Common Data Model Normalization for Disparate Enterprise Data
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Solution Overview
Problem
Existing methods for aggregating and normalizing data from disparate sources are resource-intensive and require significant expertise, as they often involve creating customized software or using incomplete common data models that need extensive customization.
Innovation Solution
A cloud-native data aggregation and normalization system that employs machine learning techniques to process data, integrate different data storage types, apply common data models, and use a data feedback loop for continuous improvement, enabling efficient data cleaning, normalization, and generation of insights and predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional brute force software creation methods are used to translate different data types, then data compatibility is achieved, but resource consumption and expertise requirements increase significantly
Solution Approach 1:
The patent introduces a common data model as an intermediary layer between disparate data sources and target applications. This mediator translates and standardizes data from multiple sources into a unified format, eliminating the need for custom translation software for each data type combination while ensuring data compatibility and consistency across the system
2Ease of operation
If common data models are used to create uniform data structure, then data standardization is improved, but the models require extensive customization to meet specific enterprise needs
Solution Approach 1:
The patent implements a dynamic common data model that can be automatically customized based on the specific characteristics of input data and enterprise requirements. The model adapts its structure and attributes dynamically during the data integration process, providing both standardization benefits and the flexibility to meet specific enterprise needs without requiring extensive manual customization
3Quantity of substance
If data is stored in disparate systems across multiple servers, then data storage capacity is increased, but data integration and accessibility become more difficult
Solution Approach 1:
The patent creates a universal data platform that can access and integrate data from multiple disparate storage systems across different servers. The common data model serves as a multi-functional interface that maintains compatibility with various storage formats and locations, enabling centralized data management and improved accessibility while preserving the distributed storage architecture's capacity benefits
Data Source
AI summary
An integrated platform system that employ a series of machine learning techniques and prediction and detection units that can process input data and extract and generate meaningful insights and predictions therefrom. The system integrates together multiple different data storage types and applications that generates data of different types, and an associated processing system for processing the different data types, store the data in a common data model to normalize the data, determine the data lineage of the data, and then process the data using different types of techniques. The data can also be processed by a prediction unit for generating meaningful insights and predictions or by an anomaly detection unit for detecting one or more anomalies in the data.