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        1. FEATURES
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          Data Cleansing

          Data standardisation & AI/ML led data cleansing
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          Data
          Enrichment

          Enrichment from unstructured data sources, proprietary and 3rd party sources
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          Hierarchy
          Management

          Automated hierarchy mapping & ML based master data categorization at scale
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          Data
          Quality

          ML based automated anomaly detection for hierarchies, categorical and numeric data types
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          Data
          Oversight

          ML based ongoing data management to ensure continuous data quality management
          IMPLEMENTATION
          AI Data Cleanser has been implemented for various master data types enabling transformation across the business value chain
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          Customer MDM with Cognitive RPA

          Enabling a unified and cleansed customer master across the enterprise by integrating multiple CRM systems
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          Product Hierarchy Management at Scale

          Enabling development of product master data by standardizing taxonomy and cleansing data from different suppliers/retailers
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          Contact MDM with External Data

          Enabling better marketing effectiveness through cleansing and 3rd party data enrichment of contact data systems
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          Product Attribute Enrichment with Computer Vision

          ML tools & algorithms to extract attributes from images of multiple formats and enrich existing product attribute master data
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          Vendor Data Quality Management

          Enabling unification of vendor database through cleansing, hierarchy & data quality management
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          Material Master Data Management

          Enabling unification of material level master data through cleansing, hierarchy & data quality management
          DEMO VIDEO

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