Data Architecture and Strategy

A data strategy says what the business needs from its data. A data architecture says how sources, platform, ownership and controls are arranged to deliver it. Programmes stall when only one of the two exists: a strategy nobody can build from, or an architecture that was never tied to a decision someone needs to make.

DataPhoenix works on both together, on AWS, so the plan you approve is the plan your engineers can build. Choosing between AWS native, Databricks and Snowflake is covered in platform consulting.

Data strategy tied to decisions

The work starts from the decisions, reports, products and AI use cases the business wants to support, and works back to the data they need. The strategy names the critical data, who owns each domain and what good looks like, in language a board can approve.

Target architecture on AWS

A target-state design for sources, ingestion, storage, processing, serving and consumers, using AWS services such as S3, Glue, Athena, Redshift and Lake Formation. The lake, warehouse or lakehouse pattern is chosen for your workloads and your team, not for fashion.

Data models, domains and contracts

Conceptual and logical data models, clear domain boundaries and data contracts between the teams that produce data and the teams that use it. A source can change without breaking everything downstream.

Governance, security and privacy by design

Access, classification, retention and lineage are designed in from the start, together with where personal data may live under UK GDPR, EU GDPR and Swiss data protection rules. The design is the control, not a review added afterwards.

Ready for AI

The same foundations serve analytics and AI: curated and documented data, a defined home for embeddings and vector stores, and controls over what a model can see. Each use case on the roadmap is checked against the data it needs, and the AI readiness assessment goes deeper where a use case calls for it.

Roadmap and business case

A sequenced roadmap with the first delivery chosen for visible value, and a business case that shows what each stage returns against what it costs.

What you receive

A data strategy, a target architecture with decision records that capture the reasoning and trade-offs, a data model and domain map, a governance and security design, and a prioritised roadmap. Everything is documented and owned by you.

How DataPhoenix works

Engagements fit your existing delivery framework, whether Scrum, Kanban or something else. DataPhoenix delivers remotely to clients across the UK, the EU and Switzerland, and stays alongside your engineers through the build.

Want to learn more? Two posts cover the thinking behind this work: AWS native, Databricks or Snowflake and data lake architectures without the vertigo. Data governance is the natural companion service.