Guides & articles
Articles
Practical guides on analytics, data quality, platform choice, and privacy-first implementation.
This is where Digilytics collects articles explaining how modern measurement works in practice — not only in theory. The focus is clarity, data quality, platform choice, and analytics setups that can actually be trusted over time.
About the content
- The articles are written to be practical and useful, not just technical.
- They connect implementation, data quality, privacy, and real analytics decisions.
- The focus is long-term sustainable measurement rather than quick hacks.
Categories
Latest articles
The guides below cover both baseline implementation and more advanced questions around privacy, consent, platform choice, and architecture.
Analytics governance: the missing layer in most organisations
Analytics systems often degrade over time despite good tools. The underlying issue is governance: ownership, change management, documentation, and clear processes for maintaining and evolving measurement across teams.
How to audit an analytics implementation properly
A practical guide to auditing an analytics implementation: GA4 configuration, events, tagging, consent architecture, and data quality. The article outlines a realistic workflow analysts and technical teams can apply to real implementations.
Designing an analytics measurement plan
An analytics measurement plan connects business goals to KPIs, metrics, and events while defining taxonomy, data layers, and governance. The result is measurement that stays consistent, interpretable, and useful as the organisation evolves.
Matomo vs GA4 vs Piwik PRO: Choosing the Right Analytics Stack
Matomo, GA4 and Piwik PRO solve different organisational problems. This article explains when each platform makes sense based on privacy requirements, data ownership, implementation complexity, and the type of organisation using it.
What Privacy-First Analytics Actually Means
Privacy-first analytics is often used as marketing language, but it has a clear legal and technical meaning. This article explains anonymization, pseudonymization, cookieless models, and how GDPR actually shapes modern web analytics.
Server-side tracking: architecture, benefits, and limits
Server-side tracking can improve data quality, control, and flexibility in modern analytics. This article explains how the architecture works, what problems it actually solves, and the costs and privacy implications organizations need to consider.
Consent Mode v2 explained for analysts
Google Consent Mode v2 changes how GA4 collects and models data when users reject cookies. This article explains what actually happens in the data and why reports can look different.
Cookieless analytics: what actually works in 2026?
The term cookieless analytics is often used loosely. This article explains what it really means, how tools like Matomo, Piwik PRO and Plausible work, and what organizations can realistically measure without cookies.
GA4 implementation 2026: measurement design and data governance
A practical 2026 guide to GA4 implementation focusing on measurement design, event taxonomy, governance, QA processes, and privacy considerations — showing how to build analytics systems that decision-makers can actually trust.
Why many GA4 implementations fail decision-makers
Many organisations implement GA4 correctly yet still struggle to use the data. The issue is rarely the tool itself but missing measurement design, ownership, and ongoing quality assurance.
Want to get measurement under control?
If you recognize the problems or questions described in these articles, the next step may be to review your current setup and see what actually needs to be built, cleaned up, or changed.