Chapter 6: Foundations of Business Intelligence - Data Bases & Information Management Flashcards

1
Q

File organisation concepts/hierarchy

A
  • database: group of related files
  • file: group of records of same type
  • record: group of related fields
  • field: group of characters as word(s), or number(s) (rows etc.)
  • entity: place, person, thing on which we store information
  • attribute: each characteristic, or quality, describing entity
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1
Q

Data hierarchy

A

Bit (0s & 1s) -> Byte (one character) -> Field -> Record -> File -> Database

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2
Q

Problems with traditional file environment

A
  • data redundancy (presence of duplicate data in multiple files)
  • data inconsistency (same attribute has different values)
  • program-data dependence (when changes in program requires change to data accessed by program)
  • lack of flexibility (cannot deliver ad hoc reports or quickly respond to unexpected info requirements)
  • poor security
  • lack of data sharing and availability
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3
Q

traditional file processing

A
  • encourages each functional area in a corporation to develop specialised applications
  • each application requires a unique data file that is likely a subset of the master file
  • these subsets of the master file lead to data redundancy, inconsistency, processing inflexibility, and wasted storage resources
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4
Q

Database Management Systems (DBMSs)

A

system to manage databases
- solves problems of tradition file environment

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5
Q

Relational DBMS

A

represent data as two-dimensional tables
- each table (referred to as file) contains data on entity and attributes
- each row represents a record, each column an attribute or field

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6
Q

Table: grid of columns and rows

A
  • rows (tuples): records for different entities
  • fields (columns): represents attribute for entity
  • key field: field used to uniquely identify each record
  • primary key: field in table used for key fields (employee ID)
  • foreign key: primary key used in second table as look-up field to identify records from original table
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7
Q

Operations of a Relational DBMS

A

three basic operations used to to develop useful sets of data (commands)
- SELECT: creates subset of data of all records that meet stated criteria
- JOIN: combines relational tables to provide user with more information than available in individual tables
- PROJECT: creates subset of columns in table, creating tables with only the information specified

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8
Q

Designing a database requires a

A
  • logical design: models the database from a business perspective, reflects its key business processes and decision-making requirements
  • physical design: shows how the database is actually arranged on direct-access storage devices
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9
Q

Normalization of a relational database

A

process of creating small, stable, flexible, and adaptive data structures from complex groups of data when designing a relational database

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10
Q

Entity-relationship diagrams (ERD)

A

graphically depict the relationship between entities (tables) in a relational database

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11
Q

Online Analytical Processing (OLAP)

A

represents relationships among data as a multidimensional structure, which can be visualised as cubes of data and cubes within cubes of data, enabling more sophisticated data analysis

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12
Q

Data Mining

A

analyzes large pools of data (including data warehouses) to find patterns and rule to predict future behavior and assist decision-making

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13
Q

Text mining tools

A

help businesses analyse large unstructured data sets consisting of text

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14
Q

Web mining tools

A

focus on analysis of useful patterns and information from the web, examining the structure and activities of website users

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15
Q

Data governance

A

encompasses organisational policies and procedures for the maintenance, distribution, and use of information in the organisation

16
Q

High data quality is crucial

A

inaccurate, incomplete, and inconsistent data creates serious operational and financial problems for businesses as they may create inaccuracies in product pricing, customer accounts, and inventory data and lead to inaccurate decisions

17
Q

Special step for ensuring high level of data quality

A
  • enterprise wide data standards
  • databases designed to minimize inconsistent and redundant data
  • data quality audits
  • data cleaning software