Which Of The Following Is Data Rather Than An Interpretation?

Data warehouses differ from traditional databases. The purpose of a data warehouse is to organize information for quick and effective queries. In outcome, they shop denormalized data, but they go 1 stride further. They organize data effectually subjects. Most often, a data warehouse is more than one database processed so that data are represented in uniform ways. Therefore, the information stored in information warehouses comes from different sources, normally databases that were set up for unlike purposes. The data warehouse concept is unique. Differences between data warehouses and traditional databases include the following:

In a data warehouse, information are organized around major subjects rather than individual transactions.
Data in a data warehouse are typically stored as summarized data rather than the detailed, raw data institute in a transaction-oriented database.
Data in a information warehouse cover a much longer time frame than data in traditional transaction-oriented databases because queries ordinarily business organisation longer-term decision making rather than daily transaction details.
Nigh data warehouses are organized for fast queries, whereas the more traditional databases are normalized and structured in such a way as to provide efficient storage of data.
Information warehouses are usually optimized for answering complex queries, known as OLAP, from managers and analysts, rather than elementary, repeatedly asked queries.
Data warehouses allow piece of cake access via data mining software (called siftware) that searches for patterns and is able to identify relationships not imagined past homo determination makers.
Data warehouses include not but one just multiple databases that have been candy and then that the warehouse's information are defined uniformly. These databases are referred to as clean data.
Information warehouses commonly include information from exterior sources (such equally an industry written report, the visitor's Security and Commutation Commission filing, or even information about competitors' products), also as data generated for internal use.

Building a data warehouse is a monumental task. The analyst needs to gather data from a diversity of sources and interpret that data into a common form. For instance, one database may store information about gender as "Male person" and "Female person," some other may shop information technology equally "Yard" and "F," and a third may shop it equally "1" and "0." The analyst needs to set a standard and convert all the data to the same format.

Once the data are clean, the analyst has to decide how to summarize the data. In one case summarized, the detail is lost, so an analyst has to predict the blazon of queries that might be asked. Then, the analyst needs to design the data warehouse by logically organizing, and perhaps even physically clustering, the data by discipline, requiring much analysis and design. The analyst needs to know a substantial amount virtually the business.

Typical data warehouses tend to exist from l gigabytes to tens of terabytes in size. Because they are large, they are also expensive. Most data warehouses toll millions of dollars.

Online Analytic Processing

Kickoff introduced in 1993 by E. F. Codd, online analytic processing (OLAP) was meant to answer decision makers' circuitous questions. Codd concluded that a decision maker had to look at data in a number of different ways. Therefore, the database itself had to be multidimensional. Many people picture OLAP equally a Rubik's Cube of data. Y'all tin can look at the data from all different sides, and tin can also manipulate the data by twisting or turning information technology so that it makes sense.

This OLAP approach validated the concept of data warehouses. It then made sense for information to be organized in ways that immune efficient queries. Of course,OLAP involves the processing of data through manipulation, summarization, and calculation, so more than a data warehouse is involved.

Information Mining

Data mining can identify patterns that a human is unable to detect. Either the decision maker cannot meet a pattern, or possibly the decision maker is not able to think about request whether that blueprint exists. Data mining algorithms search information warehouses for patterns using algorithms. Figure xiii.27 illustrates the concept of data mining.

Data mining collects personal information about customers in an effort to be more specific in interpreting and anticipating their preferences. — Data mining collects personal data about customers in an effort to be more than specific in interpreting and anticipating their preferences.

Data mining is known by another name, knowledge data discovery (KDD). Some think that KDD differs from data mining because KDD is meant to assist decision makers in finding patterns rather than turning command over to an algorithm to discover them. The decision aids bachelor are called siftware; they include statistical analysis, conclusion trees, neural networks, intelligent agents, fuzzy logic, and data visualization.

The types of patterns decision makers effort to identify include associations, sequences, clustering, and trends. Associations are patterns that occur together at the same time. For example, a person who buys cereal normally buys milk to go with the cereal. Sequences, on the other mitt, are patterns of actions that have identify over a period of fourth dimension. For example, if a family buys a firm this yr, they will most likely purchase durables (a fridge, or washer and dryer) next year. Clustering is the pattern that develops among a group of people. For instance, customers who live in a particular zip code may tend to buy a particular auto. Finally, trends are patterns that are noticed over a period of fourth dimension. For example, consumers may move from ownership generic goods to premium products.

The concept of data mining came from the desire to use a database for a more selective targeting of customers. Early approaches to direct mail service included using zip code data as a manner to decide what a family's income might exist (assuming a family must generate sufficient income to beget to live in the prestigious Beverly Hills zip lawmaking 90210 or some other affluent neighborhood). It was a way (non perfect, of class) to limit the number of catalogs sent.

Data mining takes this concept one step further. Assuming past behavior is a good predictor for future purchases, a large amount of data is gathered on a particular person from credit card purchases. The company can identify what stores nosotros shop in, what we have purchased, how much we paid for an item, and when and how frequently we travel. Data are also entered, stored, and used for a variety of purposes when we make full out warranties, use for a commuter's license, reply to a free offer, or apply for a membership bill of fare at a video rental shop. Moreover, companies share these data and often make money on the sale of them as well.

American Express has been a leader in data mining for marketing purposes. American Express will send y'all disbelieve coupons for new stores or amusement when it sends yous a credit card nib, having adamant that you have shopped in similar stores or attended similar events. General Motors offers a MasterCard that allows customers to accrue bonus points toward the buy of a new car, and and so sends out information virtually new vehicles at the most likely fourth dimension that a consumer would be interested in purchasing a new car.

The information mining approach is not without issues, however. First, the costs may be as well high to justify data mining, something that may only exist discovered after huge setup costs have been accrued. Second, data mining has to be coordinated so that various departments or subsidiaries practice not all try to attain the customer at the same time. In addition, customers may think their privacy has been invaded and resent the offers that are coming their way. Finally, customers may think profiles created solely on the basis of their credit carte du jour purchases present a highly distorted image of who they are.

Analysts should have responsibleness for because the ethical aspects of any data mining projects that are proposed. Questions about the length of time profile material is kept, the confidentiality of it, the privacy safeguards included, and the uses to which inferences are put should all be asked and considered with the client. The opportunities for abuse are apparent and must exist guarded against. For consumers, data mining is another push button engineering, and if consumers do not want to be pushed, the data mining efforts will backlash.