexts without data scientist intervention, and lack mechanisms for dynamic statistical model adaptation or visualization of statistical validity.
Innovation Solution
A parameterized statistical model that allows business users to select variables, constraints, and scope for data analysis, creating a dynamic table as input for analysis, enabling data analysis across various contexts without repeated data scientist interventions and providing visual reports on statistical validity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a statistical model is built for a specific use case with hardcoded variables, then the model provides accurate predictive analytics for that specific use case, but the model cannot be reused for different contexts or attributes without data scientist intervention
Solution Approach 1:
The patent applies parameter changes by transforming the statistical model from a hardcoded structure to a parameterized structure where variables, constraints, and scope are defined as configurable parameters. This allows the same model framework to adapt to different contexts by changing its parameters rather than requiring complete model rewriting, thus resolving the contradiction between maintaining analytical accuracy and enabling model reusability.
Solution Approach 2:
The patent implements universality by creating a single parameterized statistical model that can serve multiple functions across different business contexts. The model framework becomes universal by accepting different parameter configurations (variables, constraints, scope) that adapt it to various analytical needs, eliminating the requirement for separate models for each use case while maintaining context-specific accuracy.
2Reliability
If separate models are created for different use cases and attributes, then each model is optimized for its specific context, but the overall system complexity increases and requires repeated data scientist interventions
Solution Approach 1:
The patent reduces system complexity by replacing multiple context-specific models with a single universal parameterized model. This unified model maintains reliability for each context through parameter configuration rather than through separate model instances, thereby reducing the overall system complexity and eliminating the need for repeated data scientist interventions for model creation and maintenance.
Solution Approach 2:
The patent merges multiple separate statistical models into a single parameterized model framework. By combining the functionality of what would have been multiple context-specific models into one adaptable structure, the system achieves the same analytical reliability across different contexts while significantly reducing system complexity and maintenance overhead.
3Productivity
If business users are provided with self-service analytics tools, then user autonomy and productivity improve, but the tools are limited to visual and descriptive analytics only
Solution Approach 1:
The patent enables self-service predictive analytics by providing business users with a parameterized statistical model interface where they can independently configure variables, constraints, and scope parameters. Users can build and execute predictive analytics models without requiring data scientist intervention, thus maintaining user autonomy while expanding analytics capabilities beyond visual and descriptive to include predictive functions.
Solution Approach 2:
The patent extends self-service analytics versatility by allowing users to modify model parameters (variables, constraints, scope) to adapt the statistical model to different analytical contexts. This parameter-driven approach enables business users to perform diverse predictive analytics tasks using the same flexible framework, removing the limitation to only visual and descriptive analytics.
Data Source
AI summary
A method and system for analyzing data based on a statistical model, wherein the statistical model is used in one or more contexts without needing intervention of data scientists. This statistical model is parameterized and uploaded in an analytics platform. Parameterizing the statistical model enables end users to select scope, constraints and variables of data analysis. Acceptance indicator of this tool indicates reliability of the model on user selected scope, constraints and variables. Based on user selections, a dynamic table is created which is an input to the statistical model for data analysis. Based on values of this dynamic table, data analysis is performed on stored data on the specific context. The report id generated based on the data analysis which is presented to user as visual output.