storage assets

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Many of our customers want to reduce storage costs, and they know that just focusing on price is only part of the effort. As people understand that there are many costs associated with storage TCO, reduction of these costs first requires a basic understanding of what costs are important to them. I have done 4-5 assessment engagements this past month, and see that people quickly adopt this multi-cost approach, and I am seeing clients choose almost 20 cost categories as they move forward.

Defining and calculating total storage costs are the first few steps needed before you start to plan to reduce the costs. But many get stuck after they have a good baseline or understanding of their costs. Just knowing their cost totals does not necessarily help them to reduce the costs. Measurements are essential, but not enough.

Our work at HDS in storage economics for the past 10+ years has taught us a lot about what investments and strategies can reduce these costs. About a year ago, we put this knowledge of solutions correlated to cost categories into a tool. And this tool is available for you to use. Here is the link: http://www.hds.com/go/cost-efficiency/mapping-tool/

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Ever since we started decentralizing IT resources in the golden-era of client/server (mid-90s), we have been working to reign in the sprawl by consolidation recommendation. As soon as we consolidate, there will be another reason to sprawl (perhaps next time in someone else’s cloud infrastructure). Consolidation has been an effective technique for many years to reduce costs, and it implies several activities and resulting cost reductions:

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It is common to blame vendors and IT service providers with hidden costs. It is true that maintenance fees, transformation services, new training or adjacent system upgrades are required when new equipment is installed.

Source: http://dilbert.com/strips/comic/2009-11-23/

But I also see internal hidden costs within some IT organizations. There are many variations on what might be hidden:

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I have put together a couple blog entries reviewing some cost analysis that I did 2-3 years ago around Hadoop and Azure storage/server architectures–specifically how we worked with customers to reduce the costs of these environments (in part) with enterprise-class storage. It goes without saying—but I will anyway—the focus of these economic models and case studies was on the deployment and costs of the storage infrastructure. Some of these new cloud/big data environments do not use RAID overhead or distribute data across hundreds of nodes and disk clusters to perform the work. As I did this work, we took a myopic view of just the storage hardware aspect of these environments. I guess you would expect that from an HDS employee.

Earlier this week I had an interesting call with Ramon Chen of Rainstor, and compared notes on how they reduce DB costs, and therefore storage with their product offering. After our conversation, it was clear to me that big data cost reductions can happen on at least 2 levels:

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I’m out of the office this week—and I plan on continuing my big data case study series as soon as I return—but quickly wanted to reiterate something Claus posted last week.

Recently, we were able to sit down and cut through the misconceptions to discuss what capacity efficiency really means to us at HDS. And look, someone recorded it!

As Claus mentions, when it comes to capacity efficiency, we are really focusing on data center efficiencies, improving performance and consolidation—which gives us a pretty unique positioning in the marketplace.

So if you have a few minutes, check it out, and let us know what you think.

And for the record, I don’t think there’s anything wrong with the haircuts in the video…

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I have been developing a small mini-series on the economics of big data, with a focus on the storage approach used in Hadoop and Azure architectures. The intro blog, case study #1, and a review of bare-metal analysis have been posted to this blog over the past few weeks. I will wrap up this series with another case study, this time with a Hadoop environment.

Like so many organizations that embark on a new IT infrastructure/architecture, the start-up investments are such that planners and procurement are price sensitive. Getting a Hadoop environment genned-up can be very easy, and relatively low in cost. This is true for our case study #2 client, who is a large online retailer.

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I have had several blog entries on cloud services and the risk of just shifting costs to the cloud. There are some other entries on identifying your current costs of a class of storage (say tier 3) to accurately compare and contrast the exact same costs from a cloud vendor.

I have been helping a few partners develop business cases for cloud storage offerings, primarily for tier 4 or archive space—a popular starting place to test the feasibility and economics of public cloud infrastructure solutions.

Defining and comparing your current costs to a proposed pay-as-you-go model has to include all the same cost categories. But within the cost categories there are many different rates, which are a function of location, age, capacity and growth:

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I am a proud father of 5 kids. My 4 oldest are college graduates with meaningful careers in recreation, magazine editing, teaching/coaching and law practice. My youngest is a freshman in high school, and I am always looking to strike-up a discussion around IT (since none of my other kids chose the field) and options for a future career. I find that high school course selection can have an impact with college and eventual career choices.

Anyway, during a recent discussion I mentioned the idea of a data scientist, and how interesting that might be for work. I could sense his confusion on this as a career, and he asked if this work involved testing iPads on a Bunsen burner. I assured him it would not; like most 15-year-olds, he is fascinated with any and all things Apple.

What started this discussion was a recent article in ITworld on big data and the skills shortfall that the US is facing. Since my son graduates high school in 2015, this opportunity/shortage appears to be much more relevant in terms of supply and demand. Part of the article expressed these skills, and where we may be short:

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There is plenty of talk in the press today about big data, analytics and our next new wave for IT. I would like to present 2-3 blogs on a small but important subset of the big data world: storage infrastructure (and more importantly, optimal storage architectures). I will use our storage economics approach for the definitions of “optimal”, meaning you can address optimized storage from other dimensions as well (resiliency, scale, performance, etc.) as you develop big data strategies.

I had a period of time—2-3 years ago—where I was measuring and costing large Hadoop and Azure systems environments. I became very excited about these new distributed architectures, but at that time was not able to dedicate effort and resource for further research on their cost behaviors. Now that these systems have a new moniker (big data), the demand for big-data-economics conversations is here again. Good thing I have the models and methodology all sorted-out.

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I had a conversation with a colleague yesterday on budget consumption, and then saw this shark-eating-shark photo in National Geographic:

When you see ‘consumption’ in nature, it marginalizes the problems we often have in IT and business.

Anyway, back to budget consumption, our dialog was centered on how end-of-month (or year) budget cuts can put spending or investments on hold. We discussed an option that I had seen many years ago when HDS had a full-line of mainframes, a service around auditing software licenses and the maintenance that tends to linger—even if the software is not longer in use.