The Real Cost of Manual Asset Management

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1 The Real Cost of Manual Asset Management No Limits Software White Paper #3 By David Cole 2011 No Limits Software. All rights reserved. No part of this publication may be used, reproduced, photocopied, transmitted, or stored in any retrieval system of any nature without the written permission of the copyright holder.

2 Table of Contents Overview... 3 Cost of the Asset Management System... 3 Spreadsheets or Custom Database... 4 Visual Organizers... 4 Asset Management Software... 4 Cost to Initially Collect the Asset Data... 4 Accuracy of Manually Entered Asset Information... 5 Cost to Manually Audit Asset Information... 6 Cost of Stranded Space and Power... 6 Cost of Increased Shutdown Risk... 9 Cost of Increased Mean Time to Repair and Decreased Availability Summary About No Limits Software Bibliography No Limits Software 2

3 Overview There are a number of methods used to manage IT assets in the data center, running the gamut from simple spreadsheets to very expensive asset management solutions costing hundreds of thousands or even millions of dollars. In general, there are three primary ways to manage IT assets: Spreadsheets or custom database o Pros low initial cost o Cons limited functionality, higher cost of ongoing maintenance Visual organizers such as Visio o Pros low to moderate initial cost o Cons higher cost of ongoing maintenance Asset management software o Pros high functionality o Cons moderate to high initial cost As we move down the list from spreadsheets to asset management software, the functionality increases. At the same time, the cost of the system can grow quite rapidly. Besides the obvious costs, the hidden costs of manual asset management systems must also be taken into account. What is often lost amidst the bells and whistles of the various solutions is the true cost of managing these assets. People are often surprised to find that the cost of gathering the IT asset data is often as much as or even more than the cost of the management system itself. There are several obvious costs. First, there is the cost of the asset management system itself. Add to this the cost of the initial collection and recording of the asset data. Finally, add the cost of performing manual audits of the data on an ongoing basis. In addition to these obvious costs, there are several hidden costs which are often overlooked. These hidden costs include stranded power and increased shutdown risk due to inaccurate and incomplete data and increased mean time to repair and decreased system availability due to the inability to automatically track configuration changes in the data center. This white paper will attempt to put some real numbers around the cost of using manual data entry to maintain an accurate record of your IT assets. Cost of the Asset Management System The cost of an asset management system can vary dramatically depending on the functionality of the system and whether the system was developed internally or purchased from a third-party vendor. No Limits Software 3

4 Spreadsheets or Custom Database Internally developed systems typically consist of a set of spreadsheets or a database application which are used to keep track of the IT assets. There is a tendency to treat these as free systems, but this neglects the very real costs of the initial development and ongoing maintenance of the system. While internally developed systems are initially less expensive, they typically provide far less functionality than a purchased asset management system. In addition, they can be more difficult to maintain on an ongoing basis. In addition, there is a risk if the system was created and maintained by a single developer who later leaves the company. Often, there is not a backup person who understands the system in enough detail to be able to maintain the system once the developer has departed. Visual Organizers Visual organizers such as Visio can be used to enhance asset management by providing a visual view of asset location in addition to the asset information. Visual organizer systems tend to be relatively inexpensive but, like spreadsheets or database applications, there are initial development and ongoing maintenance costs which must be considered. These systems provide more functionality than a spreadsheet or database application, but they still typically provide far less functionality than a purchased asset management system. In addition, they can be more difficult to maintain on an ongoing basis. IT Asset management software can cost more than $1,000,000. Add another $200,000 per year to that cost for maintenance. Asset Management Software A full-blown asset management system can provide a wide variety of functionality, from basic asset management to full systems which provide capacity planning, work flows for equipment moves/add/changes, device inter-relationships and more. It is important to remember that systems with more functionality will have a steeper learning curve for users. As a rule of thumb, increased functionality also comes with a higher price tag. Asset management systems can be moderately to extremely expensive, sometimes more than $20 per square foot of data center floor space. For a 50,000 square foot data center, the initial software purchase may exceed $1,000,000 and ongoing software maintenance could be more than $200,000 per year. Cost to Initially Collect the Asset Data Once you ve chosen an asset management system (or have developed your own), the process is just getting started. Data centers can contain thousands of servers, power and cooling devices, and storage and network devices as well as a myriad of other equipment. It is a very daunting task to collect data about each piece of IT equipment, particularly if you are starting from scratch. But how much does it cost? The answer to this question depends heavily on how much data you are collecting. Let s start with the easiest case, collecting readily visible data for each device. The term readily visible means that someone collecting the data can simply look at the No Limits Software 4

