Kenneth C. Laudon,Jane P. Laudon Management Information System 12th Edition pdf

data mart

is a subset of a data ware-

house in which a summarized or highly focused portion of the organization’s

data is placed in a separate database for a specific population of users. For

example, a company might develop marketing and sales data marts to deal

with customer information. Before implementing an enterprise-wide data

warehouse, bookseller Barnes & Noble maintained a series of data marts—one

for point-of-sale data in retail stores, another for college bookstore sales, and a

third for online sales. A data mart typically focuses on a single subject area or

line of business, so it usually can be constructed more rapidly and at lower

cost than an enterprise-wide data warehouse. 

FIGURE 6-12

COMPONENTS OF A DATA WAREHOUSE

The data warehouse extracts current and historical data from multiple operational systems inside the

organization. These data are combined with data from external sources and reorganized into a central

database designed for management reporting and analysis. The information directory provides users

with information about the data available in the warehouse.

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Part Two

Information Technology Infrastructure

TOOLS FOR BUSINESS INTELLIGENCE:

MULTIDIMENSIONAL DATA ANALYSIS AND DATA

MINING

Once data have been captured and organized in data warehouses and data marts,

they are available for further analysis using tools for business intelligence,

which we introduced briefly in Chapter 2. Business intelligence tools enable

users to analyze data to see new patterns, relationships, and insights that are

useful for guiding decision making.

Principal tools for business intelligence include software for database querying

and reporting, tools for multidimensional data analysis (online analytical

processing), and tools for data mining. This section will introduce you to these

tools, with more detail about business intelligence analytics and applications in

the Chapter 12 discussion of decision making.

O n l i n e   A n a l y t i c a l   P r o c e s s i n g   ( O L A P )

Suppose your company sells four different products—nuts, bolts, washers, and

screws—in the East, West, and Central regions. If you wanted to ask a fairly

straightforward question, such as how many washers were sold during the past

quarter, you could easily find the answer by querying your sales database. 

But what if you wanted to know how many washers sold in each of your sales

regions and compare actual results with projected sales? 

To obtain the answer, you would need