Items representing facts, text, graphics, bit-mapped images, sound, analog or digital live-video segments. Data is the raw material of a system supplied by data producers and is used by information consumers to create information.

The process of accessing the data warehouse database objects using various tools such as analysis, reporting, query, statistical, and data mining.

An end-user oriented tool that allows users to build SQL queries by pointing and clicking on a list of tables and fields in the data warehouse.

The component of data integrity that deals with how well data stored in the data resource represent the real world. It includes a definition of the current data accuracy and the adjustment in data accuracy to meet the business needs.
Data warehouses of an organization are filled with data which would reflect all the activities within the group. Data may come from various sources and gathered using routing business processes. It is imperative that the processes in the data warehouse should be precise and accurate because the usefulness of data goes far beyond the software applications that generate it.

The process of extracting, transforming, and transporting data from the source systems and external data sources to the data warehouse database objects.

The process of managing the institutional data in order to provide reliable, accurate, secure and accessible data to meet strategic and management needs at all levels of the enterprise. It is the purpose of this process to improve the accuracy, reliability, and security of the institution's data; reduce data redundancy; provide ease of access, assuring that data are easily located, accessible once located, and clearly defined; and to provide data standards. It is also the purpose of the Data administration function to educate the user community on institutional data policies and to encourage the responsible use of data.

The process of redefining data into a summarization based on some rules or criteria. Aggregation may also encompass de-normalization for data access and retrieval.

Software that provides a logical view of data in a warehouse. Some create simple aliases for table and column names; others create data that identify the contents and location of data in the warehouse.

Data Architecture basically deals with designing and constructing data resource. Data Architecture provides methods to design, construct and implement a fully integrated, business-driven data resource that include real world objects and events, onto appropriate operating environments. Data Architecture also covers data resource components.

Represents a data characteristic variation that is used in a logical data model. In the realm of computer science, a logical data model is the accurate representation of a company's data. These data need to be logically represented because later on they will be the basis for data modeling. Data modeling in turn will be the basis for database implementation as the computer needs to understand business entities and activities from a digital perspective.

Represents the use of a data characteristic group in a logical data model. In data modeling, a logical data model is the representation of business data into a data model that can be the basis for the physical database implementation. It identifies a data "periodic table" which will be the basis for the business organization's functions, processes and task to be performed. Data modelers design a logical data model in order to be able to establish a data processing environment where the basic data is captured only once, stored, and then shared to data consumers who are authorized by the company for generating statistical reports or the public who may want information.

Cardinality is a property of data elements which indicates the number of allowable entries in that element. In the implementation of a structure query language (SQL), the term data cardinality is used to mean the uniqueness of the data values which are contained in a particular column, known as attribute, of a database table.

An individual characteristic that describes a data subject. It is developed, directly through measurement or indirectly through derivation, from a feature of an object or event. Each data subject is described by a set of data characteristics.

The transformation of data in its current state to a pre-defined, standardized format using packaged software or program modules.

A temporary group of data subjects for a specific purpose. It can be any useful combination of data subjects for any specific purpose that cannot be met by any of the other categorical levels.

The frequency at which data are collected from the world.

An indication of whether or not all the data necessary to meet the current and future business information demand are available in the data resource. It deals with determining the data needed to meet the business information demand and ensuring those data are captured and maintained in the data resource so they are available when needed.

Mathematical techniques used to reduce the amount of storage required for certain data.

The situation where the replicated data values at are synchronized with the corresponding data values at the official data source. When the data values at the official data source are updated, the replicated data values must also be updated so they are consistent with the official data source.

An individual, group, or application that receives data in the form of a collection. The data is used for query, analysis, and reporting.

The process of changing data from one physical environment to another. This process makes any changes necessary to move data from one electronic medium or database product to another.

The individual assigned the responsibility of operating systems, data centers, data warehouses, operational databases, and business operations in conformance with the policies and practices prescribed by the data owner.

The specification of a data element to be maintained. The specification includes datatype, size, and rules about processing: for example, derivation and validation

The process of developing the internal schema from the conceptual schema.

The process of creating a data value from one or more contribution data values through a data derivation algorithm.

A part of a database that holds definitions of data elements, such as tables, columns, and views.

A database about data and database structures. A catalog of all data elements, containing their names, structures, and information about their usage. A central location for metadata. Normally, data dictionaries are designed to store a limited set of available metadata, concentrating on the information relating to the data elements, databases, files and programs of implemented systems.

A representation of a single set of objects or events in the real world.

The process of getting data from the data resource to a client, within or without the organization, through appropriate application and telecommunication networks. Data are disseminated through client/server applications, electronic mail, and traditional business applications.

