Info Chapter 6

Front 1

Information granularity

Back 1

refers to the extent of detail within the information (fine and detailed or course and abstract)

Front 2

4 primary traits that help determine the value of information:

Back 2

1 Information type: transactional and analytical
2. Information timeliness
3. Information quality
4. Information governance

Front 3

transactional information

Back 3

encompasses all of the information contained within a single business process or unit of work, and its primary purpose is to support daily operational tasks.

Front 4

Analytical information

Back 4

encompasses all organizational info and its primary purpose is to support the performing of managerial analysis tasks. Useful when making important decisions such as whether an org. should build a new plant, hire additional salespersons, etc. Makes it possible to do many things that previously were difficult to accomplish. Ex: credit card frauds, companies can tell when a certain card has been stolen/where its used at.

Front 5

Realtime information

Back 5

means immediate, up to date information

Front 6

real time systems

Back 6

provide real time information in response to requests. Many companies use this to uncover key corporate transactional information.

Front 7

One of the biggest pitfalls associated with real time information

Back 7

continual change

Front 8

Demand for real time info stems from:

Back 8

organizations' need to make faster and more effective decisions, keep smaller inventories, operate more efficiently, and track performance more carefully.

Front 9

True/ false: Business decisions are only as good as the quality of info used to make them.

Back 9

true

Front 10

Data inconsistency

Back 10

occurs when the same data element has different values. ex: changing name after marriage, if only change in a few systems and not all then that is inconsistent

Front 11

Data integrity issues

Back 11

occur when a system produces incorrect, inconsistent, or duplicate data.
Causes managers to look at other sources for information when making decisions.

Front 12

5 characteristics common to high quality information

Back 12

- accuracy
-completeness
-consistency
-timeliness
-uniqueness

Front 13

4 primary reasons for low quality information are

Back 13

-online customers intentionally enter inaccurate info to protect their privacy
-diffferent systems have different info entry standards and formats
-data-entry personnel enter abbreviated info to save time or erroneous info by accident
-third party and external info contains inconsistencies, inaccuracies, and errors.

Front 14

Erroneous decisions can

Back 14

cost time, money, reputations, and jobs.

Front 15

Consequences that occur due to using low-quality information to make decisions are

Back 15

-inability to accurately track customers
-difficulty identifying the organizations most valuable customers
-inability to identify selling opportunities
-lost revenue opportunities from marketing to nonexistent customers
-cost of sending nondeliverable mail
-difficulty tracking revenue because of inaccurate invoices
-inability to build strong relationships with customers

Front 16

high quality info can

Back 16

- significantly improve the chances of making a good decision and directly incrase an orgs' bottom line.
-does not automatically guarantee that every decision made is going to be a good one, because people are not perfect.

Front 17

data governance

Back 17

refers to the overall management of the availabiltiy, usability , integrity, and security of company data.

Front 18

database

Back 18

maintains info about various types of objects (inventory), events (transactions), people (employees), and places (warehouses).

Front 19

database management system (DBMS)

Back 19

creates, reads, updates, and deletes data in a database while controlling access and security. Managers send requests to the DBMS and the DBMS perfoms the manipulation of the data in database.

Front 20

two primary tools for retrieving info from DBMS:

Back 20

-query by example
-structured query language

Front 21

Query by example tool

Back 21

that helps users graphically design the answer to a question against a database

Front 22

structured query language

Back 22

that asks users to write lines of code to anser questions against a database.

Front 23

data element (data field)

Back 23

is the smallest or basic unit of information. Can include customer name, address, email, shipping method, product name, quantity ordered, etc.

Front 24

data models

Back 24

logical data structures that detail the relationships among data elements using graphics or pictures.

Front 25

metadata

Back 25

provides details about data.
Ex: metadata about an image incudes size, resolution, and date created .

Front 26

data dictionary

Back 26

compiles all of the metadata about th edata elements in the data model.

Front 27

DBMS's 3 primary data models for organizing info:

Back 27

heirarchical, network, and relational database

Front 28

relational database model

Back 28

stores information in the form of logically related two-dimensional tables.

Front 29

relational database management system

Back 29

allows users to create, read, update, and delete data in a realtional database.

Front 30

entity (referred to as a table sometimes)

Back 30

stores information about a eprson, place, thing, transaction, or event.

Front 31

attributes(columns or fields)

Back 31

are the data elements associated with an entity.
Ex: under the Entity Musician the attribute would be the musician name, photo, notes, etc.

Front 32

record

Back 32

collection of related data elements.

Front 33

primary key

Back 33

field that uniquely identifies a given record in a table. Critical piece of relational database because they provide a way of distinguishing each record in a table.

Front 34

foreign key

Back 34

primary key of one table that appears as an attribute in another table and acts to provide a logical relationship between the two tables.

Front 35

advantages of using relational database vs. spreadsheet/ word doc

Back 35

-increased flexibility
-increased scalability and performance
-reduced information redundancy
-increased info intefity
- increased info security

Front 36

physical view

Back 36

of information deals with the physical storage of information on a storage device.

Front 37

logical view

Back 37

of information focuses on how individual users logicallyaccess information to meet their own particular business needs.

Front 38

data redundancy

Back 38

is the duplication of data or the storage of the same data in multiple places

Front 39

one primary goal of a database is to

Back 39

eliminate info redundancy by recording each piece of info in only one place in the database. Saves disk space, makes performing info updates easier, and improves info quality

Front 40

Information integrity

Back 40

a measure of the quality of information

Front 41

Integrity constraints

Back 41

rules that help ensure the quality of information. Database design needs to consider these constraints.

