Where Databricks may fit
Databricks may host and process customer, interaction and analytical data; create reusable data products and features; support models and AI applications; and govern data and AI assets. Its role must be connected deliberately to operational systems, identity and consent services, CDPs, decisioning, CRM, analytics and activation platforms so that customer context is timely, interpretable and governed at the point of use.
Decisions the architecture must make explicit
- 01
Which customer and interaction data products should be built or governed in Databricks?
- 02
How do identity, consent, lineage, quality and semantic context remain trustworthy across downstream uses?
- 03
Which analytical, machine-learning and AI workloads belong in Databricks versus other platforms?
- 04
How will governed context and model outputs reach operational decisioning and engagement systems at the required latency?
- 05
What ownership, cost, release and implementation-readiness controls are needed before scale?
Where independent advice can help
- Frame a bounded Databricks platform, data-foundation or AI-readiness decision.
- Define the role of Databricks in customer data, context, analytics, modelling and activation architecture.
- Assess platform overlap, data-product boundaries, governance, operating ownership and implementation readiness.
- Review architecture and delivery decisions while data engineering and platform implementation remain with qualified internal or delivery teams.
Boundaries and non-fit
- Databricks workspace implementation, pipeline development or managed platform operations
- Detailed data migration, model development or application build
- Formal cloud, security, privacy or data-governance assurance
- A predetermined recommendation that must favour Databricks