[A-D] [E-H] [I-M] [N-Q] [R-U] [V-Z]

[A-D]

Data Model: A logical map that represents the inherent properties of the data independent of software, hardware or machine performance considerations. The model shows data elements grouped into records, as well as the association around those records.

Data Modeling: A method used to define and analyze data requirements needed to support the business functions of an enterprise. These data requirements are recorded as a conceptual data model with associated data definitions. Data modeling defines the relationships between data elements and structures.

Data Owner: The individual responsible for the policy and practice decisions of data. For business data, the individual may be called a business owner of the data.

Data Partitioning: The process of logically and/or physically partitioning data into segments that are more easily maintained or accessed. Current RDBMS systems provide this kind of distribution functionality. Partitioning of data aids in performance and utility processing.

Data Pivot: A process of rotating the view of data.

Data Warehouse: An implementation of an informational database used to store sharable data sourced from an operational database-of-record. It is typically a subject database that allows users to tap into a company's vast store of operational data to track and respond to business trends and facilitate forecasting and planning efforts.

Data Warehouse Engine: Relational databases (RDBMS) and Multi-dimensional databases (MDBMS). Data warehouse engines require strong query capabilities, fast load mechanisms, and large storage requirements

Decision Support Systems (DSS): Software that supports exception reporting, stop light reporting, standard repository, data analysis and rule-based analysis. A database created for end-user ad-hoc query processing.

[E-H]

E-Commerce: A way to execute transactions and share information with other businesses, consumers or government using computer and telecommunications networks (including the Internet). Some people refer to this as E-business. E-commerce encompasses traditional electronic methods of communication, such as EFT, Email and EDI, and introduces some new ones such as intranets and extranets. Extranets are extensions of intranets that are secure, yet open to selected third parties such as partners, customers and suppliers.

Enterprise: A complete business consisting of functions, divisions, or other components used to accomplish specific objectives and defined goals.

Enterprise Data:Data that is defined for use across a corporate environment.

Enterprise Modeling: The development of a common consistent view and understanding of data elements and their relationships across the enterprise.

ERP: Stands for Enterprise Resource Planning. An information system that integrates all manufacturing and related applications for an entire enterprise.

[I-M]

Metadata: Metadata is data about data. Examples of metadata include data element descriptions, data type descriptions, attribute/property descriptions, range/domain descriptions, and process/method descriptions. The repository environment encompasses all corporate metadata resources: database catalogs, data dictionaries, and navigation services. Metadata includes things like the name, length, valid values, and description of a data element. Metadata is stored in a data dictionary and repository. It insulates the data warehouse from changes in the schema of operational systems.

Metadata Synchronization: The process of consolidating, relating and synchronizing data elements with the same or similar meaning from different systems. Metadata synchronization joins these differing elements together in the data warehouse to allow for easier access.

Methodology: A system of principles, practices, and procedures applied to a specific branch of knowledge.

Mini Marts:A small subset of a data warehouse used by a small number of users. A mini mart is a very focused slice of a larger data warehouse

Multi-dimensional Database (MDBS and MDBMS): A powerful database that lets users analyze large amounts of data. An MDBS captures and presents data as arrays that can be arranged in multiple dimensions.

[N-Q]

OLAP: On-Line Analytical Processing.

OLTP: On-Line Transaction Processing. OLTP describes the requirements for a system that is used in an operational environment.

Operational Database: The database-of-record, consisting of system-specific reference data and event data belonging to a transaction-update system. It may also contain system control data such as indicators, flags, and counters. The operational database is the source of data for the data warehouse. It contains detailed data used to run the day-to-day operations of the business. The data continually changes as updates are made, and reflect the current value of the last transaction.

Operational Data Store (ODS):An ODS is an integrated database of operational data. Its sources include legacy systems and it contains current or near term data. An ODS may contain 30 to 60 days of information, while a data warehouse typically contains years of data

Supply Chain: The material and informational interchanges in the logistical process stretching from acquisition of raw materials to delivery of finished products to the end user. All vendors, service providers and customers are links in the supply chain.

[V-Z]

Zachman Framework: A structured approach to providing an architectural context for building any product or process.  It accomplishes this through a classification scheme which ensures that most aspects of a product's life cycle receive the appropriate attention.  Each of these aspects occupy cells within a matrix pictorial of the Framework.  In this matrix, the rows represent different perspectives of the product and the columns represent its dimensions.