Chapter 9 - Business Intelligence

Question 0

Data Acquisition

Answer 0

The process of obtaining, cleaning, organizing, relating, and cataloging source data.

Question 1

Business Intelligence (BI) Systems

Answer 1

IS that process operational, social, and other data to identify patterns, relationships, and trends for use by business professionals and other knowledge workers.

Question 2

BI Analysis

Answer 2

The process of creating business intelligence.

Question 3

Push Publishing

Answer 3

Process of delivering BI to users without any request from the users; the results are delivered according to a schedule or as a result of an event it particular data condition.

Question 4

Pull Publishing

Answer 4

Requires the user to request BI results.

Question 5

Data Warehouse

Answer 5

A facility for managing an organization’s BI data.

Question 6

Granularity

Answer 6

Refers to the level of detail represented by the data. Granularity can be either too fine or too coarse.

Question 7

Data Mart

Answer 7

A data collection, smaller than a data warehouse, that addresses the needs of a particular department or functional area of the business.

Question 8

Reporting Application

Answer 8

A BI application that inputs data from one or more sources and applies reporting operations to that data to produce BI reports.

Question 9

RFM Analysis

Answer 9

Technique readily implemented with basic reporting operations, is used to analyze and rank customers according to their purchasing patterns. Considers how recently (R) and how frequently (F) a customer has ordered and how much money (M) the customer has spent.

Question 10

OLAP

Answer 10

Online Analytical Processing. Dynamic. Viewer can change the report’s format. Has measures and dimensions. A measure is a data item of interest, that is to be summed, averaged or otherwise processed. A dimension is a characteristic of a measure. The report can also be called an OLAP cube.

Question 11

Drill Down

Answer 11

To further divide the data into more detail.

Question 12

Data Mining

Answer 12

The application if statistical techniques to find patterns and relationships among data for classification and prediction. Also referred to as Knowledge Discovery in Databases (KDD).

Question 13

Unsupervised Data Mining

Answer 13

Analysts do not create a model or hypothesis before running the analysis. Analysts create a hypothesis after the analysis, in order to explain the patterns found.

Question 14

Cluster Analysis

Answer 14

A common unsupervised technique that identifies groups of entities that have similar characteristics.

Question 15

Supervised Data Mining

Answer 15

Data miners develop a model prior to the analysis and apply statistical techniques (such as regression) to data to estimate parameters of the model.

Question 16

Neural Networks

Answer 16

A popular supervised data mining application used to predict values and make classifications such as “good prospect” or “poor prospect” customers.

Question 17

Market-basket Analysis

Answer 17

An unsupervised data mining technique for determining sales patterns. Shows the products that customers tend to buy together. Creates a cross-selling opportunity.

Question 18

Support (in Market-basket terminology)

Answer 18

The probability that two items will be purchased together.

Question 19

Confidence (in Market-basket terminology)

Answer 19

A conditional probability estimate. The ratio of confidence to the base probability of buying an item is called lift.

Question 20

Decision Tree

Answer 20

A hierarchical arrangement of criteria that predict a classification or a value. It is an unsupervised data mining technique. A common application of decision trees is to classify loans by likelihood of default.

Question 21

MapReduce

Answer 21

A technique for harnessing the power of thousands of computers working in parallel. In the Map phase, BigData is broken down into pieces and sent to different computers. In the Reduce phase, the results are combined.

Question 22

Hadoop

Answer 22

An open-source implementation of MapReduce supported by the Apache Foundation. Originally part of Cassandra. Includes a query language called Pig.

Question 23

Knowledge Management (KM)

Answer 23

The process of creating value from intellectual capital and sharing that knowledge with employees, managers, suppliers, customers, and others who need that capital. KM improves process quality and increases team strength.

Question 24

Expert Systems

Answer 24

Rule-based systems that encode human knowledge in the form of if/then rules.

Question 25

Content Management Systems (CMS)

Answer 25

IS that supports the management and delivery of documents including reports, Web pages, and other expressions of employee knowledge.

Question 26

Hyper-social Knowledge Management

Answer 26

The application of social media and related applications for the management and delivery of organizational knowledge resources.

Question 27

Rich Directory

Answer 27

An employee directory that includes not only the standard name, email, phone, and address, but also organizational structure and expertise. The most popular rich directory is Microsoft’s Active Directory.