Page 80 - DCAP208_Management Support Systems

Unit 6: Data Warehousing

6.1.1 Other Definitions Notes

Other definitions of data warehouse are discussed below.
Data Warehouse: A data structure that is optimized for distribution. It collects and stores
integrated sets of historical data from multiple operational systems and feeds them to one or
more data marts. It may also provide end-user access to support enterprise views of data.
Data Mart: A data structure that is optimized for access. It is designed to facilitate end-user
analysis of data. It typically supports a single, analytic application used by a distinct set of
workers.

Staging Area: Any data store that is designed primarily to receive data into a warehousing
environment.
Operational Data Store: A collection of data that addresses operational needs of various
operational units. It is not a component of a data warehousing architecture, but a solution to
operational needs.
OLAP (On-Line Analytical Processing): A method by which multidimensional analysis occurs.

Multidimensional Analysis: The ability to manipulate information by a variety of relevant
categories or “dimensions” to facilitate analysis and understanding of the underlying data. It is
also sometimes referred to as “drilling-down”, “drilling-across” and “slicing and dicing”.
Hypercube: A means of visually representing multidimensional data.
Star Schema: A means of aggregating data based on a set of known dimensions. It stores data
multidimensionally in a two dimensional Relational Database Management System (RDBMS),
such as Oracle.

Snowflake Schema: An extension of the star schema by means of applying additional dimensions
to the dimensions of a star schema in a relational environment.
Multidimensional Database: Also known as MDDB or MDDBS. A class of proprietary,
non-relational database management tools that store and manage data in a multidimensional
manner, as opposed to the two dimensions associated with traditional relational database
management systems.
OLAP Tools: A set of software products that attempt to facilitate multidimensional analysis.
Can incorporate data acquisition, data access, data manipulation, or any combination thereof.
6.1.2 Concepts

The definition of data warehousing presented here is intentionally generic; it gives you an idea
of the process but does not include specific features of the process. To understand the role and the
useful properties of data warehousing completely, you must first understand the needs that
brought it into being. In 1996, R. Kimball efficiently summed up a few claims frequently submitted
by end users of classic information systems:
“We have heaps of data, but we cannot access it!” This shows the frustration of those who
are responsible for the future of their enterprises but have no technical tools to help them
extract the required information in a proper format.
“How can people playing the same role achieve substantially different results?” In midsize
to large enterprises, many databases are usually available, each devoted to a specific
business area. They are often stored on different logical and physical media that are not
conceptually integrated. For this reason, the results achieved in every business area are
likely to be inconsistent.

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