I was the third designer hired by the AI Empowerment incubation team within Microsoft AI & Research. Our team is bringing a brand new AI/ML product to market that unlocks the power of AI for people who otherwise wouldn’t be able to access its power.
As an early designer on a small but growing team, I was able to have a big impact. My largest overall contributions have been:
To respect my NDA, I have intentionally omitted this feature’s full product context.
I chose to showcase this feature because it is granular enough to avoid giving away sensitive product details while still demonstrating the breadth of my skill set. Many details of the larger product such as navigation, controls, and other functionality are omitted.
helps people understand unstructured data by standardizing, indexing, and unifying siloed data types such as PDFs, images, videos, and CSV though AI.
Novartis, our publicly announced partner, uses our product to aid in the drug discovery and development process.
are subject matter experts who lack the technical and data science expertise to develop their own AI models. They are typically researchers, doctors, or analysts.
A significant part of our product deals with very large tabular datasets such as CSVs. Before users can perform most analysis on tables of 1 million+ rows, they often need to run multiple filters.
During this team’s early days, the priority was to get features shipped. This meant making lots of design compromises. One of the early design decisions the team made was to copy key design patterns from Excel. However, when it came to filtering, once we shipped to our customers, we immediately started receiving feedback that I distilled down into several main observations:
Additionally, I made a more fundamental observation:
The old filter designs were an exact copy of the experience on Excel for Web.
I lead brainstorming sessions, defining the problem space and articulating the core use cases for advanced filtering.
Whiteboarding and on-paper wireframing helped us narrow down on a few promising directions: :
A selection of the most promising design directions. The team saw the most potential in the concept shown on the lower right.
During my early exploration phase, I discovered a UX pattern called a condition based interface. This pattern is commonly used for filtering UX and enables code-like functionality through a UI.
Ultimately, the design team, PM, and other stakeholders saw huge potential in the condition-based filtering concept. We decided that the power of this interaction could bring a lot of functionality to our users.
The chosen design direction. Not only did the designs work for filtering, but also for other table 'modifiers' such as sort, group by, and show and hide columns.
There were many advantages to the condition-based filtering designs. However, I also identified several potential risks:
Because my proposal was such a big departure from both the existing designs and other Microsoft products, I knew we needed to test it before committing the engineering resources to implementation.
I partnered with our user researcher to complete a user testing study. The nine participants included four from the pharmaceutical industry and five from the energy industry. All were advanced Excel users with familiarity with a wide array of data analysis tools, which was an excellent representative of our typical users.
The study was a counterbalanced presentation of a safe, incremental improvement to the existing filter experience tested against the new condition-based filter design.
The new Advanced Filtering designs were very well received and preferred over the current filtering design by all participants. Participants found the new designs to be more efficient and visually appealing than the current design
Based on strongly positive user feedback, we felt confident moving forward with the design. The research findings gave the design team the evidence we needed to make the commitment to introducing the new filtering pattern. I have outlined a few key features below:
I didn’t just create designs for advanced filtering. I applied the same patterns consistently to other table modifiers such as group by and sort by.
I included several shortcuts to ease the filter creation process. One example allows users to shorten the number of clicks it takes to create a series of filters from dozens to just a few by right-clicking on the exact values to filter by within the table.
Since our product is AI-focused, I created blue-sky concepts for how table filters might be expanded to include features such as image recognition, format conversions, and entity classification.
Although our product is still in Alpha and has not been released to the public, we have customer usage at Novartis as well as our other partners. Overall, my biggest areas of impact with this project have been: