Metadata The metadata contains The location and description of warehouse system and data components Names, definition, structure, and content of the warehouse and end-user views. Identification of authoritative data sources. Integration and transformation rules used to populate the data warehouse; these include the mapping method from operational databases into the warehouse, and algorithms used to convert, enhance, or transform data Integration and transforms rules used to deliver data to end-user analytical tools. Subscription information, which includes a history of warehouse updates, refreshments, snapshots, versions, ownership authorizations, and extract audit trail Security authorizations, access control lists, etc. Metadata is used for building, maintaining, managing, and using he data warehouse.

Metadata Interchange Initiative A Metadata standard developed for metadata interchange format and its support mechanism. The goal of the standard include Creating a vendor-independent, industry-defined and application programming interface (API) for metadata. Enabling users to control and mange the access and manipulation of metadata in their unique environments through the use of interchange-standard compliant tools Allowing users to build tool configurations that meet their needs and to incrementally adjust those configurations as necessary to add or subtract tools. Enabling individual tools to satisfy their specific metadata access requirements freely and easily within the context of an interchange model Creating a process and procedures not only for establishing and maintaining the interchange standards specification but also for extending and updating it over time as required by evolving industry and user needs.

The components of the Metadata Interchange Standard Framework are Implementation of the interchange standard metadata model must assume that the metadata itself may be stored in any type of storage facility or format; relational tables, ASCII files, fixed-format or customized-format repositories, and so on. The components of the Metadata Interchange Standard Framework are The Standard Metadata Model, which refers to the ASCII file format used to represent the metadata that is being exchanged. The Standard Access Framework, which describes the minimum number of API functions a vendor must support. Tool Profile, which is provided by each tool vendor. The Tool Profile is a file that describes what aspects of the interchange standard metamodel a particular tool supports. The User Configuration, which is a file describing the legal interchange paths for metadata in the user’s environment. This file allows customers to constrain the flow of metadata from tool to tool in their specific environments. This framework defines the means by which various tool vendors will enable metadata interchange.

Standard Access Framework Standard Metadata Model User Configuration Standard Access Framework Standard API Standard Metadata Model TOOL 1 Tool Profile TOOL 2 TOOL 3 TOOL 4

Metadata Repository The metadata itself is housed in and managed by the metadata repository. Metadata repository management software can be used to map the source data to the target database, generate code for data transformations, integrate and transform the data, and control moving data to the warehouse. Metadata defines the contents and location of data in the warehouse, relationships between the operational databases and the data warehouse, and the business views of the warehouse data that are accessible by the end-user tools. A data warehouse design should ensure that there is a mechanism that populates and maintains the metadata repository, and that all access paths to the data warehouse have metadata as an entry point.

Metadata Management Metadata define all data elements and their attributes, data sources and timing, and the rules that manage data use and data transformations. The metadata also has to be available to all warehouse users in order to guide them as they use the warehouse. A well-thought-through strategy for collecting, maintaining, and distributing metadata is needed for a successful data warehouse implementation.

Metadata Trends The process of integrating external and internal data into the warehouse faces a number of challenges Inconsistent data formats Missing or invalid data Different level of aggregation Semantic inconsistency (e.g., different codes may mean different things from different suppliers of data) Unknown or questionable data quality and timeliness