SUMMARY OF STUDY OBJECTIVES
The need for data collection and storage. Businesses need to collect and store data so that they can properly record and complete transactions in their day-to-day operations. This enables the company to carry out transactions in a manner that will satisfy customers and suppliers. It also allows the company to maintain detailed records that facilitate follow-up, feedback for future decision making, and preparation of financial statements.
Methods of storing data and the interrelationship between storage and processing. Data may be stored in characters, fields, records, files, and databases. The media on which data are stored may be sequential access or random access. The choice of data storage media may depend on the use of the data. Data that are accessed sequentially may be stored on magnetic tape, whereas disk storage is used for data that need to be accessed randomly or directly.
The differences between batch processing and real-time processing. Batch processing involves the grouping of similar transactions into a batch and processing all of these items together. On the other hand, real-time processing involves continuous processing, where transactions are processed as soon as they occur or as soon as they are entered into the system. Although real-time processing is preferred because of the timely response, batch systems are still used for processing large volumes of data due to their lower cost and ease of control.
The importance of databases and the historical progression from flat-file databases to relational databases. A database makes it possible for an organization to share data, often across widely distributed users. Flat-file databases store data in two-dimensional tables in text format. Hierarchical databases use an inverted tree structure to define relationships among data. Network databases are an advanced version of a hierarchical database because they use the inverted tree structure; however, networks share branches. Relational databases are used in practice today more than the other types of databases. A relational database stores data in two-dimensional tables that are joined in many ways to represent many different kinds of relationships in the data.
The need for normalization of data in a relational database. The two-dimensional tables used in a relational database must be flexible enough to handle an unlimited number of queries. To obtain this flexibility, the database tables must be designed according to precise specifications. Data must be translated, or normalized, in order to meet these specifications.
Data warehouse and the use of a data warehouse to analyze data. A data warehouse is an integrated set of data that spans a long period (up to ten years). The data are nonvolatile; however, they are used to support the company's strategic planning and the ongoing decision making of its managers. In order to build a data warehouse, the data must be identified, standardized, and cleansed before they are uploaded. An operational database, on the other hand, contains current-period data that are continually updated to support current operations and reporting.
The use of OLAP and data mining as analysis tools. Data mining involves analyzing data for patterns that can be used to predict future behavior. Data mining techniques are used in business organizations to predict customers' buying behaviors. OLAP is also used to find trends or patterns in data. OLAP is the software tool that allows managers to access and analyze the data in a data warehouse.
Distributed databases and the advantages of the use of distributed data. Distributed data processing is very important in companies where operations are dispersed among multiple locations. A distributed database is a dispersed system of networked databases. Centralized data processing, on the other hand, requires that the database is maintained at one centralized location, usually the company's headquarters.
Cloud-based databases. A growing trend is the use of cloud databases. A company that stores its data in a cloud database has the advantages of scalability, expanded access, reduced IT infrastructure, and cost savings. However, it must rely on the cloud provider's controls pertaining to security, availability, processing integrity, and confidentiality.
Controls for data and databases. Business organizations must exercise care in ensuring the protection and security of its data. The most crucial controls for data protection include unauthorized access, adequate backup of the data, and data integrity. These topics were discussed in detail in Chapter 4.
Ethical issues related to data collection and storage. Companies have ethical obligations to their customers and employees, while customers and employees have ethical obligations to each other and to the companies with whom they do business. Each of these parties must be discreet in their business dealings so that privileged information is not divulged to outsiders. Employees also have an ethical duty to protect information obtained in the course of their jobs; such information should not be shared with others. Finally, customers have an obligation to provide complete and accurate information to the companies with whom they are doing business.