BBA3551 Columbia Southern Data Marts and Data Warehouses Comparison Paper

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Write a one- to two-page (250-500 word) paper that discusses the differences between data warehouses and data marts. Also, discuss how organizations can use data warehouses and data marts to acquire data. You must use the CSU Online Library to locate at least two sources for your paper.

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114 January 2006/Vol. 49, No. 1 COMMUNICATIONS OF THE ACM Seven Key Interventions for DATA WAREHOUSE SUCCESS The success of data warehouses depends on the interaction of technology and social context. We present new insights into the implementation process and interventions that can lead to success. NO DATA WAREHOUSE IMPLEMENTATION CAN succeed on its own. The trick is knowing when and how to intervene. Data warehouses have tremendous potential to present information. They provide the foundation for effective business intelligence solutions for companies seeking competitive advantage. While there have been notable successes [2], there have also been significant failures [4, 7]. What accounts for such conflicting results? In the 1990s, adaptive structuration theory (AST) was developed to understand conflicting results with group decision support systems [5]. This theory analyzes the technological and contextual aspects of the application of a technology, focusing on their interactions. Using AST, we examine the interaction of context and technology, and pinpoint seven key interventions specific to that interaction for data warehouse success. We studied a large organization, in which the data warehouse implementation was successful in some units and unsuccessful in others. From interviews with users in multiple business units, both successful and unsuccessful, we derive several insights that are at odds with previous research. First, users can champion a technology just as successfully as management. Second, a wide range of data can be accessed more successfully than a narrow range (as provided in a data mart). Third, the scope and flexibility of tools offered to users should not always follow the dictum “simpler is better.” While restrictive tools make obtaining information easier for many users, some users will deem the warehouse successful only if they have the more intricate unrestrictive tools required for ad hoc queries and reports. Details of these insights will be provided later. In the organization studied the same data ware- By Tim Chenow e t h, K a r e n Cor r a l , and Hal uk D e mi r ka n COMMUNICATIONS OF THE ACM January 2006/Vol. 49, No. 1 115 house was found to be zational culture can be START both a success and a failure described as very conserby different units within vative and resistant to NO convince intervention the company. (For the change. This conser1 2 YES convince intervention Does top Do users Do users purposes of this study, sucvatism extends to the management support the data support the data YES NO support the data warehouse? warehouse? warehouse? cess was determined only company’s philosophy by whether the warehouse concerning technology. YES was used.) This provides While the company NO an opportunity for underbelieves that technology GO FOR STOP DEVELOPMENT standing data warehouses is important to its corthat is free of some of the porate success, its strong confounding aspects compreference is for mature, Figure 1. Intervention points proven technologies. The company also has a strong monly encountered in during the project’s field studies. Because we initiation phase. belief in the uniqueness of its business model, resultstudied the same technoling in a strong bias for the in-house development of ogy in the same company, as opposed to results from systems. tools or solutions are purchased, they Figure 1. Intervention points during theIfproject initiation phase. different systems and different companies, the must be flexible enough to adapt to the way the observed differences are not due to differences in company conducts business and not dictate business technology or corporate culture. We analyze these dif- processes. ferences using the theoretical framework supplied by In 1995, the company began an in-house developAST: context (social systems); technological innova- ment START effort to design and implement an enterprisetion (in this case, the data warehouse); and the inter- wide data warehouse. They chose to model the NO results indicate action of context and technology. Our warehouse using Inmon’s paradigm [3], which convince intervention 2 YES the interaction of the context with the technology is 1espouses a centralized database, referred to as the convince intervention Do top warehouse,” that is used to integrate and store the key to understanding data warehouse success. “data Do users Do users management the data the data YES inter- support Based on the nature of thesesupport interactions, seven datathe