Data Readiness for AI
Find out whether your data can support AI before you commit build money. A targeted 2–3 week assessment of your agency's data foundation — evaluating quality, schema hygiene, SSOT lakehouse maturity, vector readiness for RAG workloads, and cloud platform TCO.
Why a Data Readiness Assessment?
Over 80% of AI projects stall — and the most common root cause is not the model, the framework, or the cloud budget. It is dirty data. Fragmented source systems, inconsistent schemas, unmaintained pipelines, and data that was never designed for AI retrieval workloads all create invisible ceilings on what your AI can actually do.
A data readiness assessment conducted before your AI build sprint eliminates the guesswork. You receive a clear, quantified view of where your data stands, a target architecture blueprint, and a sequenced roadmap — so your engineering team builds on a foundation that will hold.
Any AI build sprint
Recommended before
2–3 weeks fixed scope
Duration
4 structured deliverables
Output
What We Assess
Data Quality & Schema Hygiene
Systematic profiling of null rates, duplicate records, referential integrity failures, and stale data across source systems. Identifies the data quality debt that will block AI model accuracy.
SSOT Lakehouse Architecture Maturity
Assessment of your current data architecture against a target single-source-of-truth medallion lakehouse design — covering bronze/silver/gold layer completeness, schema governance, and serving layer readiness.
Vector & RAG Context Readiness
Evaluation of whether your structured and unstructured data is ready for retrieval-augmented generation — covering embedding pipeline design, chunk strategy, metadata tagging, and vector index architecture.
Cloud Platform Fit & TCO Analysis
Independent assessment of your cloud data platform selection — Azure Fabric, Google BigQuery, Snowflake, Databricks — against your workload profile, team capability, and total cost of ownership over three years.
Data Governance & Lineage
Review of metadata catalog coverage, data ownership assignment, access policy maturity, and lineage traceability — the governance foundations that agency AI compliance requires.
Pipeline Reliability & Observability
Assessment of your data pipeline CI/CD maturity, SLA adherence, monitoring coverage, and incident response — identifying silent failures and untested transformation logic that create model drift.
What You Receive
Data Health & Metadata Hygiene Audit
Quantified view of data quality across your priority source systems — null rates, duplicates, staleness, and schema drift — with a remediation priority stack.
SSOT Target Lakehouse Architecture Blueprint
Target-state architecture design for your data platform including medallion layer structure, platform selection rationale, and schema governance model.
Vector & RAG Context Readiness Scorecard
Assessment of your data's readiness for LLM retrieval workloads, with an embedding pipeline design and recommended indexing strategy.
Cloud Platform TCO & Migration Roadmap
Effort-estimated migration roadmap to your target cloud platform, with a three-year TCO model and phased delivery sequencing.
Sprint Timeline
Discovery & Data Profiling
- Stakeholder alignment: data platform owners, analytics leads, engineering team
- Source system inventory and access provisioning
- Automated data quality profiling across priority datasets
- Architecture documentation and current-state diagram review
Architecture & Readiness Assessment
- Medallion architecture gap analysis against current lakehouse design
- Vector and RAG readiness evaluation — structure, metadata, embedding feasibility
- Cloud platform TCO modelling and fit assessment
- Data governance and lineage maturity review
Blueprint & Roadmap
- Target SSOT architecture blueprint development
- Data quality remediation priority stack
- Migration roadmap with phased effort estimates
- Final findings deck and report preparation
Presentation & Handover
- Executive findings presentation to data platform and engineering leadership
- Full deliverables handover: audit report, blueprint, scorecard, roadmap
- Optional: 30-day follow-up check-in included
Who This Is For
- CDOs and Data Platform Leads planning an AI workload
- CTOs evaluating cloud data platform options
- Data Engineering teams preparing for LLM integration
- Agencies with fragmented or siloed data estates
- Agencies planning a lakehouse migration
Common Triggers
- AI PoC stalled due to inconsistent or poor-quality data
- Multiple conflicting reports from different source systems
- Planning a move to Fabric, BigQuery, or Snowflake
- Preparing for an agentic AI or RAG deployment
- Data governance audit requirement
- Accountable authority-level AI readiness review underway
Related Advisory
DATAModern Data Foundations
Full advisory pillar →
Ready to assess your data foundation?
Fixed-scope. Senior-led. Delivered in 2–3 weeks with a blueprint and a prioritised remediation roadmap.
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