Best practices for Analyst copilot (EAP)


To get the most from Analyst copilot, focus on organizing work with projects, consistently leverage memories to ensure repeatable analysis, use both natural language and manual tools for building reports, apply permissions carefully, and take advantage of dashboards and collaboration features. Regularly review usage, share actionable results, and iterate as your organization’s needs evolve.


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Summary: ◀▼
Organize your work with projects and permissions to secure data and improve collaboration. Use memories for consistent, repeatable reports and combine natural language with manual queries for detailed analysis. Build interactive dashboards, share insights securely, and collaborate with your team. Monitor adoption and security, avoid sensitive data in memories, and continuously improve by iterating analyses and using feedback to adapt to evolving business needs.




To get the most from Analyst copilot, focus on organizing work with projects,
consistently leverage memories to ensure repeatable analysis, use both natural language
and manual tools for building reports, apply permissions carefully, and take advantage
of dashboards and collaboration features. Regularly review usage, share actionable
results, and iterate as your organization’s needs evolve.
This article contains the following topics:
Organize your projects and permissions
Build consistent, trustworthy reports
Use both natural language and manual queries
Get the most from your reports and dashboards
Collaborate and share with others
Act on insights
Monitor security
Continuously improve




Organize your projects and permissions


Use projects as containers in which you
group related datasets, reports, dashboards, and memories. This keeps work
organized and makes it easier to collaborate.
Use the group-level permissions, Assign View,
Edit, or Admin at the project level to secure your data and more effectively
collaborate. Regularly review who has access to sensitive or critical
analytics.




Build consistent, trustworthy reports


Capture every step, definition, and logic path as memories. This ensures reports are
consistent and enables faster, more transparent insight generation in the
future.
Always explicitly “remember” key business semantics as memories (for example,
VIP, churn, special SLA terms) to enforce consistency across teams and over
time.




Use both natural language and manual queries


Start by asking business questions in natural language. Let Analyst copilot
interpret and generate reports, referencing prior memories for context.
If you need advanced custom metrics or filters, switch to manual report mode.
Use expressions and logic operators as needed for sophisticated analytics.




Get the most from your reports and dashboards


Build and iterate on reports. Use drag-and-drop to add fields, adjust filters,
and change visualization types. Save versions frequently.
Use memories to combine previous analyses, narrate business stories, and build
dashboards that give holistic views to different stakeholder groups.
Enhance dashboards with narrative text, images, and links, use filters and cross
filters for interactive exploration, and tailor presentation for your
audience.




Collaborate and share with others


Share dashboards and reports as links (with enforced permissions), or export to
CSV or image formats for stakeholder presentations.
Encourage team members to use memories, fork reports, and add commentary to
build shared understanding.




Act on insights


Use dashboards to compare outcomes (for example, AI-driven vs. manual
resolutions), identify workflow bottlenecks, and measure improvement over
baseline.
Deliver actionable stories to business users, review changes with stakeholders,
and adjust processes as needed.




Monitor security


Use dataset permissions and be mindful of who can see and edit your business
information.
Avoid sensitive data in memories if not required. Refrain from analyzing PII or
confidential data in Analyst copilot during the EAP.




Continuously improve


Analyst copilot is designed for quick iteration. Save versions, use rollback,
and fork analyses as business needs evolve.
Mark memories as helpful or not helpful and take advantage of AI-driven
suggestions for new analyses or improvements.
As the product evolves, review the release notes and documentation to learn about new
features and datasets.



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