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

Overview

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 database software provides, has become essential to manage large organizations. This type of data analysis can be performed with a variety data mining, statistical analysis, and decision support software packages. This software helps managers and analysts compile or create statistics on millions of business transactions. These statistics can support business forecasting and planning efforts enabling a company to maintain a competitive edge in a competitive global marketplace.

Data analysis software has evolved over the last 60 years. For several years, such software was rather cumbersome and required custom programming. In the 1970s decision support systems (DSS) were introduced that provided assistance for specific decision-making tasks. While DSSs can be developed for and used by personnel throughout the organization, they are most commonly employed by line staff or middle and lower managers. Among the latest developments are expert systems, which capture the expertise of highly trained, experienced professionals in specific problem domains.

In the 1990s, executive information systems (EIS) or executive support systems (ESS) were being developed in large organizations (Main, 1989). At first these systems were cumbersome and most were stand alone systems requiring time consuming data entry processes. As expected, the technology for EIS has evolved rapidly, and new systems are more integrated with other applications like the DDS or Enterprise Resource Planning (ERP) systems (Watson, Rainer & Koh, 1991).

Data warehouses can also serve as analytical tools and in some cases data warehouses are developed specifically for data analysis purposes. According to Jukic (2006), there are “two main reasons that could necessitate the creation of a data warehouse as a separate analytical data store. The first reason is that the performance of operational queries can be severely diminished if they must compete for computing resources with analytical queries. The second reason lies in the fact that, even if the performance is not an issue, it is often not possible to structure a database that can be used (queried) in a straightforward manner for both operational and analytical purposes” (p. 84).

Applications

Supply Chains Extend the Scope of Business Data Management

A supply chain is a network of organizations with specialized activities that work together, usually in a sequential manner, to produce, distribute, sell, and service goods. According to Kumar (2001), “Supply chain systems support entire networks of manufacturers and distributors, transportation and logistics firms, banks, insurance companies, brokers, warehouses and freight forwarders, all directly or indirectly attempting to make sure the right goods and services are available at the right price, where and when the customers want them.

Having delivered the goods or services, the chain does not terminate. At the front end, through delivery, installation, customer education, help desks, maintenance, or repair, the goods or services are made useful to the customer. At the end of the product life, reverse logistics can ensure that used and discarded products are disassembled, brought back, and where possible, recycled. The scope of the supply chain, thus, extends from "dirt to dirt," from the upstream sources of supply, down to the point of consumption, and finally retirement and recycling” (p. 58).

Conventional strategic thinking relied on the individual firm as the basic unit of competition in a given industry. The individual company created, stored, processed, and analyzed data all produced within the company itself. In a supply chain environment, “the competitive success of a firm is no longer a function of its individual efforts-it depends, to a great extent, on how well the entire supply chain, as compared to competing supply chains, is able to deliver value to the ultimate consumers” (Kumar, 2001, p. 58). Consequently, business data management has evolved from focusing on data produced by the individual entity to that of data created by companies up and down the supply chain (Kumar, 2001).

Information technology plays a key role in the modern supply chain system by supporting business-to-business (B2B) applications. Supply chain management (SCM) is a digitally enabled interfirm process that integrates information flow, physical flow, and financial flow. Research indicates that a firm's IT-based platform capabilities have a substantial effect on supply chain process integration. This capability is deeply embedded into the structure of interfirm operational processes, such as order processing, inventory management, logistics, and distribution; financial processes, such as billing and receivables management; and information processes, such as demand planning and forecasting.

Implementation of IT-based supply chain management systems has been shown to have a positive effect on procurement of materials for production as well as distribution, marketing, and sales after production (Richardson, 2006). The integration associated with these processes is achieved through a variety of initiatives that may include trading partner agreements and supply chain partnerships and even deeply embedded IT capabilities. The development of process integration capability based on an IT infrastructure requires expertise that spans the business process domain, partnership context, and IT (Rai, Patnayakuni & Seth, 2006).

Issues

Contingency Planning for Business Data Management

Many types of incidents can disrupt computer operations, and thus critical mission and business functions. These incidents can include a power outage, hardware failure, fire, or storm. To prevent disruption, many organizations have contingency plans that directly support the “goal of continued operations. Organizations practice contingency planning because it makes good business sense” (NIST, 2004, p. 121). The U.S. National Institute of Standards and Technology (NIST) has worked to develop a model for contingency planning. NIST breaks the contingency planning process into six basic steps:

Step 1: Identify the Mission- or Business-Critical Function

In government organizations, the focus is normally on performing a mission, such as providing citizen benefits. In private organizations, the focus is normally on conducting a business, such as manufacturing widgets. Protecting the continuity of an organization is very difficult if the mission or business is not clearly identified. The definition of an organization's critical mission or business functions is often identified in detailed business plans.

Step 2: Identify the Resources That Support Critical Functions

After identifying critical missions and business functions, it is necessary to identify the supporting resources, the time frames in which each resource is used and the effect on the mission or business if the resource is unavailable. Contingency planning should address all the resources needed to perform a function, regardless of whether or not they directly relate to a computer. The analysis of needed resources should be conducted by those who understand how the function is performed and the dependencies of various resources on other resources and other critical relationships.

Step 3: Anticipate Potential Contingencies

Although it is impossible to think of all the things that can go wrong, the next step is to identify a likely range of problems. The development of scenarios will help an organization to develop a plan to address the wide range of things that can go wrong. Scenarios should include small and large contingencies. The contingency scenarios should address each of the resources described above.

