First-Party vs Third-Party Data: What Changed and What It Means for Analytics
First-party data is information an organisation collects directly from its own users in its own products, under its own legal basis. Third-party data is collected by an external party — an ad network, data broker, or tag vendor — and made available to organisations that had no direct relationship with the user. The difference determines what you may lawfully do with the data, how accurate it is, and whether it can be used to ground your own AI systems.
What is first-party data?
First-party data is behavioural, transactional, and profile information an organisation gathers through direct interaction with its own users: product events, purchases, support conversations, survey responses, and account attributes.
Three properties define it. The organisation has a direct relationship with the person the data describes. It holds the legal basis for processing. And no intermediary sits between the collection point and the organisation's own systems.
That last property is the one that gets lost. Data collected in your product but transmitted through a third-party tag to a vendor's shared infrastructure is first-party in origin and third-party in custody. Origin is not custody.
What is the difference between first-party, second-party, and third-party data?
| First-party | Second-party | Third-party | |
|---|---|---|---|
| Source | Your own users, in your products | Another organisation's first-party data, shared directly | Aggregated from many sources by a broker |
| Relationship with the user | Direct | Indirect, one hop | None |
| Accuracy | Highest | High | Variable, often inferred |
| Legal basis | Yours to establish | Contractual, dependent on original basis | Frequently contested |
| Exclusivity | Exclusive to you | Shared with one partner | Available to competitors |
| Typical use | Product analytics, personalisation, retention | Partnership targeting | Prospecting, audience expansion |
| Regulatory trajectory | Stable | Tightening | Substantially restricted |
Second-party data is simply someone else's first-party data obtained through a direct agreement. It carries the original collector's legal basis and its limits.
What actually happened to third-party data?
Something more interesting than the story the industry told itself. The expected event — the death of the third-party cookie in Chrome — did not happen. Everything else did.
Chrome kept third-party cookies. Google reversed the planned phase-out in July 2024, dropped the user-choice prompt it had proposed as a replacement in April 2025, and in October 2025 retired the remaining Privacy Sandbox APIs — Topics, Protected Audience, Attribution Reporting and the rest — citing low adoption. Third-party cookies remain in Chrome, and the consent obligations that GDPR and ePrivacy place on them are entirely unchanged by that decision. Anyone who built a strategy around a hard Chrome deadline was planning for an event that was cancelled.
The other browsers did restrict. Safari and Firefox blocked third-party cookies by default years ago and have not reversed. The mechanism did not die; it became unevenly available, which is arguably worse for anyone trying to measure consistently across a user base.
Mobile platform changes bit harder than the browser story. App Tracking Transparency on iOS made cross-app identity opt-in, and opt-in rates came in far below what the ecosystem had assumed. This was the change that actually removed signal at scale.
Regulation tightened. GDPR, CCPA/CPRA, and successors made the lawful basis for broker-sourced data difficult to establish and expensive to defend.
Accuracy decayed. Third-party audience segments were always partly inferred. As identity signals degraded unevenly across platforms, the inference degraded with them.
So the honest summary is not that third-party data disappeared. It is that it became less accurate, less legally defensible, and less differentiating — while remaining technically available in the largest browser. That combination is worse than a clean deprecation, because it lets organisations keep depending on something that is quietly getting weaker.
Why does AI change the calculation?
This is the part of the shift that is still underweighted in most analytics strategies.
Organisations building AI features on behavioural data need data they lawfully own, can retain for training and evaluation, can reprocess when a model changes, and can defend in an audit. Third-party data fails all four tests: the legal basis is external and contestable, retention is contractually limited, reprocessing rights are usually absent, and provenance cannot be demonstrated.
First-party behavioural data is the only dataset most organisations hold that satisfies all four. That makes analytics infrastructure an AI dependency rather than a reporting function — which is a different budget conversation than the one analytics teams are used to having. It is also why the provenance question has teeth: under the EU AI Act's documentation expectations, being unable to establish where training data came from and under what basis is a position you have to defend.
How do you build a first-party data foundation?
Five steps, in order.
1. Inventory your collection points. Every product surface, and for each one whether data flows to your infrastructure directly or through a third-party tag.
2. Move collection server-side where possible. Server-side collection is more reliable, harder to block, and removes the third-party intermediary. It also requires more engineering than a tag, which is why it is often deferred.
3. Establish a governed event taxonomy. Documented event names, properties, types, owners, and purposes. Without this, first-party data becomes a large pile rather than an asset.
4. Unify identity across surfaces. Web, mobile, and backend events describing the same person need to resolve to one identity, under your control.
5. Decide where it lives. First-party data held in a third party's shared infrastructure retains the accuracy advantage and loses the control advantage. This is where deployment model and data strategy converge.
Step 2 is the one with the highest ratio of value to perceived effort. Client-side tags lose a share of events to blockers, network conditions, and app terminations; server-side collection recovers much of it. Measure your own loss rate before and after rather than trusting a vendor's figure — it varies enormously by audience and platform mix.
Frequently asked questions
Is first-party data GDPR compliant by default? No. First-party collection removes the transfer and third-party processor problems, which are two of the harder parts. You still need a lawful basis, minimisation, retention limits, and working subject rights.
Did Chrome ever remove third-party cookies? No. Google reversed the phase-out in July 2024, abandoned the replacement user-choice prompt in April 2025, and retired the remaining Privacy Sandbox APIs in October 2025. Third-party cookies remain available in Chrome, though Safari and Firefox continue to block them by default and consent requirements still apply.
Can you do analytics without third-party cookies? Yes. First-party analytics uses first-party storage, server-side identifiers, or anonymous session handling. Third-party cookies were required for cross-site tracking, not for understanding behaviour inside your own product.
Is zero-party data the same as first-party data? No. Zero-party data is information users deliberately provide — preferences, survey answers, stated intent. First-party data includes zero-party data plus observed behaviour. Zero-party is a subset with the strongest consent position.
Does first-party data mean less reach? Less reach, more accuracy. Third-party data offers scale across audiences you have no relationship with. First-party data covers only your own users, and describes them correctly.
Who owns first-party data? The organisation that collects it, subject to the rights of the individuals it describes. Whether that ownership is meaningful depends on where the data is stored and what the vendor's terms permit.
Where to go next
- Governance: Data Governance for Product Analytics
- Architecture: Breaking Down Data Silos with a Unified Data Layer
- AI duties: The EU AI Act and Product Analytics
Countly is a first-party product analytics and customer engagement platform that runs self-hosted, on-premises, or in a private cloud.
Posts that our readers love
to grow your product
is here.