Storage has an RV as well. If you subscribe to IDC you may want to check the annual reports on the RV for storage systems, and what you can expect to get at the end of the term. Storage is often sold on the secondary market, even on eBay, so there is always an after-market value for the arrays that you cherish right now.

RV can and should be a factor in determining or computing your storage TCO, as a higher RV can bring in more for a trade-in or to sell in the secondary market. Since RV is a future trade-in amount, you may need to convert the RV to the present value (PV) and then add this value (it is not a cost, but an increase in the future) to the current year TCO. See here for a good overview or tutorial on defining and calculating present value.

So how do you start? First, you may need to subscribe to IDC reports that list the RV of various storage systems. Your leasing partner may be able to provide you some of these details as well. When you have that RV %, you can then calculate the present value of the RV, and use it in TCO comparisons or calculations. Let’s take an example of this process.

1. You take inventory of what you have, then get the IDC report, and determine that your storage array will have a RV of 5% after 4 years
2. You know that you paid 400K for the array, so the 5% RV would be 20K in a few years (when it is fully depreciated)
3. Say for example you have 3 more years of use from the asset, so you can use the PV formula to determine that the present value of the (future) RV is worth about 15K right now

Here is an example on how RV might impact your TCO today. In this example, we will compute the TCO of 100 TB that was purchased last year, and has 3 years left for the useful life.

Current year depreciation expense is  100,000

Power, cooling, floor space is                 40,000

Labor is                                                 150,000

Maintenance is                                        50,000

Migration is                                              50,000

Present Vale of RV                                -15,000

Current year total costs                         375,000

TCO per TB for current year              3,750/TB/Yr

IDC RV links that may be of interest:

IBM Enterprise and Midrange Storage Residual Value Forecast, Feb 2013

HDS Enterprise and Midrange Storage Residual Value Analysis, March 2013

EMC Enterprise Storage Residual Value Forecast, November 2012

In summary, don’t overlook RV as a function of the costs and benefits that your current storage has. There is future value to the equipment that you have on the floor right now. Some have higher RV than others, and that can be factored into comparisons and plans to deploy economically superior storage solutions.

Well here is a more complete view of these risks, and the rate of probability that they may happen.

This table and information is probably 12-14 years old. Risk analysis was a very popular service at HDS prior to the Y2K event, and we were busy consulting with companies to determine the cost of having an outage due to Y2K. Many companies back then (prior to 9/11) did not have formal disaster recovery (DR) capabilities, so the concept of the service was to help them determine the cost of not having a DR or protection plan, and the cost was the sum of each of the risks that they may have a problem.

Since this list is so old, some of the threat ratios or profiles may be out-of-date. Certainly the category called “External Sabotage” could be re-named terrorist attack or bombing. The world has become a more dangerous place in the last dozen years. Natural disasters are also on the rise, so you can take this old material and still see how threats that are common for you can be dollarized.

The costing exercise is simple.

1. Pick a threat (for example: lightning)
2. Based on the typical occurrence of that threat for you, pick a number between the low and high end of the range of numbers. For me here in north Texas, I would pick a number closer to 2 or 3 due to the violent storms that happen every spring
3. Determine the outage time that would result from each occurrence with your current state of preparedness or recovery
4. Determine the cost per hour of an outage

So, let’s say that lighting may hit my data center in north Texas up to 2 times per year. Each hit would cause a power outage or brown-out that would last 1 hour. Each hour of outage is valued at $40,000 per hour. The cost of risk for this one category would then be  2 times/year X  1 hour X 40,000 for a total ALE of $80,000 from lightning. I would add other cost/threats together to get my total ALE.

I am not an actuary, but this simple approach can allow us to do dollarize threat potentials and build the proper business case to recover in a cost effective time. The cost effectiveness of the recovery has to be balanced against the cost of risk.

Better go, I hear thunder outside. Time for the Texas spring storms to hit..

The author states “Capital costs are actually more of a distraction than a real representation of what storage costs.” And I agree with this 100 percent. Working on the total cost of storage over the past 12 years, we have found that the price (or CAPEX) is only a fraction of the total cost. In many of my presentations and papers I state that the price of disk is about 15-20% of total cost.

