1. Kenneth C. Laudon & Jane P. Laudon
Data Warehouses
A data warehouse is a database that stores current and historical data of potential interest to decision makers throughout the company.
The data originate in many core operational transaction systems, such as systems for sales, customer accounts, and manufacturing, and may include data from Web site transactions.
They may include legacy systems, relational or object-oriented DBMS applications, and systems based on HTML or XML documents.
The data from these diverse applications are copied into the data warehouse database as often as needed – hourly, daily, weekly, monthly.
The data warehouse consolidates and standardizes info. from different operational databases so that the info. can be used across the enterprise for management analysis and decision making.
Management Information Systems (Eighth Edition)
Kenneth C. Laudon & Jane P. Laudon

2. Data Warehouses (Cont.)
The data warehouse makes the data available for anyone to access as needed, but it cannot be altered.
A data warehouse system also provides a range of ad hoc and standardized query tools, analytical tools, and graphical reporting facilities.
Many firms use intranet portals to make the data warehouse info. widely available throughout the firm.
The data warehouse must be carefully designed by both business and technical specialists to make sure it can provide the right information for critical business decisions.
The firm may need to change its business processes to benefit from the information in the warehouse.
Management Information Systems (Eighth Edition)
Kenneth C. Laudon & Jane P. Laudon

3. Components of a Data Warehouse
Management Information Systems (Eighth Edition)
Kenneth C. Laudon & Jane P. Laudon

4. Kenneth C. Laudon & Jane P. Laudon
Data Marts
Companies often build enterprise-wide data warehouses, where a central data warehouse serves the entire organization, or they create smaller, decentralized warehouses called data marts.
A data mart is a subset of a data warehouse 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 info.
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.
However, complexity, costs, and management problems will rise if an organization creates too many data marts.
Management Information Systems (Eighth Edition)
Kenneth C. Laudon & Jane P. Laudon

5. Kenneth C. Laudon & Jane P. Laudon
Data Mining
Data mining uses a variety of techniques to find hidden patterns and relationships in large databases and inferring rules from them to predict future behavior.
The patterns and rules are used to guide decision making and forecast the effect of those decisions.
Data mining is often used to provide information for targeted marketing where personalized or individualized messages can be created based on individual preferences and to identify profitable customers.
Data mining applications can perform high-level analyses of patterns or trends, but they can also drill down to provide more detail when needed.
Data mining is both a powerful and profitable tool, but it poses challenges to the protection of individual privacy.
Management Information Systems (Eighth Edition)
Kenneth C. Laudon & Jane P. Laudon

6. Kenneth C. Laudon & Jane P. Laudon
Data Mining (Cont.)
Data mining technology can combine information from many diverse sources to create a detailed “data image” about each of us – our income, our driving habits, our hobbies, our families, and our political interests.
The types of information obtainable from data mining include:
Associations
Sequences
Classifications
Clusters
Forecasts
Management Information Systems (Eighth Edition)
Kenneth C. Laudon & Jane P. Laudon

7. Kenneth C. Laudon & Jane P. Laudon
Data Mining (Cont.)
Associations
Associations are occurrences linked to a single event.
For instance, a study of supermarket purchasing patterns might reveal that, when corn chips are purchased, a cola drink is purchased 65 percent of the time, but when there is a promotion, cola is purchased 85 percent of the time.
This information helps managers make better decisions because they have learned the profitability of a promotion.
Sequences
In sequences, events are linked over time.
We might find, for example, that if a house is purchased, a new refrigerator will be purchased within two weeks 65 percent of the time, and an oven will be bought within one month of the home purchase 45 percent of the time.
Management Information Systems (Eighth Edition)
Kenneth C. Laudon & Jane P. Laudon

8. Kenneth C. Laudon & Jane P. Laudon
Data Mining (Cont.)
Classifications
Classification recognizes patterns that describe the group to which an item belongs by examining existing items that have been classified and by inferring a set of rules.
For example, businesses such as credit card or telephone companies worry about the loss of steady customers.
Classification helps discover the characteristics of customers who are likely to leave and can provide a model to help managers predict who those customers are so that the managers can devise special campaigns to retain such customers.
Management Information Systems (Eighth Edition)
Kenneth C. Laudon & Jane P. Laudon

9. Kenneth C. Laudon & Jane P. Laudon
Data Mining (Cont.)
Clusters
Clustering works in a manner similar to classification when no groups have yet been defined.
A data mining tool can discover different groupings within data, such as finding affinity groups for bank cards or partitioning a database into groups of customers based on demographics and types of personal investments.
Forecasts
Although these applications involve predictions, forecasting uses predictions in a different way.
It uses a series of existing values to forecast what other values will be.
For example, forecasting might find patterns in data to help managers estimate the future value of continuous variables, such as sales figures.
Management Information Systems (Eighth Edition)
Kenneth C. Laudon & Jane P. Laudon