Frictionless Onboarding
USER Feedback
We started collecting feedback from analytics tools and conducting empathy-interviews. One of the most frequently received feedback was that developers have a hard time familiarizing with the notebook interface and understanding what new features have been released.
First-time onboarding tour
The initial experience a customer has with a product is vital. The aim of the first-run experience (FRE) is to ensure users quickly understand the product's value. Direct them simply and clearly to their starting point, minimizing obstacles and unnecessary friction for a smooth and rewarding beginning.
New feature tour
Properly introducing new features helps keep users aware of updates and boost feature adoptions. Use non-intrusive methods like in-context prompts or tooltips that fit seamlessly into the user's workflow. Present features when they are most relevant to boost engagement and adoption.
Easy Setup and Lightening performance
USER RESEARCH
Method: Benchmark think-aloud interviews, 90-minute 1:1 sessions.
Participants: 12 data engineers and 12 data scientists.
Theme: End to end data engineering benchmark study.
We conducted user interviews that focuses on first-time users for Fabric who had experience with other online coding tools. In the research, each participant was assigned a list of routine tasks for data engineering workflow, the tasks includes getting data, transforming data, and visualizing data. The participants were asked to performed the same tasks on Fabric and other tools with a random order. The task success were recorded in both behavioral measures (success rate and completion time) and attitudinal measures (how difficult the users feel when working on Fabric vs. other tools. High-level paint points are shared below:
Difficult Setup
To start coding, data engineers and scientists must set up a proper environment. It takes hours for every data projects to install all their preferred languages, libraries, and properties in the right version.
Slow Performance
To run codes, the market standards is to take around 15 minutes to start a session. It is perceived as "too slow", and one of the reason data scientists do not prefer web solutions for programming.
High Dependency
To transform or visualize data, data engineers and scientists need to rely on public libraries, which are sometimes laggy and unreliable. Advanced tooling usually adds up the costs of operations.
Lightening-fast start up
Our engineers had optimized backend performance, and my team focused on enabling the power of "defaults" - creating intuitive default settings that cater to the majority of our users' needs. This approach eliminated unnecessary setup steps for new notebooks.
Now, users can seamlessly choose their preferred programming languages and select a pre-built, optimized environment from our curated list, all with just a few clicks. They don't have to install anything on their computer or do any configurations. Notebook takes care of it at all.
This enhancement reduced the initial setup-to-code time from 15 minutes to under 5 seconds, dramatically boosting user productivity and workflow efficiency.
Transforming and analyzing data with built-in visualizations
In leading the design of the Fabric Notebook, my team prioritized reducing repetitive work by incorporating UI gestures. This approach eliminates the need for complex code configuration, as the notebook automates code generation. Users simply drag a table into the code editor, run it, and our built-in visualization tools—enhanced with AI recommendations—create charts and reports effortlessly.
Our visualization tools and AI recommendations have garnered praise for being available out-of-the-box, requiring no additional library installations. This achievement highlights my ability to blend innovative design with technology, significantly enhancing user experience and efficiency.
USER RESEARCH
Method: Competition benchmark, 90-minute 1:1 sessions.
Participants: 12 data engineers and 12 data scientists.
Theme: End to end data engineering benchmark study.
As a result, the Fabric notebook has the highest outcomes compared to major market competitors.
- Fabric notebook: Task Success 84%, Attitudinal 77%
- Competitor 1: Task Success 80%, Attitudinal 76%
- Competitor 2: Task Success 71%, Attitudinal 68%
secure real-time collaboration by design
USER RESEARCH
Method: Interview and wireframe testing, 90-min 1:1 sessions.
Participants: 9 participants across data engineers, scientists, and analysts with various coding experience.
Theme: Notebook collaboration testing.
Users love collaborating in coding, especially when they can see what others are working on in real-time. However, the lack of supporting features concern users. When coding on the same project, users expressed concerns about changing other's work accidentally, going back and forth with questions, exposing sensitive data to collaborators who should not have permissions.
Real-time collaboration supercharged
Real-time collaboration and comments in Fabric notebooks supercharge teamwork by letting multiple people code in different languages, comment, and brainstorm together instantly. With live updates and in-line feedback, teams can solve problems faster, share insights on the fly, and keep everyone in sync-turning data projects into a truly dynamic, collective effort.
With comments, notebook lets users leave conversations right on specific code cells, making teamwork fast and focused. Tag teammates, spark discussions, and solve problems-all without ever leaving your notebook. It’s real-time collaboration, supercharged!
USER FEEDBACK
Method: In-product feedback tool
Through our feedback tool, users expressed one concern for real-time collaboration feature - afraid of irreversible changes to others' work. This stops them of freely collaborating.
Version history safeguarded
Understanding the pain points, my team collaborated on robust built-in version control, automatically saving checkpoints and allowing manual checkpoints to mark key milestones. This not only safeguarded users' progress but also enabled them to track the evolution of my work, compare different versions through a diff view, and restore previous iterations when needed. Furthermore, for more advanced collaboration, users can leverage Fabric’s Git integration , which allows them to back up and version notebooks, revert to previous stages, and manage content lifecycle directly within the Fabric environment.
AI assisted Coding
USER RESEARCH
Method: Survey study taking around 12 minutes
Participants: 183 data professionals
Our research team sent out a survey to attendees for Fabric Conference in Las Vegas. The survey attempted to capture insights on changes in the landscape of AI tools utilization, and how data professionals are using AI in their daily work.
As a result, 87% of all respondents have low to intermediate levels of familiarity with A tools, but 90% of respondents are willing to adopt AI tools into their data workflows. Across all surveyed AI tools, ChatGPT is rated the most common and most helpful, followed by Microsoft Copilot. Two major concerns of frequently integrating AI into data workflow: 1) data security - not trusted to share data with AI tools, and 2) inefficiency to provide context to AI tools.
It gives us an advantage to build the AI-assistant tool inside of notebook, because the data are not shared outside of Fabric, and notebook automatically understands the context of all content.
AI assisted coding with Notebook Copilot
Copilot is your best partner in working with data. Copilot in Fabric notebooks offers an interactive chat panel and in-cell AI assistance, enabling users to generate code, fix errors, add comments, and optimize performance directly within their workflow. By simply typing natural language questions-such as asking for data visualizations, code snippets for machine learning models, or explanations of complex code-Copilot responds with relevant code or insights, tailored to the context of the notebook’s data and structure.
