Data Quality Management Framework Automating Validation
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Solution Overview
Problem
Manual data quality management in companies is time-consuming and costly due to issues like incorrect data, duplicates, and invalid addresses, especially in customer relationship management systems, where data is critical for organizational success.
Innovation Solution
A data quality management system that includes a central control manager, sub-task classifier, validation engines, and a KPI calculator, which selects task groups, determines sub-task types, validates data, and calculates key performance indicators (KPIs) to automate data validation and improvement processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual processes are used to clean-up database, then data quality can be improved, but the process becomes expensive and time consuming
Solution Approach 1:
The system enables self-service data validation by automatically performing validation tasks without requiring manual human intervention. The validation engines execute predefined validation rules and algorithms to clean and verify data automatically, allowing the system to serve itself in maintaining data quality while reducing time and cost expenditures.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of human operators manually cleaning and validating data, the system employs validation engines that use algorithms and automated procedures to perform the same functions, significantly reducing time consumption while maintaining or improving data quality outcomes.
2Reliability
If manual processes are used to clean-up database, then data quality can be improved, but costs increase
Solution Approach 1:
The system enables self-service data validation by automatically performing validation tasks without requiring manual human intervention. The validation engines execute predefined validation rules and algorithms to clean and verify data automatically, allowing the system to serve itself in maintaining data quality while reducing time and cost expenditures.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of human operators manually cleaning and validating data, the system employs validation engines that use algorithms and automated procedures to perform the same functions, significantly reducing time consumption while maintaining or improving data quality outcomes.
3Reliability
If data validation tasks are performed manually, then data quality can be maintained, but productivity decreases
Solution Approach 1:
The system enables self-service data validation by automatically performing validation tasks without requiring manual human intervention. The validation engines execute predefined validation rules and algorithms to clean and verify data automatically, allowing the system to serve itself in maintaining data quality while reducing time and cost expenditures.
Solution Approach 2:
The patent replaces manual mechanical processes with automated computational systems. Instead of human operators manually cleaning and validating data, the system employs validation engines that use algorithms and automated procedures to perform the same functions, significantly reducing time consumption while maintaining or improving data quality outcomes.
4Reliability
If comprehensive data validation is performed, then data quality improves, but device complexity increases
Solution Approach 1:
The system segments data validation into distinct task groups, where each task group contains specific validation sub-tasks that can be independently configured and executed. This modular segmentation allows comprehensive validation to be broken down into manageable units, reducing the perceived complexity while maintaining thorough validation coverage across different data types and requirements.
Solution Approach 2:
The validation engines are designed with multi-functionality to handle diverse validation requirements through a unified framework. Rather than requiring separate systems for different validation types, the engines can perform multiple validation functions using configurable task groups, thereby reducing overall system complexity while achieving comprehensive data validation.
Data Source
AI summary
A method of data quality management including selecting a task group wherein the task group comprises at least one data validation sub-task. In one embodiment, the method may also include arranging at least a portion of a master data into a validation group, wherein the validation group is associated with the task group. In various embodiments, the method may include, for each data validation sub-task, determining a sub-task type, validating the validation group as directed by the data validation sub-task, and calculating a set of key performance indicators (KPIs) associated with the data validation sub-task.