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Designing a SAAS data visualization tool
from ideation to launch

Astrato is an innovative cloud-based and no-code data visualization tool that empowers users to transform complex data into actionable insights. It aims to serve the needs of advanced users while being intuitive for any users.

This case study outlines the design process of conceptualizing and designing Astrato from its initial idea to a successful market launch, for sections I was responsible.

I was part of the product team working from first ideation stages towards successful launch and refinement of the product, being made responsible for the core of the product, the Workbook Editor and its chart creation experience.
Design Process
The idea for Astrato emerged from the trend of businesses to centralize their data stack in the cloud, hence allowing for faster and easier access to visualize data.

I was one of two first product designers creating first wireframes for sections of the product and the Astrato website, framing design problems by conducting workshops and brainstorming sessions with product managers and C-Suite and finding a visual language by creating mood boards, doing market research and identifying pain points of other products.

We defined our design principles to be: apply core design thinking, be conversational, and empower everyone through progressive discoverability.
User Research
I started my desing process with extensive research, interviewing around ten users of competitor data viz tools and conducting competitor analysis. My research revealed that existing tools were often complex, requiring a steep learning curve and learning of code.

During those interviews, I also learned about the workflows of BI developers, helping me to design an ideal user flow for the product.

I learned for example that users spent more than 80% with cleaning up and preparing their data and they typically keep a spreadsheet side by side as they need to see the data while creating a dashboard. Pain points I identified included that editing of objects is often difficult as the properties panel in one of the competitors tools is nested and users keep loosing context and painfully looking for features. Also, we learned that creators of such workbooks need to use external design tools for creating beautiful dashboards.
Personas
From those user interviews, I established two main design personas:

Joe, the expert: a BI developer, that is working with data clean up, creating design requirements for a dashboard, data analysis and developing the final dashboard. A core worry of the expert is user adoption of his dashboard, hence it should be intuitive and visually pleasing.

The second persona I identified for our product is "Juste, the consumer user” that does not have any formal data education and needs a simple and intuitive interface to quickly understand insights from data.

I also aligned those personas with the buyers personas that the marketing lead developed. Those personas are still used in the product design team and are continuously refined by research.
Ideation
Based on the gathered insights, the design phase focused on creating an intuitive user experience (UX) and a visually appealing user interface (UI) for the "workbook editor". I developed wireframes and low-fidelity prototypes for e.g. the properties panel where features are exposed to the expert to add, edit and style charts.

I tested those wireframes with internal experts to gather early feedback and define hierarchies of features. The following design patterns were defined in the ideation stage:

Drag-and-Drop Interface: Users should effortlessly create visualizations by dragging and dropping elements onto the canvas. Customization: A wide range of chart types, color palettes, and interactive options allowed users to tailor visualizations to their specific needs. Tabs for structuring the editing features in the properties panel, to ensure users can easily find what they look for.
Implementation
The development process was divided into sprints, each focused on implementing specific features and organized around tribes.

While we gathered more designers in the team and with increasing complexity, I became lead designer for the editing experience of the Workbook Editor, ensuring each chart has all editing features easily accessible. In some usertests I identified the colouring flow as a problem area and improved the experience by adding color rules and conditions as well as allowing users to create their own color palettes.

Agile methodologies were employed to maintain flexibility and respond to evolving requirements. Regular communication between the design, development, and testing teams ensure alignment and minimize blockers, bugs and design debth.
Reflections
Astrato was launched on schedule and is perceived as intuitive and powerful by users. Nearly all usertests with confirmed that our target persona easily works with Astrato with no learning needed.

We regularly receive user feedback from our user community, internal BI developers or through sales and customer success which we collect in a user research platform (Gleanly) to determine patterns and prioritise feature requests with product managers.

Building on the initial success, the team continues to iterate and enhance the product. New features include advanced AI-powered insights, additional integrations with popular data sources, and collaboration features.

By addressing the pain points of complex data visualisation, the product not only meets user needs but has the potential to help businesses harness the power of their data effectively.
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