Feed Inspection Tool for Continuous Data Quality Monitoring
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Solution Overview
Problem
Current data monitoring technologies fail to provide continuous and effective quality monitoring in dynamic feed environments, leading to data errors and inconsistencies that can have severe consequences, especially in domains like medicine, finance, and law enforcement, as existing solutions focus on post-collection error detection rather than real-time correction.
Innovation Solution
A system and method that incorporate data quality modules into data feed management architectures using a publish-subscribe approach, enabling continuous, passive monitoring through a feed inspection tool that analyzes data and metadata feeds in real-time, detects errors and outliers, and generates alerts at multiple scales, adapting to changing data patterns and histories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous monitoring of data feeds is implemented, then data quality detection capability is improved, but system complexity increases
Solution Approach 1:
The monitoring system is segmented into independent modules including a feed inspection tool, statistical model generation component, outlier detection engine, and alerting system. Each module performs a specific function in the data quality monitoring pipeline, allowing the complex monitoring task to be divided into manageable, independently developable and maintainable components that can be deployed incrementally
Solution Approach 2:
The patent introduces intermediary components such as the feed inspection tool that acts as a mediator between raw data feeds and the monitoring analysis system. This intermediary layer preprocesses and standardizes incoming data feeds, making them suitable for statistical analysis without requiring complex integration logic in the core monitoring engine, thus reducing overall system complexity
2Loss of time
If real-time analysis of data feeds is performed, then data quality monitoring timeliness is improved, but computational resources increase
Solution Approach 1:
The system performs partial analysis by focusing computational resources on detecting outliers and anomalies rather than analyzing every data point in depth. The statistical models are trained on historical data to establish baseline behavior, and the real-time component only needs to detect deviations from this baseline, requiring significantly fewer computational resources while maintaining timely detection capability
Solution Approach 2:
The system performs preliminary actions by pre-computing statistical models and baseline expectations from historical data feeds before real-time monitoring begins. These pre-computed models capture normal data patterns and relationships, allowing the real-time system to simply compare incoming data against these pre-established expectations rather than performing complex analysis on every new data point, thereby reducing real-time computational burden
3Measurement precision
If statistical models are built over historical data, then outlier detection accuracy is improved, but model adaptation speed decreases
Solution Approach 1:
The statistical models are designed to be dynamic rather than static, allowing them to adapt to changing data patterns over time. The system continuously updates model parameters as new historical data becomes available, and can adjust to concept drift in the data feeds. This dynamic nature allows the models to maintain high accuracy for detecting outliers while adapting to new normal patterns, balancing both detection accuracy and adaptation speed
Data Source
AI summary
A system for providing continuous monitoring of data quality in a dynamic feed environment is disclosed. In particular, the system utilizes a feed inspection tool to detect anomalies in data gathering detected from feed metadata and anomalies in data measurement detected based on file contents. In order to do so, the feed inspection tool may aggregate, for a plurality of aggregation intervals, data feeds and associated metadata feeds. Once the data feeds and metadata feeds are aggregated, the feed inspection tool may generate, for a baseline model feed, baseline statistical models by utilizing historical data of the aggregated feeds in sliding windows of different lengths. The feed inspection tool may then identify, for a plurality of monitoring time delays, data outliers by comparing the aggregated feeds with the baseline model feed. A data quality feed based on the data outliers identified may then be generated and published.