Data and Identity

Advertisers, publishers, retailers and other organisations are placing greater value on the data generated through their own customer relationships. Connecting CRM, transactional, behavioural, advertising and other first-party data can create a richer foundation for audience creation, activation and measurement.

Campaign, audience, customer, inventory and measurement data often exist across separate platforms and databases. Modern data architectures can bring these sources together, standardise them and make them available across advertising workflows.

Advertising increasingly operates across environments where different identifiers, authentication methods and privacy constraints apply. Identity strategies can combine first-party identifiers, identity graphs, clean rooms and other approaches to help organisations connect data and audiences across systems while respecting applicable permissions and controls.

Privacy can no longer be treated as something applied after data has been collected. Consent signals, permissions, retention rules and data-access policies can be incorporated directly into data architectures so that information is used according to the rules associated with it.

As more systems and teams use advertising data, organisations need clearer rules around ownership, quality, lineage, access and permitted use. AI makes this especially important because models and agents may be able to access and combine data at a scale that would be difficult for humans to manage manually.

The effectiveness of AI systems depends heavily on the data they can access. Connected, well-structured and governed advertising data can provide the context needed for AI to analyse performance, answer questions, generate recommendations and support increasingly agentic advertising workflows.