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Viva Insights Sample Code

Frontier

Turn a Viva Insights export into a finished dashboard, executive deck, or ROI analysis by pasting a prompt into a coding agent — with reusable prompts, schema guides, and example outputs.

Frontier

Turn a Viva Insights export into a finished dashboard, executive deck, or ROI analysis — by pasting a prompt into a coding agent.

Frontier is a collection of ready-to-use prompts, schema guides, and example outputs for Viva Insights. Instead of writing analysis code from scratch, you export your data, pick a prompt, and let a coding agent — such as GitHub Copilot or Claude Code — generate the deliverable for you to review and adapt.

Note: Everything in this section is sample code and starter assets — not production software. Outputs require review, validation, and adaptation to your environment before use.

Responsible use & data privacy

  • Suppress small groups. Apply a minimum group-size threshold to every breakdown, segment, and chart — Viva Insights typically uses 5–10. Raise it for smaller organizations.
  • Never expose individuals. Do not print raw UPNs or email addresses; hash, truncate, or report only aggregates. Person query PersonId is already de-identified, but Purview UserId is not.
  • Analyze groups, not individuals. These outputs are for understanding cohorts and trends — not for evaluating, ranking, or monitoring named employees.
  • Keep data in approved environments. Don't paste sensitive HR data into cloud agents unless your organization's policies allow it; prefer local or enterprise-hosted tools. Validate every agent output before sharing.

Who is this for?

  • People analytics leads building dashboards and reports from Viva Insights exports
  • HR analysts who need repeatable, transparent analysis workflows
  • Analytics consultants delivering Copilot adoption or workplace analytics engagements
  • Technically capable users comfortable with R or Python and willing to work with a coding agent

You do not need to be a software engineer. If you can export a CSV from Viva Insights and paste a prompt into a coding agent, you can use these assets.

What’s inside

Section Description
Prompt Library Structured, ready-to-paste prompts for coding agents. Covers Copilot adoption tracking, user segmentation, ROI estimation, and Purview audit log analysis.
Schema Documentation Data dictionaries for person query exports, Purview audit logs, join patterns, and common data pitfalls.

Additional resources available on GitHub:

Folder Description
examples/ Sample output specifications describing what a finished deliverable looks like.
templates/ Templates for contributing new prompt cards and schema docs.
mcp/ Concepts and sample configuration for Model Context Protocol (MCP) integration.

Getting started

Prerequisites

Before you start, make sure you have:

  1. Exported Viva Insights data. Typically a person query CSV exported from the Viva Insights Analyst portal. Person query data has a panel structure with rows keyed by PersonId and MetricDate (person-week or person-day granularity). HR attributes such as organization, function, geography, and level are included as columns.

  2. An R or Python environment. You need one of the following set up locally:
    • R (4.1+) with vivainsights installed: install.packages("vivainsights")
    • Python (3.9+) with vivainsights installed: pip install vivainsights
  3. A coding agent. Any AI-assisted coding tool that can run R or Python:

Workflow

  1. Export your data. Run a person query (or other query type) from the Viva Insights Analyst portal and download the CSV.
  2. Pick a prompt card. Browse the Prompt Library for the analysis task that matches your use case.
  3. Review the schema docs. Check Schema Documentation to understand the structure of your exported data — column definitions, expected granularity, and common pitfalls.
  4. Open your coding agent. Launch GitHub Copilot, Claude Code, or a similar tool in a workspace with R or Python available.
  5. Paste the prompt. Copy the prompt text from the card, point it at your data file, and let the agent generate the output.
  6. Review and iterate. Check the output against the documented failure modes and adaptation notes. Refine as needed.

Tips for working with coding agents

  1. Be specific about your data. Tell the agent the file name, column names, and date range. The more context you give, the better the output.
  2. Iterate in small steps. If the output is not right, ask the agent to fix one thing at a time rather than re-generating everything.
  3. Validate the output. Spot-check row counts, date ranges, and aggregation logic. Coding agents can make plausible-looking mistakes.
  4. Use the vivainsights packages. The R and Python packages handle common data validation and visualization tasks. Prompts that reference these functions tend to produce cleaner code.
  5. Keep your data private. Do not paste raw data into cloud-based agents unless your organization’s data policies allow it. Use local or enterprise-hosted agents when working with sensitive HR data.

Contributing

See the Contributing guide for how to add prompt cards and schema documentation.

This project uses the Microsoft Open Source Code of Conduct and requires a Contributor License Agreement for all contributions.

Last updated: Jun 16, 2026 Edit this page on GitHub