Increasing adoption of data products.

Project Type

Full Time Project

My Role

Product Designer

Duration

9 Months

Tools Used

Design thinking
Whiteboard
Figma
PowerBI

Client

Food Service Data Team

My Contribution

-> Heuristic Analysis
-> Accessibility
-> User Interviews
-> Wireframes & Hifi Mockups

Other Contributors

Data Science
Data Engineering
User Research
Exec Stakeholders
Insights Summary
Problem: Low adoption and usability of internal data dashboards
The company had been struggling with low adoption, misunderstood use cases and poor user experience.
Confusing versions of similar products lead to lack of adoption. Poor usability was causing busy site managers to give up.
How do site staff and managers use these dashboards?
I gathered information about the use cases of execs, site managers & implementation managers.
Through user-interviews I created personas to better understand the needs and goals of the dashboard’s users.
Design is a team sport.
Allowing teams to make design suggestions is an important part. but it must be grounded in truth, not personal opinion.
Working with product managers, engineers and other contributors needs to be done early and consistently.
This allows a common strategy to be developed where everyone is working towards common goals.
Understanding the User
I met with the users to identify how they use dashboards and what insights would be helpful to them.
Some of the feedback we received:
  • Insights into how products are selling
  • Suggestions on areas to look into
  • Organizing pages based on outcomes.
Example User Interview Questions
Insights Summary
Supporting Development
I worked closely with the data science team to ensure that the design was implemented correctly and made adjustments as needed.
The implementation team faced some challenges along the way, but I was there to help them find solutions.
We established communication with sponsor users to continue making improvements.
Results: The users were happier using the dashboard
The confusion between dashboards was alleviated by splitting up the data between insights and deep-dive analytics.
Navigation and information hierarchy changes resulted in overall user satisfaction improving.

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