Redefining Enterprise Data Discovery
Product
Enterprise analytics platform ecosystem
Timeline
4 weeks
Methods
Exploratory Interviews
Prototype Evaluation
Context
When early design work exists without research, teams are left making critical product decisions in ambiguity. I partnered with design and product to evaluate new enterprise ecosystem concepts—bringing clarity to whether this direction aligned with how users actually discover and evaluate data in their current Cloud Data Platforms.
The Problem
As the platform expanded its cloud capabilities and integrations with Cloud Data Platforms, the team sought to improve how users discover and evaluate datasets within the enterprise ecosystem before building workflows.
Early assumptions: users frequently moved between enterprise tools and Cloud Data Platforms to locate and understand their data, creating fragmented workflows and increasing cognitive overhead. Rather than staying within a single environment, analysts were piecing together context across multiple systems. The team needed to understand how data discovery within the enterprise ecosystem might alleviate the negative effects of the platform "switching" experience.
Research Goals
- Understand current data discovery pathways within Cloud Data Platforms
- Evaluate early enterprise design concepts for data discovery
- Explore how analysts interpret and use metadata
- Identify opportunities to reduce workflow fragmentation from platform "switching"
- Inform the design of a more integrated discovery experience within enterprise ecosystem
My Role
I led the research end to end, including study design, recruitment, moderation, synthesis, and stakeholder communication.
Because the problem space spanned multiple areas of the platform, I partnered closely with cross-functional teams to align on shared research questions and ensure the work addressed broader ecosystem-level needs rather than isolated feature decisions.
Methodology
I conducted remote moderated interviews combining workflow observation with prototype evaluation.
Participants were asked to:
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React to early concepts designed to support in-platform discovery
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Demonstrate how they navigate across tools during discovery
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Walk through how they currently locate and evaluate datasets
Impact
This research reframed the problem from a feature-level opportunity to an ecosystem-level challenge.
The findings:
- Provided directional confidence for investing in a more integrated data discovery experience
- Validated early design concepts focused on surfacing richer contextual metadata
Additionally, the insight around data connection ownership prompted new research exploring how administrative workflows should potentially be designed across the platform.
Reflection
A gap in scope surfaced after the study's final report around how users set up and manage data connections. However, even if we had realized this before fielding interviews, or early in the interview process, addressing it properly would have easily extended interview sessions from 60 minutes to 90 minutes, which poses the risk of scheduling conflicts and increasing participant burden and feedback quality.
Rather than reopening the study, or creating a new study, I identified an opportunity to incorporate these questions into an upcoming cross-product effort by another researcher. This allowed us to explore the gap without disrupting timelines and gather the feedback we needed.
Nonetheless, this experience reinforced the importance of early and effective alignment with adjacent teams while maintaining disciplined scope.
Takeaway: Research shouldn’t live in silos - strong alignment early on makes it easier to adapt later. But, despite best efforts, unknown objectives can still slip through the cracks. Therefore, it's important to be aware of the work being done across products and teams, allowing researchers to remain flexible and open to opportunities to gather the feedback they need.