A system for using AI and machine learning to optimize data quality, classification, and profiling across multiple data sources.

Abstract

A system for using artificial intelligence and machine learning to optimize data quality, classification, and profiling across multiple data sources, comprising: a data input module configured to collect data from heterogeneous sources, including databases, cloud platforms, IoT devices, and real-time data streams; a data preprocessing module configured to clean and standardize data using artificial intelligence (AI)-based techniques, including deduplication, error correction, and imputation of missing values; and a classification module configured to classify data into predefined and dynamically generated categories using machine learning models selected from supervised, unsupervised, and reinforcement learning methods.a data profiling module configured to analyze metadata and detect anomalies and hidden patterns in data using machine learning algorithms; an optimization module configured to continuously learn and improve classification and profiling accuracy through real-time feedback loops and predictive analytics; a reporting and visualization module configured to provide real-time insights, anomaly alerts, and performance metrics on data quality, classification accuracy, and profiling results.

Description

The present invention relates to data management and, more particularly, to a system that uses artificial intelligence (AI) and machine learning (ML) to improve data quality, classification, and profiling across multiple data sources.Given exponential data growth in industries such as healthcare, financial, electronic commerce, and manufacturing, companies must efficiently manage, classify, and maintain high-quality data. Data is generated from various structured and unstructured sources, including databases, cloud platforms, IoT devices, social media, and real-time transaction systems. Poor data quality results in inconsistencies, misclassification, and erroneous decisions, which can affect enterprise performance, legal compliance, and customer satisfaction.Conventional data management approaches rely on predefined rules, manual interventions, and basic statistical methods to ensure data quality, classification, and profiling. However, these conventional methods suffer from several limitations. A major drawback of conventional systems is their dependence on rule-based data quality management. These systems rely on predefined rules for data validation, revision, and deduplication. However, such static rules cannot be adapted to changing data patterns, which results in incomplete or incorrect data cleaning. Moreover, manual configuration of rules is time consuming and requires skills so it is inefficient to handle large and complex data sets.Another limitation is dependence on manual classification techniques. Conventional classification systems require human intervention to categorize data into predefined classes. This approach is susceptible to errors and inconsistencies, particularly in large data sets having different formats. Moreover, manual classification is time consuming and not scalable, so it is not suitable for environments with large amounts of data.Moreover, conventional data profiling techniques often use basic statistical methods and fixed thresholds to analyze data completeness, uniqueness, and redundancy. These methods are unable to detect complex anomalies or hidden patterns in large data sets, resulting in undetected errors that compromise data integrity. Finally, the lack of adaptability and automation is also a great disadvantage. Conventional data management systems do not include learning mechanisms for improving classification accuracy and data quality over time. They require frequent manual updates and adjustments, which increases operating costs and limits efficiency in dynamic data environments.To solve the problem, the present invention provides a system for utilizing AI and machine learning to optimize data quality, classification, and profiling across multiple data sources.The system for utilizing AI and machine learning to optimize data quality, classification, and profile creation across multiple data sources may automate data quality management by employing AI-controlled algorithms for data garbage collection, deduplication, non-value imputation, and error detection, thereby reducing manual interventions and improving accuracy.The system for using AI and machine learning to optimize data quality, classification, and profiling across multiple data sources, which can improve data classification using supervised, unsupervised, and reinforcement learning techniques to accurately categorize structured, semi-structured, and unstructured data.The system for using AI and machine learning to optimize data quality, classification, and profile creation across multiple data sources, which can improve data profile capabilities through the use of machine learning models to analyze metadata, detect anomalies, and identify hidden patterns for better decision making.The system for utilizing AI and machine learning to optimize data quality, classification, and profile creation across multiple data sources, which may enable real-time data processing and optimization through the integration of AI-based feedback loops that continuously refine classification models and data quality metrics.The system for utilizing AI and machine learning to optimize data quality, classification, and profile creation across multiple data sources, which can ensure compliance across multiple data sources by designing a system compatible with databases, cloud storage, APIs, IoT sensors, and real-time streams and ensures seamless data integration and transformation.The system for employing AI and machine learning to optimize data quality, classification, and profile creation across multiple data sources, which may reduce dependency on manual rule-based approaches by implementing dynamic AI-controlled models that learn from historical and real-time data to improve accuracy and efficiency.The system for utilizing AI and machine learning to optimize data quality, classification, and profile creation across multiple data sources, which can provide an interactive reporting and visualization module that provides real-time insights, alerts to detect anomalies and data quality trends across user-friendly dashboards.The system for utilizing AI and machine learning to optimize data quality, classification, and profile creation across multiple data sources, which can optimize performance through AI-controlled decision making by incorporating predictive analyses and self-improving models to improve overall data management strategies.The system for utilizing AI and machine learning to optimize data quality, classification, and profile creation across multiple data sources, which can optimize performance through AI-controlled decision making by incorporating predictive analyses and self-improving models to improve general data management strategies.In one embodiment, a system for utilizing AI and machine learning to optimize data quality, classification, and profile creation across multiple data sources is provided. A system that utilizes artificial intelligence (AI) and machine learning (ML) to optimize data quality, classification, and profile creation across multiple data sources. As data volume and complexity of data continue to increase, conventional data management techniques based on static rules and manual interventions are unsuitable for ensuring data quality and accuracy. These conventional systems are difficult to keep pace with the dynamic nature of modern data and do not adapt effectively to developing data patterns. The invention aims to overcome these limitations by automatizing and improving the processes of data acquisition, garbage collection, classification, profiling and optimization using advanced AI and ML algorithms, thus ensuring higher accuracy, consistency and usability of the data. The system is suitable for industries such as financial, healthcare, e-commerce, manufacturing, and logistics, in which data integrity and quality are decisive for operational efficiency, compliance with legal regulations, and making decisions. The system enables seamless integration of various data sources regardless of whether structured, semi-structured, or unstructured data, and optimizes data management through smart algorithms that continuously adapt and learn from the data.The invention is explained again below with reference to the figure. The following shows: FIG. 1 : shows a system for using AI and machine learning for optimizing the data quality, classification and profile creation across a plurality of data sources.FIG. 1 illustrates a system for utilizing AI and machine learning to optimize data quality, classification, and profile creation across multiple data sources. The system for utilizing AI and machine learning to optimize data quality, classification, and profile creation across multiple data sources includes a data input module, a data preprocessing module, a classification module, a data profiling module, an optimization module, and a reporting and visualization module. The data acquisition module is configured to acquire data from heterogeneous sources such as databases, cloud platforms, IoT devices, and real-time streams. The data preprocessing module is configured to clean and standardize data using artificial intelligence (AI)-based techniques, including de-duplication, error correction, and imputation of missing values. The classification module is configured to classify data into predefined and dynamically generated categories using machine learning models selected from supervised, unsupervised, and reinforcement learning methods. The data profiling module is configured to analyze metadata and recognize anomalies and hidden patterns in the data using machine learning algorithms. The optimization module is configured to continuously learn and improve classification and profiling accuracy through real-time feedback loops and predictive analyses. ReportingThe data entry module ensures seamless integration of structured and unstructured data from different sources. The data preprocessing module utilizes natural language processing (NLP) techniques and pattern recognition to clean and normalize textual and semi-structured data. The classification module is configured to automatically adapt to developing data patterns and improve classification accuracy without manual intervention. The data profiling module is configured to provide detailed insights into data completeness, uniqueness, redundancy, and quality trends using machine learning based anomaly detection methods. The optimization module includes feedback loops and self-learning models to improve classification and profiling accuracy dynamically and in real time. The reporting and visualization module includes a user-friendly dashboard that provides insight into data quality, classification metrics, and anomaly detection with real-time updates. The module for data security and government guarantees compliance with data protection regulations such as GDPR, HIPAA and ISO 27001 and ensures high standards for data integrity and data protection. The system supports cloud-based provisioning and enables scalable and flexible management of large amounts of data in distributed networks.List of reference characters100 System

Claims

An artificial intelligence and machine learning system for optimizing data quality, classification, and profile creation across multiple data sources, comprising: a data input module configured to collect data from heterogeneous sources including databases, cloud platforms, IoT devices, and real-time data streams; a data preprocessing module configured to clean and standardize data using artificial intelligence (AI)-based techniques including de-duplication, error correction, and non-value imputation; a classification module configured to classify data into predefined and dynamically generated categories using machine learning models selected from monitored, non-monitored, and reinforcement learning methods; a data profiling module configured to analyze metadata and recognize anomalies and hidden patterns in data using machine learning algorithms; an optimization module configured to continuously learn and improve classification and profiling accuracy through real-time feedback loops and predictive analyses; a reporting and visualization module configured to provide real-time insights, anomaly warnings, and performance metrics on data quality, classification accuracy, and profiling results.The system of claim 1, wherein the data input module ensures seamless integration of both structured and unstructured data from multiple sources.The system of claim 1, wherein the data preprocessing module uses natural language processing (NLP) and pattern recognition techniques to clean and normalize textual and semi-structured data.The system of claim 1, wherein the classification module is configured to automatically adapt to developing data patterns and improve classification accuracy without manual intervention.The system of claim 1, wherein the data profiling module is configured to provide detailed insights into data completeness, uniqueness, redundancy, and quality trends using machine learning based anomaly detection methods.The system of claim 1, wherein the optimization module includes feedback loops and self-learning models to dynamically refine the classification and profiling accuracy in real-time.The system of claim 1, wherein the reporting and visualization module includes a user-friendly dashboard that provides insight into data quality, classification metrics, and anomaly detection with real-time updates.The system of claim 1 further comprising a data security and recovery module that ensures compliance with data protection regulations such as GDPR, HIPAA and ISO 27001 and maintains high standards for data integrity and data protection.The system of claim 1, wherein the system supports cloud-based deployment that enables scalable and flexible management of large amounts of data across distributed networks.

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