Efforts to reduce the cost of ownership for enterprise IT environments is spurring the development and deployment of data-driven management tools. Yet, IT data is imperfect and these imperfections can lead to inappropriate decisions that have significant technical and business consequences. Current responses to imperfections in IT data are ad-hoc and incremental owing to limited awareness of the problem and to data quality being an indirect business goal. In this paper, we begin by raising awareness of the imperfect IT data problem through examples of the imperfections that occur, and a discussion of their causes and implications on IT management tasks. We then introduce a systematic approach for addressing such imperfections. Our approach allows best practices to be readily shared, simplifies the construction of IT data assurance solutions, and allows context-specific corrections to be applied during the time it takes fixes to underlying imperfection causes. We demonstrate the value of our solution through two case studies. For example, it is being used in an ongoing capacity planning effort, and reduced the (human) planner's time requirements by ≈3x to ≈6 hours, while enabling him to evaluate the data quality of ≈5x more applications and for 9 rather than I imperfection type. 12 PagesExternal Posting Date: July 6, 2008 [Fulltext].Approved for External PublicationInternal Posting Date: July 6, 2008 [Fulltext]
PDF
download
878987797
028-85220240
