Rule-Based Data Deconfliction for Enterprise Systems
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Solution Overview
Problem
The manual effort required to merge and deconflict enterprise data from multiple sources is tedious, expensive, and prone to human error, as existing automation solutions do not effectively address conflicts in data sets across different databases and formats.
Innovation Solution
A rule-based deconfliction method where human subject matter experts define rules to resolve conflicts, aggregating data into a single pool, detecting conflicting items, and ranking them using these rules, allowing for automated resolution without manual review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual merging and deconfliction of data is performed, then data accuracy can be maintained through human judgment, but the process becomes tedious, expensive, and time-consuming
Solution Approach 1:
The system enables automated self-service deconfliction by having the data aggregation system automatically detect conflicts and apply pre-defined resolution rules without requiring manual human intervention for each conflict case
Solution Approach 2:
Resolution rules are defined and prepared in advance before data conflicts occur, allowing the system to automatically resolve conflicts as they are detected without waiting for manual review
2Productivity
If automation solutions are implemented to reduce manual effort, then productivity improves, but existing solutions fail to effectively address conflicts in data sets across different databases and formats
Solution Approach 1:
The system adapts its conflict detection and resolution parameters to accommodate different data formats, schemas, and nomenclatures across multiple databases, enabling effective automated deconfliction while maintaining reliability
Solution Approach 2:
The system dynamically adjusts to changes in data structures, schemas, and naming conventions over time, allowing automated conflict resolution to remain effective as data formats evolve
3Speed
If rigid automated rules are applied to resolve conflicts, then processing speed increases, but the system cannot adapt to changes in data, schemas, and nomenclature
Solution Approach 1:
The system maintains dynamic adaptability by allowing rules to be updated and evolved over time to accommodate changing data formats, schemas, and nomenclatures while preserving the speed of automated processing
Solution Approach 2:
The system incorporates feedback mechanisms that allow monitoring of conflict resolution outcomes and enable rule updates based on observed patterns and changing data characteristics
Data Source
AI summary
Methods, computer readable media, and devices for rule-based deconfliction of overlapping data are disclosed. An example method performed by a processing system including at least one processor includes defining, with guidance from a human subject matter expert, a rule for resolving a conflict between a plurality of conflicting items of data, aggregating data from a plurality of data sources into a single pool of data, detecting a set of conflicting data items in the single pool of data, and ranking data items in the set of conflicting data items, using the rule.