5 device to gather all of the data. Collecting the data does not require logging onto a server, for example, or sliding the device out of the rack to look for a serial number. This readily visible data typically includes the following five components: Manufacturer Model Location Serial Number Device Name In a data center with 8,000 servers, initial collection of readily visible device data could cost $120,000. Expanding the data collection to include detailed system data could drive the cost to $600,000 or more! The typical cost to have an outside company come in to collect this data would range from $13 to $15 per device. Bear in mind that this cost does not include any time required on your part to manage the data collection team. For a data center with 8,000 servers, the initial data collection of readily visible data would be $104,000 - $120,000. How much would it cost to do it internally? At a rate of five servers per hour, collecting this data would require 1,600 man hours (40 man weeks). Of course, readily visible data likely doesn t include important configuration information you may need to properly manage the devices in your data center. For a server, for example, this missing information may include the hardware configuration (processor, storage, and memory), network connections, virtual machines and installed software and services. This information, while important, is more difficult to obtain. In most cases, it would involve logging into the server, using various tools to collect the information and then manually entering the collected data. While an outside company could be contracted to collect this additional information, it would require login access to all of your servers, something you may or may not be comfortable in allowing. How much would it cost to do it internally? For 8,000 servers at an hour per server, collecting this data would require 8,000 hours (200 man weeks). Accuracy of Manually Entered Asset Information In the Computer Associates technology brief Striving to Achieve 100% Data Accuracy: The Challenge for Next Generation Asset Management (Watson & Fulton, 2009), the authors point out the difficulty in maintaining the accuracy of manually entered information. The authors point out that Manual tracking with pen and clipboard, or even spreadsheets is time consuming and highly error-prone. Organizations can typically expect a 10% error rate in manual data entry due to typing and transcribing errors. In a survey of the International Association of IT Asset Managers (IAITAM) members, respondents said that 85% accuracy rate for tracking IT assets was above average and that a 90-95% rate was exceptional. Consider for a moment the impact of a 10% error No Limits Software 5

6 rate on the accuracy of manually-entered asset data. In a data center with 8,000 servers, as many as 800 of the servers will have inaccurate data recorded. Asset management systems are intended to provide data center management with the information they need to make important decisions about capacity planning, the proper placement of equipment in the rack, and so on. If the information is inaccurate, the decisions made based on the information become suspect. Cost to Manually Audit Asset Information With regulations such as Sarbanes-Oxley, HIPA and CFR-11, audits of IT equipment are required on a regular basis, sometimes as often as four times per year. Asset management systems which rely on manual data entry don t provide the ability to audit the data without performing another manual data collection. In most cases, the cost to audit the data is on the same scale as the cost of the initial data collection. This makes sense since many of the same steps must be performed. In some cases, however, the cost of the audit can exceed the cost of the initial data collection. The cost of a physical audit of IT assets typically costs as much, or even more, than the initial cost of data collection. The difference is that you may have to do this as much as four times a year! There are two general approaches to performing a physical audit. In the first approach, someone will duplicate the methodology used in the initial data collection, going rack to rack, server to server, to collect the data. This data will then be compared to that already stored in the asset management system and corrections made if necessary. This is the least expensive approach because it is performed very methodically. There is one major drawback to this approach; it only verifies data for devices which are present at the time of the audit. What about servers and other devices which have been decommissioned, moved to another location or even, in some cases, stolen? The removal or loss of these devices will not be recognized using this approach. The second approach to a physical audit is to walk through each device in the asset database and verify the device still exists in the data center and that the data for the device is accurate. This may be a little more expensive approach depending on the structure and accuracy of the data initially collected. Let s say, for example, that a server in rack C24 was mistakenly entered as being in rack B24. During the physical audit of rack B24, the server will be determined to be missing. Is the server really missing, or was it recorded in the wrong location? We won t really know until we ve finished the entire audit. A similar issue will arise when we audit rack C24. We will find a server which is not in the database. Is this a new server or is it a server which was recorded in the wrong location? Again, we won t know for certain until we have completed the audit. At the conclusion of the physical portion of the audit (data collection), we must then spend additional time to reconcile all of the records flagged as being incorrect. Remember that, on average, as many as 10% of the devices will contain incorrect information, so this reconciliation process could take some time. Cost of Stranded Space and Power One of the hidden costs of inaccurate or incomplete IT asset management data is the cost of stranded power. Consider the total cost of a cabinet in a data center. Besides the cost of the cabinet itself, there is also the cost of rack PDUs, KVM equipment, cable management, blanking panels, network devices, and raised floor grommets. No Limits Software 6

