Abstract: Embodiments relate to enrichment of a data warehouse utilizing a business compendium. Embodiments may employ a process comprising data standardization and cleansing, de-duplication of entries, and matching and enrichment, followed by manual review of an enriched record by a user. During standardization, data may be transformed into consistent content, placing correct data elements into appropriate fields, removing invalid characters, and/or standardizing names and addresses. Duplicate records are then detected and marked. During matching and enrichment, the existence of an entity (such as a supplier), may be verified by progressive matching against the business compendium. Enrichment may provide additional information regarding the entity (e.g. related to risk, diversity, and bankruptcy). The enriched record is available for manual review, allowing the user to change duplicates, matches, and parent/child linkages.

Abstract: Embodiments relate to classification of transactions based upon analysis of multiple variables. For a purchase transaction, such variables can include but are not limited to: buying location, source system, line of business, cost center, functional area, supplier capabilities, item description, account description, organization, department, custom parameters, and others. Embodiments may rely upon one or more classification schemes, such as statistical classification, semantic classification, and/or knowledge base classification, taken alone or in combination. In a purchase transaction, classification based on multivariate analysis facilitates identification of a purchased item or service, and hence accuracy in classifying and assigning a central classification code. Particular embodiments may include a feature allowing user review/revision of category assignments via a feedback loop linked to past classification.

Abstract: Embodiments relate to enrichment of a data warehouse utilizing a business compendium. Embodiments may employ a process comprising data standardization and cleansing, de-duplication of entries, and matching and enrichment, followed by manual review of an enriched record by a user. During standardization, data may be transformed into consistent content, placing correct data elements into appropriate fields, removing invalid characters, and/or standardizing names and addresses. Duplicate records are then detected and marked. During matching and enrichment, the existence of an entity (such as a supplier), may be verified by progressive matching against the business compendium. Enrichment may provide additional information regarding the entity (e.g. related to risk, diversity, and bankruptcy). The enriched record is available for manual review, allowing the user to change duplicates, matches, and parent/child linkages.

Abstract: Embodiments relate to classification of transactions based upon analysis of multiple variables. For a purchase transaction, such variables can include but are not limited to: buying location, source system, line of business, cost center, functional area, supplier capabilities, item description, account description, organization, department, custom parameters, and others. Embodiments may rely upon one or more classification schemes, such as statistical classification, semantic classification, and/or knowledge base classification, taken alone or in combination. In a purchase transaction, classification based on multivariate analysis facilitates identification of a purchased item or service, and hence accuracy in classifying and assigning a central classification code. Particular embodiments may include a feature allowing user review/revision of category assignments via a feedback loop linked to past classification.