OCN.ai - Data Collection and Visualization Tool of Ocean Data
Scope: B2B2C SaaS tool
Role: Lead UX Researcher & UX Designer
Tools: Figma, Miro, Usability Hub
Team: UX lead, 2 UX designers, 1 UX researcher/designer (me), CTO (backend developer)
Context:
Some basic research had been done before I joined the team. They had a few personas and completed some competitive analysis. There was certainly lots of opportunity for more research to better understand the users and add user groups.
Problem:
There is no comprehensive method to assign value to the ocean, which hinders effective decision-making, limits its untapped potential for sustainable growth, and restricts the development of market opportunities for natural capital assets.
Solution:
A dashboard and data visualization platform for both scientific and non-scientific users to view and analyze ocean data with the help of AI and machine learning.
Constraints:
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Pre-seed startup environment - no budget
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Constant change of product direction and user groups
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Juggle creation of mockups for investors with our UX workflow
Research
How might we...
...offer information to understand the value of a healthy ocean?
...create a dashboard for users to track the health of specific aspects of the ocean?
...provide intuitive visuals of data comparison?
After further development of the product and conversations with the CEO about users, we developed a list of several different potential user groups: scientists/researchers, government agencies, conservation organizations, carbon trading markets, & marine transportation companies.
All users have a common need - accurate and comprehensive data for specific aspects of the ocean.
The team decided to start working on the map analysis and data visualization part of the product and put the dashboard element to the side for the time. We would make mockups for investor presentations as needed on both elements but I started focusing on researching pieces for the data visualization/analysis aspect of the product.
Research Goals:
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Establish information architecture and site map
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Understand functions/features/flows that were good & bad in current products
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Identify what language should be used in our data selector elements
To better wrap our head around all of the elements of the product the team completed a card sorting type activity where we brainstormed all possible features or elements of the finished product. I then asked each member to sort the cards into whatever categories they chose in an open card sorting activity.
We then presented to each other why we chose the titles we did for each category as well as explained our reasoning for placing each card in their category. Overall we noticed that we had sorted most things in a similar manner which helped establish the initial information architecture that would later be tested by users.
Pattern Research for Data Selectors
Goal: Define the best flow through our data selector for our users.
Methodologies:
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Rapid sketching favorites and present to team
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Storyboard a user flow of data selection
Outcome:
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Lo-fi wireframes & prototype
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Nomenclature Research & Testing
Goal: Identify the best category names for our data selector that would be understood by both scientific and non-scientific users.
Methodologies:
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Research competitors/literature
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Card sorting with team members and public users
Outcome:
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Better understanding of what terminology resonates with users
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Information architecture structure developed
Outcomes & Insights
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Naming conventions for categories in the data selector have been identified
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First iteration of site map based of card sorting activity
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User flow of data selector has been chosen and will be tested in the future with prototype testing
Ideate
AI Chatbot
I worked on mockup designs of the AI chatbot to be presented to potential investors. AI and machine learning will be integrated into other elements of the software as well in the future.
