Build a Business Intelligence Stack for Data-Driven Decisions

The Cost of Deciding Without Data

Companies that use data in their decision-making are 5× more likely to make decisions faster than competitors and 6× more likely to retain customers. The cost of building a BI stack is a fraction of the cost of making the wrong strategic decisions without one.

The Modern BI Stack Architecture

• Data sources: CRM (Salesforce/HubSpot), ERP, payment processor, product analytics, marketing platforms
• ETL/ELT pipeline: Fivetran or Airbyte to sync data to warehouse automatically
• Data warehouse: Snowflake (enterprise), BigQuery (GCP-native), Redshift (AWS-native)
• Data transformation: dbt (data build tool) — SQL-based, version-controlled transformations
• Visualisation: Metabase (affordable), Looker (enterprise), Tableau (legacy enterprises), PowerBI (Microsoft shops)

The 5 KPIs Every Business Must Measure Daily

• Revenue (MRR for SaaS, GMV for marketplaces, daily revenue for e-commerce)
• Customer Acquisition Cost (CAC) by channel
• Customer Lifetime Value (LTV) — especially the LTV:CAC ratio (target: 3:1 minimum)
• Churn rate (monthly for SaaS, repeat purchase rate for e-commerce)
• Gross margin by product, customer segment, and channel

Building Your First Dashboard in 4 Weeks

• Week 1: Connect data sources, set up Fivetran/Airbyte, get raw data into warehouse
• Week 2: Build dbt models for revenue, users, and transactions
• Week 3: Build executive dashboard with 8–10 core KPIs
• Week 4: Build operational dashboards for sales, marketing, and product teams

Self-Serve vs Centralised BI

• Startup phase: centralised — data team (or one analyst) owns all data and dashboards
• Growth phase: self-serve — tools like Metabase let non-technical stakeholders build their own views
• Scale phase: data mesh — domain teams own their own data pipelines and dashboards

Common BI Mistakes

• Building dashboards before defining the questions you need to answer
• Too many metrics — executives need 5–8 metrics, not 50
• No single source of truth — sales and finance reporting different revenue numbers
• Vanity metrics over actionable metrics
• Skipping data quality — garbage in, garbage out; build data quality checks from day one