by Angela Guess
David Loshin has written a new article regarding semantic metadata. He writes, “When building a business analytics program, there is no doubt that one requires the standard types of metadata for the physical design and implementation of a data warehouse and corresponding business intelligence delivery methods and tools. For example, it would be impossible to engineer the data integration and transformations needed to migrate data out of the source systems and into an operational data store or a data warehouse without knowledge of the structures of the sources and the target models. Similarly, without understanding the reference metadata (particularly the data types and units of measure!), the delivered reports might be difficult to understand, if not altogether undecipherable.”
Loshin goes on, “But even presuming the soundness of the management of the technical, structural, and operational metadata, the absence of conceptual data available for shared information will often lead to reinterpretation of the data sets’ meanings. The availability of the business metadata, particularly semantic metadata, is somewhat of a panacea, and that means there must be some well-defined processes in place for soliciting, capturing, and managing that semantic information. Some key processes will focus on a particular set of areas of concentration, as we explore here.”