Step 4: Select Contingency Planning Strategies

A contingency planning strategy normally consists of three parts: Emergency response, recovery, and resumption. Emergency response encompasses the initial actions taken to protect lives and limit damage. Recovery refers to the steps that are taken to continue support for critical functions. Resumption is the return to normal operations. Strategies for processing capability are normally grouped into five categories: Hot site; cold site; redundancy; reciprocal agreements; and hybrids. These terms originated with recovery strategies for data centers but can be applied to other platforms.

Step 5: Implement the Contingency Strategies

Once contingency planning strategies have been selected, it is necessary to make appropriate preparations, document the strategies, and train employees. Much preparation is needed to implement the strategies for protecting critical functions and their supporting resources. For example, one common preparation is to establish procedures for backing up computer data files and applications. Another is to establish contracts and agreements, if the contingency strategy calls for them; or if necessary, renegotiate existing service contracts to add contingency services. Backing up data files and applications software is a critical part of virtually every contingency plan. Backups are used, for example, to restore files after a computer virus corrupts the files or after a hurricane destroys a data processing center.

Step 6: Test & Revise

A contingency plan should be tested periodically because the plan will become outdated as time passes and as the resources used to support critical functions change. Responsibility for keeping the contingency plan current should be specifically assigned. The extent and frequency of testing will vary between organizations and among systems. There are several types of testing, including reviews, analyses, and simulations of disasters (NIST, 2004, p. 122-132).

A review can be a simple test to check the accuracy of contingency plan documentation. For instance, a reviewer could check if individuals listed are still in the organization and still have the responsibilities that cause them to be included in the plan. This test can check home and work telephone numbers, organizational codes, and building and room numbers. The review can determine if files can be restored from backup tapes or if employees know emergency procedures.

Conclusion

There are four basic steps to business data management: Data creation, data storage, data processing, and data analysis. Business data management supports the day-to-day operations of an organization and provides managers and executives with the analytical support necessary to direct activities and plan for the future. As the global market place has become more competitive and information technology (IT) and telecommunications have evolved, IT enabled supply chain systems have become a widely used competitive business tool.

The dependence on business data management and the technology that supports data management efforts has made contingency planning an essential business function. To be effective, contingency planners must be able to identify critical systems and data and develop alternative means to provide systems and data in order to avoid disruptions in operations and the potential loss of revenue. Contingency plans must also be updated to reflect changes in an organization's needs.

Terms & Concepts

Business-to-Business (B2B) Applications: Applications software that supports interaction and transactions between businesses including supply chain systems, order entry and processing, and collaboration on design or fulfillment requirements.

Cold Site: A facility that can be readily equipped with data processing capabilities and other services necessary to maintain business operations.

Data Analysis: The process of extracting or compiling data from business data management systems that can help guide managers in making decisions or planning strategies.

Data Dictionary (DD): A DD is a database of descriptors for each piece of data used in an organization's data management activities.

Data Processing: Is a systematic approach of bringing order and meaning to data created in every-day business processes.

Data Storage: The process of managing the software and hardware necessary to store and allow access to data created in business operations.

Data Warehouse: Systems designed to manage the storage and access of a wide variety of types of data generated by the different applications software packages used by an organization.

Decision Support Systems (DSS): Applications software packages designed to provide assistance for specific decision-making tasks. While DSSs can be developed for and used by personnel throughout an organization; middle and lower managers most commonly employ them.

Enterprise Resource Planning (ERP) Software: An integrated suite of applications software modules that help organizations manage procurement, logistics, production planning, and manufacturing.

Executive Information Systems (EIS): Applications software packages designed to provide assistance for executives in making high-level management decisions.

Hot Site: A facility already equipped with data processing capabilities and other services necessary to maintain business operations.

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 (Gardner, 1998).

Reciprocal Agreement: An agreement that allows two organizations to back up each other by providing the facilities, technology, or personnel necessary to maintain business operations.

Storage Area Network (SAN): 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 (Tate, et al., 2004).

Supply Chain Management Systems: Applications software which is integrated into a communications network that enables organizations to communicate about and support their purchasing, sales, and shipping needs.

Bibliography

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Suggested Reading

Behera, J., Bhuta, C., & Thorpe, G. (2000). Management information systems: An overview of practices at Marine Container Terminals in Australia and Asia. Transportation Quarterly, 54, 59-73.

Booth, M., & Philip, G. (2005). Information systems management: Role of planning, alignment and leadership. Behaviour & Information Technology, 24, 391-404. Retrieved June 27, 2007, from EBSCO Online Database Academic Search Premier. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=18406091&site=ehost-live

Giraud-Carrier, C., & Povel, O. (2003). Characterising data mining software. Intelligent Data Analysis, 7, 181-192. Retrieved July 2, 2007, from EBSCO Online Database Academic Search Premier. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=10388834&site=ehost-live

Miller, D., & Toulouse, J. (1998) Quasi-rational organizational responses: Functional and cognitive sources of strategic simplicity. Canadian Journal of Administrative Sciences, 15, 230-244. Retrieved June 25, 2007, from EBSCO Online Database Business Source Complete. http://search.ebscohost.com/login.aspx?direct=true&db=bth&AN=1170310&site=ehost-live

Nicolaou, A. (2004). Firm performance effects in relation to the implementation and use of Enterprise Resource Planning Systems. Journal of Information Systems, 18, 79-105. Retrieved June 27, 2007, from EBSCO Online Database Academic Search Premier. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=15672357&site=ehost-live

Kettinger, W. J.; Grover, V.; Guha, S.& Segars, A. H.. Strategic information systems revisited: A study in sustainability and performance. MIS Quarterly, 18, 31-58. Retrieved June 25, 2007, from EBSCO Online Database Academic Search Premier. http://search.ebscohost.com/login.aspx?direct=true&db=aph&AN=9503310355&site=ehost-live