Information Management's Glossary


Magic arrow
An arrow used in marketing materials that gives the illusion of an integrated and automated process.

Managed availability
The ability of an organization to deliver consistent, predictable access to information for any user wherever, whenever and however the user needs it.

Market basket
The term "market basket" refers to a specific type of basket or a fixed list of items used specifically to track the progress of inflation in an economy or specific market. The list used for such an analysis would contain a number of the most commonly bought food and household items. The variations in the prices of the items on the list from month to month give an indication of the overall development of price trends.

Market segmentation
Segmentation is the process of partitioning markets into groups of potential customers with similar needs and/or characteristics who are likely to exhibit similar purchase behavior.

Market share
A company's sales expressed as a percentage of the sales for the total industry.

Marketing resource management (MRM)
Marketing resource management (MRM) refers to software that helps with the upfront planning of a marketing function and the coordination and collaboration of marketing resources.

A mashup is a lightweight tactical integration of multisourced applications or content into a single offering. Their primary business benefit is that they can quickly meet tactical needs with reduced development costs and improved user satisfaction.

Mass customization
The use of technology, such as the internet, to deliver customized services on a mass basis. This results in giving each customer whatever they ask for.

Master data
Master data represents the parties to the transactions that record the operations of an enterprise. Two examples are customer and product.

Master data management (MDM)
Master data management (MDM) is business context data that contains details (definitions and identifiers) of internal and external objects involved in business transactions (e.g., customer, product, reporting unit, NPS, market share). It explains the context within which you do business and holds the business rules. Master data management is a series of processes put in place to ensure that reference data is kept up to date and coordinated across an enterprise. Master data management is the organization, management and distribution of corporately adjudicated information with widespread use in the organization.

Meta muck
An environment created when meta data exists in multiple products and repositories (DBMS catalogs, DBMS dictionaries, CASE tools warehouse databases, end-user tools and repositories).

Metadata is data that expresses the context or relativity of 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 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 in the data warehouse to allow for easier access.

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

A framework to establish and collect measurements of success/failure on a regulated, timed basis that can be audited and verified.

Microsimulation is a computational technique used to predict the behavior of a system by predicting the behavior of microlevel units that make up the system.

Mid-tier data warehouses
To be scalable, any particular implementation of the data access environment may incorporate several intermediate distribution tiers in the data warehouse network. These intermediate tiers act as source data warehouses for geographically isolated sharable data that is needed across several business functions.

A communications layer that allows applications to interact across hardware and network environments.

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.

Million instructions per second (MIPS)
Million instructions per second (MIPS) is mistakenly considered a relative measure of computing capability among models and vendors. It is a meaningful measure only among versions of the same processors configured—with identical peripherals and software.

Model-driven architecture
Model-driven architecture is a registered trademark of the Object Management Group (OMG). It describes OMG's proposed approach to separating business-level functionality from the technical nuances of its implementation The premise behind OMG's model-driven architecture and the broader family of model-driven approaches (MDAs) is to enable business-level functionality to be modeled by standards, such as Unified Modeling Language (UML) in OMG's case; allow the models to exist independently of platform-induced constraints and requirements; and then instantiate those models into specific runtime implementations, based on the target platform of choice.

To represent how a business works and functions in such a way that it can productively be used as a means to simulate the real world. Executives, planners, managers and analysts use modeling to simulate and test operational and financial planning assumptions.*

Massively parallel processing (MPP)
Massively parallel processing is the "shared nothing" approach of parallel computing.

Multivalue attribute
Multivalue is a database model with a physical layout that allows systematic manipulation and presentation of messy, natural, relational, data in any form, first normal to fifth normal.

Data structure with three or more independent dimensions.

Multidimensional analysis
The objective of multidimensional analysis is for end users to gain insight into the meaning contained in databases. The multi-dimensional approach to analysis aligns the data content with the analyst's mental model, hence reducing confusion and lowering the incidence of erroneous interpretations. It also eases navigating the database, screening for a particular subset of data, hence asking for the data in a particular orientation and defining analytical calculations. Furthermore, because the data is physically stored in a multidimensional structure, the speed of these operations is many times faster and more consistent than is possible in other database structures. This combination of simplicity and speed is one of the key benefits of multidimensional analysis.

Multidimensional array
A group of data cells arranged by the dimensions of the data. For example, a spreadsheet exemplifies a two- dimensional array with the data cells arranged in rows and columns, each being a dimension. A three-dimensional array can be visualized as a cube with each dimension forming a side of the cube, including any slice parallel with that side. Higher dimensional arrays have no physical metaphor, but they organize the data in the way users think of their enterprise. Typical enterprise dimensions are time, measures, products, geographical regions, sales channels, etc.

Multidimensional 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.

Multidimensional query language
A computer language that allows one to specify which data to retrieve out of a cube. The user process for this type of query is usually called slicing and dicing. The result of a multi-dimensional query is either a cell, a two- dimensional slice, or a multidimensional sub-cube.