Sections
Role
Product Designer
Timeline
2026
Team
Austin (Me)
Engineers
PM
Skills
Figma
Codex
Claude
Pendo
Background
The Problem with so much data in Splunk.
Splunk customers face critical challenges with data discovery and governance. As data becomes increasingly dispersed across the platform, analysts struggle to find and leverage the data they need. Administrators can't effectively govern or manage costs without clear visibility into what data exists and where it lives. New users are left searching blindly, flooding admins and power users with "Where is X?" requests.
TLDR
- Successfully led the design that is shipped in alpha and GA release.
- Conducted multiple rounds of user research, received strong customer feedback heading into GA+.
- Leveraged AI for rapid prototyping and customer validation throughout the phases.
- Reduced 100+ monthly hours for 1,200+ admins and analysts.
Approach
My role as the lead designer.
I led design of the Machine Data Lake catalog from its earliest stage through successfully shipping Alpha and GA releases, conducting research, designing, and leveraging AI throughout the process. My challenge was creating a central interface where users could discover and explore their data across the Splunk platform, solving longstanding issues around data discovery, productivity, and missed insights.
Research
Research and designing with AI
Research was split into multiple phases throughout the design process that included usability testing, customer validation and user interviews.
Weekly enterprise customer calls in the early stages were crucial to align the understanding of what users needed. Also with such a complex product leveraging AI became a means of our research and internal alignment.
As the design matured, I used Cursor and Codex to build a working prototype that is deployed to Git for the team to use for research. Resulting in the highest quality of user feedback and understanding of the product during these customer calls.
How Prototyping and AI helped
- Enabled more realistic user research sessions, expediting customer conversations by allowing users to interact with the product rather than react to static mockups.
- Created a real product feel with lower effort, accelerating design iteration and promoting more meaningful product conversations before committing engineering resources.
- Bridged the gap between engineers, designers and PMs by helping non-designers visualize and engage with complex workflows earlier in the process.
Design
Starting the design was not easy...
Breaking down user needs, auditing technical constraints, understanding existing Splunk design patterns, and aligning with the timeline included working very closely with engineers from the start.
Alpha release
With the large scope of this project it is split into three releases alpha, GA and GA+. Alpha was my first iteration that was mainly tested within a smaller group of customers and used for initial feedback.
General availability (GA) Release
The GA release is close to the final product which underwent extensive research and different iterations within the team.
Discovery Page
A searchable listing view where analysts and admins can browse all available datasets across MDL, local, and federated sources, viewing associated metadata and statuses to quickly assess data relevance and take actions such as promoting or searching datasets.
Promotion Jobs Page
A listing view of all submitted promotion jobs, displaying each dataset's current status (whether actively promoting, completed, or failed) and available actions so users can track and manage their promotions in one place.
Promotion Flow
Walks user through activating raw datasets into a searchable state, walking them through selecting a dataset, choosing a format, and tracking the promotion job. Designed to give users the right amount of technical context to make informed decisions without overwhelming them with backend complexity.
Current state
What I'm working on now
This product is still ongoing into the next phase. I am designing GA+, adding additional features on the road map and refining current state of the product through the feedback loop we now have with the customers. Also introducing Splunk's new design pattern that modernizes many of our current design components.
Results and Takeaway
We shipped two releases!
Our team successfully shipped the alpha release and general availability (GA) release with tons of positive feedback! Also this product was successfully presented and pitched to customers at Splunk's annual Conference this year (2026).
Important Takeaways from this project
This has been one of the biggest projects I've owned at Splunk, building a completely new product that did not exist previously, which pushed me to grow in many different ways such as:
- Growth in communication across large cross functional teams as this project had many areas.
- Planning and mapping out a design that would lead to minimal repeated work.
- Communicating a very large complex technical workflow to all the different team members through various methods.
- Defending design decisions through data and principles rather than existing assumptions and biases.