The landscape of corporate data has grown into one of massive data sources, with enterprise resource planning (ERP) systems now standing out as the dominant feature on that landscape. The seismic shifts that have shaped this territory over the past decades – just-in-time manufacturing, supply chain optimization and spend analysis – have caused businesses to increasingly recognize that their corporate data is one of their most important assets. A comprehensive picture of the data that drives key business functions — such as manufacturing, supply chain management, financial, human resources and customer data — is essential to making key business decisions. ERP systems are intended to power better decisions by maintaining this data in a single database.

However, it's possible to have a comprehensive view of enterprise data and still make the wrong decisions, if the data in those systems is of poor quality. Bad data is persistent scar on the ERP landscape. ERP systems will, by their nature, have multiple points of entry for data — with every point of entry for data being an open door for bad data to get into the system. The ordinary operations of business, such as mergers and acquisitions, system upgrades and the day-today typing of human beings, can cumulatively damage the overall quality of data to the point where the systems become unusable. Bad data can take many forms - duplicate or outdated information, information that is simply incorrect and has no connection to reality, or information which may be correct in form and content but incorrect in relation to your business needs.

Whatever its source or form, once bad data is in the system, it becomes a problem that must be addressed. For this reason, data quality must be made an essential component of any ERP implementation. Today, data quality technology is available that can automate the formatting, standardization, de-duplication and cleansing of corporate data, resulting in consistent and accurate information. Building a data quality capability into an ERP system ensures that the data is usable and supports informed decision making.

This white paper examines the general principles of data quality as they apply to ERP systems, with a particular emphasis on SAP implementations. The focus here will be on the unique types of data quality management in ERP — batch processing for data loading and migration, and real-time data monitoring for ongoing quality maintenance — can complement and enhance each other if approached with the right technology. We'll also take a detailed look at how one company managed to successfully incorporate data quality into its ERP migration and implementation by working with Infosys and data quality vendor DataFlux. ERP systems are now the dominant feature on the corporate information landscape - but how reliable is the data they contain?