7 Add to this the cost of the raised floor real estate and you could be looking at $4,000 or more per cabinet. Obviously, we want to utilize as much of this space as we can to avoid the total number of cabinets we must purchase and outfit. There are five ways to determine IT device power usage: Nameplate value o Overstated value including peak operation and low-line conditions De-rated o Sometimes referred to as design value o Typically 70% of the nameplate value Calculated value o Can be determined using manufacturer calculator or through a combination of measured or real-time values and estimated usage Measured value one time or infrequent measurement Real-time value ongoing measurement The accuracy of the data increases as we move down the list from nameplate values to real-time values. One of the primary reasons cabinets are underutilized is that data center managers often don t have accurate data on power usage within the cabinet. Instead, they are forced to use nameplate or de-rated values provided with their asset management system. Unfortunately, the nameplate values are often very inaccurate for use as a gauge of actual power usage. In the Green Grid white paper Proper Sizing of IT Power and Cooling (Bean, et al., 2009), the authors point out the issues with data inaccuracy. Using nameplate values in the design, planning and deployment of power and cooling systems often results in excessive capital expense and operational costs. The inaccuracy of data centers standard process for estimating the power consumption of IT equipment has led to the under- and over-provisioning of circuits, power distribution units (PDUs), and uninterruptible power supplies (UPSs) as well as the cooling systems needed to remove the energy dissipated. This can have a significant impact on power distribution efficiencies, total power consumption, service availability, and operational costs. Unfortunately, nameplate value is still frequently used by data center planners in the design, planning, and deployment of power and cooling systems. This practice often results in excessive capital expense and operational costs due to lower efficiencies that result from overestimating resource requirements. Let s look at how this happens in practice. While a typical cabinet would more likely have multiple rack PDUs for redundancy, as a simple case we ll consider a cabinet with a single rack PDU. If the rack PDU has a 20 amp breaker, it would be able to support a continuous load of 16 amps (80% of the breaker size). For example purposes, we will populate the cabinet with servers which each have a nameplate value of 2 amps. If we use the nameplate values for design and capacity planning, we would be limited to eight servers in the cabinet. If the cabinet cost is $4,000, our cabinet cost per server would be $500. No Limits Software 7