Front 42

two types of integrity constraints

Back 42

-relational
- business critical

Front 43

relational integrity constraints

Back 43

rules that enforce basic and fundamental information-based constraints. Ex: this constraint would not allow someone to create an order for a nonexistent customer

Front 44

Business-critical integrity constraints

Back 44

business rules vital to an organization's success, and often require more insight and knowledge than relational integrity constraints. Ex: Grocery store not accepting returns of goods past 15 days because of spoilage dates.

Front 45

True/ False : security risks are increaseing as more and more databases and DBMS systems are moving the data centers run in the cloud.

Back 45

true

Front 46

data-driven website

Back 46

is an interactive website kept constantly updated and relevant to the needs of its customers using a database. Can help limit the amount of info displayed to customers based on unique search requirements

Front 47

data driven website advantages:

Back 47

- easy to manage content
-easy to store large amounts of data
-easy to eliminate human error
-costs less

Front 48

data warehouse

Back 48

logical collection of information, gathered from many different operational databases that supports business analysis activities and decision-making tasks. Primary purpose of a data warehouse is to combine info, specifically strategic info, throughout an organization into a single repository in such a way that the people who need that info can make decisions and undertake business analysis.

Front 49

Extraction, transformation, and loading

Back 49

process that extracts information from internal and external databases, transforms it using a common set of enterprise definitions and loads it into a data warehouse. Data warehouse then sends it to data marts.

Front 50

data marts

Back 50

contains a subset of data warehouse information. functional focus.

Front 51

cube

Back 51

common term for the representation of multidimensional information.

Front 52

information cleansing (scrubbing)

Back 52

process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information.

Front 53

data quality audits

Back 53

firms complete this to determine the accuracy and completeness of its data

Front 54

data mining

Back 54

the process of analyzing data to extract information not offered by the raw data alone. Companies use data mining techniques to compile a complete picture of their operations, all within a single view, allowing them to identify trends and improve forecasts.

Front 55

data mining tools

Back 55

use a variety of techniques to find patterns and realtionships in large volumes of information that predict future behavior and guide decision making.

Front 56

Ex of data mining

Back 56

Netflix uses this to analyze each customer's film viewing habits to provide recommendations for other customers with cinematch, its movie recommendation system.

Front 57

structured data

Back 57

data mining occurs on this that is already in a database or a spreadsheet.

Front 58

unstructured data

Back 58

do not exist in a fixed location and can include text documents, PDF's, voice messages, emails, and so on.

Front 59

text mining

Back 59

analyzes unstructured data to find trends and patterns in words and sentences.

Front 60

web mining

Back 60

analyzes unstructured data associated with websites to identify consumer behavior and website navigation.

Front 61

3 common forms for mining structured and unstructured data

Back 61

-cluster analysis
-association detection
-statistical analysis

Front 62

cluster analysis

Back 62

a technique used to divide information sets into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible. Helps orgs identify customers with similar behavior traits, such as clusters of best customers or onetime customers. zip codes offer valuable insight into such things as income levels, demographics, lifestyles, etc.

Front 63

unstructured data

Back 63

do not exist in a fixed location and can include text documents, PDF's, voice messages, emails, and so on.

Front 64

association detection

Back 64

reveals the relationship between variables along with the nature and frequency of the relationships. Referred to as rule generators b/c they create rules to determine the lilihood of events occuring together, or following each other.

Front 65

text mining

Back 65

analyzes unstructured data to find trends and patterns in words and sentences.

Front 66

Market basket anaysis

Back 66

analyzes such items as websites and checkout scanner information to detect customers' buying behavior and predict future behavior by identifying affinities among customers' choices of products and services.

Front 67

web mining

Back 67

analyzes unstructured data associated with websites to identify consumer behavior and website navigation.

Front 68

statistical analysis

Back 68

performs such functions as information correlations, distributions, calculations, and variance analysis.

Front 69

3 common forms for mining structured and unstructured data

Back 69

-cluster analysis
-association detection
-statistical analysis

Front 70

time series information

Back 70

is timestamped info collected at a particular frequency. Ex: Web visits per hour, sales per month.

Front 71

cluster analysis

Back 71

a technique used to divide information sets into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible. Helps orgs identify customers with similar behavior traits, such as clusters of best customers or onetime customers. zip codes offer valuable insight into such things as income levels, demographics, lifestyles, etc.

Front 72

forecasts

Back 72

are predictions based on time-series information.

Front 73

association detection

Back 73

reveals the relationship between variables along with the nature and frequency of the relationships. Referred to as rule generators b/c they create rules to determine the lilihood of events occuring together, or following each other.

Front 74

Market basket anaysis

Back 74

analyzes such items as websites and checkout scanner information to detect customers' buying behavior and predict future behavior by identifying affinities among customers' choices of products and services.

Front 75

statistical analysis

Back 75

performs such functions as information correlations, distributions, calculations, and variance analysis.

Front 76

time series information

Back 76

is timestamped info collected at a particular frequency. Ex: Web visits per hour, sales per month.

Front 77

forecasts

Back 77

are predictions based on time-series information.

Front 78

True/ False
: organizations find themselves in the position of being data rich and information poor

Back 78

true

Front 79

solution to being data rich and information poor is

Back 79

business intelligence