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Data Quality Audit
Profile missing data, completeness, accuracy, uniqueness, timeliness, and consistency — for the population this AI will serve, not only database-wide averages. Evidence, not opinions.
Missing dataRepresentativenessAccuracy
StringRay · Data Quality Audit
Audit Your Data Before You Embark on AI.
Methodology first, product second. Oxydata's AI data readiness method measures whether your data is fit for a named AI use case — StringRay is the evidence audit that runs it.
Profile critical sources, score Ready / Conditional / Not ready, and leave with a remediation roadmap — not another vague readiness slide deck.
Method → process → StringRaySix audits + scope cardBefore you build AI
The method
AI data readiness — then StringRay delivers the evidence
We teach a clear method, run a fixed process, and productise the paid engagement as StringRay. Start with the free business check if you want a plain-language snapshot before an audit.
Teach the method
Seven readiness lenses — use-case scope plus six audits — so business and IT share one language for “good enough for AI.”
Run the process
Scope card and system map first, then profile, score Ready / Conditional / Not ready, and sequence remediation.
Deliver with StringRay
Paid evidence pack: samples, severity, owners, and a roadmap — not a self-reported checklist alone.
Why it matters
AI amplifies whatever your data already is
If definitions conflict, keys break, and lineage is tribal knowledge, copilots and models will scale the mess. StringRay makes the mess visible — then actionable.
Go / no-go clarity
Know whether to proceed with AI or warehouse work — or pause and fix foundations first.
Shared definitions
Align business and IT on what “customer”, “active”, and key metrics actually mean.
Lower AI risk
Reduce hallucinations, bad forecasts, and compliance surprises caused by dirty or opaque data.
The process
From use-case card to remediation plan
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Scope & system card
Named use case, model/data inventory, system map (data → AI → decision → human), and what “good enough” means.
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Profile & sample
Inspect schemas, volumes, samples, and pipelines. Surface defects with reproducible evidence across the six audits.
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Score & rank
Ready / Conditional / Not ready by use case, plus severity-ranked findings by business impact.
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Remediate roadmap
A sequenced plan: quick wins, structural fixes, owners, and what to defer until after AI kickoff.
The product — StringRay
Six audits under StringRay
After the scope card, StringRay runs these six audits — deepened for AI lifecycle, PDPA on AI paths, and data-to-decision lineage.
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AI / RAG Data Fitness
Fitness across train / fine-tune / RAG retrieval / inference logs — coverage, freshness, approved grounding, blind spots, and a written must-not-answer list. Higher bar for public-facing agents.
RAG lifecycleGroundingOut of scope
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Semantic & Definition Alignment
Align business and IT on entities, metrics, labels, and ownership — including proxy or sensitive signals that can break trust in AI answers and reports.
DefinitionsLabelsOwnership
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PII & PDPA Exposure Review
Map personal data across CRM tables and AI paths — embeddings, prompts, chat logs, vendor APIs — and what may be used for training vs RAG vs logging.
PIIPDPAAI paths
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What to Cleanse
Prioritised backlog of AI blockers — duplicates, broken keys, orphans, free-text mess, undocumented imputation — with effort, owners, and sequencing before kickoff.
CleansingPrioritisationBacklog
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Data Lineage & Integration Traceability
Trace source → interface → ETL → model/RAG → decision → human handoff. Inventory datasets and models so provenance, freshness, and escalation points are clear.
LineageModel mapHandoff
What comes next
After StringRay, continue with the right practice
Enterprise Data & Datamarts
Build warehouses, SQL Server datamarts, and ETL on cleaned foundations.
Learn moreAI Consulting
Strategy and roadmaps once you know the data is ready enough to invest.
Learn moreAI Solutions & Development
Copilots, RAG, and agents grounded in sources StringRay has validated.
Learn moreFAQ
StringRay FAQ
What is StringRay?
StringRay is Oxydata's data quality audit offering — structured assessments of data quality, governance, and cleansing readiness so enterprises fix foundations before investing in AI, warehouses, or analytics programmes.
Why audit data before an AI project?
Most AI and RAG failures are data failures — incomplete sources, inconsistent definitions, undocumented lineage, and dirty records. StringRay surfaces those risks early with a scored findings pack and a remediation roadmap, so you do not train models on unreliable inputs.
What types of audit does StringRay cover?
StringRay typically runs six audits before AI, after agreeing a clear business AI goal: (1) Data Quality; (2) whether your approved business knowledge can ground answers; (3) shared definitions; (4) personal data / PDPA on AI journeys; (5) what to fix first; and (6) where answers come from and when humans take over. Try the free business check at /tools/ai-data-readiness before an evidence audit.
What do we receive at the end of a StringRay engagement?
A findings report with severity-ranked issues, sample evidence, recommended fixes, ownership suggestions, and a sequenced remediation roadmap. Where useful we also provide scorecards by domain or system and a go / no-go view for AI readiness.
How long does a StringRay audit take?
A focused domain or system audit typically runs 2–4 weeks. Broader multi-system or group-wide governance reviews take longer and are scoped after a short discovery call.
How does StringRay relate to Enterprise Data and AI Consulting?
StringRay is the readiness gate. Enterprise Data & Datamarts builds warehouses and ETL on trusted data. AI Consulting and AI Solutions build strategy and applications once the foundation is sound. Many clients start with StringRay, then move into those practices.
StringRay
Ready to audit before you build?
Start with the free business check, or tell us which systems and AI goals you have — we'll propose a focused StringRay evidence audit.