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- Master data management
•Collection of related files containing
records on people, places, or things.
•Prior to digital databases, business
used file cabinets with paper files.
•Generalized category representing
person, place, thing on which we store and maintain information
•E.g., SUPPLIER, PART
•Specific characteristics of each
•SUPPLIER name, address
•PART description, unit price, supplier
•Organize data into two-dimensional
tables (relations) with columns and rows.
(columns) store data representing an attribute
uniquely identifies each record
•One field in each table
•Cannot be duplicated
•Provides unique identifier for all
information in any row
•Used to clarify table relationships in a relational
•Process of streamlining complex groups of data to:
•Minimize redundant data elements.
•Minimize awkward many-to-many relationships.
•Increase stability and flexibility.
•Used by relational databases to ensure
that relationships between coupled tables remain consistent.
•E.g., when one table has a foreign key that points to another
table, you may not add a record to the table with foreign key unless there is a
corresponding record in the linked table.
- •Referential integrity
•Specify structure of content of
Data definition capabilities
•Automated or manual file storing
definitions of data elements and their characteristics.
•Structured query language (SQL)
•Microsoft Access query-building tools
- •Data manipulation
•Database that stores current and
historical data that may be of interest to decision makers
•Consolidates and standardizes data from
many systems, operational and transactional databases
•Data can be accessed but not altered
•Subset of data warehouses that is
highly focused and isolated for a specific population of users
for consolidating, analyzing, and providing access to large amounts of data to
improve decision making
•Supports multidimensional data
analysis, enabling users to view the same data in different ways using multiple
Online Analytical Processing (OLAP)
•Finds hidden patterns and relationships in large databases
and infers rules from them to predict future behavior
•Uses data mining techniques, historical data, and
assumptions about future conditions to predict outcomes of events, such as the
probability a customer will respond to an offer or purchase a specific product
•Unstructured data (mostly text files) accounts for 80
percent of an organization’s useful information.
businesses to extract key elements from, discover patterns in, and summarize
large unstructured data sets.
•Discovery and analysis of useful patterns and
information from the Web
•Content mining, structure mining, usage mining
•States organization’s rules for
organizing, managing, storing, sharing information
•Responsible for specific policies and
procedures through which data can be managed as a resource
Database design and management group responsible for
defining and organizing the structure and content of the database, and
maintaining the database
obstacle to successful customer relationship management
Poor data quality
inconsistent data produced by multiple systems
•Data input errors
Data quality problems
survey of the accuracy and completeness of data
Data quality audit
and corrects incorrect, incomplete, improperly formatted, and redundant data