IT Asset Management for cost reduction, which has often been an afterthought, has risen to be the top priority for IT investment allocations in 2010. While IT budgets will remain flat in 2010, the share of expenditures on cost reduction and corresponding technologies for consolidation, virtualization and alignment with business will increase by all accounts. The investment on projects for lowering operating costs increased the most by a whooping 19% percentage points from 39% in 2009 to 58% in 2010. Server consolidation and virtualization has been accorded the highest priority at 46% of the allocation. The times call for solutions that help determine the metrics of baseline consumption of resources in data centers and the benchmarks to be achieved. Companies can find cues for cost control by comparing industry averages with their own metrics. The data for standards for the overall efficiency of the data center are prepared by the Uptime Institute and energy efficiency from the Green Grid. Startling insights are uncovered when the number crunching is completed. The Uptime Institute, for example, revealed that 19 datacenters it surveyed wasted 1.9 watts of electricity for every watt they consume! The hard work is in measuring the devilish details of baseline numbers of their own data centers. The data, if it collected at all, is manually updated, scattered in the departments who own the specific IT assets and is likely hard to slice and dice. If the measurements are done manually, an expense of 250 hours of work for a facility with 1000 servers is incurred. Without automation of asset discovery and collation of data, the task of estimating the baseline numbers is overwhelming and most companies don’t even try. Companies can distinguish themselves by using innovative methods for estimating the baselines and the benchmarks. Intel experimented with more granular measurements of energy efficiency than those available from the Green Grid by determining the metrics for individual servers and the impact the consumption of useful energy and workloads have on their capacity utilization. The improvements were dramatic. For the period March 2008 to May 2009, it was able to replace older servers with more efficient new servers so that installed number of servers decreased by 14.2% while the compute capacity (EDA MIPS) increased by 96.4% and energy efficiency (EDA MIPS/kW) increased by 309%!  Without software to automate the measurements and the reporting of data, the servers would have continued to be underutilized.