Data Transformation Tool,
Empowering Users to Shape Their Own Data
Overview
Qrvey is a heavy-data platform where users import, prepare, and analyze complex datasets. A recurring need among customers was the ability to transform data, to change formats, merge fields, or generate new columns based on existing ones. The goal was simple yet ambitious: enable any user to transform data without code, while still offering enough power for technically advanced roles. This initiative became one of the most impactful releases of the year, enabling both engineers and analysts to work with data directly inside Qrvey for the first time.
Results
We reduced ticket requests related to data transformation features by 66%, from 15 to 5 per month. These 5 tickets, on average, were related to bugs, technical issues, or new types of transformations that were easily added to the platform given the scalable solution that was implemented.
01 - Discovery and Research
Transforming data is a fundamental step in any analytics workflow, but until recently, Qrvey’s users had to make data transformations in their own data sources outside Qrvey’s Platform, this could imply extra work and extra costs to modify and reload data, and in some case lose already finished work. Also, during customer interviews, 40–50% of prospects asked about transformation capabilities. This revealed both a gap in our offering and a strong opportunity to expand Qrvey’s value proposition.
Through these conversations we identified two key user segments:
→ Technical users, comfortable writing expressions or basic JavaScript.
→ Non-technical analysts, who needed an accessible, guided way to perform basic transformations.
This duality became the foundation of the design challenge: how to support both audiences in a single, coherent interface.
02 — Ideation & Strategy
There was already a feature in another part of the platform called “Formulas”, which allowed users to create new columns and edit existing ones. We decided to integrate that functionality into the new Transformation module to provide a powerful tool for technical users. This approach allowed for a quick first release, helping sales showcase the feature early to prospects.
Meanwhile, the design and development teams began working on a set of user-friendly transformations to lower the barrier to entry for non-technical users. We explored multiple concepts, pre-built templates, wizard-based flows, and modular blocks and ultimately chose a drag-and-drop model where transformations could be stacked in sequence.
We defined three design pillars to guide our solution:
→ Scalability — new transformations could be added as independent blocks.
→ Transparency — users could visualize how each step affected the data and test the result.
→ Flexibility — transformations executed in cascade, and order could be changed visually.
Low-fidelity prototypes were tested with internal data specialists and customer success teams to validate comprehension and flow logic before moving to high-fidelity design.
Once the core principles were defined, I moved into refining the user flow and visual consistency across transformation types.
03 — Design & Development
Design Desicions
I led the end-to-end design of the Transformation module, collaborating closely with PMs and developers. Our focus was on creating a consistent, scalable system where any user could build transformations confidently. From a UX standpoint, maintaining cohesion across a dozen of transformation types was essential to reduce the learning curve, streamline adoption, and build user confidence.
User Flows
Key Desicions Included
The following design choices were critical to ensure scalability, discoverability, and usability.
Discoverable button in a hierarchical position of importance to facilitate the access to the module.
Drag-and-drop workflow builder to provide more control and flexibility to users.
Consistent visual language across all transformation types to minimize the learning curve.
Powerful and flexible testing section to verify results and confirm a trustable experience.
See Transformation Module in action
UI Flows
04 - Testing and Lauch
A first version was developed and tested with internal stakeholders and selected customers for feedback. This is what we found:
Drag & Drop not immediately clear
❌ Issue: Some users didn’t realize transformations were added via drag-and-drop.
✅ Solution: Added a visual onboarding overlay to demonstrate cascading logic.
Incomplete transformations hidden
❌ Issue: Users often closed transformation cards with missing fields that end up broken the trasnformations flow.
✅ Solution: Introduced a red “Missing information” badge to informe clearly to users the need to take action.
Slow column loading
❌ Issue: On large datasets the users experienced slow loading of the columns, the reason was that all the existing columns were being loaded at the same time.
✅ Solution: Optimized performance through lazy loading to load data gradually based on user scrolling.
After several iterations, the feature was released as part of the Qrvey Data section, marking a major milestone in the platform’s evolution.
05 - Results and Impact
-66%
Ticket requests related to data transformations dropped by 66%, from 15 to 5 per month, a clear signal of improved usability and adoption.
+2
2 current clients renewed their subscription to Qrvey because they felt we optimized their process.
The tool remains active and continues to grow, with new transformations being added regularly. More importantly, it positioned Qrvey as a more comprehensive and self-service-oriented platform, reducing dependencies on engineering and strengthening the overall product value. This project became a key example of how thoughtful UX can simplify technical workflows and drive measurable business outcomes.
06 - Key Takeaways
→ Designing for both technical and non-technical users requires clear visual hierarchy and consistent patterns. Once a user learns to do an action is easier to lead them into other flows and features with using the same patters, consisteny over creativity.
→ Scalability through modularity: Each transformation behaves like a plug-in, making the system easy to expand without redesigning the whole interface.
→ Collaboration with engineers early in the process was essential to balance UX flexibility with backend performance constraints.