Data Analytics

Data Analytics

Data Analytics helps you answer product and business questions with data you can trust, then turn the findings into shareable reports, charts, dashboards, notebooks, and recommendations.

When to use this plugin

Use Data Analytics when a product or business decision depends on metric-backed evidence: understanding performance, explaining a metric movement, deciding where to focus, defining KPIs, checking whether data is reliable, or sizing an opportunity. Start with connected warehouses, BI or product analytics tools, docs, chats, spreadsheets, uploaded files, pasted results, or clearly labeled sample data.

Get started

Try asking:

@Data Analytics Help me get started with my first data task

The plugin will help you choose a data source, pick a useful workflow, and decide whether to use connected tools, upload data, paste results, or try sample data.

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

Example workflows

Workflow	Try this	Skill	Result
Analyze a product or business question	Analyze activation and recommend where to 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
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, PostHog, 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.