Most of us have bought a car before and sometimes we have an older vehicle to trade in. I have always been disappointed in the trade-in value of my older car, but realize that cars depreciate rather quickly. If the car is running and reasonably driveable, you will get something for the trade-in. I tend to keep cars a long time (7-8 years), so my residual value (RV) or “trade-in value” tends to be low. I like to sweat my assets.
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A few months ago I wrote a 4-part blog series on the cost of data protection. One of those entries discussed an approach to calculate the annual loss expectancy (ALE) related to risk, and the cost of risks. I had a program several years ago that helps calculate the probability of 19 of the most common threats, but failed to include the probability rate in listing those 19 elements.
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I came across this article on the total cost of storage (and VM) this past week.
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Over the last few years, it has become increasingly important to create storage service catalogs in order to align business requirements to technical storage architectures. Many organizations shy away from developing catalogs for a variety of reasons, one of them being the perceived complexity to create them. Many also tend to think that defining different tiers of storage is difficult, in that predicting exact or perfect classes of service has to be an exact science. People often ask me if there are best practices or published standards on these storage tiers, so that they can be used in a formal or informal catalog.
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I have been asked to develop a paper (more like a book) on the topic of chargeback. Since chargeback involves money, colleagues automatically assume that this is a job for the “Chief Economist” at HDS. I have had a lot of experience with charge-back schemes, though this is not a core skill that I can add to my LinkedIn page.
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Today marks my 7 year anniversary of blogging for HDS, with most of the emphasis on the economics of storage, hypervisors, converged infrastructures and cloud. My very first entry on April 4, 2006, summarizes the genesis of my material, research and learning journey that has been storage economics. As I look back on the macro and micro-economic changes that have occurred over this period of time, there are a couple of principles and concepts that have not changed too much: Read More »
This is the fourth and final entry on this series about data protection(DP) costs. I have discussed some of the reasons why these DP costs are high, how to measure them, and how to correlate these costs to risk. Finally, I’d like to discuss some ideas around DP cost reduction, as well as the correlation of these ideas to different types of methods used for protection. In a cost reduction approach, it is very important that we don’t jump to this problem-solving step first. Instead, I strongly recommend developing a Data Protection TCO as the first step. Make sure you have accounted for all types and styles of DP that may exist for the lifecycle of your data. This might be a good time to conduct a business impact analysis (BIA) to confirm the protection and risks. Then compare/contrast these costs to the risks. If you are out of balance, then start this final stage of selection cost-reduction solutions.
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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.
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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.
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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.
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