8 Nameplate Cabinet Cost/Server Server A $ 500 Server A $ 500 Server A $ 500 Server A $ 500 Server A $ 500 Server A $ 500 Server A $ 500 Server A $ 500 Total 16.0 A $ 4,000 Knowing that the actual power usage rarely approaches the nameplate value, IT asset management systems allow users to de-rate the nameplate value. What does this mean? The user will choose an arbitrary percentage of the nameplate value and use this to estimate the power usage for the device. A typical percentage would be 70% of the nameplate value. Let s see the result of using this 70% value on our servers with a nameplate value of 2.0 amps. Even using a derated value can still result in significant stranded space and power as well as increased operating cost. Nameplate 70% De-rated Cabinet Cost/Server Server A 1.4 A $ Server A 1.4 A $ Server A 1.4 A $ Server A 1.4 A $ Server A 1.4 A $ Server A 1.4 A $ Server A 1.4 A $ Server A 1.4 A $ Server A $ Server A $ Server A $ Total 16.0 A 15.4 A $ 4,000 If we use the 70% de-rated value, we estimate we can now accommodate 11 servers in the cabinet. While this is an improvement over using the nameplate value, are we still stranding power and space? Remember that the de-rated value is simply a guess of how much of the nameplate value will actually be used by the server. An Environmental Protection Agency study of 23 servers with a 1000 W output power supply showed the actual power usage ranged from 160 W (16%) to 580 W (58%). The following table shows the large difference in the number of servers supported and the cabinet cost per server based on the de-rating percentage selected. Nameplate 70% De-rated 58% De-rated 16% De-rated Number of Supported Servers Cabinet Cost/Server $ 500 $ 364 $ 308 $ 80 No Limits Software 8

9 Several observations can be made about this information. First, the nameplate value is a poor indicator of actual power usage. Second, even using a de-rated value can still result in potentially significant stranded space and power. Third, if you choose to use de-rated values, it will likely be necessary for you to de-rate servers on a model by model basis due to the wide variance between models. Finally, there is a significant cabinet cost difference per server based on the number of servers which can be supported in a cabinet. Cost of Increased Shutdown Risk While there can be considerable cost associated with being too conservative with server power estimates (leading to stranded space and power), there are also costs which can result from not being conservative enough. If we are overly aggressive with putting devices into a cabinet, we begin to increase the risk of system shutdown due to insufficient power. Consider the consequences if we de-rate the nameplate power too aggressively: De-rating the nameplate value too aggressively can increase the risk of shutdown. Nameplate 50% De-rated Actual Value Server A 1.0 A 1.1 A Server A 1.0 A 0.9 A Server A 1.0 A 1.4 A Server A 1.0 A 1.2 A Server A 1.0 A 1.0 A Server A 1.0 A 1.1 A Server A 1.0 A 1.3 A Server A 1.0 A 1.6 A Server A 1.0 A Server A 0.8 A Server A 1.1 A Server A 0.9 A Server A 0.9 A Server A 1.2 A Server A 1.0 A Server A 1.1 A Total 16.0 A 16.0 A 17.6 A Shutdown Risk! By being too aggressive with choosing a de-rated percentage, we have put systems at risk of shutdown by exceeding 80% of the rack PDU capacity. The drawback to using calculated or measured readings is that these are spot readings. They don t take into account the variability of the actual power usage. If the reading is taken at a time when the server is using more power than normal, we will overestimate the power usage and end up with stranded space and power. If the reading is taken at a time when the server is using less power than normal, we will underestimate the power usage and increase the risk of shutdown. No Limits Software 9

10 The accuracy of the data is a key factor in reducing stranded space and power, thus reducing the overall cost per server, as well as in reducing the risk of shutdown. Nameplate and de-rated values are not sufficiently accurate to serve this purpose. Calculated or measured readings are more accurate, but lack the ability to look at the power usage over time. In order to accurately plan for power usage, it is important to use real-time power to measure the power over time. Cost of Increased Mean Time to Repair and Decreased Availability Another hidden cost to manual asset management is the increased mean time to repair (MTTR) and the subsequent decrease in system availability. To explain this, it is helpful to understand the relationship between MTTR and system availability. Consider your car, for example. There are two things which affect how often your car is available for driving: How often does your car break down? How long does it stay in the shop when it needs a repair? Computer room equipment is no different. We want to maximize mean time between failures (MTBF) and minimize MTTR. In our car example, we want to minimize the number of times our car breaks down and, when it does break down, minimize the time the car stays in the shop. We can increase MTBF through good system design, preventive maintenance and by choosing reliable components, but what can we do to impact MTTR? To answer this question, it is important to understand the components which make up MTTR. Time to recognize we have a problem Time to diagnose the cause of the problem Time to resolve the problem Automated change management can reduce overall MTTR by 58%, increasing system availability. Reducing any of these three areas recognition, diagnosis and resolution reduces the mean time to repair. A manual asset management system can do very little to reduce time spent in any of these areas. An automated system, however, can reduce the time to recognize a problem has occurred as well as greatly reducing the time required to diagnose the cause of the problem. In the book The Visible Ops Handbook: Implementing ITIL in 4 Practical and Auditable Steps (Behr, Kim, & Spafford, ), the authors examined a number of high performing IT organizations and found that 80% of their outages were due to a change, and that 80% of their MTTR was trying to find out what changed. These organizations learned to first look at the scheduled and authorized changes for the affected asset, as well as the actual detected changes on the asset. By just looking at this information, problem managers could recommend a fix to the problem over 80% of the time, with a first fix rate of over 90%. The authors found that organizations No Limits Software 10

