Reports do not agree
Different teams use different sources or definitions. We trace discrepancies, compare business rules and agree the definition and owner of each critical measure.
MS6 / DATA QUALITY SERVICES
Know where data fails.
Keep quality visible.
Data quality assessment and monitoring for organizations whose reports, processes or AI depend on reliable information. MS6 helps you define what good data means, identify the causes of defects and establish an improvement process.
UNDERSTAND THE SERVICE
What is a data quality assessment?
A data quality assessment checks whether information is fit for a particular business use. It tests dimensions such as completeness, validity, consistency, uniqueness and timeliness. Accuracy requires comparison with an authoritative source or other credible evidence; a correctly formatted field is not necessarily correct.
Based in Abu Dhabi, MS6 provides advisory and implementation services for organizations in the UAE and internationally. Scope, supported sources and deployment requirements are agreed for each engagement.
PROBLEMS WE HELP ADDRESS
Start with the issue
affecting your business.
Required fields are unreliable
Key identifiers, dates or statuses are missing or invalid. We define checks at the points where data is entered, transferred and consumed.
Recurring errors return after cleansing
Fixing a dataset does not fix the upstream process. We identify recurring causes, assign owners and connect remediation to operating workflows.
Quality is hard to measure
An overall score hides important defects. We define measures by dataset and business use, with thresholds, exceptions and escalation paths.
HOW MS6 WORKS
A clear path from evidence to action.
Define the business use
Identify the decisions or operations that rely on the data and the consequences of specific defects.
Profile and reconcile
Examine relevant fields, relationships and source definitions. Confirm findings with data owners and document evidence limitations.
Prioritize remediation
Rank issues by business impact and effort. Assign accountable owners and agree checks, correction rules and acceptance criteria.
Monitor and improve
Design monitoring, alerts and issue workflows. Use AI-assisted anomaly detection where suitable, with evaluation and human investigation.
WHAT YOUR TEAM RECEIVES
Findings you can act on.
Deliverables and acceptance criteria are agreed before work begins.
- Assessment of critical datasets and quality dimensions
- Documented rules, definitions and thresholds
- Issue register with severity, owners and root-cause findings
- Prioritized remediation and monitoring plan
- Quality measures linked to business acceptance criteria
EXAMPLE ENGAGEMENT
A practical starting point.
Finance and operations report different totals for the same period. MS6 can compare source definitions, date logic and record relationships, identify the discrepancy and help the teams agree a reconciled metric and recurring checks.
Illustrative scope, rather than a reported client result.
COMMON QUESTIONS
Before you begin.
How is data quality different from data cleansing?
Quality assessment identifies and measures problems; cleansing corrects agreed issues. Monitoring helps detect recurring defects. Many engagements connect all three.
What does AI contribute?
AI may support classification, anomaly detection, record matching or proposed rules. It is evaluated against the use case and complements business rules and source evidence.
Which data quality measures matter?
Measures depend on the use: completeness of required fields, valid values, consistent definitions, unique records, freshness and verified accuracy. We agree thresholds with the business.
Can we begin without a large transformation project?
Yes. A scoped assessment of one important dataset can establish the baseline and identify a practical first improvement.
MS6 / حلول البيانات
تقييم جودة البيانات ومراقبتها
عندما تختلف أرقام التقارير أو تتكرر أخطاء البيانات، تساعدك MS6 على معرفة السبب وتحديد ما يجب إصلاحه أولًا. نقيس اكتمال البيانات، وصحة الصيغ، والاتساق، والتكرار، وحداثة المعلومات وفقًا لاستخدامها في الأعمال. التحقق من دقة المعلومات يحتاج إلى مصدر موثوق للمقارنة. نحدد قواعد الجودة وأصحاب المسؤولية، ثم نضع خطة لمعالجة المشكلات ومراقبة عودتها. يمكن للذكاء الاصطناعي المساعدة في اكتشاف الأنماط غير المعتادة، بينما يراجع المختصون النتائج ويعتمدون الإجراءات.
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