data extractedNOfrom support multiple warehouse? operational systems. warehouse? warehouse? ventions are identified. This data warehouse, in turn, feeds area-specific data marts. Over the years the data warehouse has grown YES in size and complexity as more operational systems COMPANY OVERVIEW NO The company studied is a large, global financial have been integrated into its environment. From the GO effort was very much an institution based in the U.S. TheFORcompany is highly beginning, this development STOP DEVELOPMENT successful and enjoys an excellent reputation both IT-driven initiative. The business justification for the within its industry and among its clients. Its organi- warehouse was never fully developed, and the poten- Chenoweth fig 1 (1/06) If there is no management champion for the warehouse, strongly supportive users of the technology can convince management of the technology’s value. Both management and end users must be convinced of the technology’s value if the project is to go forward. 116 January 2006/Vol. 49, No. 1 COMMUNICATIONS OF THE ACM tial business uses of the warehouse were not carefully proceeded in a top-down fashion, there was a notable enumerated. The prevailing assumption of the devel- success in which the push was bottom-up (intervenFigure 2. Intervention points during the design phase. opment team was that if the warehouse was built and tion point 2 in Figure 1). The users championed the filled with data, then business units would find a use warehouse because they recognized the task fit and value to the organization. They convinced their unit for it. During the fall of 2001, the company sponsored a leader, who initially had been unsupportive of the study to evaluate its enterprise data warehouse, data warehouse, of the warehouse’s value and thereby including how the warehouse was being used. Repre- converted the leader into a supporter. This result is sentatives from the business units affected by the surprising in that it is counter to most prescriptions 3 warehouse were interviewed to determine the nature for technology adoption. It shows that not having a o champion is not necesof those impacts and the want to a sarily a death sentence for degree to which each unit YES ange of YES a technology. If there is had been able to use the a? 3 no management chamwarehouse. We use the Do users want pion for the warehouse, AST framework to anaGO FOR access to a YES DEVELOPMENT broad range of strongly supportive users lyze the results of those data? of the technology can interviews. NO convince management of BUILD A POINTS the technology’s value. INTERVENTION SINGLE Both management and ASTREPOSITORY recognizes that both BUILD BUILD A end users must be conthe features of a technolDATA SINGLE MARTS REPOSITORY vinced of the technoloogy and the context in gy’s value if the project is which it is implemented to go forward. affect the use of that tech4 4 Do Do AST states that the nology. “No matter what users want users want limited data access NO YES ted data access features are YESdesigned into more structured the use and analysis and analysis tools? of a technology is, the a system, users mediate tools? easier it will be to develop technological effects, a consensus among the adapting systems to their PROVIDE PROVIDE UNRESTRICTIVE RESTRICTIVE users about how the needs, resisting them, or TOOLS TOOLS innovation should be refusingPROVIDE to use them at RESTRICTIVE used and the appropriateall” [5]. Because there is a TOOLS Do users ness of that use. Ambiguparticularly high degree understand the task fit? ity about use of the of interaction between technology erodes the the technological dimenDo users users’ comfort with the sions and the contextual understand Figure 2. Intervention points technology, which in turn erodes their respect for it. the task fit? features of a data wareduring the design phase. The third intervention point (see Figure 2) occurs house, AST is an ideal with the design of the warehouse. Data warehouses lens for this study. Important contextual aspects are the “rules and are generally designed around one of two plans. Some resources actors use to generate and sustain this sys- data warehouses are designed with a set of data marts tem” [5]. One of the frequently cited necessary con- that partition the data warehouse into smaller, focused textual conditions for the successful implementation databases tailored to the information needs of a subset Chenoweth fig 2 (1/06) of almost any technology is a champion [1]. It is no of users. Other data warehouses provide a single different for data warehouses. The attitude of a unit’s repository that gives the users a very wide range of leader affects all the factors leading to warehouse data. In the company studied, business units that acceptance. At the company studied, when a leader was strongly supportive (intervention point 1 in Fig- accessed their data through data marts were usually ure 1), users were willing to