Over the last few years, this percentile has continued to drop. Working with an HDS customer last week (they operate with over 70PB of storage), their depreciation expense (or CAPEX) is less than 10% of the total cost. Depending on what costs are included in a TCO, I have seen the CAPEX rate as low as 6% of costs.

There are several cost categories that, over time, are higher than the purchase price of a storage system:

• Power and cooling
• Migration
• Management
• Data protection

When addressing a cost reduction initiative within your organization, you have to look beyond the price negotiations to produce real cost savings. You need to ensure that the products and architecture you consider for storage can reduce other cost. Simple questions to ask your vendor:

• Does this solution provide an easy on-ramp and off-ramp to other storage systems at the beginning and end of its life?
• How do the total power, cooling and floor space requirements compare to other solutions?
• Can I effectively manage 200, 500, 1000 TB with one storage engineer for the total solution set?
• Can you provide me optimal data protection techniques (backup, snaps, dedupe, copies, replication, disaster recovery solutions) to reduce the total hardware spend on this solution?

Sometimes to get to an optimal cost, you may need to be prepared to pay a higher price. These solutions are not always cheap or free. There is a simple test – ask your CFO if they are willing to pay a higher price to get a lower total cost. The answer will always be “it depends on how much higher of a price and how much lower of a cost”. That is where you need to start your own storage economics effort. Here are some links to some of my relevant blogs that may help provide further insights:

So Let’s Do a TCO

Think Like an Economist, Talk Like an Accountant, Act Like a Technologist

Storage Economics Talking Points

As far as I am aware, there are no published industry best practices on setting up storage tiers. The definition of tiers is different for each customer and industry around the world. One person’s tier 1 can be someone else’s tier 2.

Most storage architects do see some general patterns of these tiers, and I have outlined a highly simplified version here.

As I mentioned, every organization will have different tier definitions based on their applications and requirements. The above definitions are average or illustrative in nature. The key point is that the last row (total cost ratio) is how we provide real differentiation or separation of costs. This is not the price of the media for the tier, but the total cost of the tier over a several year period (annualized though).

The costs that are used can vary from company to company, but tend to include the following popular costs out of the possible 34 costs that we use in Storage Economics at HDS:

1. Hardware and software depreciation
2. Hardware and software maintenance
3. Labor for management
4. Power and cooling
5. Floor space
6. All data protection costs (backup, Disaster Recovery)
7. SAN and WAN costs

Since you want to move data to lower-cost-to-own tiers over time, this 11:7:3:1 ratio is important. If 3 of the 4 tiers are all roughly the same cost, then there are no options to reduce the cost of storage. Having multiple tiers at multiple/differentiated costs is a necessary incentive as we move to chargeback and pay-as-you-go (cloud) models. In the least, departments and IT consumers need to know what their storage tier selection really costs the company over time. This might influence different selections or assignments of IT resources.

The types of services and capabilities have to be designed in order to achieve this differentiating 11:7:3:1 cost ratio. If not, subsidies and tariffs may have to be introduced to artificially set the costs so that these ratios can be instituted. I don’t think there is anything wrong with artificial cost setting, since this is a tactic to drive the right behavior in storage data at the right tier for the life of the data asset.

Don’t be discouraged that you do not have a comprehensive storage catalog in place. I see many customers start with a simple matrix like the one above (perhaps put into a colorful and informative 1-page brochure format) to communicate differences in cost, performance, protection and availability. Communicating the differences and capabilities can start with some simple tables, and over time this format can evolve into a style or format that works best for your organization. Just don’t forget to include the costs for each tier.

