analysis-planning
When to use
After requirements are gathered and before any data is touched. Planning is especially important when the analysis involves multiple steps, uncertain data availability, or a tight deadline where sequencing matters. A 15-minute planning session prevents hours of wrong-direction work.
Process
- Decompose the question — break the business question into sub-questions using
references/scoping_framework.md; each sub-question should be answerable with a single data pull or calculation. - Identify data dependencies — for each sub-question, list the required tables/datasets and assess availability (confirmed / likely / unknown); flag blockers early.
- Sequence the work — order sub-questions so that each output feeds the next; identify which steps can run in parallel.
- Estimate effort — use
references/effort_estimation.mdto assign time estimates per step; sum to a total and compare against the deadline. - Log risks and dependencies — use
references/risks_dependencies.mdto document anything that could delay or invalidate the plan (data gaps, external approvals, methodology uncertainty). - Produce the plan — fill in
assets/analysis_plan_template.md; for projects with stakeholder kickoffs useassets/kickoff_doc_template.md.
Inputs the skill needs
- Analysis brief or requirements doc (from
stakeholder-requirements-gatheringskill) - Available data sources
- Deadline and resource constraints
More from nimrodfisher/data-analytics-skills
funnel-analysis
Conversion funnel analysis with drop-off investigation. Use when analyzing multi-step processes, identifying conversion bottlenecks, comparing segments through a funnel, or optimizing user journeys.
45executive-summary-generator
Create concise executive summaries from detailed analysis. Use when preparing board decks, executive briefings, or condensing complex analysis into decision-ready formats for senior audiences.
41insight-synthesis
Transform data findings into compelling insights. Use when converting analysis results into actionable insights, connecting findings to business impact, or preparing insights for stakeholder communication.
41data-narrative-builder
Build compelling data-driven narratives. Use when presenting analysis results, creating stakeholder reports, or transforming a set of findings into a story that drives a specific decision or action.
40data-quality-audit
Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating data pipeline outputs before production use, auditing a dataset against defined business rules, or producing a quality scorecard for a data asset.
39time-series-analysis
Temporal pattern detection and forecasting. Use when analyzing trends over time, detecting seasonality, identifying anomalies in time series, or building simple forecasting models for planning.
39