Data Quality Analysis System Using Dynamic Control Limits
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Solution Overview
Problem
Businesses, particularly financial institutions, face challenges in identifying and resolving data quality issues across various metrics, which affects their confidence in making decisions due to unpredictable behavior of metrics used for forecasting and analysis.
Innovation Solution
A method is implemented to calculate a normalized quality score for metrics by determining a forecast, upper control limit, and lower control limit based on transaction information, allowing for the evaluation of data quality and identification of metrics requiring improvement, using a system that enables simultaneous changes to forecasting and control limit calculations across thousands of metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If businesses collect and analyze more data to improve decision-making, then the quantity and variety of information increases, but the complexity of managing and ensuring data quality across multiple metrics increases
Solution Approach 1:
The patent segments data quality assessment into distinct components: data completeness, data accuracy, and data timeliness. Each component is evaluated separately through specific metrics and control limits, allowing organizations to manage complex data quality issues systematically by addressing each aspect independently rather than overwhelming the entire data quality management process at once.
Solution Approach 2:
The patent implements feedback mechanisms through control limits (upper and lower thresholds) that provide real-time information about data quality status. When metrics exceed these limits, the system generates alerts and notifications, enabling organizations to respond to data quality issues promptly. This continuous feedback loop transforms complex data quality management into an actionable, iterative process.
2Reliability
If businesses monitor multiple metrics to evaluate data quality, then the comprehensiveness of quality assessment improves, but the difficulty of identifying and resolving issues increases
Solution Approach 1:
The patent employs visual indicators and color-coded status representations to communicate data quality conditions. Metrics are displayed with different visual states (such as color changes or status symbols) based on whether they fall within acceptable control limits or exceed them. This visual encoding transforms complex quantitative data quality information into intuitive visual signals, making it easier to identify problematic metrics at a glance without requiring deep analysis of each metric's underlying data.
Solution Approach 2:
The patent introduces control limits as intermediary thresholds that mediate between raw data metrics and organizational decision-making. These control limits translate complex data quality variations into clear pass/fail decisions, simplifying the identification of issues. The control limits act as a buffer layer that aggregates and interprets data quality information, making it more manageable and actionable for stakeholders.
3Measurement precision
If businesses use complex metrics to analyze data, then the depth of analysis improves, but the unpredictability and lack of confidence in metric behavior increases
Solution Approach 1:
The patent implements dynamic control limits that adapt to changing data characteristics and business conditions. Rather than using fixed static thresholds, the control limits are designed to respond to variations in data patterns, allowing the system to maintain reliable predictions even as business environments evolve. This dynamic adjustment capability ensures that metrics remain predictable and trustworthy while continuing to provide deep analytical insights.
Solution Approach 2:
The patent performs preliminary analysis by establishing baseline control limits and monitoring thresholds before actual data quality issues occur. By pre-defining acceptable ranges and triggering conditions, the system prepares detection and response mechanisms in advance, reducing the unpredictability of metric behavior. This preliminary setup allows organizations to anticipate and prepare for data quality variations, transforming uncertain metric behavior into manageable, predictable patterns.
Data Source
AI summary
Methods, computer readable media, and apparatuses for analyzing data quality are presented. Transaction information may be received from a database, and the transaction information may describe various aspects of a plurality of transactions handled by an organization. Subsequently, a forecast may be calculated based on the transaction information, and the forecast may predict the future value of a metric. An upper control limit and a lower control limit for the metric may be determined based on the transaction information. Thereafter, the latest actual value of the metric may be computed. A normalized quality score for the metric then may be calculated based on the latest actual value of the metric, the forecast, the upper control limit, and the lower control limit. Optionally, a chart may be generated to evaluate data quality for a plurality of metrics, and metrics that exceed a control limit may be added to an issue log.