Information Systems Exam 1

Front 1

DATA

Back 1

Comprises facts, observations, or perceptions (which may or may not be correct). By itself, it represents raw numbers or assertions, and may therefore be devoid of context, meaning or intent.

Front 2

2 TYPES OF DATA

Back 2

1. Quantitative

2. Qualitative

Front 3

QUANTITATIVE DATA

Back 3

Data in numerical form and that can run mathematical operations on

Front 4

QUALITATIVE DATA

Back 4

Data not in numerical form and could include just text, pictures, videos, observations, sound recordings, etc. Can be represented by numbers; e.g., 1=Male 2=Female

Front 5

STRUCTURED DATA

Back 5

Data that are organized in a pre-defined manner or have a "data-model" determine their organization. e.g., data stored in a database table with rows and columns

Front 6

UNSTRUCTURED DATA

Back 6

Data that are typically not organized in a pre-defined manner. e.g., emails, word docs, tweets, product reviews, customer service call logs, video, audio, etc.

Front 7

INFORMATION

Back 7

A subset of data, only including those data that possess context, relevance, and purpose; typically involves the manipulation of raw data to obtain a more meaningful indication of trends or patterns

Front 8

KNOWLEDGE

Back 8

Is justified beliefs about relationships among concepts relevant to that particular area.

Front 9

2 BROAD TYPES OF KNOWLEDGE RELEVANT TO IS

Back 9

1. Tacit Knowledge

2. Explicit Knowledge

Front 10

TACIT KNOWLEDGE

Back 10

Includes insights, intuitions, and hunches. It is difficult to express and formalizes, and therefore difficult to share. More likely to be personal and based on individual experiences and activities.

Front 11

EXPLICIT KNOWLEDGE

Back 11

Typically refers to knowledge that has been expressed into words and numbers. Such knowledge can be shared formally and systematically in the form of data, specifications, manuals, drawings, audio and video tapes, computer programs, patents, and the like

Front 12

INFORMATION SYSTEMS

Back 12

Combinations of hardware, software, and telecommunications networks that people build and use to collect, create, and distribute useful data, typically in an organized setting.

Front 13

INF. SYS. IS A COMBINATION OF THESE 5 ITEMS

Back 13

1. People

2. Hardware

3. Software

4. Data

5. Telecommunications networks

Front 14

BUSINESS PROCESSES

Back 14

The manner in which work is organized, coordinated, and focused to produce a valuable product or service. They are workflows or a set of activities

Front 15

3 INTER-RELATED WAYS TO USE IS TO ADD VALUE TO AN ORGANIZATION:

Back 15

1. Automation (operational role of IT)

2. Organizational Learning (process improvement role of IT)

3. Strategic use of IS (strategic role of IT)

Front 16

IS FOR AUTOMATION

Back 16

Develop or acquire application software that can turn manual business processes into "computerized" or automated ones so hat tasks can be completed more efficiently

Front 17

BENEFITS OF AUTOMATION

Back 17

1. Cheaper

2. Faster

3. More Accurate

4. With Greater Consistency

5. Focus Predominantly on Efficiency

Front 18

IS FOR ORGANIZATIONAL LEARNING

Back 18

Helps capitalize on automation efforts

Front 19

BENEFITS OF ORGANIZATIONAL LEARNING

Back 19

1. Improve Processes

2. Provide better products and services

3. Improve both efficiency and effectiveness

Front 20

BUSINESS INTELLIGENCE (BI)

Back 20

Providing decision makers with valuable information and knowledge by leveraging a variety of sources of data as well as structured and unstructured information

Front 21

BI PROCESSES UTILIZE:

Back 21

Data and information as inputs and produce knowledge that enables decision making as output.

Front 22

DATA WAREHOUSE

Back 22

a single logical repository for an organization's data

Front 23

DATA WAREHOUSE DATA IS OBTAINED FROM:

Back 23

Multiple operational systems such as a point-of-sale system, a customer relationship management system, etc.

Front 24

ETL TOOLS

Back 24

Data is combined together using tools to Extract, Transform (to make data consistent), and Load data.