I probably have a slightly different perspective since I think that catalogs and chargeback are key initiatives to reduce costs (this is a phase 3 event as outlined in this cost reduction time-frame). At this time when customers are considering utility models, the idea of developing or understanding chargeback is also relevant. So even though I am not ready to write a formal paper or a book, there is enough for a blog entry. So here I go…

What are the goals or objectives expected to be achieved with a chargeback? You should document and track these goals:

• Change behavior – what new utilization behaviors are needed?
• Recover IT costs and charge them to the departments or groups that use them
• Is IT turning into a profit center?
• Drive down unit cost – what is your cost now? What should it be? How will utility and chargeback help this?
• Accountability – people pay for what they consume
• Reward good behavior and usage, and penalize the bad
• Review and revisit goals every year; compare with what the utility brings

Storage Total Cost Baseline is needed. You cannot randomly set a rate to charge, unless you can measure your current cost of goods (COGS). HDS has outlined 34 different types of costs. Your plan should be able to define:

• Current unit costs
• What costs (out of the 34) are relevant or important?
• Total cost difference between file and block, tier 1, 2 and 3, disk and tape, etc.
• Differentiation between services and service levels: Cost of SAN, Cost of backup, Cost of disk only, Cost of DR protection

Before you start selling disk, you should create a “Storage Service Catalog”. This is your menu of offerings, by service type and by costs. Not all storage and storage services are created equal. The Storage Service Catalog allows you to present information in the language the ‘customer’ will understand:

• Different tiers, different services, different prices
• Drive the right behavior
• Tied to referential architecture
• Tied to Business Impact Analysis (BIA)

A Storage Service Catalog is a business level abstraction of the technical or referential architecture. The two need to be linked to each other, but consumers or users should not be selecting drive type, RAID level, cache size etc. The catalog has to abstract the technology and define the services in the context of applications or business.

Now with goals defined, total cost and COGs understood and basic service catalogs developed, you are ready to start creating the ‘chargeback’ system for storage. Some things to consider (this is a partial list):

• Unit of measure
• Frequency of metering
• Automation for metering and costs
• Collection, reconciliation and billing system
• Consider a tiered price ratio that drives the right behavior (my experience is that for a 3-tiered storage service, a total cost ratio of 7:3:1 is optimal)
• Annual true-upAnnual price reduction
• Billing and journal entry accounting systems
• Benchmark of commercial systems in order to be competitive
• Are there SOX or other compliance groups that need to be involved in setting your rate?

I have written in the recent past about how utility computing and chargeback will in conflict with many traditional project-based procurement processes. I suggest these blog entries to be read as you create the chargeback scheme. Read them here and here.

So perhaps a book on this topic is in order. Perhaps I can use the Betty Crocker template and really simplify this process in the context of a cookbook…

1. The price of IT is not the same as IT itself
2. Just as all are “so easily seduced” by the power of the ring in Lord of the Rings, so too are IT professionals drawn to low prices. We are often hood-winked into architecture that cost more over time.
3. Whether we are looking to improve costs per VM, cost of an IOP, or cost of a TB – we have to start with a baseline. We cannot improve what we cannot measure
4. Micro and macro economic factors in the world do affect IT economics. IT professionals have to learn to speak the business language of money and (unit) cost reduction
5. Since IT is expanding (data, systems, apps, users, etc.) we have to focus on unit cost reductions
6. ROI is good for business case support, but in the end TCO (or an equivalent) is much more meaningful
7. The list of levers or options to reduce costs keeps getting longer and more intriguing over time
8. We have to learn to map or correlate cost reduction areas to cost reduction levers

So what will the IT economic landscape look like in the coming years? No one can predict future events, but what the heck—here I go…

• Depreciation of IT assets will diminish
• We will turn to consumption models
• IT elements will keep getting cheaper (disk, core, memory) while enablement resources (labor, power, risk) will keep rising
• Rate of change, demand for business agility, standing-up and tearing-down of IT resources will accelerate to levels not yet seen
• Cloud, web, and hosting options will adapt and change to meet security and protection requirements for larger percentages of IT systems
• Social innovation/systems will transform traditional IT systems, and shift some costs more to the consumer of IT
• The need for cost efficient solutions will not go away, as consumer/social innovation trends will demand lower variable rates, and lower unit costs

I look forward to continuing to journal this IT economic journey, and will have to come back every year or so to see if my predictions are close.

