There are consequences to bad quality master data, which impact the organization across functions – sales, procurement, supply chain, finance, human resources, production, maintenance, reliability…i.e., every function in the organization. When we speak of bad quality master data, we are referring materials and services data that is – inaccurate, incomplete, blank fields, obsolete, duplicate, not classified, not following an industry standard.
With reference to master data programs, companies today are of four mindsets:
Having good quality material master data will involve the need to analyse, and then address challenges around – data, systems, people, and processes. The process of addressing master data quality issues can be overwhelming in many cases, and not having internal expertise to address it means considerable time, resources and bandwidth by leadership. Master Data Management is not a core business or technical function in organizations; therefore, organizations do not have the – internal expertise, or tools to address it. The best solution is to hire a dedicated program manager and that person to lead the whole initiative via an external partner.
While the focus is on master data, which primarily entails master data management (MDM), it is imperative that organizations prepare to maintain the data after the MDM program, which is Master Data Governance (MDG).