pursue continuing knowl- more successful than units that accessed it through a edge of the technology even after introductory single repository. That is, most units either requested training. If users do not want to use the data ware- a data mart, or simply did not use the single reposihouse, then it is necessary to provide additional train- tory. Data marts help reduce the inherent complexity and ambiguity associated with data warehouses by ing or motivation to change their attitudes. While acceptance of the data warehouse generally providing a slice of the data that is tailored to meet the COMMUNICATIONS OF THE ACM January 2006/Vol. 49, No. 1 117 PROVIDE UNRESTRICTIVE TOOLS NO PROVIDE RESTRICTIVE TOOLS 5 Do users understand the task fit? PROVIDE TRAINING ON BUSINESS APPLICABILITY YES NO Do users perceive IT as supportive? 6 CREATE COOPERATIVE ENVIRONMENT BETWEEN USERS AND IT YES NO NO Does the unit have one or more power user? NO 7 CREATE POWER USER ROLE YES IMPLEMENTATION AND MAINTENANCE STOP Figure 3. Intervention points during the training and support phase. specific requirements of a business unit. However, one of the most successful uses of this data warehouse was through a single repository. That unit had a strong need for a very broad range of data as well as contextual characteristics that distinguished it from the units that used data weth fig 3 (1/06) marts. In particular, people in that unit were proactive in trying technology. As a result, they were not intimidated by technology and were generally technically knowledgeable. The fourth intervention point (see Figure 2) is in selecting the tools that will be available to the users. Tools range from the highly restrictive, which limit the users’ choices and thereby reduce ambiguity and complexity, to the highly unrestrictive, which require the user to have more expertise. Most units that were successful with data marts preferred restrictive tools 118 January 2006/Vol. 49, No. 1 COMMUNICATIONS OF THE ACM for accessing the data, but the unit that was successful with the single repository rejected restrictive tools. Those users wanted the greater flexibility offered by the unrestrictive tools, and were willing to expend additional effort for more capability. The relevance of the task to the organization, the degree to which a technology supports a task, and the degree to which users understand the task fit can influence the acceptance of the technology. This is the fifth intervention point (see Figure 3). In this study, those business units that successfully used the warehouse clearly understood the relationship between the warehouse and the business issues relevant to the unit. In other words, they saw the applicability of the data warehouse to their tasks. This was especially true of units that received data marts, which not only reduce the ambiguity of the technology, but if properly focused, make the applicability to the unit’s task more obvious. Giving a unit a single repository (instead of the more narrowly focused data mart) can overload the users with information. Beyond the design of the technology, users can be supported by training on the business applicability of the warehouse. This helps them see how the warehouse can assist them in performing their jobs. A lack of knowledge of a technology can lead to difficulties using the technology, or even abandonment of the technology; this leads to the sixth intervention point (see Figure 3). In this study, the business units that successfully used the warehouse typically had two mechanisms to acquire knowledge. First, they had a good working relationship with the warehouse development team. Because of this relationship, members were comfortable going to the warehouse development team for help with problems concerning their data warehouse applications. Conversely, those business units that were experiencing difficulty utilizing the warehouse did not draw on this resource, and generally characterized the development team as unresponsive and “difficult to deal with.” The perceived availability of the data warehouse development team as a support group is an important intervention point in the success of the warehouse. If users lack the perception of support, an intervention will be needed to create a spirit of cooperation. Without that perceived support, users will lack an understanding of the purpose of the warehouse, as well as having difficulty learning how to use it. The second mechanism successful business units had for acquiring knowledge was experts within the unit. These super users understood the data warehouse technology itself and the business issues facing the unit, at least to the degree necessary to extract relevant data from the warehouse. In other words, they The relevance of the task to the organization, the degree to which a technology supports a task, and the degree to which users understand the task fit can influence the acceptance of the technology. understood both the business