Role and Value of Data Warehousing - Coursework Example

Introduction At the present, there are extensive developments in the field of database technology. Watson & Ariyachandra (2006) state, new and advanced technology of the database is used to manage large volumes of organizational and business data. Since, this utilization of database technology supports the business and query based report production and this is the main traditional utilization of this technology. Though, the size as well as volume of data being handled elevates new and interesting concerns. Presently this technology is used to facilitate businesses in attaining effective business advantage and underlying business processes. However, this new database based technology is also facilitating the improvements in business processes and decision making. Thus, organizations use the database based business intelligence system those could comprise the data warehouse, data mining tools, and OLAP technology. The fundamental goal of the business intelligence systems (BIS) and data mining is to put embeddable analytic techniques that offer the potentials to discover, model, and summarize the data for effective business decision making. However, prior to applying these techniques to data, the data has to be usually controlled into business history data repositories; those are acknowledged as the Data warehousing and analytics. In addition, Data warehousing and analytics could necessitate incorporation of numerous sources of data that can engage dealing with numerous database systems, numerous formats, cleaning the data, distributed databases as well as creating unified logical inspection of the fundamental non-homogeneous data (Watson & Ariyachandra, 2006; Stair & Reynolds, 2003). This paper presents a detailed analysis of the role and value of data warehousing and aspects of business intelligence. In addition, this paper will provide a comprehensive analysis of the issues and challenges of development/implementation of current technologies and approaches for the different business intelligence systems. Data warehousing and analytics The description of fundamental terms is essential before starting discussion on main topic. At the present, the data and information are most important and precious resources for an organization. In addition, the process of decision making depends on information. Here, information is a collection of data that have been processed and transformed into a form that is vital and useful to human beings. On the other hand, the data are collection of unprocessed facts showing events taking place in organizations or the physical environment prior to they have been transformed and managed into a form that people can recognize and utilize. In addition, the data can comprise text, numbers, images and videos (Shelly et al., 2005, p.6; Norton, 2001, p.4; Laudon & Laudon, 1999, p.7). An information system collects, processes, stores, evaluates, and distributes information for a particular function. Similar to any other system, an information system encompasses inputs (for instance, instructions and data) and outputs (reports, calculations). It performs operations on the inputs by using technology like PCs and develops outputs that are offered to users or to other systems via electronic networks (Turban et al., 2005, p.18). In addition, “the term information system can be defined straightly as a set of interconnected components that bring together, process, store, and present information to carry decision making and control in a corporation”. In addition, the information systems keep and maintain information about important people, places, and things inside the corporation or in the set up surrounding it (Laudon & Laudon, 1999, p.7; Hoffer et al., 2007, p.429). A most important improvement in the development of decision support systems has been the development of data warehousing. Since, a data warehouse is a large size database developed to support executive support systems, decision support systems, and other analytical and business operations. In addition, the utilization of Data warehousing and analytics is a most important element of business intelligence, the collection and utilization of huge amounts of data for analysis or query by DSS, ESS, and intelligent systems (Turban et al., 2005, p.55). Data warehousing and analytics facilitate or support corporation’s executive information systems and decision support systems. Here, a decision support system is an interactive, computer-based information system that offers an elastic tool for situation analysis and facilitates managers to put attention on the future. However, to accomplish the decision support system level of complexity in information technology, a corporation must have developed a management information system (a computer-based information system that utilizes data collected by TPSs as input into programs that create regular reports as output) and a transaction processing system (a computer-based information system that maintains the record of the transactions required to carry out business activities). An executive information system is a user-friendly decision support system that is developed mainly for executives (top managers), since, it mainly facilitates strategic decision making. Additionally, an EIS is also acknowledged as an executive support system (ESS) (Hutchinson & Sawyer, 2000). A data warehouse is a read-only, informational database that is filled with comprehensive, outline, and immunity data and information produced by other management information systems and transaction systems. The data warehouse can then be used by managers and executives with DSS tools that produce an almost unlimited range of information for unstructured decisions. In addition, DSS tools encompass PC-database management systems for instance, Microsoft Access, spreadsheets for instance, Microsoft Excel, custom reporting tools for instance, Brio Technology’s Brio Query and Seagate Software’s Crystal Reports, and statistical analysis programs for instance, SAS Institute’s SAS (Whitten et al., 2000, p.48). The data warehouse supports the storage of metadata (data about data). Both the data and the meta-data are sent to the data warehouse. Since, the metadata comprises, principles for managing data, software programs regarding data, and data summaries that are uncomplicated to index and explore (Turban et al., 2005, p.502; Hutchinson & Sawyer, 2000). In addition, the decision makers require brief, consistent information about existing activities, developments, and changes. However, the corporations hold present data only (past data were obtainable through particular IS reports that took a long time to generate). In addition, data frequently divided and stored into detach operational databases for instance, sales or payroll with the intention that different managers make decisions from incomplete knowledge bases. Thus, users and information system experts may have to spend excessive time finding and collecting data. However, the data warehousing deals with this issue by combining the important operational data from the corporation in a structure that is reliable, trustworthy, and straightforwardly accessible from reporting (Laudon & Laudon, 1999, pp.246-47; Watson & Haley, 1998). CHARACTERISTICS OF A DATA WAREHOUSE The main features of data warehouse are: (Turban et al., 2005, p.502) 1. Organization. Data are arranged and categorized by subject for instance, vendor, customer, product, price level, and region, and include information appropriate for decision support only. 2. Consistency. Data in diverse operational databases could be programmed differently. For instance, gender data could be programmed “m” and “f” in one operational database and 0 and 1 in other database. However, in the data warehouse this data will be coded in the same way. 3. Time variant. The data in the data warehouse are stored for a lot of years consequently they can be utilized for predictions, trends, and comparisons over time. 4. Nonvolatile. Once the data are inserted into the warehouse, they could not be updated. 