| name | effort-estimator-data |
| description | This skill estimates analytics and data engineering effort for product initiatives. Use when asked to size instrumentation work, estimate dashboard build time, or scope a data pipeline. Also consider when sprint planning requires analytics capacity allocation. Suggest when a PRD is approved without a data workstream estimate.
|
| department | data-growth |
| agent | analytics-lead |
| version | 1.0.0 |
| complexity | simple |
| related-skills | ["instrumentation-planner-data","instrumentation-spec-data"] |
| triggers | ["estimate analytics effort","data project estimation","analytics work estimation","scope data initiative","effort sizing data"] |
effort-estimator-data
Agent: Analytics Lead
L1 analytics leader (1x) responsible for search demand validation, market sizing, goal framing, instrumentation strategy, and north star metric governance.
Department ethos: ideal-data-growth.md
Skill Description
The effort estimator sizes the analytics and data engineering work required for an initiative, producing t-shirt estimates for instrumentation, pipeline, and dashboard deliverables so product and engineering can plan capacity accurately.
When to Use
- When a new product initiative is entering sprint planning and the analytics workstream has no estimate.
- When a PRD includes metrics requirements but no data engineering scope.
- When the analytics team needs to negotiate capacity trade-offs across competing initiatives.
Workflow
- Extract data requirements: Parse the PRD or initiative brief for all metrics, dashboards, instrumentation, and reporting deliverables.
- Decompose into work units: Break each deliverable into discrete tasks — event schema design, SDK integration, pipeline configuration, dashboard build, QA verification.
- Estimate per unit: Assign t-shirt sizes (S/M/L/XL) to each task based on historical velocity and complexity factors (new event taxonomy vs. extending existing, number of platforms, real-time vs. batch).
- Identify dependencies: Flag tasks blocked by engineering (SDK releases, API changes) or external teams (third-party integrations).
- Produce estimate summary: Deliver a table of tasks, sizes, dependencies, and total capacity required in analyst-days.
Anti-Patterns
- Estimating without the spec: Sizing effort before the instrumentation spec exists produces guesses, not estimates. Why: unknown event schemas and property sets make scope unpredictable.
- Ignoring QA and verification: Scoping only implementation without verification time leads to under-allocation. Why: instrumentation verification typically consumes 20-30% of total effort.
- Single-point estimates: Providing one number without a range hides uncertainty. Why: stakeholders treat single-point estimates as commitments rather than forecasts.
Output
Success:
- An effort estimate table listing each analytics task, t-shirt size, dependency, and total analyst-days with a confidence range.
Failure:
- Estimates are consistently off by more than 40% from actuals. Report which task categories were mis-estimated and recalibrate the sizing model.
Related Skills
