by Angela Guess
Stephen Baker recently wrote in Data Informed, “There has always been an uneasy truce within large organizations between those who control access to data – the IT group, usually – and those who need that data to improve business performance. In a perfect world, the IT group would like to see a single source of truth manifested in master data management (MDM) and the enterprise data warehouse (EDW). Let’s consider MDM. A paper by Wilbram Hazejager of DataFlux Corporation (acquired by SAS in 2000) notes that MDM’s origins go back to the early 2000s. Its proponents did – and still do – see MDM as the way to solve the problem of disparate, disjointed data spread across different lines of business. Nevertheless, according to Gartner, the majority of MDM initiatives fail. There can be many reasons for this. But one reason is simple: To succeed, MDM demands strict adherence to data-governance policies by everyone in the enterprise, all the time. That’s not very realistic.”
Baker goes on, “But the effort to implement MDM, even if only partially realized, reinforces IT’s role as the gatekeeper of enterprise data. Rapidly growing supplies of data make it all the more difficult to streamline the data supply chain that delivers raw material for analysis to business users. And it puts IT in the unenviable position of trying to deliver more data sets, faster, while the greater enterprise population yearns for data democracy. Along with MDM, the enterprise data warehouse also represents a legacy approach to handling critical business data. Large and expensive to maintain, the typical EDW fulfills a narrow, often application-specific purpose. Moreover, data architects must use extract, transform, and load (ETL) tools to add data to an EDW, which consumes substantial time and money. Simply adding a row of data to an EDW could take months.”
Photo credit: Flickr/ Materials Aart