I was just reading an article about IT’s desire to better understand costs (see here). The article reminded me that back in July, I posted (here) an invitation to a survey that Burton Group was conducting as part of my ongoing research on data center cost models. At the time I promised to share some of the results on the blog. Before I go into the details of the survey I’d like to link to some additional information that may be helpful.
- If you are Burton Group customer, you can download “Counting the Cost of the Elephant in the Data Center” which contains a more detailed look at cost modeling and also includes a sample spreadsheet that can be used a starting point for looking at infrastructure costs (i.e. what does it cost each year to run a server).
- There is a recording of my 1/2 day workshop on data center economics from Catalyst 2009 which is available to anybody with a browser. In the interests of full disclosure, this is $199 to purchase.
And while I’ve got your attention, if anybody is interested in talking to me about data center cost models and how they are used in your business, please get in touch via email to email@example.com.
Anyway, on the results of the survey…
The 80+ respondents to the study were well distributed across industry segments and company size:
- Participants in all major IT sectors including; education, government, pharmaceuticals, aerospace, energy and consulting.
- Companies ranging in size from less than $50 million to greater than $10 billion in annual revenues.
Out of all the companies surveyed, less than 25% had a comprehensive model for their data center costs. A similar number had no cost model of any kind. The rest of the companies surveyed had some form of cost model that was limited in scope and application.
Of the companies that did not maintain a cost model or only maintained a limited model, the key problems were either lack of motivation (i.e. costs are not a major part of the decision making process) or difficulty in collecting the necessary information because of it’s spread across multiple fiefdoms within the organization (i.e. facilities, purchasing, human resources, etc.)
As to the contents of the data center cost model, at least 25% of those using a cost model did not include any information about power, cooling, or facilities costs. This represents a serious shortcoming as these costs are a significant part of the overall IT costs.
Another point that emerged is that the old idea that a data center should be expected to last twenty years is no longer valid. Of the survey respondents, more than 85% had built or upgraded a data center since the year 2000, with 45% having done the work in the last year to 18 months. Yet 65% of the respondents expect to outgrow their current facility within five years. The major factors dictating data center life were limits on floor space and power distribution, with cooling coming in a distant third.
The biggest gap in terms of data required for a data center cost model was in the area of power. Only about 25% of respondents could track power consumption down to the individual piece of IT equipment, rest had varying levels of insight into their power usage. Interestingly this 25% figure was mirrored almost perfectly by the number of respondents who had calculated PUE for their data center.
Another weakness common to the majority of respondents was the availability of detailed purchase price information for equipment in the data center. Roughly 20% of respondents had the purchase price information in the corresponding Configuration and Management Database (CMDB) entry for the equipment. Another 23% had a spreadsheet that has to be maintained by somebody in the IT organization. But over 50% of respondents would have to track down the information from other sources in the company such as a purchasing department.
Overall, the survey indicated that many IT organizations are not in a position to fully understand the costs of running their data center. The lack of a complete understanding of data center costs creates a potentially dangerous hole in the IT decision making process, especially because IT decisions are increasingly cost driven.
Posted by: Nik Simpson