Enterprise GenAI Data Explorability Best Practices
GenAI delivery slows when teams spend more time hunting for data than using it. This workshop makes “explorable” measurable and prioritizes the experience and tooling improvements that reduce time-to-data across use cases.
Leave with an explorability baseline, a prioritized set of improvements, and a plan to reduce time-to-data for GenAI delivery.
Most enterprises invest in data platforms and catalogs—but still struggle to make data truly usable for GenAI teams at scale.
- Data exists, but isn’t easy to explore: Teams can’t quickly determine what a dataset contains, how it should be used, or whether it’s fit for their GenAI needs.
- Tooling is fragmented across data types: Structured and unstructured data are explored through disconnected experiences, slowing analysis and limiting reuse across teams.
- No feedback-driven improvement loop: Without usage signals and exploration logs, teams can’t systematically improve what’s confusing, hard to find, or frequently misused.
When data isn’t explorable, GenAI work becomes slow, duplicated, and inconsistent—blocking scale.
We help teams define “explorable” in practical terms, then improve experiences and behaviors that drive reuse and speed.
- Define metrics for measuring explorability: Establish a simple, shared set of indicators that reflect how easily users can find, understand, and assess data for GenAI use.
- Design intuitive exploration experiences for all data types: Identify the interface patterns and information cues that make structured and unstructured data easier to interpret quickly.
- Integrate visualization and query tooling for AI teams: Align the exploration experience to the workflows teams actually use—so insights and evaluation can happen faster.
- Promote reuse through metadata tagging and lineage mapping: Strengthen the signals that help users discover relevant assets, understand provenance, and avoid reinventing work.
- Improve continuously using exploration logs: Define how usage and friction signals will guide iterative improvements to the exploration experience over time.
- Defining metrics for measuring data explorability
- Enabling intuitive interfaces for structured data exploration
- Enabling intuitive interfaces for unstructured data exploration
- Integrating visualization tooling for AI teams
- Integrating query tooling for AI teams
- Promoting reuse through metadata tagging
- Promoting reuse through lineage mapping
- Improving user experience through exploration logs
- Define what “explorable” means for GenAI data in your enterprise and how to measure it
- Identify the biggest friction points slowing data discovery, understanding, and reuse
- Map a target exploration experience across structured and unstructured data workflows
- Prioritize metadata and lineage improvements that increase reuse and reduce duplication
- Leave with a plan to use exploration logs and signals to continuously improve explorability
Who Should Attend:
Solution Essentials
Facilitated workshop (interactive discussion + working session)
4 hours
Intermediate
Virtual whiteboard and shared document workspace