SUCCESS FACTORS IN DATA WAREHOUSE PROJECTS - Essay Example

?SUCCESS FACTORS IN DATA WAREHOUSE PROJECTS Success Factors in Data Warehouse Projects Affiliation Introduction A data warehouseis a large database containing reporting and query tools that stores recent and past data collected from various operational systems and merged for management reporting, analysis, and decision making. Why organizations need to bring data together from different working systems? Obviously, the answer is, to be more beneficial, to be more competitive, or to grow by adding value for customers. This can be achieved by mounting the pace and flexibility of decision making, developing business processes effectively, or gaining a clearer idea of customer activities. The data warehouse is a huge collection of the past and current business data that analyze the old business data for offering special discounts and trend assessment in the past business. These systems also facilitate decision makers to retrieve data as many times as they need without disturbing the performance of the core working systems. A data warehouse merges data that are scattered all over the different working systems and makes them readily accessible for decision support applications (Laudon & Laudon, 1999, p. 247; Inmon, 2002, p. 3; Hoffer, Prescott, & McFadden, 2007, p. 47). There are many factors that play a significant role in the implementation of a data warehouse. This essay presents a detailed analysis of the critical success factors in the implementation of data warehouse projects. Data Warehouse: An overview A data warehouse is a large size subject-oriented database that is designed and implemented with organization-wide access in mind. Additionally, a data warehouse collects and process a mountain of data from a number of sources and the basic purpose of this data collection and processing is to allow its users to be familiar with the data and information they want for decision making and get access to that information by making use of easy to use applications and tools. In addition, data warehouse encompasses a wide variety of tools and technologies such as multidimensional and relational databases, graphical user interfaces, client/server architecture and many more. In the context of a data warehouse system, all these components work with the purpose of combining raw data and facts from a variety of sources into a particular and reliable warehouse that provides an excellent support for decision making and analysis inside a particular domain of the business. In this scenario, the majority of large size business organizations develop data warehouse systems as a key element of their main information systems environment (Alshboul, 2012; Swalker, 2011). Data Warehouse Projects A few years ago, it was a serious challenge for the business organizations to actually make use of the covered data and information and facts stored in the functional systems for management and decision tasks. In this scenario, data management is seen in the sense of data as a significant asset belonging to the entire business organization for management and decision tasks, and not only as the belongings of specific tools and applications, personnel or business areas. Basically, this data collected from a variety of sources is supplied to a managerial part, which is responsible for transforming collected data into understandable and useful information for instance high-class subject orientated information will be accessible just in due course. In view of the fact that data can play a significant role in supporting functioning tasks very competently, hence it does not repeatedly make available information that can transform knowledge and improve the efficiency of business processes efficiency. In the past, these operational data were not accessible in a way that end users could straightforwardly recognize and utilize. In this scenario, in the form of a theoretical framework in the direction of contemporary information processing system a data warehouse was developed for a useful and well-organized practice of the available data necessary for decision support and management (Lehmann & Jaszewski, 1999; Adamala & Cidrin, 2011). In addition, modern data warehouse environment is not only records and collection of business data but it provides excellent and easy ways for accessing and utilizing business information. Today’s data warehouses contain a wide variety of tools and applications which are intended to convince the data and information requirements of business executives and managers all the way through the business organization. In addition, the basic purpose of these data warehouses is not only to provide a support for complex data queries, however generally they allow their users to be able to get rapid, correct, and frequently perceptive information. The research has shown that a data warehouse is believed to be similar to a physical warehouse. In the context of a data warehouse, a number of operational systems generate data parts that can be inserted and loaded into the warehouse. In this scenario, most of those components are summarized into information elements that are kept in the warehouse (Perkin, 2003; Watson & Haley, 1997; B. & Gray, 1999; Sakaguchi & Frolick, 1997). In order to get the required data, users of data warehouse make requests and in response to their requests the desired data and information products that are developed from the stored parts and components are delivered to those users. It is an admitted fact that data warehousing has emerged as one of the most attractive business trends for a number of reasons. One of the most important