Tracking plans, naming conventions, and event architecture
João Lousada outlines how a data governance strategy prevents analytics drift. He emphasizes the role of casing rules, ownership tags, and source-of-truth documentation in keeping instrumentation aligned with product changes. Great for teams scaling analytics without chaos.
In this Amplitude reintegration story, Moss Pauly shows how reducing events to a <30 schema improved usability for analysts. Events were refactored to prioritize clarity and team ownership, with event routing and validation set up before hitting Amplitude.
Based on product audits, this post explains how exceeding 30 core events leads to confusion, duplicative metrics, and inflated tooling costs. Timo recommends modeling a small set of core entities and using properties to capture UI-specifics. Includes governance tips.
Paul Koullick argues that naming events well is a UX challenge. He lays out conventions for naming (verb-noun, title case, property bias), and introduces a workflow where every new event is reviewed for clarity and governance. Great for scaling teams.
This post introduces the Core Entity Test to decide when to create new events versus reusing existing ones with more properties. It includes real-world pitfalls around analytics debt and rising costs, with insights on designing for long-term maintainability.
Victor Ackerhans suggests starting from the business questions you want to answer and designing events backwards from the dashboard. His three-step guide is practical for small teams and early-stage companies just getting into structured analytics.
This guide explains the purpose and structure of a tracking plan, including what to log, where, and why. It includes tactical steps for plan rollout, collaboration, and long-term maintenance. Useful for teams looking to formalize their tracking strategy.
This case study shows how Pipedrive split responsibilities for event instrumentation: PMs define, engineers implement, and analysts advise. It walks through their workflow for designing, documenting, and QA'ing event data. A pragmatic guide for collaborative tracking setups.
This framework balances two pillars: how to structure events (verb-noun naming, hierarchies, required properties) and how to govern them (ownership, versioning, validation). It's a great blueprint for teams dealing with scale and event sprawl.
This post lays out a practical naming convention for analytics events using the object-action schema. It emphasizes clarity, casing consistency, and dot/underscore delimiters to reduce mystery events and promote scalability. Great starting point for taxonomy design.
This workbook introduces the 'double three-layer' framework for event structures, organized by customer, product, and interaction layers. It's designed to help teams build a lean, analyzable event taxonomy that ties directly to business questions. Essential reading for anyone starting a tracking setup from scratch.