bi-analyst_skill

This skill helps you master BI fundamentals including metric definitions, KPI calculations, dimensional modeling, and dashboard optimization for rapid data

Python

2

GitHub Stars

1

Bundled Files

2 months ago

Catalog Refreshed

4 months ago

First Indexed

Readme & install

Copy the install command, review bundled files from the catalogue, and read any extended description pulled from the listing source.

Installation

Preview and clipboard use veilstrat where the catalogue uses aiagentskills.

npx veilstrat add skill pluginagentmarketplace/custom-plugin-sql --skill bi-analyst

SKILL.md8.6 KB

Overview

This skill teaches BI fundamentals: metric definition, KPI calculation, dimensional modeling, dashboard optimization, and data storytelling. It includes 40+ metric examples and reusable calculation patterns to standardize reporting. The content emphasizes SQL patterns, fact/dimension design, pre-aggregation strategies, and practical analytics recipes.

How this skill works

I provide concrete SQL patterns and modeling guidance that inspect data grain, calculate core business metrics, and optimize queries for dashboards. The skill covers transaction- and summary-grain fact tables, KPI formulas (MAU, CAC, retention, NPS), cohort and RFM analytics, and pre-aggregation queries for fast dashboards. Guidance includes variance and trend analysis queries, window functions, and aggregation best practices for operationalizing metrics.

When to use it

Designing a new analytics schema or selecting fact table grain
Standardizing metric definitions across teams and dashboards
Optimizing slow dashboard queries with pre-aggregations
Building KPI pipelines for monthly/weekly reporting
Running cohort, retention, or RFM analyses for segmentation

Best practices

Define metrics at atomic grain and store canonical calculations as views
Use conformed dimensions so multiple facts share consistent keys and attributes
Pre-aggregate or build summary fact tables to speed dashboard queries
Implement slowly changing dimensions where history matters and document SCD policy
Automate data quality checks and monitor query performance (EXPLAIN PLAN)
Use incremental loads for large fact tables and apply appropriate indexes for common joins and filters

Example use cases

Monthly revenue and AOV reports from transaction-level orders with customer attribution
Dashboard optimized by fact_sales_summary for near-real-time executive KPIs
CAC and marketing ROI calculated monthly by joining customers to marketing spend
Cohort LTV and month-since-acquisition revenue tables for product growth analysis
Customer segmentation using RFM and NTILE for targeted marketing campaigns

FAQ

The patterns use standard SQL concepts and window functions common across Postgres, Redshift, Snowflake, and similar systems; minor syntax adjustments may be needed per engine.

How do I choose between atomic and summary fact tables?

Use atomic grain for flexibility and accuracy; add summary fact tables when performance or dashboard latency requires pre-aggregation. Maintain both if you need drill-down and fast dashboards.