Databases, Chapter 13

A comprehensive, cohesive, and integrated set of tools and processes used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information to support business decision making.

In a data warehousing environment, the integrated processes of getting data from original sources into the data warehouse. ETL includes retrieving data from original data sources (extraction), manipulating the data into an appropriate form (transformation), and storing the data in the data warehouse (loading).

In business intelligence, a web-based system that presents key business performance indicators or information in a single, integrated view with clear and concise graphics.

In terms of business intelligence, a unified, single point of entry for information distribution.

In business intelligence, a collection of concepts, techniques, and processes for the proper identification, definition, and management of data elements within an organization. In general, methodology that provides a “master copy” of entities such as customers, that appear in numerous systems throughout the organization. MDM helps prevent dirty data by coordinating common data across multiple systems.

In business intelligence, the methods for controlling and monitoring business health and promoting consistent decision making.

In business intelligence, quantifiable numeric or scale-based measurements that assess a company’s effectiveness or success in reaching strategic and operational goals. Examples of KPIs are product turnovers, sales by promotion, sales by employee, and earnings per share.

An arrangement of computerized tools used to assist managerial decision making within a business.

To decompose data into more atomic components—that is, data at lower levels of aggregation. This approach is used primarily in a decision support system to focus on specific geographic areas, business types, and so on.

(1) To aggregate data into summarized components, that is, higher levels of aggregation. (2) In SQL, an OLAP extension used with the GROUP BY clause to aggregate data by different dimensions. Rolling up the data is the exact opposite of drilling down the data.

Database that contains huge amounts of data—gigabyte, terabyte, and petabyte ranges are not unusual.

A small, single-subject data warehouse subset that provides decision support to a small group of people.

A data modeling technique used to map multidimensional decision support data into a relational database. This also represents data using a central table known as a fact table in a 1:M relationship with one or more dimension tables.

In a data warehouse, the measurements (values) that measure a specific business aspect or activity. For example, sales figures are numeric measurements that represent product or service sales. Commonly used in business data analysis include units, costs, prices, and revenues.

In a data warehouse, the star schema table that contains facts linked and classified through their common dimensions. A one-to-many relationship with each associated dimension table.

In a data warehouse, numeric facts that measure a business characteristic of interest to the end user.

In a star schema design, qualifying characteristics that provide additional perspectives to a given fact.

In a data warehouse, tables used to search, filter, or classify facts within a star schema

The ability to focus on slices of a data cube (drill down or roll up) to perform a more detailed analysis.

A top-down data organization that is used for two main purposes: aggregation and drill-downoll-up data analysis.

A type of star schema in which dimension tables can have their own dimension tables. The snowflake schema is usually the result of normalizing dimension tables.

The process of splitting a table into subsets of rows or columns.

The process of creating and managing duplicate versions of a database. Replication is used to place copies in different locations and to improve access time and fault tolerance.

Information about the time span of data stored in a table, usually expressed as current year only, previous years, or all years.

Analytical processing functions that use relational databases and familiar relational query tools to store and analyze multidimensional data.

An extension of online analytical processing to multidimensional database management systems.

A database management system that uses proprietary techniques to store data in matrix-like arrays of n dimensions known as cubes.

The multidimensional data structure used to store and manipulate data in a multidimensional DBMS. The location of each data value in the data cube is based on its x-, y-, and z-axes. Data cubes are static, meaning they must be created before they are used, so they cannot be created by an ad hoc query.

In multidimensional OLAP, the shared, reserved memory area where data cubes are held. Using the cube cache assists in speeding up data access.

In multidimensional data analysis, a measurement of the data density held in the data cube.

A subset of business intelligence functionality that encompasses a wide range of mathematical, statistical, and modeling techniques with the purpose of extracting knowledge from data.

Data analysis that provides ways to discover relationships, trends, and patterns among data.

Data analytics that use advanced statistical and modeling techniques to predict future business outcomes with great accuracy.

A process that employs automated tools to analyze data in a data warehouse and other sources and to proactively identify possible relationships and anomalies.

A dynamic table that not only contains the SQL query command to generate rows but stores the actual rows. The materialized view is created the first time the query is run and the summary rows are stored in the table. The materialized view rows are automatically updated when the base tables are updated.

The process of abstracting data to provide a visual representation of the data that enhances the user’s ability to effectively comprehend the meaning of the data.

A large centralized data repository to store all company data (structured, semi-structured, and unstructured) in its raw/natural format.