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
- Nobody is sure which source is authoritative
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.
From inconsistent records to trusted data
- Source Records
- Profile
- Match & Standardise
- Validate & Govern
- Trusted Use
The goal is not to create a perfect universal master record for every business object. It is to make the critical data reliable enough for the reporting, integration, automation, and AI use cases that depend on it.
Data profiling
Measure the problem before designing the fix
- Null / missing values
- Duplicate keys
- Invalid formats
- Unexpected categories
- Out-of-range values
- Broken relationships
- Source-to-source differences
- Unmapped records
- Changes over time
Profiling creates evidence for prioritisation so the project focuses on the quality issues that materially affect business decisions or system processes.
Customer master data
- Duplicate customer records
- CRM vs ERP customer mapping
- Parent / child account relationships
- Naming standardisation
- Region / segment mapping
- Inactive / merged records
- Customer hierarchy
The appropriate matching process depends on available identifiers, business rules, privacy requirements, and the consequence of merging the wrong records.
Product and SKU master data
- SKU and barcode mapping
- Supplier product codes
- Category / brand hierarchy
- Pack and unit mappings
- Legacy product codes
- Channel-specific identifiers
- Active / discontinued status
Product master-data quality is especially important for retail, ecommerce, inventory, procurement, and margin analytics.
Supplier, location and entity data
- Supplier IDs and aliases
- Warehouse / store mappings
- Region / territory hierarchy
- Legal entity vs management entity
- Department / cost-centre mappings
- Site / branch identifiers
Shared location and entity mappings help finance, operations, sales, and management reporting use the same organisational structure.
Reference data
Small code lists can create large reporting problems
Reference data often includes:
- Statuses
- Categories
- Channels
- Regions
- Reason codes
- Service types
- Priority levels
- Business units
Controlled reference data reduces the number of one-off mapping tables and manual fixes embedded in reports.
Matching and deduplication
Treat 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
- Required-field checks
- Format validation
- Allowed-value checks
- Relationship integrity
- Control totals
- Date-sequence checks
- Duplicate detection
- Schema / column change detection
Validation rules should be explicit, documented, and owned so the organisation knows what ‘good data’ means.
Data quality monitoring
Move from one-off cleansing to ongoing visibility
- Exception count
- Unmapped records
- Duplicate candidates
- Missing required data
- Source-feed failures
- Quality trend by system
- Open vs resolved issues
- Recurring root causes
A data-quality dashboard should help teams understand where problems originate and which issues remain unresolved.
Remediation workflow
A practical quality process can include:
- Issue detected
- Owner assigned
- Source verified
- Correction approved
- Source or mapping updated
- Downstream data refreshed
- Issue closed
Where possible, corrections should be made at the authoritative source rather than repeatedly patched in downstream reporting.
Source-of-truth and ownership
Define which system owns which attribute
- Authoritative system
- Data owner
- Business steward
- Update process
- Change approval
- Exception ownership
- Downstream consumers
A single system does not need to own every attribute. Clear attribute-level ownership can be more practical than claiming one universal source of truth.
Data quality for Power BI
Improved master data can reduce:
- Manual mapping files
- Many-to-many model problems
- Duplicated dimensions
- Inconsistent slicers
- Report-specific corrections
- KPI reconciliation issues
Data quality for AI and automation
AI cannot reliably act on records it cannot identify
Better data quality supports:
- Permission-aware AI retrieval
- Customer / product matching
- Workflow routing
- Agentic tool calls
- Exception summarisation
- Reliable system updates
Master data without overengineering
Not every business needs a dedicated enterprise MDM platform. Datazeb can help determine whether the problem can be solved with:
- Reference tables
- Curated warehouse dimensions
- Controlled mapping workflows
- Data-quality rules
- Existing platform capabilities
- A dedicated MDM tool where genuinely justified
Existing data-quality review
Datazeb can review:
- Current mapping spreadsheets
- Duplicate-handling methods
- Reference tables
- Data warehouse dimensions
- Power BI fixes
- Source-system validations
- Integration errors
- Known reconciliation issues
The result may be a focused mapping clean-up, automated validation layer, domain-specific master-data process, or a broader data-quality roadmap.
Why Datazeb for data quality
- Strong data engineering and BI capability
- Practical SQL, Python, API, warehouse, and Power BI experience
- Focus on the business use case rather than abstract governance
- Ability to connect data quality to reporting, integrations, AI, and automation
- Domain mapping across customers, products, entities, suppliers, and locations
- Flexible project and managed-support models
- Senior-led global delivery
Engagement options
- Data-quality assessment
- Customer master-data project
- Product / SKU mapping project
- Reference-data standardisation
- Duplicate / matching analysis
- Validation and monitoring framework
- Master-data operating model
- Managed data-quality support
How we work
- Prioritise
Identify the business reports, workflows, integrations, or AI use cases most affected by poor data. - Profile
Measure quality issues across the selected data domains and source systems. - Define
Agree identifiers, mappings, quality rules, authoritative sources, ownership, and remediation paths. - Build
Implement mappings, validation, monitoring, reference data, and integration logic. - Operate & Improve
Track exceptions, resolve recurring issues, document ownership, and improve the rules as systems change.
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.
Do you provide ongoing support?
Yes. Managed support can cover new mappings, recurring exceptions, source changes, validation failures, and continuous improvement.