- Spend data sits within multiple systems that need to be aggregated in order to get visibility into overall spend
- Different codes are often used to describe the same supplier or commodity across these systems. Aggregated spend information from multiple systems may not be accurate.
- Item codes used by systems do not relate an item to an industry standard classification. Consequently, it becomes difficult to aggregate similar and equivalent data and identify opportunities to save money by combining spend across commodities, locations, suppliers and programs
- Systems rarely identify relationships between suppliers. Your system may not tell you that Lab Safety Inc. is a subsidiary of WW Grainger. You may be spending a lot more money with WW Grainger than you thought
- Minority status of suppliers, shipment performance and quality data from last 12 months or even D&B credit rating usually does not exist within these systems. Such information is critical to assessing risk
Due to these issues, it is impossible to do a comprehensive spend analysis simply by bringing data from all the systems into a spreadsheet or a business intelligence system. The data has to be cleansed to remove errors, normalized to ensure that suppliers are represented in a consistent manner, and finally enriched with commodity classification data, subsidiary relationships and supplier performance data. Only then can the data analysis be performed to get a picture of the overall spend.