advantages of data warehouse is that a well-planned and appropriately implemented data warehouse can be acknowledged as a significant competitive tool for the business organizations. On the other hand, there are many unique characteristics and features of a data warehouse that make the implementation of a data warehouse different from the development and implementation of just another software application. The research has shown that not all the business organizations are able to effectively and beneficially develop and implement a successful data warehouse. In fact, there are many more chances of failures than successes. There are many factors and aspects that need to be kept in mind all the way through the implementation process of the data warehouse development project. In fact, these aspects are believed to be more critical for the success of a data warehouse project and ignorance of these aspects can lead this development towards failure. Some of the critical success factors are outlined below (Perkin, 2003; Watson & Haley, 1997; B. & Gray, 1999; Sakaguchi & Frolick, 1997): Critical Success Factors (CSF) Some of the critical success factors for data warehouse projects are: Management and Participation Organization’s Needs and Requirements Selection of an Enterprise Architecture Design and Architecture of a Data Warehouse Selection of a Technology for Data Warehouse Quality of Information and Information Sources Data Warehouse Development Environment Management and Participation In view of the fact a project is a team oriented activity in which a number of stakeholders work together or communication with each other in order to achieve a common goal (Kerzner, 2006). In the context of a data warehouse project, the management of the business organization is the main stakeholder for the reason that this system is basically developed for their use. In this scenario, their active involvement and participation is critical for the success of the data warehouse development project. Similar to top management, all other stakeholders should also take equal part all the way through this development and implementation. In fact, without both the management participation and stakeholders’ involvement projects usually fail. In this scenario, one of the most important responsibilities of top management is to completely support by providing adequate funds and resources throughout the development of data warehouse and usage. Additionally, sponsorship and support can comprise making sure adequate resources will be provided throughout the project development and management will be available whenever their support will be needed. In addition, the sponsorship also comprises constant obligation and interest to developing a data warehouse that is the exclusive source for business dimension and decision support data. In some cases, the development and implementation of a data warehouse can require considerable change to the business environment and culture. On the other hand, without top management’s involvement it cannot be made possible. In the same way, top management must make sure that all the potential users of the data warehouse, even heads and managers from every department of the organization must aggressively take part all the way through the data warehouse development project, which can include data warehouse design, implementation, and management. In view of the fact that these potential users of the data warehouse have the significant influence on approval of the warehouse, hence it is very important that their requirements and needs are effectively addressed. Moreover, these potential users are also the owners and sources of operational data and as a result are the most excellent source for subject matter knowledge (Perkin, 2003; Watson & Haley, 1997; B. & Gray, 1999; Sakaguchi & Frolick, 1997). Organization’s Needs and Requirements Without a doubt a project is initiated in order to fulfill some of the business requirements. In the same way, the basic purpose of initiating the data warehouse development project is to fulfill some of the strategic business requirements. Not considering these strategic requirements while implementing a data warehouse certainly lead to project failure. The development team can extract these business requirements from business strategic plan and the performance measures mentioned in the plan. In this scenario, these business requirements turn out to be the foundation for the enterprise architecture and the data warehouse design and architecture. The research has shown that the business organizations should not commence data warehouse system development project, until they have determined their strategic information and business requirements. In the same way, implementing the appropriate performance measures is critical to successful business management. The basic purpose of implementing these measures is to let the firm tell the project team whether development and progress is being made towards achieving its critical goals and whether stakeholder expectations are not being considered. In this scenario, the significant and functional performance measures are believed to be cross-functional which are associated with the suitable strategies, goals, and performance criteria. In addition, business executives and managers make use of the information generated from the data warehouse to strengthen plans, reward management and change plans. On the other hand, business employees make use of this information to regulate processes and act in response to business strategic needs. Furthermore, associating appropriate and precise measures to definite business objectives and