E-Commerce Information Systems Term Paper

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[. . .] These are the technologies upon which a program is either explicitly or implicitly-based or susceptible, and they are often the root cause of change in today's program management and control environment. It is thus considered crucial that they be specifically recognized and fully integrated into the program management (Thorn 2001).

Relational Databases

Relational databases have dominated database management systems for the past decade, and they are now facing challenges from object-oriented databases. These new databases enable users to access vastly different types of data and do so in a way which is efficient and which increases productivity. In this way, types of information (such as graphics, sounds and pictures) which were difficult to organize into relational databases, can be put into databases and easily accessed by users in either distributed or nondistributed environments. As hardware becomes more accommodating (in the sense of being more powerful and less expensive), these types of databases are likely to gain in popularity over traditional relational databases.

These issues are solved in different ways for different purposes. For instance, most accounting information systems and enterprise resource planning systems rely on databases to store and retrieve information, and a single database can supply information to accounting, operations, marketing, personnel, and sales. At the same time, many of these functional areas may have their own specialized software applications, but still they may derive their data from one general database. For this reason, this database is to be maintained separately and independently from other functional system applications. Analysts state that the benefits of having one database for all applications are considerable in terms of efficiency and costs. Data redundancy can also lead to data inconsistency as errors result when obsolete data are used for management decisions. Therefore, data should be stored and maintained independently to make sure that updates, manipulations, and deletions occur in only one place. The relational model structures data into tables, making them flexible enough to allow the data to be manipulated into almost any configuration. The relational model has the ability to decrease data redundancy and increase data integrity (Tribunella 2002).

In choosing the technology solution for a given data need, certain features should be considered. This means making a careful study of needs, the complexity of the infrastructure, the resources and skill level of the IT staff, and the capital available for investment. To make an informed choice, the manager should consider four areas: reliability, scalability, security, and total cost of ownership. The meaning of reliability is self-evident, and it means features to ensure the integrity of the data and to assure that the data is safe in the event of a system failure or disaster. The feature of scalability is measured in both its ability to manage increasingly large volumes of data and increasingly large numbers of users or transactions without compromising the system's overall worth. There are two approaches for dealing with scalability, known as scale-up and scale-out. Scale-up is achieved by increasing the computing power of a single machine by adding CPUs, memory, or enhancing the networking components and storage systems. Scale-out, on the other hand, is accomplished by the addition of more servers to the system to spread the workload over a number of machines (Keehn 2002).

In developing data management technology, while the amount of data to be managed increases all the time, what is sought in the technology is to store the growing amount of data in a smaller and smaller space. Knowledge management and Knowledge databases are supposed to be about linking people to serve customers, or people needing expertise with people who have expertise. This means not that the technology not only stores information but also connects each user to others who may have answers they do not, allowing for the free exchange of data instantly. One analyst notes how this would work in a university setting, stating the possibility of creating a database that includes information on the most recent research different colleagues have conducted or reviewed, on recent successful professional development efforts, on recent media and multimedia that they have created, on recent teaching innovations, and so on. Each individual instructor could then access this information as needed and make use of it in a different context. This would be a form of expert system bringing together the knowledge of different people in a way that could be accessible to all (Vanhorn 2001).

Oversight

Information technology has traditionally been the domain of a company's management information system (MIS) department. Companies-based decisions to implement such applications on demonstrated cost savings or avoidance and the availability of MIS resources (both system resources and labor resources for development and support). The advent of personal computers has strengthened the argument for implementing a database application because it greatly relieves the burden on the MIS department (Inmon 1998, p. 88NA1). ADVANCE d3?

In many operational information systems, the data represent a structured collection. One record exists for each item and each has the same set of attributes. Information systems also have validation and referential integrity requirements. There should be no duplications, and multiple references for the same classification of data should have the same characteristics (for example, the same address for multiple contacts at a single company in a customer database).

In transaction-based environments such as banking systems and accounts payable, the cycle and the pre- and post-transaction processing work is predictable. Therefore, the format and schedule of reporting is also predictable. In these applications, standard, cyclical reporting has proven successful for monitoring and controlling transaction processing systems. Knowledge-based work, however, is neither repetitive nor predictable. A knowledge worker uses a wide variety of resources, including information resources, to manage a heterogeneous workload. Because it is impossible to predict the order, style or timing of the worker's information requirements, the information system should not compromise variety, flexibility or responsiveness.

Managers

Many organizations' reporting tools require significant programming expertise, which usually prevents a knowledge worker from working directly with information systems databases. The need for unencumbered access to information has made end-user reporting a key issue for the information system industry. In order to give knowledge workers flexible, responsive access to information system databases, software vendors have developed four broad categories of products: programmers' report-writing tools, database connect tools, business views, and natural language.

Relational databases, which have dominated database management systems for the past decade, are facing challenges from object-oriented databases (Vowler 36). ADVANCE d3? These new databases enable users to access vastly different types of data and do so in a way which is efficient and which increases productivity. In this way, types of information (such as graphics, sounds and pictures) which were difficult to organize into relational databases, can be put into databases and easily accessed by users in either distributed or nondistributed environments. As hardware becomes more accommodating (in the sense of being more powerful and less expensive), these types of databases are likely to gain in popularity over traditional relational databases.

Data warehouses address the weaknesses in relational databases by offering historical data, integrating data and offering detailed as well as summary data. By bringing together all of the components of a company's various computer systems, including those relating to customers, employees, vendors, products, inventory, and financials, companies are able to create large repositories of data. This is what led to the term "data warehouse." Various methods have been developed for retrieving information from the data warehouse; commonly referred to collectively as data mining, these techniques give companies the ability to use data in ways which were previously unavailable because of the sheer size of the data involved (Inmon 1998, p. 88NA1). ADVANCE d3?

Although a number of large companies have implemented data warehouses, the size of the data stored in such warehouses and the costs associated with developing these projects has prevented some companies from taking this route. Instead, a number of organizations have decided to use data marts, which are smaller and more focused than data warehouses. Regardless of whether a data mart or data warehouse is used, companies are advised that they should not expect dollar savings; instead, they will see increased productivity through better management of their data. However, although data marts are easier to develop than data warehouses, they still require a high initial investment relative to the simple relational databases of previous years (Deck 1998, p. 17). ADVANCE u3?

ADVANCE d3?

Throughout the development of data warehouses, academics have been critical of the concept and its application. The research and development which has supported the creation of data warehouses and data marts has been largely driven by the market: as customers have demanded greater access to vast stores of information kept on large servers, software vendors have responded with various data warehouses and data marts. This market focus is likely to… [END OF PREVIEW] . . . READ MORE

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