Frolick, Mark

Lindsey, Keith

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Association for Information SystemsAIS Electronic Library (AISeL)AMCIS 2002 Proceedings Americas Conference on Information Systems(AMCIS)December 2002CURRENT ISSUES IN DATAWAREHOUSINGKeith LindseyUniversity of MemphisMark FrolickUniversity of MemphisFollow this and additional works at: http://aisel.aisnet.org/amcis2002This material is brought to you by the Americas Conference on Information Systems (AMCIS) at AIS Electronic Library (AISeL). It has been acceptedfor inclusion in AMCIS 2002 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contactelibrary@aisnet.org.Recommended CitationLindsey, Keith and Frolick, Mark, "CURRENT ISSUES IN DATA WAREHOUSING" (2002). AMCIS 2002 Proceedings. 7.http://aisel.aisnet.org/amcis2002/738 2002  Eighth Americas Conference on Information SystemsCURRENT ISSUES IN DATA WAREHOUSINGKeith LindseyThe University of Memphiskllindsy@memphis.eduMark N. FrolickUniversity of Memphismark@frolick.netAbstractThis study examined recently published cases of data warehouse failure in an attempt to find a set of factorsthat are commonly associated with data warehouse project failure.  In addition, the purported rate of datawarehouse failure is questioned, a possible cause for the high reported rate of failure is offered, and a newdefinition of data warehouse failure is provided.Keywords:  Data warehouse failureIntroductionThere is a common perception that the failure rate of data warehousing projects is 70 to 80 percent (Inmon 2001) and one studyreported a 90 percent failure rate (Conning 2000).  Given the high failure rate, it was expected that there would be a significantnumber of data warehouse failure cases reported in the academic and practitioner literature.  This study was undertaken to performa detailed analysis of these cases to determine whether the presence (or absence) of any specific factors or combination of factorsmight be correlated with instances of failure.  Watson  et al. (1999) reported the details of eight data warehouse projects that were considered failures.  Beyond those, only nineadditional cases of data warehouse failure have been reported since 1995 and many of these did not contain information thatdescribed the mode of failure.  The simultaneous lack of documented failures juxtaposed with the common perception that mostdata warehouse projects are failures does not stand to reason.  Common Failure FactorsThough the evidence was anecdotal, the new cases of data warehouse failure were evaluated and the reasons for failure were foundto fit within the framework provided by Watson et al. (1999).  Common failure factors included weak management and inadequateuser involvement.  Scope creep and turnover of organizational personnel did not appear to be as common or as detrimental aspreviously considered.Ramifications of the Reported Failure RateAbout 3,000 data warehousing projects are undertaken each year.  If the lowest perceived data warehouse failure rate (70%) isaccurate, then each year there are 2,100 failures.   Yet, with the inclusion of the cases analyzed by Watson et al. (1999), only 17cases have been reported since 1995.  The discrepancy calls into question the credibility of the industry statistic.  In addition, the reported rate of failure continues to rise, indicating that data warehouse developers are unable to capitalize onimproved hardware, software, and system development tools and methods.  Finally, it is inconceivable that businesses continueto suffer losses amounting to billions of dollars each year without widespread analysis of these losses.Lindsey & Frolick/Current Issues in Data Warehouse Failure2002  Eighth Americas Conference on Information Systems 39Definition of FailureA possible reason for the discrepancy between the high documented rate of data warehouse failure and low documented instancesof data warehouse failure is the ambiguity of the phrase data warehouse failure.   A standard definition used for surveys andin industry periodicals could help bridge this gap.  Our proposed definition is the formal or informal cancellation of a datawarehouse project, or the condition where a completed projects performance falls significantly short of the business need.  ConclusionsA review of industry statistics might cause one to expect to find details concerning hundreds of cases of data warehouse failureeach year.  The remarkable fact that fewer than 20 cases have been reported in the literature since 1995 precludes a rigorousanalysis of the reasons for data warehouse failure, and further, it causes one to question the common perception that more than70% of data warehouse projects are failures.   A potential source of this discrepancy is the broad definition of the phrase datawarehouse failure.  Finally, it is proposed that a standard definition, based on failure to fulfill the business need, would helpreconcile the simultaneous existence of two mutually exclusive industry conditions.  ReferencesConning, 2000 Data Warehousing and Data Mining in the Insurance Industry: Floods of Information, Fountains of Knowledge,Conning & Company Insurance Research and Publications, 2000.Inmon, W. Information Management: Charting the Course: Little White Lies, DM Review, August 2001, online athttp://www.dmreview.com/master.cfm?NavID=198&EdID=3819 accessed 8/21/01.Watson, H., Gerard, J., Gonzalez, L., Haywood, M., & Fenton, D. "Data Warehousing Failures: Case Studies and Findings,"Journal of Data Warehousing, (4:1), 1999, pp. 44-55.

CURRENT ISSUES IN DATA WAREHOUSING

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CURRENT ISSUES IN DATA WAREHOUSING

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