Use Case Data Configuration for Adaptive Source Selection
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Solution Overview
Problem
Existing data management systems rely on subject matter expert knowledge for selecting data attributes and sources, leading to single points of failure, inability to adapt to new sources, and governance/audit challenges, without clear evidence for data source selection.
Innovation Solution
Utilize machine learning models to automatically determine a use case-specific configuration for data management, selecting optimal data sources and attributes, with real-time onboarding of new sources, and validate through an independent control process to improve accuracy and facilitate auditing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If subject matter expert knowledge is used for selecting data attributes and sources, then data management accuracy is improved, but system reliability deteriorates due to single points of failure and inability to adapt to new sources
Solution Approach 1:
The system uses machine learning models to automatically determine optimal data sources and attributes without requiring subject matter expert intervention. The model self-learns from historical data and use case requirements, enabling the system to serve itself in making data management decisions, thus eliminating the single point of failure represented by expert dependency.
Solution Approach 2:
The system dynamically adapts to new data sources by continuously learning from available sources and updating its model. When new data sources become available, the system can evaluate them and determine optimality without requiring reconfiguration by experts, making the system flexible and adaptable to changing data landscapes.
2Adaptability or versatility
If multiple data sources are used with different attribute definitions, then data availability is improved, but data consistency deteriorates
Solution Approach 1:
The system applies use case-specific configurations that define optimal data sources and attributes for each particular use case. Instead of enforcing uniform data definitions across all sources, the system tailors the data selection to match specific use case requirements, allowing different data sources to contribute their strengths while maintaining consistency within each use case context.
Solution Approach 2:
The machine learning model determines optimal attribute definitions and data source selections based on use case requirements. The system can change which attributes are considered optimal and which data sources are selected, adapting parameters dynamically rather than maintaining fixed definitions, thus resolving consistency issues across diverse data sources.
3Reliability
If manual expert configuration is used for data management, then governance control is improved, but productivity deteriorates due to inability to quickly adapt to new sources
Solution Approach 1:
The system incorporates an independent control process that validates use case-specific configurations and provides feedback to the machine learning model. This feedback loop maintains governance control by verifying configuration validity while enabling automated adaptation, as the model learns from validation results and improves future configurations without requiring manual expert intervention for each change.
Solution Approach 2:
The machine learning model acts as an intermediary between governance requirements and data source selection. The independent control process serves as another intermediary that validates configurations. These intermediaries automate the governance control function, replacing manual expert configuration while maintaining accountability and validity through automated validation mechanisms.
4Adaptability or versatility
If use case-specific configurations are automatically determined, then adaptability to new data sources is improved, but system complexity increases
Solution Approach 1:
The system replaces manual expert configuration mechanisms with machine learning-based automated determination. The machine learning model processes use case requirements and data source characteristics to automatically generate optimal configurations, substituting the mechanical process of expert analysis and decision-making with an automated computational system that scales more efficiently.
Solution Approach 2:
The independent control process provides validation feedback that ensures automated configurations meet governance requirements. This feedback mechanism manages system complexity by verifying configurations automatically, reducing the need for complex manual review processes while maintaining governance control through automated validation loops.
Data Source
AI summary
A data management method may include receiving a use case, and receiving a configuration specific to the use case associated with an item type having a plurality of attributes. The configuration may define a target data store, a subset of the plurality of attributes associated with the use case, and a subset of optimal data sources from a plurality of available data sources each configured to provide one or more attributes within the subset of the plurality of attributes. The data management method may further include, based on the configuration, accessing, from the subset of optimal data sources, values corresponding to the subset of the plurality of attributes, and storing the values in the target data store.