CHP. 8

Question 1

Information overload

Answer 1

An overabundance of irrelevant data.

Question 2

Dirty data

Answer 2

Problematic data. Examples are a value of B for customer gender and a value of 213 for customer age. Other examples are a value of 999-999-9999 for a North American phone number, a part colour of green, and an email address of WhyMe@GuessWhoIAM.org. All these values are problematic when data mining.

Question 3

Granularity

Answer 3

The level of detail in data. Customer name and account balance are large granularity data. Customer name, balance, and the order details and payment history of every customer order are smaller granularity.

Question 4

Clickstream data

Answer 4

E-commerce data that describe a customer’s clicking behaviour. Such data include everything the customer does at the website.

Question 5

Online transaction processing (OLTP)

Answer 5

Collecting data electronically and processing transactions online.

Question 6

Online Analytic Processing or (OLAP) and Decision support systems (DSSs)

Answer 6

Systems that focus on making data collected in OLTP useful for decision making.

Question 7

Online analytic processing (OLAP)

Answer 7

A dynamic type of reporting system that provides the ability to sum, count, average, and perform other simple arithmetic operations on groups of data. Such reports are dynamic because users can change the format of the reports while viewing them.

Question 8

Drill down

Answer 8

With an OLAP report, to further divide the data into more detail.

Question 9

Data resource challenge

Answer 9

Occurs when data are collected in OLTP but are not used to improve decision making.

Question 10

Business intelligence (BI) system

Answer 10

A system that provides the right information, to the right user, at the right time. A tool produces the information, but the system ensures that the right information is delivered to the right user at the right time.

Question 11

Group decision support systems (GDSSs)

Answer 11

An application that enables more than one individual to undertake a decision. Often includes voting and brainstorming functions.

Question 12

Reporting systems

Answer 12

Systems that create information from disparate data sources and deliver that information to the proper users on a timely basis.

Question 13

Data-mining system

Answer 13

Information system that processes data using sophisticated statistical techniques, such as regression analysis and decision-tree analysis, to find patterns and relationships that cannot be found by simpler operations, such as sorting, grouping, and averaging.

Question 14

Market-basket analysis

Answer 14

A data-mining technique for determining sales patterns. A market-basket analysis shows the products that customers tend to buy together.

Question 15

Knowledge management (KM) systems

Answer 15

Information systems for storing and retrieving organizational knowledge, whether that knowledge is in the form of data, documents, or employee know-how.

Question 16

Expert systems

Answer 16

Knowledge-sharing systems that are created by interviewing experts in a given business domain and codifying the rules used by those experts.

Question 17

RFM analysis

Answer 17

A way of analyzing and ranking customers according to their purchasing patterns.

considers:

-How Recently a customer has ordered
-How Frequently a customer orders
- How much money the customer spends per order

Question 18

Data warehouse

Answer 18

Is used to extract and clean data from operational systems and other sources for reporting and data mining.

Question 19

Metadata

Answer 19

Data that describe data.

Question 20

Data mart

Answer 20

A database that prepares, stores, and manages data for reporting and data mining for specific business functions.

Question 21

Data mining

Answer 21

The application of statistical techniques to find patterns and relationships among data and to classify and predict.

Question 22

Unsupervised data mining

Answer 22

A form of data mining whereby the analysts do not create a model or hypothesis before running the analysis. Instead, they apply the data-mining technique to the data and observe the results. With this method, analysts create hypotheses after the analysis to explain the patterns found.

Question 23

Cluster analysis

Answer 23

An unsupervised data-mining technique whereby statistical techniques are used to identify groups of entities that have similar characteristics.

A common use for cluster analysis is to find groups of similar customers in data about customer orders and customer demographics.

Question 24

Supervised data mining

Answer 24

A form of data mining in which data miners develop a model prior to the analysis and apply statistical techniques to data to estimate values of the parameters of the model.

Question 25

Regression analysis

Answer 25

A type of supervised data mining that estimates the values of parameters in a linear equation. Used to determine the relative influence of variables on an outcome and also to predict future values of that outcome.

Question 26

Neural networks

Answer 26

A popular supervised data-mining technique used to predict values and make classifications, such as good prospect or poor prospect.

Question 27

Lift

Answer 27

In market-basket terminology, the ratio of confidence to the base probability of buying an item. Lift shows how much the base probability changes when other products are purchased. If the lift is greater than 1, the change is positive; if it is less than 1, the change is negative.

Question 28

Big Data

Answer 28

An imprecise term that generally refers to large volumes of a variety of data over a long period of time that are used to draw general and specific inferences and analysis—for example the spread of disease, customer preferences, or individual behaviors.