Building a revenue data pipeline that actually works

Original: Revenue Automation Series: Building Revenue Data Pipeline

May 29, 2025

13 min read

Case Study

Advanced

Business Understanding

Visit Original Article

Revenue Automation Series: Building Revenue Data Pipeline

Summary

An in-depth look at the technical and analytical considerations around revenue recognition, such as aligning with ASC 606/IFRS-15 rules, handling multi-element allocations, and analyzing ratable vs. point-in-time recognition patterns. Explores how to build revenue waterfalls, cohort aging tables, and other financial reporting to ensure data integrity and accounting compliance.

Who This Is For

Analytics Engineers

Data Engineers

Revenue Ops

Key Takeaways

Create a glossary dictionary to translate business requirements into engineering-friendly specifications
Use data gap analysis to bridge custom systems with standard third-party integrations
Handle complex revenue recognition scenarios like fair value allocation and multi-product bundling
Implement composite data solutions when direct field mapping isn't available between systems

Tools & Technologies

Revenue Recognition SaaS (REVREC service) ETL (Extract, Transform, Load) architecture Yelp's custom order-to-cash system Product Catalog system Data pipeline infrastructure

Topics Covered

revenue-recognition accounting-alignment saas-metrics financial-reporting

Ready to dive deeper?

Read Full Article on engineeringblog.yelp.com

Related Resources in Business Understanding

Why your big ideas matter more than perfect data

This article argues that powerful ideas, even if imperfectly measured, are more useful for business decision-making than perfect measures for less impactful ideas. It discusses the importance of focusing on high-leverage metrics and acknowledging the limitations of data, rather than getting overly bogged down in pursuit of perfect measurement.

John Cutler June 2, 2025

Intermediate

Why most teams skip the messy middle between strategy and features (and how to fix it)

This article discusses the data-informed product cycle, which involves translating high-level strategy into models, testing hypotheses, and iterating based on data. It covers key topics like freemium, free trials, usage-based pricing, activation metrics, and product-qualified leads (PQLs).

John Cutler June 2, 2025

Intermediate

John Cutler's North Star workshop goodies (finally organized)

Establishing the core foundations for a successful go-to-market strategy, including defining the ideal customer profile, target market segments, and aligning the product's value proposition with the right sales and distribution channels.

John Cutler June 2, 2025

Intermediate

Topics

revenue-recognition accounting-alignment saas-metrics financial-reporting