Analytics thinking, clearly explained.
Practical perspectives on measurement design, data architecture, and building analytics programs that support real decisions.
Why Measurement Design Must Come Before Implementation
Most analytics implementations begin with a tool. They should begin with a question. Here's why getting the order wrong is the root cause of most data trust problems.
Adobe Analytics vs GA4: How to Make the Right Call for Your Organization
The Adobe vs Google question comes up in almost every enterprise analytics review. Here's a framework for making the decision based on your actual needs — not vendor positioning.
Data Layer Design: The Foundation That Determines Everything Downstream
A data layer is not an analytics implementation detail. It is infrastructure. The decisions made here determine data quality, governance, and the ability to scale for years.
Customer Journey Analytics: When It Adds Value and When It Doesn't
CJA is one of the most powerful tools in the Adobe stack — and one of the most frequently misunderstood. Here is an honest assessment of when it delivers and when it overcomplicates.
Why Experimentation Programs Fail Without Analytics Alignment
A/B testing programs stall when analytics and experimentation are treated as separate systems. Here is what alignment looks like and why it determines whether your testing program produces decisions or debates.
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