Most analytics resources explain tools.
This library explains what happens between the tools: how data is collected, structured, stored, interpreted, reported, and maintained over time.
Start with the problem you’re seeing, or explore the system from the foundations up.
Start With the Problem
If something doesn’t look right, start here
Analytics problems usually appear in reports first.
But the cause is often somewhere upstream.
Foundations
The concepts behind reliable analytics
Reliable analytics depends on more than collecting data.
It requires structure, consistent logic, clear definitions, and an understanding of how the pieces work together.
Tracking & Collection
How reliable data enters the system
Everything downstream depends on what happens at collection.
These pages explain how events are defined, generated, transmitted, and governed before they reach analytics and reporting systems.
Why Analytics Breaks
How reliable systems become unreliable
Analytics rarely fails all at once.
Websites change. Platforms change. New tags and integrations are added. Definitions drift. Assumptions that were once true stop being true.
These pages explain how analytics systems degrade and why problems that appear in reports often begin somewhere else.
Data Architecture & BigQuery
When analytics needs a stronger foundation
As data volume, reporting requirements, and analytical complexity grow, the architecture behind the reports becomes increasingly important.
These pages explore how data can be stored, transformed, governed, and made available beyond the limits of individual analytics platforms.
Reporting & Looker Studio
Reliable reporting starts before the dashboard
A reporting tool can only work with the data architecture behind it.
These pages explain where reporting platforms work well, where they begin to struggle, and when the underlying architecture needs to change.
Attribution
Why platforms tell different stories
Attribution is not simply a setting inside GA4 or an advertising platform.
It is the result of how users, sessions, events, channels, and conversions are observed and interpreted across the system.
These pages explain why attribution changes, why platforms disagree, and what more reliable attribution actually requires.
AI-Ready Data
What AI actually depends on
AI does not fix unreliable data.
It makes the consequences of unreliable data more visible.
Conversational analytics, data agents, and AI-assisted reporting depend on structured data, stable definitions, and shared meaning.
These pages explore the data foundation required before AI can reliably interpret an analytics system.
Data Maturity & Decision-Making
What becomes possible when the foundation is reliable
Better analytics is not simply more reporting.
As the underlying system matures, organizations can move from collecting and describing data toward interpreting it, comparing outcomes, forecasting, and making better decisions.
Strategy & System Design
How to make better analytics decisions
Some of the most important analytics decisions are not configuration decisions.
They involve choosing the right architecture, understanding the limits of platforms, and deciding which capabilities should be bought, built, or managed as part of a larger system.
Tools
Understand the components without confusing them for the system
GA4, Google Tag Manager, BigQuery, and Looker Studio each perform an important function.
But no individual tool determines whether your analytics can be trusted.
Understanding what each tool does — and where its responsibility ends — makes it easier to design the larger system correctly.
What This All Leads To
Analytics you can actually rely on
Reliable analytics is not created by adding more dashboards or more technology.
It comes from a system with:
- structured data collection
- consistent logic
- stable definitions
- clear ownership
- controlled change
- ongoing validation
When those pieces remain aligned, reports become easier to trust, problems become easier to diagnose, and the data becomes more useful throughout the organization.
Not Sure Where Your System Is Breaking?
An Evaluate engagement examines how your analytics system is actually behaving.
It identifies:
- where data is being lost or distorted
- where systems have fallen out of alignment
- which assumptions no longer hold
- what should be fixed first
The goal is not simply to find problems. It is to understand what is causing them and establish a clear path toward a more reliable analytics system.
Doug McCaffrey
Designs and maintains analytics systems that remain reliable over time.