Data Quality & Master Data
Make the data behind your reporting consistent enough to trust
Datazeb helps organisations identify, reconcile, and monitor the data-quality problems that create conflicting reports, broken integrations, unreliable automation, and weak AI outcomes.
Common symptoms of poor master data
- The same customer appears under multiple names or IDs
- Product codes do not match across POS, ERP, ecommerce, or supplier files
- Locations, departments, or entities are named differently in different systems
- Reports require manual mapping spreadsheets every month
- Finance, sales, and operations use different hierarchies
- Integrations fail because reference values do not match
- Duplicate records inflate counts or split activity
- AI and automation cannot reliably identify the right record
What Datazeb can deliver
-
Data Profiling
Measure completeness, uniqueness, validity, consistency, and common quality issues across selected data domains.
-
Master Data Mapping
Reconcile customer, product, supplier, account, entity, location, or other identifiers across systems.
-
Reference Data Design
Create controlled mappings for categories, statuses, departments, channels, regions, and other shared values.
-
Duplicate & Match Logic
Identify likely duplicate records and define practical matching rules for review or remediation.
-
Validation Rules
Create automated checks for missing fields, invalid values, broken relationships, unexpected changes, and mapping gaps.
-
Data Quality Monitoring
Track quality exceptions, trends, unresolved issues, and source-feed problems in recurring reporting.
-
Remediation Workflows
Route quality issues to the right owner, capture corrections, and maintain an auditable process where needed.
-
Governance & Ownership
Clarify authoritative sources, data owners, update responsibilities, and change-control expectations.
Data quality cycle
Find problems at the source and keep them fixed
Quality cycle
- Profiling System
- Rules Rules
- Validation System
- Exceptions Trigger
- Ownership Person
- Correction Result
- Monitoring System
Person: a human reviews or approves before the workflow continues.
In detail
Explore the detail
Data profilingMeasure the problem before designing the fix
Profiling creates evidence for prioritisation so the project focuses on the quality issues that materially affect business decisions or system processes.
- Null / missing values
- Duplicate keys
- Invalid formats
- Unexpected categories
- Out-of-range values
- Broken relationships
- Source-to-source differences
- Unmapped records
- Changes over time
Customer master data
The appropriate matching process depends on available identifiers, business rules, privacy requirements, and the consequence of merging the wrong records.
- Duplicate customer records
- CRM vs ERP customer mapping
- Parent / child account relationships
- Naming standardisation
- Region / segment mapping
- Inactive / merged records
- Customer hierarchy
Product and SKU master data
Product master-data quality is especially important for retail, ecommerce, inventory, procurement, and margin analytics.
- SKU and barcode mapping
- Supplier product codes
- Category / brand hierarchy
- Pack and unit mappings
- Legacy product codes
- Channel-specific identifiers
- Active / discontinued status
Supplier, location and entity data
Shared location and entity mappings help finance, operations, sales, and management reporting use the same organisational structure.
- Supplier IDs and aliases
- Warehouse / store mappings
- Region / territory hierarchy
- Legal entity vs management entity
- Department / cost-centre mappings
- Site / branch identifiers
Reference dataSmall code lists can create large reporting problems
Reference data often includes: Controlled reference data reduces the number of one-off mapping tables and manual fixes embedded in reports.
- Statuses
- Categories
- Channels
- Regions
- Reason codes
- Service types
- Priority levels
- Business units
Matching and deduplicationTreat fuzzy matching as a controlled decision, not an automatic merge
Potential matching logic may use:
- Exact identifiers
- Normalised names
- Email / phone where appropriate
- Address components
- Product attributes
- Reference combinations
- Fuzzy similarity as a review signal
Validation rules
Validation rules should be explicit, documented, and owned so the organisation knows what 'good data' means.
- Required-field checks
- Format validation
- Allowed-value checks
- Relationship integrity
- Control totals
- Date-sequence checks
- Duplicate detection
- Schema / column change detection
Data quality monitoringMove from one-off cleansing to ongoing visibility
A data-quality dashboard should help teams understand where problems originate and which issues remain unresolved.
- Exception count
- Unmapped records
- Duplicate candidates
- Missing required data
- Source-feed failures
- Quality trend by system
- Open vs resolved issues
- Recurring root causes
FAQ
Frequently asked questions
Can you help clean duplicate customer records?
Yes. Datazeb can profile duplicate candidates, develop matching logic, and design a controlled review or remediation process. Final merge rules should reflect the business risk of incorrect matches.
Can you reconcile product codes across systems?
Yes. SKU, barcode, supplier-code, category, and legacy-code mapping is a common data-quality use case.
Do we need an MDM platform?
Not necessarily. Many organisations can improve critical master data through clearer ownership, reference tables, curated models, validation, and controlled workflows before considering dedicated MDM software.
Can you build ongoing data-quality monitoring?
Yes. Validation checks, exception reporting, alerts, and quality dashboards can be built into data pipelines and analytics.
Can this improve our AI readiness?
Yes. Consistent identifiers, trusted sources, permissions, and controlled reference data improve the reliability of many AI and agentic workflows.
Learn it yourself · Datazeb Training
Learn SQL & Data Analytics with Datazeb Training
Give analysts the SQL to query, validate and prepare data themselves, taught live on realistic datasets.
training.datazeb.com
Stop fixing the same data problem in every report
If customer, product, supplier, location, or entity data requires repeated manual correction before it can be used, tell us where the inconsistencies appear today. Datazeb can help create clearer mappings, validation, ownership, and monitoring so downstream reporting, integration, automation, and AI are built on more reliable data.