Awesome Business Intelligence

A curated list of the best resources about business intelligence, data analytics, and the modern data stack for business leaders and data teams.

Inspired by awesome lists. Maintained by Rework.

Traditional BI was IT-led, slow, and disconnected from the business questions that actually mattered. Modern BI is business-led, self-service, and built on a data stack that can be assembled and maintained by small teams. These resources cover both - because most organizations are somewhere in between and need to understand where they're going.

Contents

Articles

Books

Videos & Talks

Tools & Software

  • Tableau - Industry-leading data visualization and BI platform, now part of Salesforce ecosystem.
  • Microsoft Power BI - Microsoft's BI platform with strong Excel integration and competitive pricing for M365 users.
  • Looker - Google Cloud's enterprise BI platform with LookML for consistent, governed metrics.
  • Metabase - Open-source BI tool ideal for teams who want self-service analytics without enterprise pricing.
  • Apache Superset - Open-source data exploration and visualization platform, widely used in data-mature organizations.
  • dbt (data build tool) - The standard tool for data transformation in the modern data stack, used by thousands of teams.
  • Fivetran - Fully managed ELT data pipeline tool connecting hundreds of sources to your data warehouse.
  • Snowflake - Cloud data platform used as the warehouse layer in most modern data stacks.

Templates & Frameworks

Case Studies & Real-World Examples

  • Netflix - Netflix's recommendation engine is one of the most documented BI success stories in tech: the company estimated its personalization and recommendation system saves approximately $1 billion per year in reduced subscriber churn. Every element of the Netflix UI — artwork, row ordering, search results — is driven by behavioral data from over 200 million subscribers and tested through continuous A/B experiments. Source

  • Starbucks - Built a store analytics capability using customer purchase data, mobile app behavior, and location intelligence to inform new store placement decisions and hyper-personalized loyalty offers. Starbucks' "Deep Brew" AI platform powers over 400,000 personalized offers per week to Rewards members, contributing to the Rewards program representing more than 50% of U.S. tender. Source

  • Coca-Cola - Used AI and social media data analysis to identify that its customers were creating their own cherry-vanilla Sprite combinations at self-serve fountain machines, which led directly to the development and launch of Cherry Sprite as a new product. This demand-sensing use of BI — letting behavioral data surface product opportunities — has become a standard reference for how CPG companies can use analytics to reduce product development risk. Source

  • Amazon - Amazon's entire retail operation is a BI machine: its pricing algorithm adjusts prices roughly 2.5 million times per day based on competitor pricing, demand signals, inventory levels, and customer behavior patterns. Its forecasting systems predict demand weeks in advance and pre-position inventory in fulfillment centers accordingly, which is why next-day delivery is operationally possible at its scale. Source

  • UPS and ORION - UPS deployed its On-Road Integrated Optimization and Navigation (ORION) system to optimize delivery routes across its 55,000-driver fleet, using BI and operations research to reduce left turns (which waste fuel waiting in traffic). ORION saved UPS an estimated 100 million miles per year, reducing fuel consumption by roughly 10 million gallons annually — a direct, measurable financial outcome from operations analytics at scale. Source

  • Target's pregnancy prediction model - Target's data team built a model that could predict customer pregnancies from purchasing behavior changes and began sending targeted coupons accordingly — famously before some customers had announced their pregnancies publicly. The case became a landmark study in both the power of retail BI and the ethical boundaries of predictive analytics, leading to industry-wide conversations about data use policies. Source

Communities & Newsletters

  • dbt Community Slack - The most active analytics engineering community online, with channels for every aspect of the modern data stack.
  • Locally Optimistic Slack - Practitioner community for data analysts, analytics engineers, and data team leads.
  • Data Council - Community and conference for data practitioners across analytics, engineering, and ML.

Rework Resources

Contributing

Know a great business intelligence resource we've missed? Let us know.

Last updated: March 2026. Links verified. Covers traditional BI platforms and the modern data stack for teams at every stage of data maturity.