Data Analytics

Answer product and business questions with data, explain why metrics changed, and turn analysis into reports, dashboards, and clear decisions.

When to use this plugin

Use Data Analytics when you need to understand product or business performance, explain why a metric changed, define a success measurement plan, assess whether data is trustworthy, or package analysis for stakeholders. You can start from connected data in a data warehouse, dashboards, notebooks, spreadsheets, uploaded files, or pasted context.

Onboarding

Ask XpertAI:

@Data Analytics Help me get started and set up reusable data context for future data work

Data Analytics has a guided onboarding flow that helps confirm the right data sources, save reusable metric and source-of-truth context you provide, and build a context-specific first analysis prompt.

Onboarding is an interactive, step-by-step conversation. XpertAI will guide you through setup, ask for approval before making changes, and help you try a first workflow.

Already have a focused task? Start directly with one of the workflows below.

Example workflows

The primary hero workflow is bolded.

| Workflow | Try this | Skill | Result | | --- | --- | --- | --- | | Analyze a product or business question | Analyze activation and recommend where the team should focus next | product-business-analysis | A decision-ready analysis with evidence, measurable opportunities, and a clear recommendation | | Diagnose a metric movement | Diagnose why weekly active users dropped last week | metric-diagnostics | A calibrated explanation of verified drivers, likely contributors, unresolved questions, and next actions | | Design KPIs | Design a KPI framework for this new product area | design-kpis | A measurement plan with outcome metrics, drivers, guardrails, targets, and validation priorities | | Prepare a KPI readout | Turn this month's metrics into a leadership-ready operating update | kpi-reporting | A concise KPI update with actuals, comparisons, validated drivers, and operating implications | | Build a dashboard | Build a dashboard for monitoring activation, retention, and conversion | build-dashboard | A source-backed dashboard with metrics, filters, visual hierarchy, QA, and handoff | | Size a market | Estimate the market opportunity for this product and show the assumptions | market-sizing | A transparent market or opportunity sizing estimate with sensitivity, uncertainty, and validation priorities | | Build an analytical report | Create an executive report explaining the biggest growth drivers this quarter | build-report | A polished report with answer-first narrative, charts, tables, caveats, and source metadata | | Improve or render a chart | Turn this analysis into a clear chart for the product review | visualize-data | A production-ready visual with the right chart type, labels, hierarchy, and accessibility checks | | Create a notebook | Build a reproducible notebook for this experiment readout | jupyter-notebooks | A clean SQL or Python notebook that can be skimmed, rerun, and extended | | Work with spreadsheets | Analyze this workbook and add a polished summary tab | spreadsheets | A verified spreadsheet artifact with formulas, formatting, charts, tables, and a clear readout | | Validate an analysis | Review this analysis before I share it with leadership | validate-data | A QA pass covering methodology, sources, calculations, analytical pitfalls, caveats, and conclusion strength | | Assess data quality | Check whether this table is reliable enough for our retention analysis | analyze-data-quality | A source-backed quality assessment covering grain, freshness, missingness, duplicates, joins, and material risks |

Integrations

Data Analytics can use available tools when they are connected:

| Source | Supported integrations | What they unlock | | --- | --- | --- | | Warehouses and query tools | Databricks, Databricks Genie, BigQuery, Snowflake | Schema inspection, query-backed analysis, and source-grounded metric investigation | | Product analytics and BI | Amplitude, Mixpanel, Omni Analytics, Metabase, ThoughtSpot, Statsig | Behavior analysis, dashboard context, experiment evidence, and reusable reporting inputs | | Notebooks and analytical workspaces | Hex, Deepnote | Reproducible analysis, notebook handoff, and shared analytical context | | Docs and collaboration | Google Drive, SharePoint, Notion, GitHub, Slack, Microsoft Teams | Business definitions, source-of-truth documents, implementation context, and stakeholder evidence | | Email and calendar | Gmail, Outlook Email, Outlook Calendar | Supporting context for stakeholder questions, operating cadence, and analytical handoff |

You can also start with spreadsheets, uploaded files, pasted query results, schema descriptions, or manually provided business context.

Local development

The published plugin does not require users to install Node.js dependencies. For local development of the Data Analytics MCP server and widgets, install dependencies from the plugin directory:

npm ci