View source for 20240020436. AUTOMATED DATA QUALITY MONITORING AND DATA GOVERNANCE USING STATISTICAL MODELS simplified abstract (Capital One Services, LLC)

=AUTOMATED DATA QUALITY MONITORING AND DATA GOVERNANCE USING STATISTICAL MODELS=


==Organization Name==

[[:Category:Capital One Services, LLC|Capital One Services, LLC]]


[[Category:Capital One Services, LLC]]

==Inventor(s)==

[[:Category:Thomas Oliver Cantrell of Maidens VA (US)|Thomas Oliver Cantrell of Maidens VA (US)]][[Category:Thomas Oliver Cantrell of Maidens VA (US)]]

[[:Category:William Conner Ritchie of Ashland VA (US)|William Conner Ritchie of Ashland VA (US)]][[Category:William Conner Ritchie of Ashland VA (US)]]

[[:Category:Sanjay Daga of Chantilly VA (US)|Sanjay Daga of Chantilly VA (US)]][[Category:Sanjay Daga of Chantilly VA (US)]]

==AUTOMATED DATA QUALITY MONITORING AND DATA GOVERNANCE USING STATISTICAL MODELS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240020436 titled 'AUTOMATED DATA QUALITY MONITORING AND DATA GOVERNANCE USING STATISTICAL MODELS

==Simplified Explanation==

The abstract of this patent application describes a data quality system that uses historical data to generate statistical summaries and confidence intervals for a data element. The system then compares the current value of the data element with the predicted range defined by the confidence interval to determine if it falls within the range.

* The data quality system obtains a historical dataset with historical values for a data element.
* It generates statistical summaries for the data element based on the historical values.
* Using a statistical model, the system creates a confidence interval with upper and lower thresholds.
* The confidence interval defines a predicted range for the current value of the data element.
* The system receives a current dataset with the current value for the data element.
* It generates an output indicating whether the current value falls within the predicted range.

Potential applications of this technology:

* Data quality assurance in various industries such as finance, healthcare, and manufacturing.
* Monitoring and validation of sensor data in IoT systems.
* Fraud detection and anomaly detection in transactional data.

Problems solved by this technology:

* Ensures data accuracy and reliability by comparing current values with historical patterns.
* Helps identify data inconsistencies or outliers that may indicate errors or anomalies.
* Provides a quantitative measure of data quality and confidence in the current value.

Benefits of this technology:

* Improves decision-making by providing a reliable assessment of data quality.
* Reduces the risk of making decisions based on inaccurate or unreliable data.
* Enables proactive identification and resolution of data quality issues.
* Enhances data governance and compliance efforts by ensuring data integrity.


==Original Abstract Submitted==

in some implementations, a data quality system may obtain a historical dataset that includes historical values for a data element. the data quality system may generate one or more statistical summaries for the data element based on the historical values for the data element. the data quality system may generate, using a statistical model, a confidence interval defined by an upper threshold and a lower threshold based on the one or more statistical summaries, wherein the upper threshold and the lower threshold define a predicted range for a current value for the data element. the data quality system may receive a current dataset that includes the current value for the data element. the data quality system may generate an output that indicates whether the current value for the data element is within the predicted range defined by the upper threshold and the lower threshold.



[[Category:G06F30/20]]