Front 25

DATA MINING

Back 25

The process of discovering hidden patterns and large quantities of data stored in electronic form

Front 26

TYPICAL TYPES OF ANALYSES INCLUDE:

Back 26

1. Cluster Analysis

2. Anomaly Detection

3. Association Rule Mining

Front 27

BUSINESS ANALYTICS

Back 27

The application of statistical analyses, and mathematical modeling or optimization techniques, to business data in order to describe/explain, predict, or improve business performance. Goal is typically to provide practical guidance based on data

Front 28

DATABASE (DB)

Back 28

An organized (structured) collection of related data

Front 29

DATABASE MODEL

Back 29

A model that determines the logical structure of a database

Front 30

RELATIONAL DATA MODEL

Back 30

The most common data model used; specifies that data is structured within tables. 2 or more tables may be related to each other

Front 31

COLUMNS

Back 31

Fields or Attributes that describe an entity (person, place, thing, relationships, etc.)

Front 32

ROWS

Back 32

Records or Tuples; a set of values across columns that describes a particular instance of an entity

Front 33

TUPLES

Back 33

Technical name for an entity

Front 34

RELATIONAL DATABASE

Back 34

A database that organizes its data using a relational database model. (Relationships among tables)

Front 35

PRIMARY KEY

Back 35

In a database table, it is the column that UNIQUELY identifies each particular row; typically auto-generated or it may be a combination of 2 or more columns that together uniquely identify a row

Front 36

FOREIGN KEY

Back 36

A column in one table whose value matches the primary key value in some other table

Front 37

CHARACTERISTICS OF A FOREIGN KEY

Back 37

1. It associates one or more rows in one table to a row in the other table

2. A table can have more than on, resulting in this table being related to more than one other table

3. The data type and values must match exactly to the primary key it is associated with

Front 38

TYPES OF RELATIONSHIPS COMMON IN A RELATIONAL DATABASE

Back 38

1. One-to-Many

2. Many-to-Many

Front 39

ONE-to-MANY

Back 39

One record in a table is associated with one or more records in another table

Front 40

MANY-to-MANY

Back 40

Requires a third "junction" table to implement such that the relationship is broken down into two one-to-many relationships

Front 41

ENTITY RELATIONSHIP DIAGRAMS (ERD)

Back 41

Diagrams used by database designers to depict entities and the relationships between them

Front 42

2 COMMON TYPES OF ER MODELS

Back 42

1. Logical

2. Physical

Front 43

LOGICAL ER MODEL

Back 43

Abstract models from a business perspective

Front 44

PHYSICAL ER MODEL

Back 44

Detailed models that can be actually used to start creating the database

Front 45

STANDARD QUERY LANGUAGE (SQL)

Back 45

A computer language that allows us to "talk" to relational database management systems; vendor neutral; allows us to ask questions to the database

Front 46

SQL PROVIDES FUNCTIONALITY SUCH AS:

Back 46

1. Creating tables, users, and assigning user permissions

2. Updating records in tables, deleting records from tables

Front 47

FORMULAS

Back 47

User created expressions using cell references, built-in functions or literal values along with operators. They return a numeric text, yes/no, or a date value

Front 48

FUNCTIONS

Back 48

Pre-programmed functionalities tha treturn a value based on the type being used

Front 49

RELATIVE

Back 49

Cell references that are always interpreted in relation to the local of the cell containing the formula

Front 50

ABSOLUTE

Back 50

Cell references in which both the column and row names remain fixed. Uses a $ to "fix"; e.g., $A$2

Front 51

MIXED

Back 51

Cell reference containing both relative and absolute references; e.g., $A2 or A$2

Front 52

A COLON -- :

Back 52

A range operator; produces one reference to all the cells between the two references, including the two references

Front 53

A COMMA -- ,

Back 53

Union operator; combines multiple references in one reference

Front 54

A SPACE

Back 54

Intersection operator; produces one reference to cells common to the two references

Front 55

HORIZONTAL AXIS

Back 55

Categories axis

Front 56

VERTICAL AXIS

Back 56

Values axis

Front 57

DATA SOURCE

Back 57

The range of cells that contains the data to display in the chart