AI/ML driven Data Quality Management
FEATURES
Data Cleansing
Data standardisation & AI/ML led data cleansing
Data
Enrichment
Enrichment from unstructured data sources, proprietary and 3rd party sources
Hierarchy
Management
Automated hierarchy mapping & ML based master data categorization at scale
Data
Quality
ML based automated anomaly detection for hierarchies, categorical and numeric data types
Data
Oversight
ML based ongoing data management to ensure continuous data quality management
Data Quality
Data Standardization, Cleansing, De-dupe and golden record creation wired to an interactive dashboard with configurable data quality metrics
Data Validation
Global address validation & correction using postal directories and 3rd party APIs. Contact/lead email and phone number verification
Data Enrichment
Customer/contact enrichment through 3rd party partnerships. Product/material enrichment through web scraping, image processing, unstructured data analysis
Hierarchy Management
Customer/vendor hierarchy validation, creation and management through 3rd party partnerships. ML algorithms for product/material hierarchy assignment at scale (including GPC/GS1 migration)
Feedback Loop
Active Learning based feedback module allows business users to pass feedback and augment the algorithm’s results if required
Data Governance
Ensure on-going data is kept clean by managing Stewardship, policies etc. Individual modules for metadata management, data quality dashboards/metrics and more
IMPLEMENTATION
Sancus has been implemented for various master data types enabling transformation across the business value chain
Customer MDM with Cognitive RPA
Enabling a unified and cleansed customer master across the enterprise by integrating multiple CRM systems
Product Hierarchy Management at Scale
Enabling development of product master data by standardizing taxonomy and cleansing data from different suppliers/retailers
Contact MDM with External Data
Enabling better marketing effectiveness through cleansing and 3rd party data enrichment of contact data systems
Product Attribute Enrichment with Computer Vision
ML tools & algorithms to extract attributes from images of multiple formats and enrich existing product attribute master data
Vendor Data Quality Management
Enabling unification of vendor database through cleansing, hierarchy & data quality management
Material Master Data Management
Enabling unification of material level master data through cleansing, hierarchy & data quality management
RELATED CASE STUDIES
DEMO
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