AI-powered data quality monitoring and automated alarm system for cloud-based data warehouses

Abstract

An AI-powered data quality monitoring and automatic alerting system (100) for cloud-based data warehouses, comprising: a. an AI-driven data quality monitoring module configured to continuously assess data in the cloud-based data warehouse for accuracy, completeness, consistency, and timeliness; b. an anomaly detection and warning system configured to automatically detect data anomalies based on the assessment performed by the data quality monitoring module and to generate real-time alerts based on detected anomalies; c. an automated data cleansing and remediation workflow that triggers corrective actions in response to detected anomalies, including data imputation, record merging, and validation to maintain data integrity.

Description

The present invention relates to data quality monitoring systems, and more particularly to an AI-based solution for ensuring the accuracy, consistency, and completeness of data stored in cloud-based data warehouses. The invention includes mechanisms for abnormality detection, warning generation, and data clean-up workflows automation to ensure high data integrity in cloud-based environments.As companies and organizations increasingly employ cloud-based data warehouses for storing and analyzing large-scale data, ensuring the quality of these data becomes a critical challenge. Data integrity issues such as inconsistencies, missing values, or legacy records may result in inaccurate findings, ineffective decisions, and operational inefficiencies. Current solutions are often based on manual reviews or simple rule-based systems that cannot keep pace with the growing volume and complexity of the data.To address these challenges, the invention utilizes artificial intelligence (AI) and machine learning algorithms to continuously monitor the quality of data in cloud-based data warehouses, detect anomalies, and automatically trigger alerts or corrective actions. The system has been developed to reduce the manual effort for monitoring data quality and to improve the efficiency of the entire life cycle of data management.An object of the present disclosure is to provide continuous monitoring and correction of data quality while maintaining high accuracy and consistency.Another object of the present disclosure is to automatically detect anomalies in real time, thereby reducing manual intervention and accelerating problem solution.Another object of the present disclosure is the ability to process large amounts of data in cloud-based environments, thereby supporting enterprise growth.Another object of the present disclosure is to immediately notify critical data issues, allowing for a quick response and minimizing potential interruptions.Another object of the present disclosure is to automate data quality management tasks, resulting in lower resource costs for manual checks and corrections.Another object of the present disclosure is to improve the AI-controlled system over time by adapting to new data patterns and increasing its detection accuracy.Another object of the present disclosure is to ensure that the data used for analyses is clean and reliable, resulting in more informed and accurate business decisions.Another object of the present disclosure is to enable data clean-up and correction processes to ensure that the system meets the specific organizational requirements.The present invention relates to a system for AI-based monitoring of data quality in cloud-based data warehouses.Another embodiment of the present invention is the AI-based data quality monitoring module that continuously monitors data in real time and evaluates various aspects of data quality such as accuracy, completeness, consistency and up-to-dateness. AI models are trained to recognize patterns in historical data and may detect deviations from expected trends and exhibit potential issues such as missing or inconsistent data, outliers, or unexpected values.Another embodiment of the present invention utilizes advanced machine learning algorithms. This module identifies anomalies in incoming data streams based on predefined rules, historical trends, and contextual factors. The system can detect anomalies in metrics such as data volume, value ranges, duplication, or temporal consistency. When an anomaly is detected, the system automatically generates a warning with detailed information about the problem and its possible effects on downstream processes.Another embodiment of the present invention is that upon detection of data quality problems, the system automatically initiates predefined operations for data clean-up and correction. These operations may include data corrections, data set merging, data validation from external sources, or manual intervention request. The AI system may also recommend corrective action based on historical trends and context to ensure minimal disruption of data operation.Another embodiment of the present invention is the centralized dashboard that provides data quality metrics and real-time insight into the state of the cloud-based data warehouse. The dashboard displays key performance indicators (KPIs) relating to data quality, e.g., error rates, abnormality frequencies, and the status of corrective actions. The reports are automatically generated and can be adapted for various user roles, e.g., for data technicians, analytics, and guide-workers.A further embodiment of the present invention provides that the AI models used for monitoring the data quality continuously learn from new data and improve their accuracy over time. The system can adapt to changing data patterns and business requirements by incorporating user and new data source feedback and thus ensuring continuous optimization of the data quality recognition algorithms.The present invention relates to an AI-based data quality monitoring and automated alert system (100) for cloud-based data warehouses that provides continuous monitoring and enhancement of data integrity in cloud environments. The system consists of key modules: the AI-controlled data quality monitoring module that evaluates data accuracy and completeness; the anomaly detection and warning system that identifies problems and triggers real-time warnings; and the automated data clean-up and correction workflow that cancels detected anomalies. A centralized dashboard provides real-time views and reports. These modules cooperate to optimize data quality management and support data controlled decisions.The invention is explained again below with reference to the figure. The following shows: FIG. 1 : the AI-based system for monitoring the data quality and for automated alerting (100) of cloud-based data warehouses.FIG. 1 illustrates the AI-based data quality monitoring and automated warning system ( 100) for cloud-based data warehouses. The components of the system are as follows:AI-Controlled Module for Monitoring Data Quality:This module continuously monitors the data within the cloud-based data warehouse and evaluates important data quality attributes such as accuracy, completeness, consistency and upduality. It uses machine learning techniques to analyze historical data patterns and detect deviations in real time. By identifying discrepancies such as missing values, outliers, or inconsistencies, this module helps maintain high data integrity across large data sets.Anomaly Detection and Warning Messages System:The anomaly detection system processes the data evaluated by the monitoring module and detects anomalies based on predefined thresholds and learned patterns. When an anomaly is detected - e.g., an unexpected data spike, missing values or outliers - it triggers real-time warnings to inform the participants. These alerts are forwarded over various channels such as email, SMS, or integrated cloud monitoring platforms (e.g., AWS CloudWa or Google Stackdriver) to ensure that potential data quality issues are promptly solved.Automated Data Clean-Up and Correction Workflow:Once anomalies are detected, the system activates the data clean-up and correction workflow. This module automatically initiates corrective action, such as filling missing values (imputation), removing or merging duplicate records, and normalizing inconsistent data. The automated processes reduce manual intervention and ensure that data quality is maintained efficiently. In cases where the problems are more complex, the system may identify records for verification by an employee, thus providing data technicians or analytics the necessary context for their decisions.Dashboard for Data Quality and Report Interface:The centralized data quality dashboard provides users with real-time insight into the state of the data warehouse. It visualizes important performance indicators (KPIs) such as error rates, anomaly frequency and the status of corrective actions. The dashboard is customizable and allows users to examine specific data problems, track trends over time, and generate reports tailored to different user roles, from data engineers to business managers. This module provides transparency in data quality management and improves the arbitration processes.Continuous Learning and AI Model Optimization:The system is designed to continually improve its data quality monitoring capabilities. The AI models embedded in the system learn over time from new data patterns and user feedback. As the system processes more data and receives corrections or additional input, it refines its anomaly detection and data purification algorithms. This module ensures that the system adapts to changing data environments and improves the accuracy and efficiency of data quality management over time.

