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Data Quality Audit
Profile missing data and nulls, completeness, accuracy, uniqueness, timeliness, and consistency across critical tables and fields — with evidence, not opinions.
Missing dataCompletenessAccuracy
StringRay · Data Quality Audit
Audit Your Data Before You Embark on AI.
StringRay is Oxydata's data quality, governance, and cleansing audit — so AI, RAG, and analytics programmes start on foundations you can trust.
Profile critical sources, score defects by business impact, and leave with a remediation roadmap — not another vague readiness slide deck.
Quality · Governance · CleansingAI & RAG fitnessBefore you build
Why StringRay
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.
Audit types
The audits we run under StringRay
Six audits before AI — from missing data and quality through lineage, ending with a clear list of what to cleanse and trust.
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AI / RAG Data Fitness
Check whether sources are fit for copilots, RAG, and predictive models — coverage, freshness, grounding docs, and known blind spots.
AI readinessRAGGrounding
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Semantic & Definition Alignment
Align business and IT on what key entities and metrics mean — ownership, definitions, and stewardship gaps that break AI and reporting trust.
DefinitionsOwnershipStewardship
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PII & PDPA Exposure Review
Identify personal data in unexpected places, access risks, and handling gaps before you expand AI or analytics workloads.
PIIPDPAAccess
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What to Cleanse
Turn findings into a cleansing backlog — what to fix first (duplicates, broken keys, orphans, free-text mess), effort estimates, owners, and sequencing before AI kickoff.
CleansingPrioritisationBacklog
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Data Lineage & Integration Traceability
Map where critical data comes from and how it moves — source systems, interfaces, ETL/feeds, and break points — so AI and analytics teams know provenance, freshness, and which integrations can be trusted.
LineageInterfacesProvenance
How it works
From discovery to a remediation plan
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Scope & discover
Agree domains, systems, and success criteria — what “good enough for AI” means for this engagement.
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Profile & sample
Inspect schemas, volumes, samples, and pipelines. Surface defects with reproducible evidence.
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Score & rank
Severity-ranked findings by business impact — not an endless dump of technical noise.
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Remediate roadmap
A sequenced plan: quick wins, structural fixes, owners, and what to defer until after AI kickoff.
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: (1) Data Quality — including missing data, completeness, accuracy, and consistency; (2) AI / RAG Data Fitness; (3) Semantic & definition alignment; (4) PII / PDPA exposure; (5) What to Cleanse — a prioritised cleansing backlog; and (6) Data Lineage & Integration Traceability — source-to-pipeline provenance and feed reliability.
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?
Tell us which systems and AI or analytics goals you have in mind — we'll propose a focused StringRay scope.