Selective Enterprise Application Integration Framework
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Solution Overview
Problem
Existing enterprise application integration (EAI) practices often result in over-engineered solutions due to broad application, leading to inefficient use of resources and data inconsistencies across systems, as manual updates are tedious and prone to errors.
Innovation Solution
A method for selective application of EAI practices using an IT governance model framework that classifies applications, business objects, and business processes to determine the optimal EAI approach based on short-term and long-term requirements, costs, and benefits, allowing for a full, partial, or minimal EAI approach to maximize return on investment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If EAI is applied broadly to all applications, then integration coverage and reusability are improved, but solution complexity and cost increase leading to over-engineering
Solution Approach 1:
The patent applies local quality by differentiating the degree of EAI integration based on the specific characteristics of each application. Instead of uniform integration, the system evaluates individual applications and implements appropriate integration levels (full EAI, partial EAI, or minimal EAI) based on factors like data sharing requirements, system criticality, and business value, thereby avoiding over-engineering while maintaining necessary integration coverage
Solution Approach 2:
The patent implements partial action by providing three distinct EAI approach levels (full, partial, minimal) rather than requiring complete integration. This allows organizations to apply EAI practices selectively to the extent necessary for each specific situation, avoiding excessive integration effort and cost while still achieving sufficient integration benefits where needed
2Device complexity
If manual updates are used to maintain data consistency, then system simplicity is preserved, but labor intensity and error rate increase
Solution Approach 1:
The patent implements self-service by enabling systems to automatically update and synchronize their own data through EAI integration mechanisms. Instead of requiring manual intervention to maintain data consistency across applications, the integrated systems automatically exchange and update information, thereby eliminating tedious manual updates while maintaining system simplicity through automated self-maintenance
3Adaptability or versatility
If full EAI approach is implemented, then long-term flexibility and reuse are maximized, but short-term investment cost increases
Solution Approach 1:
The patent applies partial action by offering three EAI implementation levels that allow organizations to invest only the necessary amount based on their specific needs and time horizon. Organizations can select full EAI for long-term strategic initiatives requiring maximum flexibility, partial EAI for moderate-term needs, or minimal EAI for immediate requirements, thereby aligning investment cost with actual business needs rather than universally implementing the most expensive approach
4Quantity of substance
If EAI practices are selectively applied, then investment efficiency is improved, but determination complexity increases
Solution Approach 1:
The patent implements feedback by establishing evaluation criteria and frameworks that continuously assess application characteristics, business requirements, and integration benefits. This feedback mechanism provides structured guidance for determining the appropriate EAI approach level, making the determination process more systematic and less complex by using predefined evaluation dimensions and comparison frameworks
Data Source
AI summary
A method for selective application of enterprise application integration (EAI) for a project under consideration involves a computer implemented model. The method includes defining a plurality of sets of EAI practices including a full EAI approach, a partial EAI approach, and a minimal EAI approach. Classifications are defined for each approach. Classifications are defined for applications, business objects, and business processes. The model generates an output indicative of a recommended set of EAI practices. In determining the output, the model considers factors including the classifications for the applications, business projects, and business processes involved in a project.