Data Management Architecture for Enterprise Metadata
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Solution Overview
Problem
Managing large quantities of enterprise data across various business operations is cumbersome and tedious, as data stored for one purpose is often accessed for another, requiring efficient tools and processes to facilitate seamless collaboration and analysis.
Innovation Solution
A Data Management Architecture (DMA) is introduced, comprising an enterprise portal with service tools like report, configure, define, administrative, analytics, and collaborate tools, along with adapters for data sources, and a set of metrics for data analysis, enabling stakeholders to perform services and analyze data effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large quantities of enterprise data are stored and accessed for multiple purposes, then data utility and versatility are improved, but data management complexity and workload increase
Solution Approach 1:
The system segments data management into distinct functional modules including a data extraction module, data transformation module, data loading module, and metadata management module. Each module handles specific aspects of data processing, allowing complex data management tasks to be divided into manageable operations that reduce overall system complexity while maintaining high data utility across multiple purposes.
Solution Approach 2:
The patent introduces an ETL (Extract-Transform-Load) framework as an intermediary system between data sources and various business functions. This intermediary layer standardizes data formats, manages data flows, and provides unified access points, thereby reducing the complexity of managing diverse data for multiple purposes while preserving data versatility.
2Device complexity
If manual data management processes are used, then system complexity is reduced, but time consumption and labor requirements increase
Solution Approach 1:
The system implements automated data extraction, transformation, and loading processes that self-manage data flows without requiring manual intervention. The ETL framework automatically connects to data sources, extracts relevant data, transforms it into standardized formats, and loads it into appropriate destinations, thereby eliminating time-consuming manual tasks while maintaining manageable system complexity through automation.
Solution Approach 2:
The patent establishes predefined data extraction rules, transformation schemas, and loading configurations before data processing begins. These preliminary setups enable the system to automatically process data according to predetermined protocols, reducing the need for manual configuration and execution, thereby saving time while keeping the automated system relatively simple to manage.
3Loss of information
If comprehensive data analysis tools are implemented, then data insights and decision-making are improved, but system complexity and resource requirements increase
Solution Approach 1:
The system extracts and transforms only the necessary data elements from large datasets, focusing on key metrics and relevant information rather than processing entire data volumes. This selective extraction approach provides meaningful data insights for decision-making while reducing the computational resources and system complexity required compared to comprehensive analysis of all data.
Solution Approach 2:
The patent implements data analysis capabilities that process partial datasets or use sampling techniques to derive insights without requiring complete data processing. This partial action approach provides sufficient data intelligence for effective decision-making while significantly reducing system resource consumption and complexity compared to full-scale analysis of all enterprise data.
Data Source
AI summary
A method and a system for managing enterprise data are provided. The system comprises a processor and a memory coupled to the processor. The memory comprises a definition module defining a Data Management Architecture (DMA). Further, the DMA comprises at least one of an enterprise portal, an enterprise model, at least one adaptor, and a set of metrics. The enterprise portal comprises a set of service tools. The set of service tools is configured to perform a set of services on the data. The enterprise model is configured to analyze an enterprise process to create and modify enterprise metadata of the data originating from the enterprise process. Further, the adapter is configured to communicate with a data source associated with the enterprise process for extracting the enterprise metadata from the data source. Furthermore, the set of metrics may represent an analysis of the data.