The placement and maintenance of replicated data at one or more data sites on a mainframe computer or across a telecommunications network. This part of developing and maintaining an integrated data resource that ensures data are properly managed when distributed across many different data sites. Data distribution is one type of data deployment, which is the transfer of data to data sites.

A term used to identify data that are captured, processed, or stored redundantly. It results in unknown, uncontrolled, and unmanaged data redundancy. It is not orderly and creates additional disparate data.

The most elementary unit of data that can be identified and described in a dictionary or repository which cannot be subdivided.

Represents a data subject from the common data model that is used in the logical data model.

The process of routinely searching evaluational data for patterns, trends, and exceptions. Data exploration usually starts with an incomplete definition of the search criteria and an unknown volume of data. As patterns, trends, and exceptions are discovered, the search criteria are refined and the volume of data may be changed.

A term given to express the increase in stored data when using MultiDimensional Database Systems. The amount of data stored in these systems is often a multiple of the size of the raw data entered into the systems from the existing operational databases. Hence, the data undergoes an “Explosion” to several times (or many times) its original size.

Data which normally resides on an operational system and which is removed from that system for loading into a data warehouse.

The process of pulling data from operational and external data sources in order to prepare the source data for the data warehouse environment.

Software that reads one or more sources of data and creates a new image of the data.

A representation of a data entity from the logical data model that is implemented with a physical data model. It is a physical file of data that exists in a database management system, as a computer file outside a database management system, or as a manual file outside a computer that represents a data entity.

A diagram that shows the normal flow of data between services as well as the flow of data between data stores and services.

An unorderly process of placing data at various data sites. It is not done within the common data architecture, is not well-managed or documented, and results in unknown, undocumented, redundant data.

The process of creating successive layers of summary data in an evaluational database. It is a process of zooming out to get a broader view of a problem, trend or situation. It is also known as rolling-up data.

See data mining.

The movement of data between two co-existing systems. The interfacing of this data may occur once every hour, once a day, etc.

The quality of the data residing in the database objects. The measurement which users consider when analyzing the value and reliability of the data.

A statement that defines the actual data values or coded data values that are allowed.

Verification that converted data is accurate and functions correctly within a single subsystem or application.

An actual data value or a coded data value that is allowed.

a set of one or more data characteristics that have a special meaning and use in addition to describing a feature or trait of a data subject. Data keys are important for uniquely identifying data occurrences in each data subject and for navigating through the data resource.

A separate and distinct set of related spatial data that are stored and maintained in a spatial database. It represents a particular theme or topic of interest in the real world and is equivalent to a data subject.

The portion of a data layer extent for which data are not captured and stored. It is the reverse of a data layer coverage.

The process of populating the data warehouse. Data loading is provided by DBMS-specific load processes, DBMS insert processes, and independent fast load processes.

Controlling, protecting, and facilitating access to data in order to provide information consumers with timely access to the data they need. The functions provided by a database management system.

Software that converts data into a unified format by taking derived data to create new fields, merging files, summarizing and filtering data; the process of reading data from operational systems. Data Management Software is also known as data extraction software.

A technique for establishing a match, or balance, between the source data and the target data warehouse database object. This technique identifies data shortfalls and recognizes data issues.

The process of assigning a source data element to a target data element.

A data warehouse data class organized for a business functional area or department. The database contains data summarized at multiple levels of granularity and may be designed using relational or multidimensional database structures.

The logical representation of the specific information requirements organized around a department of functional area.

The movement of data from one database to another database -- but not necessarily to a working application or subsystem tables.

A technique using software tools geared for the user who typically does not know exactly what he's searching for, but is looking for particular patterns or trends. Data mining is the process of sifting through large amounts of data to produce data content relationships. This is also known as data surfing.

A representation of the specific information requirements of a business area; see also ENTITY RELATIONSHIP MODEL.

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.

A convention established to resolve problems with Traditional data names. Many of these conventions are in use today, such as the Of Language, entity—attribute—class, role—type—class, prime—descriptor—class, entity—adjective—class, entity—attribute—class word, entity—description—class, entity keyword—minor keyword—type keyword, and entity keyword—descriptor—domain.

A process to develop the conceptual schema from the external schema.

A process that prepares the logical schema from the data view schema. It is the counterpart of data deoptimization.

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.

A technique to improve application performance or security by splitting tables across multiple locations.

A process of rotating the view of data.

A software service, organization, or person that provides data for update to a system-of-record.

A software service, organization, or person that provides data for update to a system-of-record.

Indicates how well data in the data resource meet the business information demand. Data quality includes data integrity, data accuracy, and data completeness.

An activity in the data architecture component that ensures the maintenance of high-quality data in an integrated data resource.