goals initiates to make business management processes more of a science and less of an art (Perkin, 2003; Watson & Haley, 1997; B. & Gray, 1999; Sakaguchi & Frolick, 1997). Selection of Enterprise Architecture Enterprise Architecture (EA) is a connection among the various architectures such as the enterprise business architecture (EBA) (which includes strategic goals, plans, objectives, measures) with its enterprise information architecture (EIA) enterprise service component architecture (ESA) and enterprise technical architecture (ETA). Basically, this architecture is believed to be an organized way for the management of business information requirements, definitions and explanations of business systems that provide support for the business strategic requirements. Additionally, this connection can comprise the relationships between a variety of application systems by means of collective software elements and collective data components. In addition, the enterprise information architecture is also responsible for determining and guiding about standards and functional processes that identify the organization’s computing environment. In this scenario, prior to defining, designing, implementing the architecture for its strategic information management systems, such as data mart, data warehouse, decision support, and other business information systems, the business organizations must initially identify and understand the environment in which these new systems will be implemented and work. In this scenario, the enterprise information architecture is a completely normalized data model that is aimed at defining and describing all the data and information that are required for the business organization. Additionally, it shows the relationships between a variety of business data objects, recognition of the proprietor of the data and business rules with reference to the use of the data components. In the same way, enterprise service component architecture is responsible for identifying and documenting all the existing business information systems implemented and used by the business organization to generate, interpret, keep informed, and eliminates business data. Moreover, in order to make the implementation of a data warehouse project a success, a business organization must link all these information architectures. Moreover, every system should also be associated with suitable components of the enterprise technology architecture (Perkin, 2003; Watson & Haley, 1997; B. & Gray, 1999; Sakaguchi & Frolick, 1997). Design and Architecture of a Data Warehouse The research has shown that the most important success factor in scalable data warehouse development and implementation that is critical to success of a data warehouse a data warehouse architecture. Hence, if a firm wants to make the implementation of a data warehouse a success then design and architecture of data warehouse should imitate the business requirements and performance measurement of the business organization (Perkin, 2003; Watson & Haley, 1997; B. & Gray, 1999; Sakaguchi & Frolick, 1997). Selection of a Technology for Data Warehouse A business organization should start searching for and selecting and putting into practice its data warehouse technology only after the data warehouse architecture has been defined and finalized. If not, the selected technology will not be able to support business requirements. In the same way, if the prospected data warehouse system is designed for a particular technology it will be problematic to change or update the implemented technologies with the changing requirements and even when technologies progress and grown-up. At the present, there a large number of technologies are available to implement data warehousing solutions. Some of these technologies can include user interfaces, hardware platforms, warehouse engines, system software, and system security. In this scenario, user interfaces are believed to the most important part of technology for the reason that data warehouse users communicate and access the desired data and information from a data warehouse through user interfaces. In addition, user interfaces have the critical impact on how practical and efficient the data warehouse will be apparent. As discussed above, end- users must actively take part in all the stages of data warehouse implementation. They must take part in the selection of their own interface to the data warehouse. In some cases, end-users having technical knowledge can have a great deal of inspiration towards learning and using all the features and aspects of the data warehouse system they can get their hands on. Basically, these users want to have everything under their control for instance the way they access and format information. In this scenario, these users can include systems or business consultants and analysts who are assigned with additional responsibilities in a particular business department. Additionally, they want to have grip over all those tools and functions of the data warehouse development that is being used by staff members. In fact, the majority of business organizations have all of these kinds of users. Hence, this makes it sensible for the data warehouse development team to provide each type of data warehouse user with a specific user interface. However, the ultimate choice for the development of user interface is that it must support the access metadata intended for the data warehouse. Without a doubt, if the user interface of a data warehouse is user-friendly, easy to use, supports all prospective users to have access to the information they want in the format they want, and performs all these tasks in