11 which implemented automated change auditing were surprised and alarmed to see how many changes are being made under the radar. The authors recommend scanning systems for changes at least once a day. The ability to track both authorized changes and detected changes changes made but not necessarily authorized is key functionality needed to reduce MTTR and increase overall system availability. It is difficult to associate a cost with availability as each system may perform a different function. Suffice it to say, however, that companies spend a great deal of money on providing redundancy to reduce downtime and increase availability. Let s analyze the impact of using automated change management on the overall MTTR. If problem managers can use the list of detected changes to recommend a fix 80% of the time and 90% of these recommendations result in a solution, this means we ve reduce the time to diagnose the problem by 72%. Since the diagnosis phase represents 80% of the total MTTR, automated change management can reduce MTTR by 58%. At the same time, we ve also reduced the amount of labor required to troubleshoot and resolve problems by a similar amount. If real-time system monitoring is also added into the equation, the time to recognize a problem will also be reduced considerably, further reducing MTTR and increasing availability. Besides the obvious costs, the hidden costs of manual asset management systems must also be taken into account. Summary What is often lost amidst the bells and whistles of IT asset management solutions is the true cost of managing these assets. Besides the obvious cost of the IT asset management solution itself (whether a purchase price or the cost of the labor to develop a system internally), there are a number of other costs which must be considered: Annual maintenance of purchased software (or the cost of labor to maintain an internally developed system) Initial data collection and recording of the asset data Ongoing costs for audits of the data to meet regulatory requirements such as CFR-11 and Sarbanes-Oxley Stranded space and power due to conservative use of nameplate values Increased shutdown risk due to aggressive use of de-rated values Increased mean time to repair and decreased system availability due to the inability to automatically track configuration changes About No Limits Software No Limits Software is a leading provider of data center solutions, including asset management, capacity planning, and power and environmental monitoring. No Limits Software provides a unique solution by taking asset management to the rack unit. The RaMP (Rack Management Platform) solution eliminates the need for physical audits, dramatically reduces the time to find and repair equipment, improves system availability and improves data center energy efficiency by providing accurate capacity planning. No Limits Software 11

12 No Limits Software solutions are fully scalable and all data is fully accessible via published web services to allow you to easily integrate the solutions with your existing management platforms. No Limits Software was founded in 2009 by industry experts in data center monitoring and management solutions. To learn more, visit or Bibliography Bean, J., Bednar, R., Jones, R., Jones, R., Morris, P., Moss, D., et al. (2009). Proper Sizing of IT Power and Cooling Loads. Retrieved from Green Grid: %20of%20IT%20Power%20and%20Cooling%20Loads.ashx?lang=en Behr, K., Kim, G., & Spafford, G. ( ). The Visible Ops Handbook: Implementing ITIL in 4 Practical and Auditable Steps. IT Process Institute (ITPI). Watson, T., & Fulton, J. (2009, March). Striving to Achieve 100% Data Accuracy: The Challenge for Next Generation Asset Management. Retrieved from No Limits Software 12

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