Contextual Advisory System for XaaS Transformation
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Solution Overview
Problem
Enterprises face challenges in re-architecting their products into Software as a Service (SaaS) due to the complexity of cloud native technology ecosystems and the lack of comprehensive solutions that consider the interdependencies between technology, business, and design areas, leading to an expensive and time-consuming process.
Innovation Solution
A method and system for generating contextual advisory for Everything as a Service (XaaS) that captures user inclination and navigates users through complex decisions by using a process involving inference nodes, decision nodes, knowledge graphs, and machine learning-based approaches to provide dynamic recommendations for building XaaS models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If enterprises use conventional trial and experimentation methods to achieve target architecture, then they can eventually realize their business objectives, but the process becomes expensive and time-consuming
Solution Approach 1:
The system performs preliminary assessment of the enterprise's current state, maturity level, and requirements before the actual transformation begins. By evaluating the starting position and pre-calculating the transformation path, the system avoids trial-and-error experimentation and directly guides enterprises through the appropriate transformation steps, significantly reducing time and cost while ensuring reliable achievement of business objectives.
2Ease of operation
If enterprises use isolated decision-making methods for individual areas, then specific local decisions can be made, but it becomes technically challenging to consider the impact of one solution choice on other areas and provide a unified view of the entire SaaS ecosystem
Solution Approach 1:
The system creates a unified decision-making framework that simultaneously handles multiple areas including technology, business, and design aspects of SaaS transformation. The contextual advisory engine evaluates interdependencies across all these dimensions together, providing comprehensive recommendations that consider the impact of decisions in one area on other areas, thus maintaining ease of operation while managing system integration complexity.
Solution Approach 2:
The system implements a hierarchical decision-making structure where local area decisions are nested within the broader context of the entire SaaS ecosystem. Each local decision (technology, business, or design) is evaluated considering its place within the larger transformation framework, allowing enterprises to make specific local decisions while maintaining awareness of and alignment with the overall system goals and interdependencies.
Data Source
AI summary
State of art techniques hardly provide technical solution to address complex dependencies that exist between different areas of SaaSification. Embodiments of the present disclosure provide a method and system for a contextual advisory for a platform, a process, a technology, and technical components to build Everything as a service (XaaS) for a domain of interest. The method dynamically generates contextual advisory and recommendations to build the XaaS on users input from assessment and decision navigation. Decision navigation captures complex interdependencies of business, design, technology etc. User inclination/inferences are captured from assessment. In accordance with initial inclination of the user, the user is navigated through decision making process involved in building the XaaS model for the domain of interest of the user. The system recommends the decision and generates the dynamic information based on user inputs which is used to generate the advisory documents.