As the sole designer at Vega Cloud, I led the redesign of the rules engine from start to finish. I employed various methods and tools, including user research, affinity mapping, sketching, wireframing, prototyping, and usability testing, along with collaborative workshops to ensure the design met both user and business needs.

Vega Cloud's rules engine is a core feature enabling users to automate resource allocation. However, the existing design lacked flexibility in defining rules with complex logic and prioritization. Users expressed difficulties when setting up rules that required conditional layers. This gap in functionality led to inefficient workflows and low user engagement, limiting the platform's automation potential. I undertook the challenge of redesigning this feature to address these concerns and improve user satisfaction.

The rules engine was critical for automating resource allocation by applying complex rules. Despite its importance, it faced challenges that hindered its effectiveness:

To address the identified issues and enhance the effectiveness of the rules engine, the following goals were set for the redesign:

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"How can the rules engine be redesigned to add complexity while remaining intuitive?"

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To better understand and resolve these issues, I conducted the following research:

• User Interviews: Collected feedback from users to understand their pain points and needs related to rule configuration for resource allocation.
• Competitor Analysis: Analyzed how other systems handle complex rule setups and prioritization to identify best practices for automating resource allocation.
• Usability Testing: Evaluated the existing rules engine to identify usability problems and gather data on user interactions.

Key insights included the need for a modular approach to rule creation, clearer visual representations of rule layers, and the ability to assign priorities to improve automation accuracy.

After collecting and analyzing results from my research, I created an affinity map to organize insights and challenges when designing a rules engine for Vega Cloud, helping to address key issues like flexibility, usability, and user engagement.

I came up with the following solutions for the redesign which focused on the following improvements:

I began sketching some ideas for a rule engine with the key focus on how users would logically set conditions and sub-conditions, and how to ensure the prioritization process was intuitive.

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Initial wireframes and prototypes were created to test with a small group of platform users as well as some internal team members to gather feedback and make necessary adjustments.  

I conducted usability testing sessions with internal and external users to observe how they interacted with the updated interface, focusing on ease of use, clarity, and efficiency in setting up automated resource allocation rules. Then, I collected insights from testing to refine the design, making adjustments based on user feedback to address any pain points and enhance the overall experience.

There were significant improvements in user adoption, satisfaction, and error reduction, as demonstrated by key performance metrics:

The redesign of the rules engine for Vega Cloud successfully transformed a complex and underutilized feature into a user-friendly tool that effectively automates resource allocation. By prioritizing flexibility, intuitive design, and iterative user feedback, the new system not only improved adoption rates and user satisfaction but also drastically reduced configuration errors.

This project underscored the importance of balancing complex functionality with simplicity while reinforcing the value of a user-centered, iterative design process. Close collaboration with users, continuous testing, and adapting solutions based on real-world feedback were key to success. The experience also highlighted the need for scalable and flexible designs, especially when handling complex logic like resource allocation—lessons that will continue to guide my approach to future design challenges.