Transaction Data Deployment with Predictive Validation
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Solution Overview
Problem
Current approaches for deploying transaction data across different database systems require manual modification of dependent master data, which is inefficient and prone to errors, and may result in failed deployments due to structural misalignment or missing/inaccurate master data.
Innovation Solution
The solution involves generating multiple instances of a transaction data template with known master data values, using a predictive model to determine deployable instances, and deploying only those instances that can be successfully integrated into the target system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual modification of master data is performed for each transaction data deployment, then deployment customization is possible, but deployment efficiency deteriorates and error rate increases
Solution Approach 1:
The system performs preliminary actions by pre-generating multiple instances of transaction data templates with placeholder master data references before deployment. This allows the deployment process to automatically resolve these placeholders against the target system's master data, eliminating the need for manual modification while maintaining customization capability.
Solution Approach 2:
The patent implements self-service by enabling transaction data instances to automatically resolve their own master data dependencies during deployment. The system autonomously matches placeholder references with actual master data in the target system, performing what would traditionally require manual intervention without human involvement.
2Adaptability or versatility
If manual modification of master data is performed, then some customization is achieved, but accuracy deteriorates due to human error
Solution Approach 1:
The system enables transaction data instances to autonomously resolve master data references through automated matching algorithms, eliminating manual data entry and modification. This self-service approach ensures consistent and accurate resolution of master data dependencies without human error.
Solution Approach 2:
The patent incorporates validation mechanisms that provide feedback during the deployment process, verifying that placeholder references are correctly resolved against the target system's master data. This feedback loop ensures accuracy by detecting and preventing mismatches before they propagate.
3Speed
If transaction data is deployed without predictive validation, then deployment speed is maintained, but failure rate increases due to structural misalignment
Solution Approach 1:
The system performs preliminary validation of transaction data instances against the target system's schema and master data structure before actual deployment. This pre-check identifies structural misalignments and missing master data references in advance, allowing corrections to be made without slowing down the overall deployment process.
Solution Approach 2:
The patent applies preliminary anti-action by proactively identifying and preventing deployment failures through predictive validation. The system anticipates potential issues such as structural misalignment or missing master data and takes corrective action before deployment begins, thereby preventing failures rather than reacting to them.
Data Source
AI summary
A system and method include reception of a data object template comprising a plurality of fields and a respective value for each of the plurality of fields, determination of at least one master data-dependent field of the plurality of fields, generation of a plurality of data instances comprising the respective value for each of the plurality of fields except for the at least one master data-dependent field, where a value of the at least one master data-dependent field in each of the plurality of data instances is different from the respective value of the at least one master data-dependent field in the data object template, input of each of the plurality of data instances into a machine learning model to determine a likelihood of successful deployment for each of the plurality of data instances, and determination of a second plurality of data instances for deployment based on the determined likelihoods.