needs of the unit and the potential of the data warehouse to meet those needs. The super users were also highly skilled at using the tools. These users were recognized as authorities within the unit concerning matters related to the warehouse and played a pivotal role in disseminating knowledge. Providing the resources necessary to create the role of super users is another intervention point for management. We have expressed the seven intervention points as sequential; however, each of these points can be addressed throughout the project. CONCLUSION Organizations are spending millions each year on data warehouse development, but the majority of the efforts fail [6], and little is understood about why these failures occur or how to prevent them. Most previous recommendations for data warehouse applications have suggested one-dimensional approaches. By considering the interaction of the contextual and technical dimensions (the human factors and the specifics of the technology design), the previous results become less contradictory. For example, conventional wisdom holds that having a management champion with a tightly focused (data mart) design and restrictive tools will lead to success. In this case study, we observed that the reverse situation can be just as successful. If the users see the potential of the data warehouse to deliver value to the organization, they can be the champions and convince management to adopt the technology. Similarly, because of its simplicity, the data mart approach is frequently recommended as the preferred approach. Providing what the users want and need is more important. If users understand both the technology and the organization’s business processes, a single data repository may actually be more satisfying for them. In the same way, too little flexibility in tools can be just as harmful as too much. The level of tool flexibility that users require for success varies based on their technical knowledge and their business needs. This article not only explains those paradoxes, but identifies the key points at which interventions may have to occur in order to achieve the level of leadership, focus, and flexibility required for data warehouse success. c References 1. Beath, C.M. Supporting the information technology champion. MIS Q. 15, 3 (Sept. 1991), 354–370. 2. Cooper, B.L., Watson, H.J., Wixom, B.H., and Goodhue, D.L. Data warehousing supports corporate strategy at First American Corporation. MIS Q. 24, 4 (Dec. 2000), 547–567. 3. Inmon, W.H., Imhoff, C., and Sousa, R. Corporate Information Factory. John Wiley, New York, 1997. 4. Kelly, S. Data Warehousing in Action. John Wiley, New York, 1997. 5. Poole, M.S., and DeSanctis, G. Understanding the use of group decision support systems: The theory of adaptive structuration. Reprinted in Organizations and Communications Technology, J. Fulk and C. Steinfield, Eds., Sage Publications, Newbury Park, CA, 1990. 6. Vatanasombut, B., and Gray, P. Factors for success in data warehousing: What the literature tells us. J. Data Warehousing 4, 3 (Fall 1999), 25–33. 7. Watson, H.J., Gerard, J.G., Gonzalez, L.E., Haywood, M.E., and Fenton, D. Data warehousing failures: Case studies and findings. J. Data Warehousing 4, 1 (Spring 1999), 44–55. Tim Chenoweth ( is an assistant professor of networks, operations and information systems at Boise State University. Karen Corral ( is an assistant professor of information systems in the W.P. Carey School of Business at Arizona State University. Haluk Demirkan ( is an assistant professor of information systems in the W.P. Carey School of Business at Arizona State University. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. © 2006 ACM 0001-0782/06/0100 $5.00 COMMUNICATIONS OF THE ACM January 2006/Vol. 49, No. 1 119 INTERNATIONAL REVIEW OF L AW C OMPUTERS & TECHNOLOGY , VOLUME 11, N UMBER 2, P AGES 251–261, 1997 The Data Mart: A New Approach to Data Warehousing PAM ELA PIPE Introduction Vendors have recently begun to deliver low-cost and integrated data warehouse packages intended for the rapid development of departmental data warehouses, or so-called data marts. The availability of these packages requires organizations to consider the role of a data mart in a data warehousing system, and whether a data mart should be built before, after, or in parallel with a corporate enterprise data warehouse. In some situations a set of distributed data marts may even eliminate the need for an enterprise-le vel data warehouse solution. This paper discusses the role of data marts, reviews the pros and cons of the different approaches to building a data warehousing system involving data marts, and also looks at data mart product requireme nts. Throughout the paper, the SmartM art package from Information Builders Inc. is used to describe the characteristics of an integrated data mart package. Types of Data W arehouse · Data warehouses come in all shapes and ...
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