As the Design Lead, I led a team of 6 to design a code-free visual workspace for Stardog's flagship product - Stardog Studio.
Our capstone project team was commissioned by the CEO of Stardog, a fast-growing Enterprise Knowledge Graph Platform provider, to redesign their existing knowledge graph product that could be used not only by technical users but also business analysts who typically rely on IT to create a graph for them. We identified a need for a simpler data unification process. We collaborated closely with their product and engineering team on a mission to design various Graphical User Interfaces (GUIs) that would allow non-technical users to do data mapping, data modeling on their own without any coding.
I managed a team of 6 designers and collaborated with stakeholders and customers, led the design operations, user research and product evaluation, conceptualized solutions, and owned 4 critical features in the final interface design.
What is the Knowledge Graph?
A knowledge graph (KG) is a flexible graphical representation of data derived from a variety of structured, semi-structured, and unstructured data sources that are connected together to create machine-understandable knowledge.
Enterprise data in large companies is stored in silos and fails to provide insights required to make better decisions. KGs allows users to build flexible models and connect data from different silos.
The process of creating and using KGs (detailed workflow) requires a number of large, complex steps which are illustrated below. Our project focus on frame 2 to 4, supporting users to do data modeling and mainly data mapping.
Storyboard illustrating the knowledge graph creation process.
Lead ontologists, data architects etc. with 5+ years of experience in the KG domain. Needs ability to code & has many advanced use cases.
Junior data architects, software Enggs. etc. with <5 years of experience with KGs. They need to be able to create and modify data models & mappings easily.
Domain experts, business analysts, managers etc. with some or no understanding of KGs. Wants to query & find answers to business questions.
Knowledge transfer sessions with Stardog engineers.
Analyzed existing solutions in knowledge graph space.
Conducted interviews with 8 High-techies and 3 Low-techies.
Conducted 5 interviews to capture observational data.
Evaluated lo-fi prototype with 5 participants.
1. Modeling and mapping are very closely tied.
When users work on mapping, they need to edit/add new nodes or properties as needs arise. So we treated each project as a virtual graph where users can create data models and do the mapping on the same canvas.
4. A subset of data is selected before users start mapping.
Users select some data-columns to work with before mapping so they don’t get overwhelmed by a huge amount of data. So we created a data-field selection user flow.
2. High-techies prefer coding.
High-techies prefer coding because they are more comfortable with writing code than using a visual interface. So we decided to provide the flexibility to switch between synced visual and code modes.
5. Properties are defined separately.
Users may define properties and classes separately and later assign the properties to the classes and map them. So we created a new place for defining properties separately.
3. Auto-mapping is the default starting point for techies.
The majority of current users use Auto-mapping functionality as a starting point. So we highlighted the ‘Auto mapping’ feature when users first create the virtual graph project.
6. 'Click to map' vs 'drag and drop' to map.
Users found both 'click to map' and 'drag and drop' mapping functionalities to be useful. So we supported both 'click to map' and 'drag and drop to map' interactions.
Stardog was definitely the most challenging product that I worked on considering its unique industry space targeted towards developers. The unique challenges posed by this project forced me to push my limits and made me more comfortable managing both the research and design roles.
Leading a team of 6 designers made me think from a perspective of design operations and helped me realize the importance of different perspectives while designing something. Throughout the project, I got to lead Design Operations, Research, and Design which helped me grow tremendously as a designer.
Reflecting back, most of the challenges occurred because of our limited knowledge of this space, and I would have spent more time on Desk Research and learning Modeling and Mapping through code so that I could have understood the complex use cases better.