Centralized Data Warehouse for Compliance Prediction
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Solution Overview
Problem
The complexity and decentralization of enterprise data management pose challenges for corporate compliance, as each application stores data in different formats, making it difficult to review and maintain compliance effectively.
Innovation Solution
A system that processes enterprise data from multiple sources and stores it in a centralized repository, normalizing the data and applying metadata labeling for efficient analysis and compliance assessment. This system generates a graphical user interface (GUI) that allows users to request specific data types, applies machine learning for compliance prediction, and includes interactive elements for corrective actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If enterprise data is stored in multiple decentralized applications with different formats, then data can be maintained in their original formats, but compliance review becomes difficult and complex
Solution Approach 1:
The patent introduces a centralized data warehouse as an intermediary layer between decentralized applications and compliance review systems. The data warehouse receives data from multiple applications in their native formats, normalizes it to a unified schema, and stores it in a standardized structure. This mediator enables compliance reviewers to access consolidated data in a consistent format without needing to understand the varied formats from source applications, thereby reducing review complexity while maintaining format compatibility.
Solution Approach 2:
The patent segments the data management system into distinct components: source applications, a centralized data warehouse, and compliance review interfaces. Each application continues to store data in its own format, but the data warehouse segments and reorganizes this data into a unified structure. This segmentation allows each component to maintain its original characteristics while the overall system achieves consistency for compliance purposes.
2Loss of information
If manual data collection from multiple applications is performed, then data can be gathered for compliance review, but time consumption increases significantly
Solution Approach 1:
The patent implements preliminary action by establishing automated data extraction and loading processes that continuously or periodically pull data from applications into the data warehouse before compliance reviews are needed. Metadata about data structure, format, and source is captured and stored in advance. This preliminary preparation ensures data is ready for immediate analysis during compliance reviews, eliminating time-consuming manual collection while maintaining complete data records.
Solution Approach 2:
The data warehouse system performs self-service by automatically maintaining its own data repository through scheduled data extraction, transformation, and loading operations. The system self-updates with new data from applications without requiring manual intervention from compliance reviewers. This automation ensures data completeness is maintained continuously while significantly reducing the time reviewers need to spend on data collection activities.
3Productivity
If real-time data processing is implemented, then compliance analysis can be updated continuously, but system computational requirements increase
Solution Approach 1:
The patent applies partial action by processing only the changes and updates in data rather than continuously processing entire datasets. The system identifies and processes only the delta (changes, additions, modifications) from applications since the last synchronization point. This selective processing approach enables real-time compliance analysis for changed data while significantly reducing computational resource consumption compared to full dataset processing.
Solution Approach 2:
The patent utilizes parameter changes by adjusting the frequency and scope of data processing based on data characteristics and compliance requirements. The system can change processing parameters dynamically - processing data in real-time for critical compliance areas while using batch processing for less time-sensitive data. This parameter flexibility allows the system to optimize between productivity and computational resource consumption based on actual needs.
Data Source
AI summary
A system according to the present disclosure may include a processor, a centralized data warehouse, and a non-transitory computer readable medium storing thereon instructions that are executable by the processor to cause the system to perform operations. The operations may include comprising training a machine learning model based on enterprise data from the centralized data warehouse, the machine learning model trained to determine a value corresponding to a metric, receiving, from at least one external source, updated enterprise data, determining, in real-time by the machine learning model, a predicted change to the value based on the updated enterprise data, and presenting, via a graphical user interface (GUI), a suggested action based on the predicted change.