Data Quality Management System for Automated Rule Execution

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Solution Overview

Problem

Traditional data quality solutions are labor-intensive and inefficient in defining and applying data quality rules, hindering effective data management and quality assurance in sophisticated data warehousing and business intelligence architectures.

Innovation Solution

A data quality management system that includes a rules repository for storing profiling and cleansing data quality rules, a rules management module for organizing and managing these rules, and a data quality job management module for migrating and executing these rules across different data quality processing systems, enabling coordinated and automated data profiling and cleansing processes.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Productivity

If traditional data quality solutions are used to define and apply data quality rules, then data quality management can be performed, but the process becomes labor intensive and inefficient

Engineering Contradiction:

Improvedata quality management efficiencyVSAvoidtime required to define and apply data quality rules

Core Design Contradiction:

ProductivityVSLoss of time

Solution Approach 1:

The system enables self-service through automated rule execution and coordination. The data quality management system automatically executes profiling and cleansing rules without requiring manual intervention for each rule application. The coordinated execution mechanism automatically manages the sequence and dependencies between rules, allowing the system to serve itself rather than requiring continuous human oversight for routine operations.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces manual mechanical processes with automated computational mechanisms. Instead of manually defining and applying data quality rules through repetitive manual operations, the system uses automated rule engines that interpret and execute rules programmatically. The coordination mechanism automatically manages rule execution sequences, substituting manual coordination with systematic automated control.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Reliability

If data quality rules are manually defined and applied in traditional solutions, then data quality can be maintained, but the process is labor intensive

Engineering Contradiction:

Improvedata quality assuranceVSAvoidease of defining and applying data quality rules

Core Design Contradiction:

ReliabilityVSEase of operation

Solution Approach 1:

The system performs self-service by automatically executing and coordinating data quality rules without requiring manual intervention for each rule application. The automated execution engine handles rule interpretation, sequencing, and coordination, allowing the system to maintain data quality through autonomous operation rather than continuous manual effort.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The data quality management system provides multi-functionality by handling multiple operations through a unified automated platform. The system can execute profiling rules, cleansing rules, and coordinate between them through a single integrated mechanism, eliminating the need for separate manual processes for each function and simplifying operation across the board.

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Productivity

If automated data quality rule execution is implemented, then efficiency is improved, but system complexity increases

Engineering Contradiction:

Improvedata processing efficiencyVSAvoidcomplexity of data quality management system

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system segments the data quality management functionality into distinct modular components: profiling rule execution, cleansing rule execution, and coordination mechanisms. Each module handles specific aspects of data quality processing independently, making the overall complex system manageable through segmentation. This modular architecture allows automated execution while maintaining clarity through separation of concerns.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent introduces a coordination mechanism as an intermediary layer that manages the complexity of rule execution. This intermediary component handles the coordination between profiling and cleansing rules, managing dependencies and sequencing automatically. By placing this coordination layer in between the rule definition and execution components, the system achieves automated efficiency while the intermediary absorbs and manages the inherent complexity.

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS9082076B2Data quality management for profiling, linking, cleansing, and migrating data

Publication Date: 2015.07.14 ACCENTURE GLOBAL SERVICES LTD

AI summary

A data quality management system includes a rules repository configured to store profiling data quality rules, cleansing data quality rules, and linking data that links profiling data quality rules to cleansing data quality rules. The data quality management system also includes a rules management module configured to manage the rules repository. The data quality management system further includes a data quality job management module configured to migrate data quality rules from the rules repository to a data quality processing system and manage a data quality process performed by the data quality processing system using the migrated data quality rules.