data, and data with multiple sources and destinations, need to be identified. These discrepancies most often include source data with no target data field, target data fields with no identified or existing source, one source field with multiple destinations and multiple sources with only one destination field. To resolve unmapped data, the IIC must define procedures to normalize data with multiple sources and develop mapping rules as to which of the multiple sources will provide what data elements. Identification of Conversion Metrics and Critical Success Factors. Metrics and Critical Success Factors (CSFs) provide key measurements for a successful conversion. When comparing data from before and after a conversion, typically there are four data sets on which an IIC will focus. These CSFs and metrics provide input to help develop the acceptance test and make "go/no go" decisions for production. Financial information. The IIC can compare the reported value of receivables from before and after conversion. Account information. The IIC can compare the active number of customers reported before and after conversion. Operational data. The IIC can compare order volume and status information from before and after the conversion. Statistical measurements. The IIC can, for example, take a random sample of customer accounts and analyze the converted data against the legacy data. Conversion Business Process Development. The IIC must also develop a conversion process to address needs for manual intervention, error handling routines, back-up and restart procedures, operational run sheets, data cleansing, data transformation and business analysis reports (BARs). These processes define the conversion execution - from how to run and document the data conversion to what to do when data irregularities are found. They are the essential "worst case scenario" plans and procedures that must be prepared and communicated to the people involved in the migration. At the close of Phase 1, when the mapping, planning and analysis have been acc