By 2026, digital analytics is no longer a narrow technical concern.
It has become part of an organization’s critical infrastructure. Analytics shapes strategic decisions, processes personal data, and intersects with broader risk considerations. As a result, analytics decisions have moved up the organization. They now involve legal teams, CIOs, risk managers, and boards.
I have written this article for this context.
I explain what you should understand when migrating from GA4 to Matomo Analytics. This question has fundamentally changed over the past few years.
Fragility as the new baseline
In the 2010s we assumed stability:
- stable legal frameworks
- stable political alliances, and
- stable international cooperation.
That assumption no longer holds. Data transfer mechanisms are fragile and legal certainty is provisional. Long-term dependencies are harder to justify than they once were.
Today, we should accept uncertainty as a permanent condition. Also when planning digital analytics infrastructure. It is a critical dependency just like cloud hosting for many businesses.
Why GA4 is being reconsidered
Most organizations do not look for GA4 alternatives because it lacks functionality. On the contrary, GA4 provides excellent set of features, integrations and customizability.
They consider alternatives it because GA4 embodies Google’s incentives rather than their own.
GA4 is designed for scale, deep integration with Google’s advertising ecosystem. It also uses modeling, automation and abstraction for giving us back some data we lost because of GDPR.
For many organizations, this works well.
For others it creates tension that is not technical, but structural.
At executive level, the concerns are familiar. Transparency into how numbers are produced is limited. Dependence on a US-governed platform for a core data asset feels increasingly uncomfortable.
Explaining data processing logic to regulators or auditors is hard (as we don’t really know what happens behind the scenes). The gap between what is compliant and ethical analytics widens continuously.
These are governance problems, not usability issues.
What actually changes when you move away from GA4
A migration from GA4 to Matomo is not a simple tool replacement. It is a shift in responsibility, and in several practical assumptions.
GA4 often acts as a central data source for Google Ads reporting and optimization. When GA4 data is no longer available, Google Ads tracking often needs to be improved or rebuilt. Conversions that “just worked” through GA4 integrations must be explicitly defined.
More broadly, GA4 makes many decisions on behalf of the organization. It defines defaults, smooths uncertainty through modeling, and hides complexity behind automation. This reduces effort, but it also reduces visibility and control.
Matomo works differently. It makes fewer assumptions. It doesn’t use AI or machine learning to “improve” our data. That gives organizations more control, but it also removes the comfort of delegation. With Matomo, the organization decides
- what is measured
- how consent is implemented in practice
- how long data is retained, and
- where it is stored.
The logic behind the numbers is no longer outsourced to a vendor. Accountability moves inward.
For decision-makers, the core trade-off is straightforward: convenience versus control.
Two analytics philosophies
At leadership level, feature comparisons are rarely decisive. Underlying philosophies are.
GA4 reflects a worldview where
- scale matters more than explainability
- automation more than inspection
- and tight integration with advertising ecosystems is a primary objective.
Control over platform logic ultimately remains with the vendor.
Matomo reflects a different worldview. Data ownership is central. Processing logic should be inspectable. Privacy is a design constraint rather than an afterthought. The organization is the data controller not only legally, but operationally.
This does not make one approach universally superior. It makes them suitable under different assumptions about risk, responsibility, and the future.
When Matomo fits—and when it does not
Matomo is not a default choice, and it should not be presented as one.
It fits organizations that operate under regulatory scrutiny, and are willing to invest in governance. It suits environments where long-term accountability matters more than short-term efficiency.
It is a poor fit for organizations that rely heavily on Google Ads automation, and expect insights without internal effort.
Being explicit about this is not a weakness. It is essential for making a sound decision.
Migration is not “just replacing GA4 tags”
Migrations often underestimate how much stays the same and how much fundamentally changes.
Consent management usually remains unchanged. The consent management platform, legal texts, and approval flows typically stay in place. The complexity lies elsewhere.
Key architectural decisions must be made early. Should tracking be implemented via Google Tag Manager or Matomo Tag Manager? Matomo Cloud or on premises? Should server-side tracking be introduced, or is client-side sufficient? These are not technical details; they shape data quality, governance, and long-term maintainability.
Event tracking also differs significantly between the platforms. GA4’s event model and Matomo’s event tracking logic are conceptually different. This means that existing measurement plans cannot simply be copied. Events must be redefined, not translated.
Also certain ecommerce tracking features available in GA4 do not exist in Matomo in the same form.
This does not make Matomo unsuitable for ecommerce. However, it requires prioritization: deciding which metrics are essential, and which just nice to have.
What migrations usually underestimate
Analytics migrations rarely fail because of technology.
They fail because organizations underestimate the human and structural dimension.
Historical data cannot be made identical. KPIs will differ before they stabilize. Parallel tracking is usually necessary to rebuild trust. Reporting must be redesigned rather than copied. Stakeholders need time to recalibrate their expectations.
A GA4-to-Matomo migration is, above all, a change-management exercise. The tooling supports the change, but it does not drive it.
A pragmatic way forward
From a leadership perspective, a sensible migration approach is deliberately cautious.
The organization must first be clear about why it is migrating and which decisions analytics is expected to support. Non-negotiable KPIs should be defined. GA4 and Matomo should run in parallel long enough to rebuild confidence in the numbers. Only once trust is restored should GA4 be retired.
This approach avoids symbolic decisions and reduces unnecessary risk.
The illusion of free analytics
GA4 is often described as free.
It is not.
Its costs surface elsewhere: in legal uncertainty, engineering workarounds, data extraction pipelines, and recurring internal debates about whether numbers can be trusted.
Matomo is open source but introduces visible costs.
For CFOs and CIOs, the relevant comparison is not license pricing. It is total, risk-adjusted cost over time.
The real question behind the migration
By 2026, the central question is no longer “Which analytics tool is best?”
It is: Which analytics model can we justify under uncertainty?
Analytics can no longer be treated as a nice-to-have. It must be treated as critical information infrastructure.
Migrating from GA4 to Matomo is not about rejecting a vendor. It is about reducing dependency on assumptions of regulatory and political stability.
Ultimately, the decision is not about tools.
It is about who controls knowledge about your users, your services, and your organization.