Exam 12: Decision Making and Business Intelligence

What are some of the obstacles to developing data-mining applications in the real world? Does this mean that data-mining projects should be avoided?

Is it better to have data that is too fine or too coarse? Explain.

________ projects are risky because you never know how they will turn out. Even with a good model, bad luck may prevail.

Systems that focus on making data collected in OLTP useful for allowing multiple decision makers to collaborate are often referred to as

________ measure(s) the impact of a set of variables on another variable.

Thomas Davenport noted that a major grocery chain used less than ________ percent of its scanner data

________ analysis is a data-mining system that computes correlation of items on past orders to determine items that are frequently purchased together.

The purpose of a(n) ________ is to extract and clean data from operational systems and other sources.

Problematic data are termed dirty data.

A ________ is a subset of a data warehouse that addresses a particular component or functional area of the business.

There are two general categories of data-mining techniques; supervised and ________.

Operational data may not be suitable for more sophisticated analyses. What are the problems with operational data? What is a data warehouse and how is it designed to go beyond these issues?

Which of the following systems fosters innovation, improves customer service, and increases organizational responsiveness by sharing knowledge of products, product uses, and best practices?

________ is the problem of having many data points and generating the same number of equations to predict those points.

Online transaction processing (OLTP) systems are the backbone of all cross-functional systems in a company.

A(n) ________ is a data collection that is made to address the needs of a particular business function, problem, or opportunity.

OLTP provides the ability to sum, count, average, and perform other simple arithmetic operations on groups of data.

Sometimes, large organizations break out ________ from their data warehouses so they can be used for a specific functional area of the organization like marketing or finance.

With cluster analysis, analysts create a model or hypothesis before running the analysis.

________ refers to the relative level of detail that data represents.