Change Validation Engine for Enterprise Data Integrity
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Solution Overview
Problem
In enterprise systems, mobile users are typically restricted from making changes to central data repositories due to the risk of incorrect data modifications, which can lead to widespread dissemination of errors across multiple locations, and existing solutions either disallow changes or allow versioning, but lack a robust validation mechanism.
Innovation Solution
A method is introduced that analyzes proposed changes to an electronic entity using a validity index calculation based on contextual and user-related quantified values, ensuring that only valid changes are applied, thereby maintaining data integrity within the knowledge system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If mobile users are allowed to make changes to central data repositories, then the knowledge system can evolve and adapt to user needs, but incorrect changes can be made leading to widespread dissemination of errors
Solution Approach 1:
A validation engine is introduced as an intermediary component between mobile users and the central data repository. This engine receives proposed changes, validates them against predefined rules and criteria, and only allows changes that pass validation to be applied to the repository. This mediator approach enables user-initiated changes while filtering out incorrect modifications before they can propagate throughout the system.
Solution Approach 2:
The system performs preliminary validation of proposed changes before they are applied to the central data repository. The validation engine checks proposed changes against multiple criteria including user authority levels, change content validity, and contextual appropriateness in advance. This preliminary action prevents incorrect changes from being implemented, allowing the knowledge system to evolve safely.
2Reliability
If changes to central data repositories are completely disallowed, then data integrity is maintained, but the knowledge system cannot evolve and users cannot make necessary corrections
Solution Approach 1:
The validation engine serves as a controlled intermediary that allows changes to proceed only after they have been validated against predefined rules. This maintains data integrity by blocking invalid changes while enabling the knowledge system to evolve by allowing valid user-initiated modifications to be applied to the central repository.
Solution Approach 2:
The system applies different levels of validation authority to different users based on their roles and expertise. Rather than a uniform block on all changes, the validation engine applies localized quality control where users with higher authority levels can propose changes that are validated against stricter criteria, while less authoritative users have more restricted change capabilities. This enables evolution where appropriate while maintaining integrity where needed.
3Reliability
If administrators verify all changes manually, then data integrity is maintained, but system complexity and administrative burden increase
Solution Approach 1:
The validation engine performs automated self-validation of proposed changes against predefined rules and criteria without requiring manual administrator intervention for each change. The system automatically checks user authority levels, validates change content against business rules, and determines whether changes should be applied. This self-service approach maintains data integrity through consistent automated validation while significantly reducing administrative burden and process complexity.
Solution Approach 2:
The validation engine provides automated feedback to users about whether their proposed changes are valid and why. When a change fails validation, the system returns specific feedback indicating the problem with the proposed modification. This automated feedback mechanism maintains data integrity through consistent validation while simplifying the administrative process by eliminating the need for manual review and decision-making for each change proposal.
Data Source
AI summary
One implementation provides a method for analyzing the validity of a proposed change to an electronic entity. The method includes receiving the proposed changes to an electronic entity from an external device, and calculating a validity index based on a set of definable rules. The inputs to the calculation include quantified values that represent either contextual information about the proposed change or information about a user proposing the change, or some combination thereof. After the calculation of the validity index, the method includes deciding, based on the calculated validity index, whether to proceed further in applying the proposed change to the electronic entity.