IBM Watson Analytics is a data exploration tool that enables people without “analyst” in their title to make sense of data by:
- Easy Data Access and Cleansing
- NLP and the ability to gather Insights
- Visual Exploration
- Static and Dynamic Reporting
- Sharable Artifacts
When my team was given the project, the goal was to make Watson more accessible to entry level data explorers as well as make it align more with the IBM brand, in order to generate more users of the tool.
Personas helped us narrow our focus on who this product was created for.
Understanding our users
We were able to inquire with users of Watson Analytics, and conduct various research activities. through this we were able to validate assumptions or find user needs we had not expected.
- Contextual Inquiries – Observing how our users interacted with product interfaces while on their own environments.
- Card Sorting – Users did various card sorting activities related to data visualization, work flows and data definitions.
- Usability Testing – We presented various low-fi prototypes of the purposed new framework and asked users to complete various tasks while asking them to talk though their thought process