Data cleansing the process of standardizing the data in a structured format. It includes the following step by step process to reduce the pain points data disaster. First step is to analyse characteristics and structure by Experts. Second step is product name identification and extracting the associated specifications with the help of specified schema in a structured format by selecting and populating the appropriate attributes, Third is data normalization for improving consistency and quality. Final step is duplicate resolution by identifying and removing the duplicates by keeping only unique records.
Data enrichment is the process of managing the data with the help of Internet Research-EDV with the legacy information's supplied by the clients, By sourcing the manufacturer/supplier catalogues and information's from the World Wide Web. Enrichment process carried out in the customer specified schema by extracting the attribute values for the material characteristics as followed, If and when required, the data is cleansed and normalized based on the requirement. Images could be sourced, resized, cleaned up and thumbnails could be created based on the requirement.
Data classification is the categorization of data or categorizing of items in a logical and hierarchical order for its most effective and efficient use. This is the must exercise for every company to strengthen the roots of data as it plays the vital role of data protection throughout its lifetime. PIPL provides group of classification that suits to the business needs in global Schemas like UNSPSC, ECCMA, SIC, NATO, NAICS or Custom Schema.
Proper Data Classification results Improves spend visibility and purchasing process, Reduction in duplicates, Vendor evaluation and assessment, spend reduction and strategic procurement.