MISCHAP5REAL

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Anonymous
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105143
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MISCHAP5REAL
Updated:
2011-09-29 02:42:29
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MISCHAP
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  1. enables
    companies like R.R. Donnelley to eliminate outdated, incomplete or incorrectly
    formatted data.
    • Master data management
    • (MDM)
  2. •Collection of related files containing
    records on people, places, or things.

    •Prior to digital databases, business
    used file cabinets with paper files.
    Database
  3. •Generalized category representing
    person, place, thing on which we store and maintain information

    •E.g., SUPPLIER, PART
    Entity
  4. •Specific characteristics of each
    entity:

    •SUPPLIER name, address

    •PART description, unit price, supplier
    Attributes
  5. •Organize data into two-dimensional
    tables (relations) with columns and rows.
    Relational database
  6. (columns) store data representing an attribute
    Fields
  7. uniquely identifies each record
    Key field
  8. •One field in each table

    •Cannot be duplicated

    •Provides unique identifier for all
    information in any row
    Primary key
  9. •Used to clarify table relationships in a relational
    database
    • •Entity-relationship
    • diagram
  10. •Process of streamlining complex groups of data to:

    •Minimize redundant data elements.

    •Minimize awkward many-to-many relationships.

    •Increase stability and flexibility.
    •Normalization
  11. •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
    • rules
  12. •Specify structure of content of
    database.
    Data definition capabilities
  13. •Automated or manual file storing
    definitions of data elements and their characteristics.
    Data dictionary
  14. •Structured query language (SQL)

    •Microsoft Access query-building tools
    • •Data manipulation
    • language
  15. •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
    Data warehouse
  16. •Subset of data warehouses that is
    highly focused and isolated for a specific population of users
    Data mart
  17. tools
    for consolidating, analyzing, and providing access to large amounts of data to
    improve decision making
    Business intelligence
  18. •Supports multidimensional data
    analysis, enabling users to view the same data in different ways using multiple
    dimensions
    Online Analytical Processing (OLAP)
  19. •Finds hidden patterns and relationships in large databases
    and infers rules from them to predict future behavior
    Data Mining
  20. •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
    Predictive analysis
  21. •Unstructured data (mostly text files) accounts for 80
    percent of an organization’s useful information.
    allows
    businesses to extract key elements from, discover patterns in, and summarize
    large unstructured data sets.
    Text Mining
  22. •Discovery and analysis of useful patterns and
    information from the Web

    •Content mining, structure mining, usage mining
    •Web Mining
  23. •States organization’s rules for
    organizing, managing, storing, sharing information
    •Information policy
  24. •Responsible for specific policies and
    procedures through which data can be managed as a resource
    •Data administration
  25. Database design and management group responsible for
    defining and organizing the structure and content of the database, and
    maintaining the database
    • •Database
    • administration
  26. major
    obstacle to successful customer relationship management
    Poor data quality
  27. •caused by
    •Redundant and
    inconsistent data produced by multiple systems

    •Data input errors
    Data quality problems
  28. structured
    survey of the accuracy and completeness of data
    Data quality audit
  29. detects
    and corrects incorrect, incomplete, improperly formatted, and redundant data
    Data cleansing

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