IM 101
Fundamentals of Database System Data vs Information To understand what drives database design, you must understand the difference between data and information. Data are raw facts. The word raw indicates that the facts have not yet been processed to reveal their meaning. For example, suppose that you want to know what the users of a computer lab think of its services. Typically, you would begin by surveying users to assess the computer lab’s performance. Information is the result of processing raw data to reveal its meaning. Data processing can be as simple as organizing data to reveal patterns or as complex as making forecasts or drawing inferences using statistical modeling. To reveal meaning, information requires context. Keep in mind that raw data must be properly formatted for storage, processing, and presentation. In this “information age,” production of accurate, relevant, and timely information is the key to good decision making.
In turn, good decision making is the key to business survival in a global market. We are now said to be entering the “knowledge age.”2 Data are the foundation of information, which is the bedrock of knowledge—that is, the body of information and facts about a specific subject. Knowledge implies familiarity, awareness, and understanding of information as it applies to an environment. A key characteristic of knowledge is that “new” knowledge can be derived from “old” knowledge.
Let’s summarize some key points:
Data constitute the building blocks of information.
Information is produced by processing data.
Information is used to reveal the meaning of data.
Accurate, relevant, and timely information is the key to good decision making.
Good decision making is the key to organizational survival in a global environment. Timely and useful information requires accurate data. Such data must be properly generated and stored in a format that is easy to access and process. And, like any basic resource, the data environment must be managed carefully. Data management is a discipline that focuses on the proper generation, storage, and retrieval of data. Given the crucial role that data play, it should not surprise you that data management is a core activity for any business, government agency, service organization, or charity.
Introducing Database Efficient data management typically requires the use of a computer database. A database is a shared, integrated computer structure that stores a collection of:
End-user data, that is, raw facts of interest to the end user.
Metadata, or data about data, through which the end-user data are integrated and managed.
The metadata provide a description of the data characteristics and the set of relationships that links the data found within the database. A database management system (DBMS) is a collection of programs that manages the database structure and controls access to the data stored in the database. In a sense, a database resembles a very well-organized electronic filing cabinet in which powerful software, known as a database management system , helps manage the cabinet’s contents. 1.3 Role and Advantages of the DBMS The DBMS serves as the intermediary between the user and the database. The database structure itself is stored as a collection of files, and the only way to access the data in those files is through the DBMS
The DBMS manages the interaction between the end user and the database
Having a DBMS between the end user’s applications and the database offers some important advantages. First, the DBMS enables the data in the database to be shared among multiple applications or users. Second, the DBMS integrates the many different users’ views of the data into a single all-encompassing data repository.
Increased end-user productivity. The availability of data, combined with the tools that transform data into usable information, empowers end users to make quick, informed decisions that can make the difference between success and failure in the global economy. The advantages of using a DBMS are not limited to the few just listed. In fact, you will discover many more advantages as you learn more about the technical details of databases and their proper design.
Types of Database A DBMS can support many different types of databases. Databases can be classified according to the number of users, the database location(s), and the expected type and extent of use. The number of users determines whether the database is classified as single- user or multiuser. A single-user database supports only one user at a time. In other words, if user A is using the database, users B and C must wait until user A is done. A single-user database that runs on a personal computer is called a desktop database. In contrast, a multiuser database supports multiple users at the same time. When the multiuser database supports a relatively small number of users (usually fewer than 50) or a specific department within an organization, it is called a workgroup database. When the database is used by the entire organization and supports many users (more than 50, usually hundreds) across many departments, the database is known as an enterprise database. Location might also be used to classify the database. For example, a database that supports data located at a single site is called a centralized database. A database that supports data distributed across several different sites is called a distributed database. The extent to which a database can be distributed and the way in which such distribution is managed are addressed in detail in Chapter 12, Distributed Database Management Systems. The most popular way of classifying databases today, however, is based on how they will be used and on the time sensitivity of the information gathered from them. For example, transactions such as product or service sales, payments, and supply purchases reflect critical day-to-day operations. Such transactions must be recorded accurately and immediately. A database that is designed primarily to support a company’s day-to-day operations is classified as an operational database (sometimes referred to as a transactional or production database). In contrast, a data warehouse focuses primarily on storing data used to generate information required to make tactical or strategic decisions. Such decisions typically require extensive “data massaging” (data manipulation) to extract information to formulate pricing decisions, sales forecasts, market positioning, and so on. Most decision support data are based on data obtained from operational databases over time and stored in data warehouses. Additionally, the data warehouse can store data derived from many sources. To make it easier to retrieve such data, the data warehouse structure is quite different from that of an operational or transactional database. The design, implementation, and
use of data warehouses are covered in detail in Chapter 13, Business Intelligence and Data Warehouses. Databases can also be classified to reflect the degree to which the data are structured. Unstructured data are data that exist in their original (raw) state, that is, in the format in which they were collected. Therefore, unstructured data exist in a format that does not lend itself to the processing that yields information. Structured data are the result of taking unstructured data and formatting (structuring) such data to facilitate storage, use, and the generation of information. You apply structure (format) based on the type of processing that you intend to perform on the data.
Some data might not be ready (unstructured) for some types of processing, but they might be ready (structured) for other types of processing. For example, the data value 37890 might refer to a zip code, a sales value, or a product code. If this value represents a zip code or a product code and is stored as text, you cannot perform mathematical computations with it. On the other hand, if this value represents a sales transaction, it is necessary to format it as numeric. To further illustrate the structure concept, imagine a stack of printed paper invoices. If you want to merely store these invoices as images for future retrieval and display, you can scan them and save them in a graphic format. On the other hand, if you want to derive information such as monthly totals and average sales, such graphic storage would not be useful. Instead, you could store the invoice data in a (structured) spreadsheet format so that you can perform the requisite computations. Actually, most data you encounter are best classified as semistructured. Semistructured data are data that have already been processed to some extent. For example, if you look at a typical Web page, the data are presented to you in a prearranged format to convey some information. The database types mentioned thus far focus on the storage and management of highly structured data. However, corporations are not limited to the use of structured data. They also use semistructured and unstructured data. Just think of the very valuable information that can be found on company e-mails, memos, documents such as procedures and rules, Web pages, and so on. Unstructured and semistructured data storage and management needs are being addressed through a new generation of databases known as XML databases. Extensible Markup Language (XML) is a special language used to represent and manipulate data elements in a textual format. An XML database supports the storage and management of semistructured XML data.