Claims

A AI-based data quality monitoring and automatic warning system (100) for cloud-based data warehouses, comprising: a. an AI-controlled data quality monitoring module configured to continuously evaluate data in the cloud-based data warehouse for accuracy, completeness, consistency, and up-to-date; b. an anomaly detection and warning system configured to automatically detect data anomalies based on the evaluation performed by the data quality monitoring module and generate real-time warnings based on detected anomalies; c. an automated garbage collection and correction workflow that triggers corrective actions in response to detected anomalies, including data emulation, record merging, and validation to maintain data integrity.The system (100) of claim 1, wherein the AI-controlled data quality monitoring module uses machine learning algorithms including monitored and unsupervised learning techniques to detect data patterns and deviations from expected trends.The system (100) of claim 1, wherein the anomaly detection and alert system generates alerts over communication channels selected from a group consisting of email, SMS, and cloud monitoring platforms such as AWS CloudWa or Google Stackdriver.The system (100) of claim 1, wherein the automated data clean-up and correction workflow comprises data emulation methods for padding missing values, de-duplication of records, and normalization of inconsistent data.The system (100) of claim 1, further comprising a centralized data quality dashboard that provides real-time metrics and visualizations of data quality indicators including error rates, anomaly frequency, and status of corrective actions.The system (100) of claim 1, wherein the AI-controlled data quality monitoring module is configured to identify anomalies on multiple data levels, including individual records, database tables, and entire records.The system (100) of claim 1, wherein the anomaly detection and warning system is further configured to prioritize anomalies based on their potential impact on business operations such that users can address the most critical issues first.The system (100) of claim 1, wherein the AI models within the system continuously learn and adapt over time, thereby improving the ability of the system to detect and address data quality issues as new data and feedback is incorporated into the system.

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