The following is a simple table that lists (on the left hand column) popular methods to target and reduce costs. These are some of the levers you have available to reduce the cost of DP. Across the columns are impacts to the different DP methods that you may be using. Not all solutions or tactics impact all DP methods the same.

Now the cycle is complete. You have levers or solutions to implement that can impact your cost of DP. After a period of time, you can start the process again by measuring the TCO of DP and aligning it to your new risks. Hopefully, with improvements and changes the ratio and cost factors are more favorable.

As a postscript to this 4-part series on data protection, I would like to reference a 451 Research report entitled “Storage protection strategies shift under server virtualization” which states: “Data protection architectures are where we see differences between physical and virtual servers. While a majority still uses the same protection schemes for both, those taking the opportunity for change are experimenting with relying on hardware functions of snapshot, replication and de-duplication, while leaving backup software out of the picture.”

The report identifies server virtualization (Virtual Machine) related capacity growth as having the largest single impact on storage, especially on data protection. I will write some more about these DP methods that are specific to the VM world.

What to do with that number? By itself, it is impossible to know if it is too high or too low for your environment. You do not want to be defending or challenging the number if you cannot do so in a proper context. I suggest that the best way to evaluate this number is to compare it to your cost or level of risk. Balancing the cost of protection against a cost of (potential) risk is a key method to understand if you are over-or-under-insured relative to your data. If your cost of data protection is $1M (all the infrastructure, copies, backup etc.) and your cost of risk is $5M, then you will have to work through the 1:5 ratio.

• Are your protection methods highly optimized?(in order to allow the 1:5 ratio)
• Is the risk level so high that you may not be protecting your environment adequately?

This approach gives you some financial methods to compare and contrast your protection and risk. Conversely, if your data protection costs are $5M, and your risk is only $1M, then there may be some opportunities to reduce the protection costs to be commensurate with the risk level that you have calculated.

Dollarizing risk and comparing/contrasting to protection costs allows us to get away from subjective statements (“this is just too expensive”) and knee-jerk reactions. Balancing risk and protection is not a perfect science, but putting a dollar amount on both sides of these equations can help with IT projects in the future to optimize or reinforce protection methods.

Now the effort to dollarizing risk may not be as easy as it sounds. Sometimes external (3^rd party) help is required to bring some credibility to the problem/solution. Many years ago we had a formal risk-calculation framework based on CORA. You can Google CORA to find products and services that are available today. 15 years ago our CORA services were essential to Y2K planning services. We had to dollarize the risks of Y2K outages to balance against the Y2K development efforts. A similar method to CORA is ALE, or annualized loss expectancy. This can be a faster way to determine the annual probability of certain events, apply a probability rating, and then derive a cost. This adds up all the types of threats and costs results in ALE. ALE can be a shortcut to determining total cost of data risk.

In the ALE approach, there are hundreds of events that can cause an outage, data loss or disruption of service. Years ago I built some simple tools to calculate ALE by narrowing this list to the 19 most common or popular event types. Here is the list I came up with (in no particular order):

1. Fire (Minor)
2. Fire (Major)
3. Fire (Catastrophic)
4. Flood
5. Hurricane
6. Tornado
7. Lightning
8. Earthquake
9. Power Outage
10. Power Surge/Transiens
11. Network Outage
12. A/C Failure
13. Input Error
14. Software Error
15. Hardware Error
16. External Sabotage
17. Employee Sabotage
18. Fraud/Embezzlement
19. Strike/Riot

So for example, if a data center is located in central Florida, then several of these 19 would be included, and some would be excluded. If catastrophic hurricanes is chosen, and the pattern is that one occurs every 20 years, with an average outage period of 2 days, at a business impact of $10K/hour. The ALE for this one category would be:

.05 (annual probability) X  2 days X 24 hours X $10,000

The total amount is now calculated at $24,000. If other categories from the 19 are similarly calculated and added together, this would constitute the ALE.

In any kind of data protection assessment, you have to compare the cost of risk to the cost of protection. ALE can be a simple approach to dollarize outage or loss risks, and then compare this rate to the protection rate. I can publish some standard probability ratings of these 19 cost elements, drop me a note or leave a comment on the blog and I can get this information to you to help you create your own ALE.

