Data Loads & Recurring Data Load Configurations
In order to generate patient predictions, ClosedLoop's AI needs pre-existing data.
ClosedLoop's process for uploading data onto the platform relied entirely on the internal data science team's coding. How could we create an experience that allows users - including those with limited data science knowlegde - to upload and configure their data within the platform's interface?
Role: Product Designer
Timeline: December 2022 - April 2023
Org Collaborators: Product Management, FE and BE Engineering, and Data Science
Existing UX & UI
The current UX/UI for data loads was limited. The platform had a file upload screen, but it lacked the necessary information and required the internal data science team to handle all uploads on the back end.
Metrics for Success:
Metrics for Success:
Limitations & Scope
MAPPING & LOW-FIDELITY ITERATIONS
This flow integrates Table Sources, Data Load Configurations, and Data Loads History to enable users to:
Wireframes & Stakeholder Approval
User flows and wireframe iterations were shared with stakeholders and internal data science users for feedback and approval.
This process ensured alignment with engineering and incorporated necessary functionality updates as the backend was further scoped. Overall, enabling effective progress into high-fidelity screens.
V1 HIGH FIDELITY DESIGNS & INTERNAL USER TESTING
High-Fidelity Designs v1
Considering user and business goals, as well as technical limitations, we developed the initial high-fidelity designs, and prototyped them for user testing.
Internal User Testing
For the first round, testing was done with four internal data science users.
After user testing was complete, the findings and design solutions were synthesized and presented to stakeholders.
Key pieces of feedback
How I addressed it
V2 HIGH-FIDELITY DESIGNS & INTERNAL/CUSTOMER USER TESTING
High-Fidelity Designs v2
Along with research takeaways and action items, the next version of high-fidelity designs included smaller UI states, corner cases, and additional refinements to enhance the user experience.
Customer & Internal Non-DS User Testing
Testing was done with three customer data science and two internal non-data science users.
The designs were easily understood and users successfully completed tasks without issues. Positive feedback was received for this final version:
“This will be really helpful for us to not be as reliant on data scientists to load data.”
“I can easily connect and see how the load is rather than wait a day or two.”
“I know the DS team would be excited to give this to the customer rather than spending 2 hours doing it themselves.”
“It fits really well with what I’ve seen so far with the ClosedLoop platform - nice consistency.”
After user testing was complete, the findings were synthesized and presented to stakeholders.
Next steps for design included:
What I learned
What I would do differently