a reasonable amount of time, it can be acknowledged as the most accurate interface (Perkin, 2003; Watson & Haley, 1997; B. & Gray, 1999; Sakaguchi & Frolick, 1997). Quality of Information and Information Sources One of the most important success factors for data warehousing projects is the quality of information supplied to data warehouse users as well as the sources from which that data are extracted or collected. Without a doubt, in order to make effective use of a data warehouse, data provided to a data warehouse should be of the uppermost possible quality. In addition, it must be correct, pertinent, comprehensive and brief. Additionally, it must be appropriate and up to date. In the same way the sources from which data is collected should be of the highest quality. However, it can be made possible by adopting practices for instance implementing a single point of entry for any data element and thorough edits (Perkin, 2003; Watson & Haley, 1997; B. & Gray, 1999; Sakaguchi & Frolick, 1997). Data Warehouse Development Environment The last but not least the most important success factor for data warehouse implementation is the selection of a data warehouse environment. In fact, it has the critical impact on the overall data warehouse development. With the purpose of consistently designing, developing, and implementing a data warehouse, the business organizations must select a suitable development environment that allows the development team to put into practice best tools and techniques. Some of the important elements of this environment can be project methodology, teams and tools (Perkin, 2003; Watson & Haley, 1997; B. & Gray, 1999; Sakaguchi & Frolick, 1997). Recommendations By identifying the critical success factors of a data warehouse project, the management of a business organization along with project manager can be able to pay their full attention to strategies that can lead this development to successful project completion. The business organization that is going to implement a data warehouse must start this process from a thorough planning. At this stage, it must identify its goals, objectives, requirements and resources. In addition, this plan should be strictly followed and monitored in order to complete a project on time and within resources. The firm should also ensure that all its employees are satisfied with the decision of implementation and their participation should be ensured and encouraged (Humphries, Hawkins, & Dy, 1998; Adelman, 2005). Conclusion A data warehouse is a latest emerging trend which has become a most important factors in the success of a large number of business organizations. Basically, a data warehouse is a large collection of data that is gathered for the purpose of supporting decision making and managerial processes. At the present, the majority of business organizations implements data warehouses. However, the majority of data warehouse development projects prove to be failures because there are a number of factors involved in the successful implementation of a data warehouse. This paper has presented a detailed analysis of data warehouse technology. This paper has discussed a number of success factors that need to be considered before the implementation of a data warehouse project. This paper has also presented recommendations against each success factor that can help project managers to deal with a specific challenge. In conclusion, if a data warehouse is implemented effectively then it can play a significant role in the success of a business organization. References Adamala, S., & Cidrin, L. (2011). Key Success Factors in Business Intelligence. Journal of Intelligence Studies in Business, Volume 1 Issue 2011, pp. 107-127. Adelman, S. (2005, February 09). Critical success factors for data warehousing projects. Retrieved April 22, 2013, from Techtarget.com: http://searchsqlserver.techtarget.com/news/1052587/Critical-success-factors-for-data-warehousing-projects Alshboul, R. (2012). Data Warehouse Explorative Study. Applied Mathematical Sciences, Volume 6 Issue 61, pp. 3015-3024. B., V., & Gray, P. (1999). Factors for success in data warehousing: What the literature tells us. Journal of Data Warehousing, Volume 4 Issue 3, pp. 25-30. Hoffer, J. A., Prescott, M. B., & McFadden, F. R. (2007). Modern Database Management, Eighth Edition. Pearson Education, Inc. Humphries, M., Hawkins, M. W., & Dy, M. C. (1998). Data Warehousing: Architecture and Implementation. London: Prentice Hall. Inmon, W. H. (2002). Building the Data Warehouse, 3rd Edition. New York: Wiley Computer Publishing. Kerzner, H. (2006). Project management: A systems approach to planning, scheduling, and controlling (10th ed.). New Jersey: John Wiley & Sons. Laudon, K. C., & Laudon, J. P. (1999). Management Information Systems, (6th ed.). New Jersey: Prentice Hall. Lehmann, P., & Jaszewski, J. (1999). Business Terms as a Critical Success Factor for Data Warehousing. Proceedings of the International Workshop on Design and Management of Data Warehouses (DMDW'99), (pp. 71-75). Heidelberg, Germany. Perkin, A. (2003). Critical Success Factors for Data Warehouse Engineering. Lexington, MA: Visible Solutions. Sakaguchi, T., & Frolick, M. (1997). A review of the data warehousing literature. Journal of Data Warehousing, Volume 2 Issue 1, pp. 34-54. Swalker. (2011, September 27). Critical Success Factors for Data Warehousing. Retrieved April 20, 2013, from https://blogs.oracle.com/emeapartnernews/entry/critical_success_factors_for_data Watson, H., & Haley, B. (1997). Data warehousing: A framework and survey of current practices. Journal of Data Warehousing, Volume 2 Issue 1, pp. 10-17. Read More