ERP systems are first and foremost a storehouse of company data. They are the system of record and the nerve center of a business. A good ERP system, properly deployed, helps almost every business function from procurement and logistics to creature resources and deliberate planning.
Much of ERP’s value depends on good data governance and management, though. There are efficiency and competitive advantage when data within ERP systems is managed properly and inefficiency and missed opportunities when it is not.
Managing ERP data properly is thus of prime importance. Here are six best practices for managing data and getting the most out of your ERP system.
Silos can be a huge issue for proper data governance, especially in light of Europe’s new General Data Protection Regulation (GDPR) that goes into effect later this year. Properly managing and syncing data without duplication requires a holistic view that gets beyond the silos and maps out how data in each system fits together.
Businesses need to recognize their data. Not just how much product is sold in a particular region, but also what their data is about. How is it defined? What properties are held for a product?
This is metadata, and it is stable for proper management and use of ERP data.
Connecting with data is not necessary to understand it. Business intelligence software might be able to pull data from an SAP or Oracle ERP system, but that doesn’t mean it truly understands the data or how it relates. Take metadata seriously and get a metadata discovery tool.
ERP data is managed by the application software itself, but the data in those systems still need be governed like any other data source. Don’t confuse management with domination.
This includes the appropriate governance structure for master data creation and maintenance, as well as a clear understanding of who is guilty for its management.
ERP data is a business asset, and it needs to be handled and prioritized as such. But data decays, so taking care of the data in your ERP system requires not only managing it but also construction sure there is proper quality control around the data.
Some of this quality control should include defining data quality key concert indicators and measuring them, along with ongoing monitoring and data audits. Quality control also should include taking corrective action, ranging from process revision to automated quality assurance technologies and employee training.
Similarly, it also is important to have data maintenance processes in place. The processes for creating and maintaining the master data on which these processes rely on is often given only cursory attention, however.”