As fate would have it, IDC published a recent report from Laura DuBois, Richard L. Villars, Brad Nisbet entitled “The Copy Data Problem: An Order of Magnitude Analysis.” It is an excellent read, with high points being:

• IDC estimates that in 2012, more than 60% of enterprise disk storage systems (DSS) capacity may have been made of copy data
• Similarly, in 2012, copy data made up nearly 85% of hardware purchases and 65% of storage infrastructure software revenue
• IDC projects that data protection infrastructure will cost businesses $44 Billion in 2013
• They go on to say that the growth of data is bordering on paranoia

So with this as a backdrop, let’s walk through a process to actually calculate the costs of data protection. First, we have to determine what cost elements we should use in a TCO analysis. Luckily HDS has a robust methodology called Storage Economics that provides 34 different cost areas to use in this approach. See these papers and books for more on the 34 costs.

Next, we need to isolate the costs and align them to the different data protection techniques outlined in my first blog. These costs tend to be hard costs, or direct costs. You can add soft costs as well, but for this exercise hard cost calculations will be more impactful to management for them to see what this insurance is really costing. We will handle the risk costs in another part of this data protection blog series, as these risks need to be quantified and dollarized as part of the annualized loss expectancy.

Here is a simple correlation to some of the costs on the far left side, and how these costs may exist in the several data protection methods.

With this framework you are now able to do this math on your own, to calculate techniques unit costs and total costs of data protection. HDS has tools and services to help you if you want to do this a little faster and with people who do this several times a month instead of once in a career.

I also recommend not only measuring data protection cost per usable TB, but also dividing these costs by the 1^st instance capacity that you are protecting or in other words, the total cost of data ownership (TCDO). This gives a better metric going forward with improvement and cost reduction plans. With this framework in place, you are now able to start calculating the total cost of your data protection strategy.

I believe this same problem exists with data protection in that data protection methods (and there are many of them) lead me to believe that data protection may be over-engineered and IT departments may be paying too much for data protection. There are many reasons and functions for data protection:
• Business reasons – monetary loss, customer attrition, brand erosion, compliance, vendor viability, operating costs, changing business needs, operational efficiency, business disruption
• Technical reasons – data loss, data corruption, physical disaster, disparate systems, storage and apps, in-house expertise, technical flexibility, solution mix, business resumption delays, recovery point and time, management challenges, recovery complexities, application protection

Looking over recent results of global storage economic assessment, we see that data protection can easily and often be 40-60% of storage TCO (TCO/TB/Year). So when management is looking to reduce costs, can we stand up and state (with a straight face) that we should look at data protection costs, or insurance costs, to see if there are cost reduction opportunities?

I will dedicate the next few blogs on this topic. But first we have to outline the insurance options are being used today to protect data:
• RAID
• Snap copies, CDP
• Volume copies (local, remote)
• Backup to tape
• Backup disk, VTL (then tape)
• 2DC (passive or active)
• 3DC

These techniques protect against different types of risk, but there is often overlap. Therefore, with overlapping coverage, there are extra costs. And sometimes there are gaps in the data protection coverage that can increase risks. As we take a larger look at the problem, there is compounded complexity when you include application and system-level protection techniques (clustering, OS, application level). So developing a data protection economic framework may not be an easy task.

In this short blog series, I will focus on infrastructure methods and costs. High availability and clustering in the server does protect data, but is targeted at the application layer. I will focus on the storage infrastructure layer. In the next blog, I will cover the methods to calculate the TCO of data protection, then look at the various ‘levers’ available to reduce the costs. These cost reduction efforts have to be aligned with a cost-to-risk-equilibrium (RTO, RPO, cost of outages, Annualized Loss Expectancy (ALE), total spend, service levels of protection, risks) to make sure that we are not creating more risk costs.

Speaking of risk – the TCO of data protection has to be balanced with ALE. More on ALE in a later blog.

I hope this can be an interesting dialogue to identify, measure and eventually reduce the total cost of data protection.