Data · Data Quality & Governance
Every decision, report and AI model inherits the quality of the data beneath it. We measure that quality objectively with DMOne™, fix what’s broken, and put the ownership, standards and monitoring in place so it stays fixed — governing the data assets your enterprise runs on.
Home› Data › Data Quality & Governance
Why Data Quality & Governance
Data quality problems are rarely dramatic; they are cumulative. Duplicate suppliers, stale employee records, inconsistent codes — each individually small, collectively the reason reports disagree, migrations overrun and AI models learn the wrong lessons.
One-off cleansing treats the symptom. We pair remediation with governance of the data itself: named owners, agreed definitions, stewardship that meets, and DMOne™ monitoring that measures quality continuously — so improvement holds instead of eroding back to baseline. (Governing AI systems is a separate discipline — see our AI Governance service.)
DMOne™ profiling quantifies completeness, validity, consistency and duplication across your enterprise data — a baseline nobody can argue with.
Cleansing and enrichment executed with documented, repeatable rules — so the fix is auditable and re-runnable, not a one-off heroic effort.
Named owners and stewards for the data domains that matter, with the forums and authority to act — not titles on a slide.
Quality KPIs tracked on live data, with thresholds and alerts — so decay is caught in days, not discovered at the next crisis.
We present the same work in the language each stakeholder needs — commercial for the boardroom, plan-ready for the programme, technical for the architecture.
For the CXO
A quantified quality baseline, visible improvement, and named ownership — so “whose data is this?” finally has an answer.For the Programme Manager
Quality issues found and fixed before migration or analytics delivery — the single most common cause of programme overrun, removed early.For the Solution Architect
Documented, versioned quality rules and monitoring that integrate with your pipelines and platforms — governance you can implement, not just read.Talk to our Oracle EBS experts today
Six services that measure, fix and protect the quality of your enterprise data.
Every engagement is scoped around a defined set of deliverables agreed at the outset.
AI and analytics are only as good as the enterprise data beneath them. That data is our home ground.
A quality baseline typically lands in three to four weeks; governance and monitoring build from there.
DMOne™ profiling across in-scope domains produces the quantified baseline and a prioritised issue log.
Cleansing and enrichment executed in priority order with documented rules and full audit trail.
Owners, stewards, definitions and standards agreed and stood up — with the authority to keep quality decisions moving.
Quality KPIs, thresholds and alerts running continuously — with periodic reviews that turn trends into action.
Data Quality & Governance connects naturally with these services across our Data & AI portfolio.
