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10.3. Metadata Management > 10.3. Metadata Management - Pg. 172

172 Chapter 10 METADATA AND DATA STANDARDS applications. Sizes and types are just the tip of the iceberg. Inte- gration of records from different data sets can only be done when it is clear that the data elements have the same meaning, that their valid data domains are consistent, that the records represent similar or the same real-world entities. There are more complex dependencies as well: Do the client applications use the same entity types? Do the different applications use different logical names for similar objects? How is access for reading and writing data objects controlled, along with many other important vari- able aspects? This means that the scope of work for collecting, collating, and assembling a true enterprise metadata repository may exceed the appetite of potential business sponsors. A conceptual view of metadata starts with basic building blocks and grows to maintain comprehensive views of the information that is used to support the achievement of business objectives. The metadata stack described in this chapter is driven by business objectives from the top down and from the bottom up and is intended to capture as much information as necessary to drive: · The analysis of enterprise data for the purpose of structural and semantic discovery, · The correspondence of semantics to data element types, · The determination of commonly used data element types, · Mapping data element concepts to business applications, · Usage and impact scenarios for data element concepts, and · The data quality directives. We can look at different types of metadata that are of value to the data quality practitioner, starting from the bottom up: · Business definitions, which look at the business terms used across the organizations and the associated meanings · Reference metadata, which details data domains (both con- ceptual domains and corresponding value domains) as well as reference data and mappings between codes and values · Data element metadata, focusing on data element definitions, structures, nomenclature, and determination of existence along a critical path of a processing stream · Information architecture, coagulating the representations of data elements into cohesive entity structures, how those structures reflect real-world objects, and how those objects interact within business processes · Business metadata, which captures the business policies driving application design and implementation, the corres- ponding information policies that drive the implementation decisions inherent in the lower levels of the stack, and the management and execution schemes for the business rules that embody both business and information policies