Systematically improving the quality of IT utilization data

Efforts to reduce the cost of ownership for enterprise IT environments are 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. In this paper, we begin by raising awareness of this 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 until the root cause(s) of the imperfections can be fixed. To demonstrate the value of our solution, we describe a capacity planning use case. Application of our solution to an ongoing capacity planning effort 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 imperfection types rather than 1.