5. Relational. Normally the data warehouse uses a relational framework. 6. Client/server. The data warehouse uses the client/server framework principally to offer the managers an easy access. 7. Web-based. At the present, new data warehousing and analytics offer a well-organized computing environment for web-based applications. Jarke et-al. (2003) outlined that data warehousing has newly achieved a significant momentum as a standard for driving daily company analytical processes (Jarke et al., 2003). Watson & Ariyachandra (2006) stated that business analytics (BA) refer to the technologies, skills, practices, and applications for constant investigation and iterative exploration of past business performance to achieve insight as well as drive business planning. The paradigm of business analytics spotlights on emergent of new insights and recognition of business performance, on the other hand, BI traditionally focuses on the utilization of a steady set of metrics to both carry out present business planning and measure past performance (Watson & Ariyachandra, 2006). According to Kashner (2003), business analytics can formulate extensive utilization of corporate data, quantitative and statistical analysis, predictive and explanatory modeling, as well as fact-based management to develop business decision. In addition, the business analytics can be employed like an input for corporate strategic decisions or can drive completely automated decisions. Additionally, the business intelligence is reporting, querying, alerts and OLAP. In other words, reporting, querying, OLAP, and "alert" technology and tools are able to answer the questions: how many, what took place; where; how often, what actions should be taken and where accurately is the problem. On the other hand, the paradigm of business analytics offers response to the questions like that why is this occurrence; what if these developments carry on; what is the most excellent that could happen- means optimize or what will happen in future for example forecast (Kashner & Zaima, 2003). Chaudhuri & Daya (1997) outlined that data analytics is accurate statistical analysis software that is employed by the people who have achieved proper post-secondary training in statistics and analysis. In addition, these data analytics systems are frequently employed to distinguish trends in the business data gathered over a period of time. Since a huge amount of data employed for this kind of analysis is as well necessary for a business intelligence tool, thus, a number of corporations have united the features and tools of these two software products into one (Chaudhuri & Daya, 1997). Issues and challenges of business intelligence solutions Jarke et-al. (2003) outlined some of main issues those can arise in case of developing/implementing business intelligence solutions. The main and fundamental issue is about that business intelligence solutions such as data warehousing, analytics systems, and data mining tools are not could not work with unstructured data or data having some complicatedness. Thus, they need huge efforts and energy in case of making this data optimal and useful for the business intelligence systems (Jarke et al., 2003). Another main issue comes in case of developing/implementing business intelligence solutions like data warehouse that is about latency. This comes out when huge business data in passed through the ETL (extracted, transformed and loaded) process. Thus, it takes a lot of time and effort and cost augments. Furthermore, the biggest issue that any organization faces is high costs of these systems development and maintenance (Jarke et al., 2003). Use of data warehousing and analytics for effective business intelligence The use of data warehousing and analytics for effective business intelligence is really necessary. In case of effective enterprise resource plans and decision making organizations need more effective business reporting and intelligence. In this scenario the data warehouse based business intelligence system offers better support for the corporate decision making. This also offers competitive advantage through the effective business decision making. In addition, these systems offer quality data management and on time decision making. These systems also offer quality business support through quality data management. These paradigms are used to manage huge business data that can be used for a lot of other business tasks those are impossible to handle with the simple database systems (Turban et al., 2005; Inmon, 2002). Discussion (Opinion) This section covers my thoughts about the topic. The modern and up-to-date tools and techniques of business intelligence technology (data warehouse and analytics) are offering various business advantages and operational support to the organizations. Thus, by implementing data warehousing and analytics organizations can have enhanced business decision power that facilitates them in making business decision. In our business environment we need to take some effective business decisions in interest of business. The implementation of business intelligence technology like that OLAP, data mining tools, data warehouse and other decision support technologies can offer enhanced business support and management power to the organizations. Conclusion This paper has presented the discussion on the role of business analytics and data warehouse in case of effective business decision making and future trends prediction. This research has highlighted the main areas and aspects of the new business intelligence technology and its implication for the enhanced business decision making and performance enhancement. At the present, the implementation of data warehouses and analytical systems has become necessary for the organizations to survive in the competitive business world. I hope this research will offer a comprehensive overview of new technology of business decision support and decision making. References Chaudhuri, S. & Daya, U., 1997. An Overview of Data Warehousing and OLAP Technology. 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