Documents and improves data quality by using both the deductive and inductive techniques. It is a systematic process of examining the data resource to determine its level of data quality and ensuring that the data quality is adjusted to the level necessary to support the business information demand.

The process of moving data replicates from one data site to another to meet business needs. It is a process that constantly balances data needs, data volumes, data usage, and the physical operating environment.

A process that refines disparate data within a common context to increase the awareness and understanding of the data, remove data variability and redundancy, and develop an integrated data resource. Disparate data are the raw material and an integrated data resource is the final product.

The process of updating active data replicates based on a regular, known schedule. The frequency and timing of data refreshing must be established to match business needs and must be known by clients.

The master copy of the data associated with a business object. Several databases may share access to a common data registry to ensure consistency and eliminate redundant entries across multiple applications and databases. An example of a data registry would be a shared customer master. All updates and changes would be made to the customer master data registry and then propagated to subscribing sites. All systems requiring customer information would interface with the customer data registry.

An interface that transfers data registry data between similar or dissimilar applications.

The process of copying a portion of a database from one environment to another and keeping the subsequent copies of the data in sync with the original source. Changes made to the original source are propagated to the copies of the data in other environments.

A logical (and sometimes physical) partitioning of data where multiple databases which apply to specific applications or sets of applications reside. For example, several databases (revenues, expenses) which support financial applications (A/R, A/P) could reside in a single financial Data Repository.

A component of information technology infrastructure that represents all the data available to an organization, whether they are automated or nonautomated.

The process to restructure the source data to the target data during data transformation.

A subset of data integrity that specifies criteria for preventing the loss of critical data through updates or deletion. It considers the future value of data to determine what data should be retained and how they should be retained. It looks to the future to determine the unknown or hidden usefulness of the data.

A concept that provides a structure or framework for managing the integrated data resource.

A diagrammatic representation of the structure of data. It represents any set of data that is being captured, manipulated, stored, retrieved, transmitted, or displayed.

The process of filtering, merging, decoding, and translating source data to create validated data for the data warehouse.

An operational system, third-party organization, or external system that provides the data to support the information requirements of the client. The data source is accessed during the data acquisition process.

A temporary or permanent storage concept for logical data items used by specified business functions and processes.

A representation of the arrangement, relationship, and contents of data subjects, data entities, and data files in the common data architecture. It includes all logical and physical data within the common data architecture.

A component of the metadata warehouse that contains the structure of data within the common data architecture.

A subset of data integrity that specifies the integrity for data relations

The process of summarizing primitive evaluational data or derived evaluational data to create more generalized derived evaluational data.

See Data Mining.

The process of identifying active data replicates and ensuring that data concurrency is maintained. Also known as data version synchronization or data version concurrency because all replicated data values are consistent with the same version as the official data.

For an item that is populated across multiple systems (like social security number) name the source system.

A component of the metadata warehouse that contains a set of data name synonyms to help people locate the particular data they need. It provides a reference between similar names or business terms and the common data names.

The process of tracking data from their data origin to their current data site.

The process of moving data from one environment to another environment. An environment may be an application system or operating environment.
See Data Transport.

The process of redefining data based on some predefined rules. The values are redefined based on a specific formula or technique.
Creating "information" from data. This includes decoding production data and merging of records from multiple DBMS formats. It is also known as data scrubbing or data cleansing.

The process of redefining data in a manner differing between its original representation and its final representation.

The mechanism that moves data from a source to target environment. See Data Transfer.

The process of moving and loading the transformed data into the data warehouse database objects.

The form of a data value, such as date, number, string, floating point, packed, and double precision.

The process of ensuring correct data based on error and exception handling rules. This process directly impacts data integrity.

The individual facts and figures contained in data characteristics, data characteristic variations, data attributes, and data items.

A subset of data integrity that specifies the allowable values for each data characteristic and each relation between data characteristics within the common data architecture. Data value integrity is specified as data integrity values or data integrity rules.

The process of creating and presenting a chart from a set of data based on a set of attributes. It deals with understanding patterns, trends, and relationships in historical data, and providing visual information to the decision maker.

An enterprise structured repository of subject-oriented, time-variant, historical data used for information retrieval and decision support. The data warehouse stores atomic and summary data. The data warehouse is the source data stored in the data marts.

A person or group of people that administer and manage a data warehouse

The logical representation of the historical information requirements structured for the enterprise.

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

A combination of technologies and the interaction of technologies that support a data warehousing environment.

The process on reconciling each data warehouse increment with the strategic data warehouse architecture.

Software that extracts and transforms data from operational systems and loads it into the data warehouse.

A structured method for full life-cycle custom development data warehouse projects.

