data-collect

To define data requirements from KPIs, this skill automates reverse-calculating necessary data fields from business goals and mapping them to specific data sources. It reads your analysis context to generate a structured data collection plan.

The best way to catalog data sources and trace requirement gaps is using an automated requirement tracing workflow. This skill generates a data-source catalog and identifies missing data assets relative to your defined KPIs.

Automating data collection workflows handles analysis context by reading the analysis_context.md file directly. It extracts business objectives from this context to outline specific data acquisition steps and map required data stores.

You need an analysis_context.md file to start automating data requirements definition. This markdown file provides the business goals and analysis context required to map KPIs and generate the data collection plan.

Yes, you can use this for projects needing end-to-end data sourcing. It satisfies complete data collection workflows by outlining acquisition steps, creating a data-source catalog, and tracing requirements across data stores.

You should outline data acquisition steps before data collection to identify data gaps early and ensure all required data sources align with your KPIs. This proactive planning prevents missing data assets during analysis.