Master Data Profiling Analytics for ERP Data Quality
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Solution Overview
Problem
In complex ERP environments, maintaining high data quality in master data records is challenging due to the complexity of relational data models, where many users lack expertise to understand field relationships and dependencies, leading to incorrect data creation and changes.
Innovation Solution
An analytics tool is provided that offers insights into master data models, including field relationships, distributions, and dependencies, allowing users to identify important fields, detect anomalies, and implement data quality rules, using either a business object implementation or real-time calculation via API calls, with data stored in an in-memory database for faster processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users directly maintain master data records in complex ERP systems, then data creation and changes can be performed, but data quality deteriorates due to lack of expertise in understanding field relationships and dependencies
Solution Approach 1:
The patent introduces an analytics tool as an intermediary between users and master data records. This tool automatically analyzes the data model, identifies important fields based on dependencies and relationships, and provides guidance to users without requiring them to understand the complex underlying data structure. The analytics tool mediates the interaction by translating complex relational models into simple, actionable insights.
Solution Approach 2:
The system performs self-service by automatically analyzing the data model, calculating field importance metrics, and generating recommendations without human intervention. The analytics engine autonomously traverses the data model, identifies dependencies, and determines which fields require special attention, eliminating the need for users to manually analyze complex relationships.
2Reliability
If comprehensive data analysis and analytics tools are implemented, then data quality and integrity improve, but system complexity and processing requirements increase
Solution Approach 1:
The patent extracts the complex analytics functionality into a separate, standalone tool that can be independently configured and executed. Rather than embedding complex analysis logic throughout the entire ERP system, the analytics engine is extracted as a distinct component that focuses solely on analyzing the data model and providing recommendations, thereby reducing the complexity burden on the core ERP system.
Solution Approach 2:
The analytics tool segments the analysis process into distinct, manageable components: data model traversal, dependency identification, field importance calculation, and recommendation generation. Each component handles a specific aspect of the analysis, making the overall system more manageable and easier to maintain despite the comprehensive nature of the analysis.
3Reliability
If real-time data validation and analysis are performed, then errors are detected immediately improving data quality, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis by pre-calculating field importance metrics and identifying critical fields before users actually enter or modify data. The analytics engine traverses the data model in advance and prepares recommendations, so when users interact with the system, the validation and guidance are already in place, reducing real-time processing requirements.
Solution Approach 2:
The system applies partial analysis by focusing computational resources on identifying and validating only the most critical fields rather than analyzing every single field in the data model. The analytics engine calculates importance metrics and prioritizes validation efforts on fields with highest impact, performing excessive analysis only where necessary to ensure data quality.
Data Source
AI summary
In an example embodiment, a specialized in-memory database abstraction component is introduced in a cloud cluster. The in-memory database abstraction component may receive lifecycle commands from a client-facing application and interface with a container service to create an in-memory database resource. When parameters are received by the in-memory database abstraction component from the client-facing application, the in-memory database abstraction component may act to validate the parameters, determine if a service plan is available, and determine whether the parameters meet the service plan requirements. If the service plan requirements are not met, the in-memory database abstraction component translates the parameters for the in-memory database resource.