The process of designing, building, and maintaining a data warehouse system.

A collection of data, usually in the form of tables or files, under the control of a database management

A person (or group of people) responsible for the maintenance and performance of a database.

The collective application and database instances that comprise the complete system.

A mechanism to locate and access data within a database. An index may quote one or more columns and be a means of enforcing uniqueness on their values.

One set of database management processes and an allocated area in memory for managing those processes. Typically, a database instance is associated with one database. Note that a database instance may process data for one or more applications.

A software environment that structures and manipulates data, and ensures data security, recovery, and integrity.

The logical and physical definition of a database structure.

A technique for expressing the significant dataflows of a business system.

A temporary or permanent storage concept for logical data items used by specified business functions and processes.

Database Administrator.

A centralized database that has been partitioned according to a business or end-user defined subject area. Typically ownership is also moved to the owners of the subject area.

A remote data source that users can query/access via a central gateway that provides a logical view of corporate data in terms that users can understand. The gateway parses and distributes queries in real time to remote data sources and returns result sets back to users.

A set of software applications intended to allow users to search vast stores of information for specific reports which are critical for making management decisions.

See on-line analytical processing.

An application primarily used to consolidate, summarize, or transform transaction data to support analytical reporting and trend analysis.

A tangible, measurable output of a task.

A term derived from demos meaning population and graph in meaning to write or describe. Literally it means describing populations.

Any data that locate, identify, or describe populations. Demographic data can be related to the Earth the same as geographic data.

A database design activity that restructures a database by introducing derived data, replicated data, and/or repeating data to tune an application system and increase performance.

Data that have been through data denormalization. The data in the physical schema and internal schema.

A data store that does not comply to one or more of several normal forms. See Normalization.

The relationship of one task to another where the start or end date of the second task (successor) is constrained by the start or end date of the first (predecessor).

A value that is derived by some algorithm from the values of other attributes; for example, profit, which is the difference between revenue and expense.

A value derived by some algorithm from the values of other columns; see also DERIVED ATTRIBUTE, DERIVED DATA ITEM , and DERIVED FIELD. Derived Data Item A value derived by some algorithm from the values of other data items; for example, profit, which is the difference between revenue and expense.

Data that is the result of a computational step applied to reference or event data. Derived data is the result either of relating two or more elements of a single transaction (such as an aggregation), or of relating one or more elements of a transaction to an external algorithm or rule.

A multidimensional structure which represents a side of a multidimensional cube. Each dimension represents a different category, such as region, time, product type. Discovery The evaluation and validation of the implemented data warehouse increment, experiences and lessons learned, and scope for next increment to be developed.

A table that contains discrete values (usually a countable text field like school or degree). Also see fact table. Imagine viewing a spreadsheet. The row and column names would be the dimensions and the numeric data within would be the facts.

A type of data modeling suited for data warehousing. In a dimensional model, there are two types of tables: dimensional tables and fact tables. Dimensional table records information on each dimension, and fact table records all the "fact", or measures.

Dimension tables store records related to this particular dimension. No facts are stored in a dimensional table.

Data that are essentially not alike, or are distinctly different in kind, quality, or character. They are unequal and cannot be readily integrated to adequately meet the business information demand. Disparate data are heterogeneous data.

Databases or database management systems that are not electronically or operationally compatible. Disparate databases are known as heterogeneous databases.

A self-perpetuating cycle where disparate metadata are being produced faster than ever before.

The current-value operational data that support daily business transactions. They are the disparate data, including both tabular and nontabular data, that most organizations currently use to support their daily business operations.

A data set from one data subject or data occurrence group that is distributed.

A database that is physically located on more than one computer processor. It is connected via some form of communications network. An essential feature of a true distributed database is that users or programs work as if they had access to the whole database locally.

A software product that manages and maintains the distributed database and makes it transparent to clients. Data flow freely over any network or combination of networks by using one or more network protocols.

The ability to have several computers working together in a network, where each processor runs different activities for a user, as required.

A set of business validation rules, format constraints, and other properties that apply to a group of attributes or database columns; for example: a list of values, a range, a qualified list or range, or any combination of these.

Data analysis across dimensions.

An analytical operation which accesses and evaluates detail data which has been aggregated into interrelated data.

Data analysis that goes from an OLAP cube into the relational database.

Data analysis to a parent attribute.

See Decision Support System.

The situation where both data occurrence partitioning and data characteristic partitioning are done on the same data subject. Dual data partitioning is common in most data distribution.

The situation where distributed data need to be continually evaluated and adjusted to meet the business information demand in an optimum manner.

Dynamically constructed SQL that is usually constructed by desktop-resident query tools. Queries that are not pre-processed and are prepared and executed at run time.