Business Data Management
Business data management is an essential activity in all types of companies. This article explains the four basic steps in business data processing: Data creation, data storage, data processing, and data analysis. Various methods to accomplish the four steps are examined along with changes in technology that have impacted how the steps are being accomplished in a modern enterprise. As business practices have changed over the last few decades so have business data management methods. The emerging supply chain business model is explained along with its implications for business data management. The necessity for contingency planning for business data management is examined and the basic steps to contingency planning are explained.
Keywords Business Information Systems; Contingency Planning; Data Dictionary; Data Storage; Data Warehouse; Decision Support Systems; Executive Information Systems; Management Information Systems; Information Storage & Retrieval Systems
Business Information Systems: Business Data Management
Over the last two decades corporations have been placing increased emphasis on the management of data (Goodhue, Quillard & Rockart, 1988). Business data management is a core activity for all businesses and supports a wide array of activities including financial management, accounting, purchasing, sales, human resource management, facilities management, product planning, manufacturing, and strategic planning. The activities of virtually every employee in every organization are dependent on business data management. There are four basic steps to business data management: Data creation, data storage, data processing, and data analysis.
Generally, it is the central Management Information Systems (MIS) department that designs, implements, and maintains the computer systems, networks, and applications software that support the four basic steps of business data management. The director of the MIS department, or Chief Information Officer (CIO) often participates in business decision making at the highest management level in the organization (Moynihan, 1990). This participation helps to align the activities of the MIS department with the strategic business goals of an organization (Grant, 2003). The strategic alignment of MIS activities and business goals can provide a company with a competitive edge as well as reduce overhead by avoiding expenditure for less than useful management information systems.
Trained information technology professionals staff the MIS department. MIS staff specialize in the many different disciplines necessary to create and maintain systems to support business data processing. These include operations specialists that support the data centers that house computer and storage systems, network staff that maintain the data communications systems that link systems together, and applications programmers who design and maintain software. Other specialties include database administrators who are responsible for database software and applications, systems analysts who keep large systems up-to-date and operational, and helpdesk staff that support end-users throughout the company.
The Creation of Data
Data is created through every-day business processes such as the production of items, the consumption of supplies or resources, the sale of goods or services, and customer service activities. In a consumer goods retailer, for example, data is created when inventory is ordered, sales are made in stores, employees clock in and out for work, and when accounts are paid or collected. The larger the retail operation the more data that is created on a daily basis, and the more important it is for data to be accurate and readily available to support business processes.
Achieving good data management requires an understanding of data, data management systems, and data management software (Chalfant, 1998). This means that the staff in the MIS department must understand the data needs of the organization in order for them to best apply their skills to business problems. But this requires that managers and data users throughout the orgnization understand their data and how they use it. Interdepartmental teams can be established to address business information needs. These teams can identify the organization's data management needs, what data is needed to meet those needs, where the data will come from, how it will get into a database, and what can be done with it after it is stored.
One of the most important steps in creating and maintaining good data is the establishment of a data dictionary (DD). A DD is a database of descriptors for each piece of data used in an organization's data management activities. A wide assortment of DD software packages is available. In general, a DD system will hold data that describes data along with its associated structures, processes, users, applications, and equipment (Vanecek, Solomon & Mannino, 1983).
The Storage of Data
Data storage has three major elements: The software used to manage stored data (most often database software); the technology used to store data (disk drives); and the networks which connect computers and computer users to data storage systems. The importance of database software has increased over the last three decades and has enabled banks, retailers, and manufacturers to grow beyond small local operations into global giants. Disk drive technology has also dramatically changed with increases in storage capacity, manageability, reliability, and accessibility. Data communications networks have become like the nervous system of an organization; allowing data to be instantly collected from locations across the country or around the world. The networks also allow data to be utilized by managers and decision-makers in offices far from where data is created or stored.
The primary tool for managing large amounts of data is database software. IBM, Oracle, Microsoft, and other software companies offer a wide variety of database software packages. The packages are capable of managing several thousand up to billions of pieces of data. Database software can operate on desktop and laptop computers as well as on servers and giant mainframe complexes. Database software is used in virtually all industries especially those that are transaction focused and need to track large quantities of items or activities.
Organizations with large amounts of data are turning to data warehousing models of data storage. The five basic steps required to build a data warehouse is planning, design, implementation, support, and enhancement. In the planning and design phases, metadata is created. In data warehousing, “metadata refers to anything that defines a data warehouse object, such as a table, a column, a query, a report, a business rule, or a transformation algorithm. Building a data warehouse is a complex process requiring careful planning between the IT department and business users” (Gardner, 1998, p. 59).
Data storage technology has rapidly evolved over the last two decades. In large organizations there are still what many refer to as disk farms, which are vast conglomerations of high-density disk drives, capable of storing billions and billions of business records. New approaches to storage technology include the storage area network (SAN), which is a “specialized, high-speed network attaching servers and storage devices. A SAN allows any-to-any connection across the network, using interconnect devices such as routers, gateways, hubs, and switches. It eliminates the traditional dedicated connection between a server and storage. It also eliminates any restriction to the amount of data that a server can access, usually limited by the number of storage devices attached to the individual server” (Tate, Lucchese & Moore, 2004, p. 1.1).
The Processing of Data
There are two major components required for data processing: The software used for processing data and the computer systems on which data is processed. The goals of data processing procedures are to take large amounts of data and make it useful to the personnel responsible for operations, managers that oversee various business functions, and planners who rely on data to forecast business activity. Data is processed for day-to-day operations in many ways using several different types of software ranging from accounting software to inventory control or payroll. In addition to helping to manage the storage of data, database software can also be used to generate planned reports or on-the-spot queries necessary to make business decisions.
The second essential element in processing data is the computer system on which the data is processed. These systems can range from servers capable of supporting small organizations to large complexes of mainframe systems capable of processing billions of pieces of data in a few hours or in many cases just a few minutes.
IBM has dominated the business data processing field for several decades. Ever since computing started to be used commercially, IBM has been a key player in providing businesses with information technology. Historically, the mainframe has performed the role of a central data server for many large enterprises and has typically provided high data throughput, scalability and strong security capabilities. However, over time business computing has evolved and now most companies have a multi-tier hardware infrastructure with various types of servers spread throughout the enterprise.
For decades, mainframes have been viewed as large and very expensive systems. However, over the last ten years are so, mainframe technology has become more scalable and systems are available to support the largest global companies as well as small companies. The new age mainframe can have from one to 54 processors in a single system. In addition to flexibility in the number of processors, the new mainframe provides scalability in memory and in input/output capabilities.
The Analysis of Data
Complex data analysis, beyond what...
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