Data Quality Monitoring via Rule-Based Flagging
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Solution Overview
Problem
Existing data processing systems lack the ability to perform individual rule-based flagging of upstream data quality errors, leading to limitations in data use, error detection, and unintended results due to the inability to accurately identify and correct low-quality or incorrect data.
Innovation Solution
The system receives data from various sources, applies multiple rules to identify tagged populations by determining which outputs meet rule thresholds, and activates downstream execution by updating or overriding errors, ensuring only high-quality data is used for downstream tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data processing is performed without individual rule-based flagging, then processing speed is maintained, but data quality and error detection capability deteriorate
Solution Approach 1:
The patent segments the data processing workflow into distinct stages: initial data collection, rule application stage, flagging stage, and downstream task execution. By dividing the processing pipeline and applying rules selectively to specific data segments rather than uniformly to all data, the system achieves improved data quality monitoring without proportionally increasing overall processing complexity.
Solution Approach 2:
The patent introduces an intermediary flagging mechanism that sits between data collection and downstream task execution. This intermediary layer applies rules to generate flags indicating data quality issues, allowing the system to maintain processing flow while identifying and isolating problematic data without requiring complete reprocessing of all data.
2Measurement precision
If individual rule-based flagging is implemented, then error detection capability is improved, but processing time and computational resources increase
Solution Approach 1:
The patent applies partial action by implementing rule-based flagging selectively rather than uniformly across all data processing operations. Rules are applied to specific data elements or subsets that require quality monitoring, allowing the system to achieve adequate error detection precision for critical data while avoiding the time cost of applying comprehensive rules to all data indiscriminately.
Solution Approach 2:
The system implements feedback mechanisms where flagging results inform subsequent processing decisions. When flags indicate data quality issues, the system can adjust processing behavior, such as requesting additional validation, using alternative data sources, or prioritizing certain data elements, thereby reducing overall processing time while maintaining detection precision for important errors.
3Reliability
If comprehensive rules are applied to all data, then data quality monitoring is improved, but system efficiency and productivity decrease
Solution Approach 1:
The patent applies local quality by tailoring the strictness and type of rule-based monitoring to specific data contexts, sources, or elements. Different data streams or categories can have different rule sets applied based on their quality requirements, allowing the system to maintain high data quality monitoring for critical data while preserving system efficiency for less critical data paths.
Solution Approach 2:
The system dynamically adjusts processing parameters such as rule application intensity, threshold values, and monitoring frequency based on data characteristics, source reliability, and current system load. This allows the system to maintain adequate data quality monitoring while adapting processing intensity to preserve overall productivity under varying conditions.
Data Source
AI summary
Computer-implemented methods and systems include downstream execution for individual rule-based flagging of upstream data quality errors by receiving upstream data from a plurality of sources, identifying a downstream task to be executed, applying a plurality of rules to the upstream data, generating a plurality of outputs including at least one output for each of the plurality of rules applied to the upstream data, each of the plurality of outputs being associated with a corresponding rule of the plurality of rules, identifying a tagged population based on the plurality of outputs, determining that at least one of the plurality of outputs does not meet a corresponding rule threshold, and activating the downstream execution for the tagged population after at least one of (i) updating the corresponding rule threshold or (ii) overriding an error.