Storage Economics

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My last 2 blogs “Are We Over-protected/Over-insured” and “Calculating the Total Cost of you Data Protection” have been on the cost of data protection, and the categories that make up data protection costs. In a blog format, I do not have time/space to present a complete calculation methodology, but rather set up the framework that you can start to determine what the total cost of data protection is for your environment. If you go through this exercise, you will have a number–the total (annual) cost of data protection for your environment.

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.

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In my last blog post, I introduced a 4-part blog series on the economics or costs of data protection. My colleagues Ros and Claus also are writing a parallel set of blogs on the technology behind data protection. My boss Hu Yoshida also posted compelling points in a blog from 2 months ago, and an experience where only 12% of a customer’s data storage was 1st instance data.

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:

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Recently I experienced an insurance audit – to correlate and review all my insurance between home, life, car etc. The audit was helpful in identifying some gaps in my coverage, and to look at ways to reduce my total insurance bill. In the end, I found that I had better coverage at a lower cost.

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

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I am working in Australia for several weeks, and find that many sourcing companies (including HDS) have been in the Storage as a Service business for several years. Most companies are aware of these offerings and general acceptance seems to be higher here than in other parts of the world. Part of that may be that these national resources are here in-country, and a threat of data or systems moving off-continent seem to be less likely. The distinctions of utility services compared with traditional outsourcing are mostly well understood. Recently I  met with  a few customers who still have a bad recollection of old-fashioned outsourcing and are skeptical that these new consumption methods are really a disguise for bulky, inflexible outsourcing deals. They also do not see how these options can reduce real costs.

In this blog, I will outline the theory of storage cost savings with a utility (scale up and down) pay-as-you-go storage service. Let’s just call this “storage utility” for now. And for this blog let’s focus on the CAPEX impact of savings/differences.

I will start by describing an overly simplistic, multi-year storage growth model. First, let’s look at the written-to data requirements of a company.

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After my blog entry last week discussing TCO and ROI, a colleague sent me this good article on how ROI and the economic buyer within IT is alive and well. I agree with the concept of “The Rise of the Economic Buyer” in that more people are trying to link economic benefits to technical investments. IT professionals need to be able to convert technical and operational benefits into cost savings and payback measurements.

Enjoy the reading, and keep sending more links….

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A colleague sent me this article about using ROI to justify IT investment requirements to business managers/leaders. Some follow on discussions have opened up an old debate about TCO versus ROI to justify IT spending in the face of potential savings. Since the inception of our Storage Economics work at HDS in 1999, I have always been a big proponent of ROI. For all the same reasons as defined in the article, ROI is a valuable tool when competing with other internal spending/funding initiatives. ROI can be a single measurement that management can use to contrast investment options from IT, marketing, new properties, R&D or whatever companies spend money on. So when you have to compare and compete, ROI is an excellent method.

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In my last entry, the concept of project-based funding for IT resources was started. I want to take a deeper dive into some of the problems of project-based IT funding, and how these methods may have to change in the future as procurement and ownership of IT will likely change in the next few years.

One set of traditions in project-based funding goes like this:

I. The project gets approved and receives funding to get started

1.  The budget includes some IT funds—some of which are for IT infrastructure such as storage

2.  The storage budget is reviewed and presented to the storage team

a.    The project managers will not want to come back for more resources, so they tend to request and buy all the capacity up front

1. The storage team uses the project funding to purchase new storage

a.    The project personnel often assumes that since the assets were bought with their money, the storage assets belong to the project and they have some say on how the assets are used, shared, managed.

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There are probably a dozen ways IT departments get funding (primarily CAPEX) to handle new growth and daily operations. I am going to present a few of these traditional funding models, and some of the risks that these funding types may experience to non-traditional IT ownership models. I have been writing about some of these non-traditional methods for the past few months, and you can see them here:

Back to 1999 CAPEX Ratio
Challenging the Tradition of Depreciation for IT Ownership
Alternative Acquisition Methods
An 11-Step Program

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So, did we go over a cliff this week? I was sort of expecting a more grandiose situation, like the scene when Denethor runs (while on fire) over the edge of the promenade of Gondor (in Return of the King).

Our U.S. national tax situation was not quite as spectacular. But the recent changes in my local tax laws (including the annual change of insurance and withholdings) caused me to spend some time this holiday break looking at my financial plan for 2013. It is good to challenge older strategies and tactics and to take action to develop the best financial plan for my family and me.

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I like looking at trends. Let’s take a look at one storage economics trend—the ratio of CAPEX to OPEX in the total cost of storage ownership.

In 1999, we saw a pattern that the purchase cost was about 50% of storage cost of ownership. Disk was relatively expensive (one million dollars per TB) so when we analyze the total cost of a GB back then, we saw that CAPEX was the major contributor. This time period was before the shift to SAN and enterprise-pooled storage. Disk was local to the server or mainframe.

When we look at TCO circa 2005, we notice that this rate changed radically. The CAPEX portion went from 50% to about 20% of the TCO. The total cost was reduced by 80% over this 6-year period, resulting in a change of cost ratios driven by: