Metadata-based enterprise data architecture system for automated data collection, processing, and reporting.

Abstract

A metadata-driven enterprise data architecture system for automated data collection, processing, and reporting, consisting of: a centralized metadata control repository configured to store canonical enterprise data models and business semantic definitions, source-destination mapping rules and transformation logic, data quality thresholds and matching parameters, governance policies and compliance controls, data provenance relationships and audit tracking rules, security and access control configurations, planning instructions and reporting specifications; an execution and orchestration engine that is operationally connected to the metadata control repository and configured to dynamically interpret metadata definitions, automatically implements data ingestion pipelines to extract data from legacy systems, cloud platforms, application interfaces, files, and streaming sources, performs transformation operations such as normalization, enrichment, deduplication, aggregation, and format harmonization to align the ingested data with the canonical enterprise data model, applies metadata-driven data quality validation, exception handling, and reconciliation processes to ensure accuracy and consistency, and enforces metadata-defined security policies, including role-based access control, encryption, and data masking.Automatic capture of complete data provenance and audit records across all processing stages and creation of standardized operational, analytical, and regulatory reports based on metadata-defined reporting structures and business metrics; where changes to metadata definitions automatically modify the collection, processing, governance, security, and reporting behavior without requiring manual redesign of data pipelines or application code.

Description

[0001] The present invention relates to enterprise data systems used for managing and analyzing organizational information. More specifically, the invention relates to a metadata-driven data system that automatically collects, processes, and prepares data from multiple sources to generate accurate and timely reports. The system was developed to simplify complex data operations, reduce manual effort, and improve data consistency in enterprise environments.

[0002] Today, businesses rely heavily on data to support daily operations, strategic planning, and regulatory reporting. Large organizations, particularly in industries like banking and finance, collect information from numerous legacy systems, cloud platforms, and external data sources. These systems often generate data in disparate formats and structures, making it difficult to combine and manage information consistently and reliably. Traditional data management approaches typically rely on manually built data pipelines, rigid transformation rules, and separate reporting tools. Each new data source or business requirement usually necessitates additional programming, redesign, and testing. This results in lengthy implementation times, high maintenance costs, and frequent data inconsistencies.Furthermore, limited transparency regarding data origin, quality, and governance hinders the fulfillment of audit and compliance requirements. With increasing regulatory demands, organizations need accurate, traceable, and timely data reporting. However, existing data platforms typically lack an integrated mechanism that automatically governs how data is captured, processed, managed, and reported based on consistent business rules. Instead, most systems rely on fragmented tools and hard-coded logic that requires repeated updates whenever data structures or regulations change. Therefore, there is a need for an improved enterprise data architecture that can intelligently manage data flows across diverse systems while reducing manual intervention.A solution is needed that uses centralized metadata to control data collection, transformation, management, and reporting in an automated and consistent manner. Such a framework would enable faster onboarding of new data sources, improved data quality, better audit readiness, and more reliable business reporting.

[0003] To solve this problem, the present invention offers a metadata-driven enterprise data architecture system for automated data collection, processing and reporting.

[0004] The system automatically manages data collection, processing, management, and reporting across multiple heterogeneous data sources.

[0005] The system can reduce the manual effort and technical complexity involved in building and maintaining data pipelines by dynamically generating data workflows based on configurable metadata definitions.

[0006] The system ensures consistent data standards, business rules, and semantic definitions throughout the company by using a centralized metadata repository.

[0007] The system can improve data quality, traceability, and audit readiness by automatically applying validation rules, reconciliation processes, and provenance tracking.

[0008] The system enables the rapid integration of new data sources and quick adaptation to regulatory or business changes without requiring extensive code changes.

[0009] The system supports secure and controlled access to company data through metadata-driven governance and compliance mechanisms.

[0010] The system delivers reliable reports in real time or on a schedule, meeting operational and regulatory requirements.

[0011] The present invention provides a metadata-driven enterprise data architecture system that enables the automated collection, processing, governance, and reporting of data across various enterprise systems. The framework is designed to simplify complex data operations by using centralized metadata definitions to control how data is collected, transformed, validated, secured, and presented. According to the invention, a centralized metadata control repository stores business rules, data models, source-destination mappings, data quality parameters, governance policies, lineage relationships, and reporting configurations. An execution engine interprets this metadata to automatically generate and orchestrate data pipelines, transformation workflows, quality checks, reconciliation processes, and report outputs without requiring manual coding for each data source.The system supports integration with both legacy and cloud-based platforms, enabling organizations to quickly incorporate new data sources while maintaining consistent data standards. Automated provenance tracking and audit logging ensure transparency and regulatory compliance, while integrated data governance mechanisms guarantee secure and controlled data access. By managing all operational processes through metadata, the invention significantly reduces development time, operating costs, and the risk of data inconsistencies. The framework enables scalable, flexible, and reliable enterprise data management, making it particularly suitable for regulated environments that require accurate, traceable, and timely reporting.

[0012] The present invention discloses a metadata-driven enterprise data architecture system (100) that automates the entire lifecycle of data collection, transformation, management, quality control, security enforcement, provenance tracking, and reporting across multiple heterogeneous enterprise data sources by replacing traditional hard-coded pipelines with dynamically generated workflows that are fully controlled by centralized metadata definitions. A metadata control repository manages enterprise-wide canonical data models, business terminology, source-to-target mapping configurations, transformation rules, scheduling parameters, validation thresholds, matching logic, governance policies, access control permissions, encryption requirements, provenance relationships, audit logging rules, and reporting templates.An execution and orchestration engine continuously interprets these metadata definitions to automatically create and execute data extraction processes from legacy databases, cloud platforms, transactional systems, application programming interfaces, flat files, and streaming sources, followed by transformation processes such as normalization, enrichment, deduplication, aggregation, and format harmonization. Integrated data quality and governance components apply real-time and batch validation checks, exception handling mechanisms, compliance controls, and reconciliation workflows to ensure accuracy, consistency, and regulatory compliance. A lineage and audit module records the complete data flow histories and transformation activities, providing transparent traceability for compliance reporting and operational monitoring.Security mechanisms enforce metadata-driven, role-based access, masking, and encryption to protect sensitive information throughout the entire data lifecycle. A reporting and analytics layer automatically generates standardized enterprise dashboards, regulatory submissions, and business intelligence outputs using metadata-defined metrics and structures. By centralizing operational intelligence in metadata rather than code, the invention enables rapid integration of new data sources, seamless adaptation to regulatory changes, reduced development and maintenance costs, improved data reliability, and scalable, enterprise-wide information management.

Images