Duplicate customers or suppliers
The same entity appears under different names, spellings or identifiers. We scope matching rules and AI-assisted similarity checks, then review uncertain matches before merging.
MS6 / AI DATA CLEANSING
Clean the data.
Trust the next decision.
AI-assisted data cleansing for organizations dealing with duplicate records, inconsistent formats and unreliable business information. MS6 combines data discovery, matching and validation with accountable human review.
UNDERSTAND THE SERVICE
What is AI data cleansing?
Data cleansing identifies and corrects errors, inconsistencies and duplicates. AI can help recognize similar records, classify information and propose corrections where fixed rules alone are insufficient. Business rules and data owners determine which changes are accepted.
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.
Inconsistent formats and values
Dates, addresses, currencies or category labels differ across sources. We define approved formats, standardize values and preserve exceptions for review.
Missing or conflicting information
Required fields are absent or sources disagree. We flag gaps, compare approved evidence and route conflicts to owners; inferred values are identified rather than treated as verified facts.
Data trapped in documents
Relevant information sits in forms, PDFs or scanned records. Where in scope, extraction and classification help turn it into structured fields, with validation against the source.
HOW MS6 WORKS
A clear path from evidence to action.
Discover and profile
Agree the use case, source systems, access and sensitive fields. Establish a baseline for completeness, consistency and duplication.
Define correction rules
Confirm business definitions, authoritative sources, matching thresholds and exceptions with data owners.
Review AI suggestions
Use AI where it adds value to classification or matching. Compare proposals with deterministic checks and send uncertain cases for human review.
Validate and release
Reconcile record counts and key business totals, review changes and exceptions, then agree the controlled release into the target workflow.
WHAT YOUR TEAM RECEIVES
Findings you can act on.
Deliverables and acceptance criteria are agreed before work begins.
- Data quality baseline and issue register
- Approved cleansing and matching rules
- Reviewed correction proposals and exception queue
- Before-and-after validation results and change history
- A scoped plan for integration and ongoing monitoring
EXAMPLE ENGAGEMENT
A practical starting point.
A company has customer records in a sales application, billing system and spreadsheets. MS6 can assess duplicate patterns, propose matches and help business owners validate the master record before a migration. The acceptance criteria are agreed before correction begins.
Illustrative scope, rather than a reported client result.
COMMON QUESTIONS
Before you begin.
Does AI automatically change every record?
No. The engagement defines which corrections may be automated, which require review and which must remain unresolved. Ambiguous matches and business-critical changes need agreed approval controls.
Can MS6 work with our existing applications?
The service is scoped around your existing systems and approved access. Supported formats, connectors, data volumes and deployment requirements are confirmed during discovery.
Can you guarantee perfect data?
No. Quality is evaluated against agreed rules and the evidence available. Unknowns, source limitations and unresolved exceptions remain visible.
Where should we start?
Start with one high-value dataset and a clear business outcome, such as reducing duplicate suppliers or preparing customer records for migration.
MS6 / حلول البيانات
تنظيف البيانات بالذكاء الاصطناعي
تساعد MS6 المؤسسات على اكتشاف السجلات المكررة، وتوحيد صيغ البيانات، ومعالجة التعارضات والنواقص قبل التحليل أو نقل البيانات أو استخدام الذكاء الاصطناعي. نستخدم الذكاء الاصطناعي لاقتراح المطابقات والتصحيحات، مع قواعد واضحة ومراجعة من أصحاب البيانات للحالات غير المؤكدة. تبدأ الخدمة بتحديد مصادر البيانات والمشكلة، ثم تقييم الجودة، واعتماد قواعد التصحيح، والتحقق من النتائج. لا تُعامل القيم التي يستنتجها الذكاء الاصطناعي على أنها حقائق مؤكدة.
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