Strategy & Design
Data and AI strategy that is ready to be built
A current-state assessment, a target architecture, a prioritized roadmap and a focused pilot, so your journey starts with clear priorities, named owners and governance designed in.
What it is
Strategy and design is where most engagements begin. We review your current data estate, business objectives and regulatory context, in financial services or any other sector, and turn them into a roadmap your teams can act on. The result is specific enough to build from: which use cases come first, what the target architecture looks like, who owns which data, and how success will be measured.
The work covers the choices a data and AI program involves, including current-state assessment, target architecture, operating model, business case, build versus buy, vendor selection, pilot design and value tracking. A roadmap that cannot be turned into a first pilot is a document rather than a strategy, so we keep it practical.
Problems it addresses
- Many data and AI ideas compete for attention, and there is no agreed way to rank them by value, risk and effort.
- Platforms were chosen before the business questions were clear, so teams work around them.
- Pilots stall because ownership, data access or approval paths were never settled.
- Leaders in technology, data, operations and compliance hold different views of what to do first.
- There is uncertainty about build versus buy, or about which services on AWS, Microsoft Azure or Google Cloud fit the existing estate.
What we deliver
- A current-state assessment of data sources, platforms, skills, spend and risks.
- A prioritized roadmap of use cases with value, risk, effort and dependencies noted.
- A target architecture covering ingestion, storage, a semantic layer, analytics, machine learning (ML) and generative AI (GenAI), with a short list of platform options.
- A governance and operating model with ownership, stewardship roles and human oversight points.
- A business case and value-tracking approach with measures agreed in advance.
- Vendor evaluation criteria and a documented build versus buy comparison.
- A scoped pilot plan with success criteria, data needs and an approval path.
- An executive summary and a delivery plan for the teams who will build.
Our method
How we work
We follow our four-step method, and each step ends with a review where you can pause, adjust or stop. See the full method.
Discovery & Strategy
Workshops and a review of systems, data sources, policies and constraints produce a current-state assessment and a ranked list of use cases.
Pilot & Validation
A small pilot with success measures set in advance tests the riskiest assumptions on real or representative data.
Build & Scale
The roadmap becomes a staged implementation plan with a target architecture, funding view and delivery team structure.
Optimize & Evolve
Value tracking and periodic roadmap reviews keep the plan aligned with results, budgets and changing rules.
Technologies
Technologies we commonly work with
These are examples of tools we commonly work with when we assess options and design target architectures. We recommend what fits your estate, and often that is what you already own.
Cloud
The three major clouds we design and build on, shaped around the estate you already run.
- AWSAmazon Web Services: broad infrastructure, data and AI services.
- Microsoft AzureMicrosoft's cloud, closely tied to Microsoft 365, Fabric and Entra ID.
- Google CloudGoogle's cloud, known for BigQuery, Dataplex and Vertex AI.
- Cloud object storageAmazon S3, Azure Data Lake Storage and Google Cloud Storage as lake foundations.
Data platforms
Warehouses, lakes and lakehouses where governed data is stored, modeled and shared.
- DatabricksLakehouse platform for data engineering, SQL analytics and machine learning.
- SnowflakeCloud data platform for warehousing, sharing and analytics.
- Microsoft FabricUnified analytics platform with OneLake, pipelines and Power BI.
- Google BigQueryServerless data warehouse with built-in machine learning.
- Amazon RedshiftCloud data warehouse in the AWS ecosystem.
- Azure Synapse AnalyticsAnalytics service combining warehousing and big data on Azure.
- Delta LakeOpen table format that adds reliability to data lake storage.
- Apache IcebergOpen table format for large analytic tables across engines.
AI and GenAI
Machine learning, generative AI and agents, built with evaluation, guardrails and human review.
- Azure OpenAI ServiceLarge language models delivered through Azure with enterprise controls.
- Amazon BedrockManaged access to foundation models, agents and guardrails on AWS.
- Google Vertex AIGoogle Cloud platform for machine learning and generative AI.
- Amazon SageMakerManaged service to build, train and deploy machine learning models.
- Azure Machine LearningAzure service for training, deploying and monitoring models.
- Hugging FaceOpen models, datasets and libraries for machine learning.
- LangChainFramework for building retrieval and agent workflows.
- MLflowExperiment tracking and model registry for MLOps.
- Vector databasesEmbedding stores such as pgvector, Azure AI Search and OpenSearch for retrieval.
Governance and security
Catalogs, lineage, access control and key management that make data and AI auditable.
- Microsoft PurviewData catalog, classification and governance across Microsoft estates.
- Unity CatalogUnified governance for data and AI assets on Databricks.
- CollibraData governance and catalog platform for policies and stewardship.
- AWS Lake FormationFine-grained access control for data lakes on AWS.
- Google DataplexData management and governance across Google Cloud data.
- Azure Key VaultManaged storage for secrets, keys and certificates on Azure.
- AWS KMSManaged encryption keys for services and applications on AWS.
- Microsoft Entra IDIdentity and access management for people and workloads.
- OpenLineageOpen standard for collecting data lineage across tools.
- Great ExpectationsOpen source framework for data quality checks.
These tools are examples of what we commonly work with, listed as text only. We are vendor-neutral and recommend what fits your estate. Product names belong to their owners, and their appearance here does not imply a partnership, endorsement or certification.
Governance and compliance
Strategy has to account for oversight from the start. We map each candidate use case to the policies and obligations that touch it, such as privacy law (for example Personal Information Protection and Electronic Documents Act (PIPEDA) in Canada, General Data Protection Regulation (GDPR) in Europe and state laws in the United States), health privacy rules in healthcare and life sciences, and model risk and recordkeeping expectations in financial services and insurance. The roadmap records what governance, human oversight and evidence each use case needs, and treats data residency and third-party risk as design inputs. Frameworks such as ISO/IEC 42001 and the National Institute of Standards and Technology (NIST) AI Risk Management Framework serve as references. Your own compliance function determines what applies.
Ways to start
Engagement options
Most clients begin with an assessment or a pilot. Scope, owners and success measures are agreed in writing for each option, and the options can be combined.
Assessment
A short, fixed-scope review of your current state with a ranked list of opportunities and a recommended first step.
Pilot
Design and support for one focused use case, with success measures agreed before work starts.
Build
Detailed architecture and delivery planning for the teams who will implement, with ARCHR available alongside them.
Advisory retainer
Ongoing senior guidance on roadmap reviews, vendor questions and governance decisions, sized to your needs.
This page is general information about ARCHR services. It is not legal, financial or investment advice, and it does not assure compliance with any regulation.
FAQ
Frequently asked questions
What does a strategy engagement produce?
Typically a current-state assessment, a prioritized roadmap, a target architecture, a governance model, a business case and a scoped pilot, agreed at the outset.
Do we have to commit to a full program?
No. You see the result of a focused pilot before deciding whether to scale, and each step ends with a review.
Which cloud platform will you recommend?
We work on AWS, Microsoft Azure and Google Cloud and shape the design around your existing stack, skills, contracts and budget rather than starting from a preferred vendor.
How do you approach build versus buy decisions?
We compare options against your requirements, data sensitivity, skills and total cost of ownership, and we state the trade-offs openly.
Let's talk about your data and AI priorities
Tell us what you are trying to solve. We aim to reply within one business day.