Legacy Administrative System Conversion Through AI Data Alignment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Legacy administration systems are cumbersome, siloed, and unscalable, leading to error-prone and difficult-to-scale processes that hinder efficient conversion to target systems.
Innovation Solution
Utilize AI-empowered techniques, including machine learning and robotic process automation, to analyze, predict, and migrate data, processes, and products from legacy to target administration systems, aligning data structures and definitions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used to convert legacy systems to target systems, then flexibility and adaptability are maintained, but productivity is low and error rates are high
Solution Approach 1:
An AI intermediary system is introduced between the legacy system and target system. This AI system analyzes the legacy system's data structures, business rules, and processes, then automatically translates and converts them to the target system's format. The AI acts as a smart mediator that handles the complex conversion tasks, achieving high productivity while maintaining accuracy through intelligent automation rather than simple robotic process automation.
Solution Approach 2:
The system dynamically adjusts conversion parameters and data transformation rules based on the specific characteristics of the legacy system being converted. The AI analyzes the source system's data structures, business rules, and processes, then automatically modifies conversion parameters to optimize the transformation to the target system. This allows the system to adapt to different legacy systems while maintaining high conversion speed and accuracy.
2Adaptability or versatility
If legacy system data structures are maintained, then compatibility with existing processes is preserved, but adaptability to new target systems is reduced
Solution Approach 1:
The conversion system segments the legacy data structure into distinct components: core data elements, business rules, and process logic. The AI analyzes each segment separately and transforms them independently to the target system's structure. This segmentation allows the system to maintain compatibility with the legacy system's business logic while adapting the data structure to the target system's requirements, reducing overall complexity.
Solution Approach 2:
The system adds a transformation layer that operates in a different dimensional space between the legacy and target systems. Instead of directly mapping one-dimensional data structures, the AI creates a multi-dimensional transformation model that considers data elements, business rules, and process contexts simultaneously. This dimensional transformation simplifies the conversion process while ensuring compatibility with both systems.
3Reliability
If comprehensive data validation is performed during conversion, then result accuracy is improved, but processing time increases
Solution Approach 1:
The AI performs preliminary analysis and validation of the legacy system's data structures, business rules, and processes before the actual conversion begins. It identifies potential data quality issues, inconsistencies, and validation requirements in advance. During the conversion process, the system applies pre-defined validation rules and performs spot checks on critical data elements, rather than comprehensive validation of every data point, thereby maintaining high accuracy while reducing processing time.
Solution Approach 2:
The system replaces mechanical, step-by-step data validation processes with AI-driven intelligent validation. The AI uses machine learning models to predict data quality issues and automatically validate critical conversion elements. This substitution of AI intelligence for mechanical validation processes achieves high conversion accuracy significantly faster than traditional validation methods.
Data Source
AI summary
Methods, systems and apparatuses, including computer programs encoded on computer storage media, are provided for automatically converting a legacy administrative system to a target administrative system. The data sets, product rules, business functions/processes, business rules, and calculation modules of both systems are analyzed by an analysis system to determine elements in common. Elements of the legacy system not present in the target system are generated and the legacy system is then fully migrated to the target system. For data required by the target system not present in the legacy system, data-driven prediction models may be used to predict the required data.