Platform Architecture Planning Using Type Unit Definitions
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Solution Overview
Problem
Current platform architecture planning processes lack standardized methods for evaluating and managing architecture types, leading to inefficiencies in resource allocation and project management within cloud computing environments, particularly in transitioning existing systems to cloud platforms like Salesforce.
Innovation Solution
The implementation of a platform architecture planning process utilizing architecture type unit definitions, which categorizes architectures into Go-To-Market, Platform Environment, Platform Component, and Platform Information architectures, along with success gates and a platform pulse metric, to ensure feasible, preliminary, detailed, and scheduled levels of detail and compliance, facilitating agile development and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional platform architecture planning processes are used, then existing systems can be transitioned to cloud platforms, but the evaluation and management of architecture types lacks standardization leading to inefficiencies in resource allocation and project management
Solution Approach 1:
The architecture planning process is segmented into four distinct architecture types (Go-To-Market, Platform Environment, Platform Component, Platform Information), each with its own evaluation criteria and success gates. This segmentation enables standardized evaluation across different architecture domains while maintaining the ability to address specific needs of each type, thereby improving both standardization and resource allocation efficiency.
Solution Approach 2:
The patent introduces quantitative metrics and success gates that transform qualitative architecture assessment into measurable parameters. By defining specific criteria for each architecture type and establishing measurable success conditions, the system enables standardized evaluation and tracking of progress, improving resource allocation efficiency through data-driven decision-making.
2Productivity
If architecture planning processes are standardized with success gates and metrics, then resource allocation efficiency improves, but the complexity of the planning process increases
Solution Approach 1:
By dividing the complex architecture planning into four manageable segments (Go-To-Market, Platform Environment, Platform Component, Platform Information), each with its own success gates, the system reduces the cognitive load on planners while maintaining comprehensive coverage. This segmentation makes the standardized process more manageable despite the increased formality.
Solution Approach 2:
The success gates provide structured feedback mechanisms at defined stages of architecture planning. This feedback system guides resource allocation decisions by clearly indicating when objectives are met or not met, simplifying the decision-making process despite the standardized framework. The feedback loops enable efficient resource allocation through objective assessment rather than subjective judgment.
3Reliability
If architecture deliverables are required to be complete, consistent, correct, and compliant, then platform maturity and health visibility improves, but the time required for planning and delivery increases
Solution Approach 1:
The success gates are designed to be checkpoints that can be validated in advance, allowing teams to prepare and demonstrate compliance before final delivery. By planning for and validating completeness, consistency, correctness, and compliance at intermediate stages rather than only at the end, the system reduces overall delivery time while maintaining high reliability standards.
Solution Approach 2:
The structured feedback mechanism through success gates enables continuous validation of deliverables against the four criteria (complete, consistent, correct, compliant). This ongoing feedback allows for early detection and correction of issues, reducing rework time and ensuring platform maturity is achieved more efficiently rather than through lengthy post-delivery corrections.
Data Source
AI summary
One or more implementations relate generally to a platform architecture planning process utilizing architecture type unit definitions. For example, an architecture for realizing a customer system on a cloud computing platform may be defined in terms of a plurality of architecture types, each type (AT) defined by plural architecture type units (ATUs), and each ATU comprising a set of ATU Details.