HDS products

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As IT departments struggle with growing the volumes and velocity of data, and especially the many copies of data that they generate, one of the biggest headaches is email. Mailboxes get crammed with copies of messages and attachments,  not to mention all the trivial and junk email that we receive.

The “fix” in most organizations is to limit the size of users’ mailboxes: once you reach a certain size limit, you get a warning; then a little later you get locked out of being able to send email until you delete enough old email to get you under the threshold. Many of us call this situation “mail jail.”

Of course, this approach does not serve the needs of either the user or the organization. The user is pulled away from doing productive work, unable to respond to critical messages, while they spend time sifting through their mailbox to decide what to delete. And in this process, users often delete email records that may be needed by the business at a later time.

So how do we solve this problem? We need to set policies for email retention and deletion that meet business and regulatory requirements. Then we need to move older, static messages out of the email database and into a secure, scalable, searchable, and cost-effective active archive content storage platform. And it needs to happen automatically without affecting users.

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Earlier this month, the 5th annual Germany Hitachi Information Forum was held in Mainz. With over 300 attendees, 20 presentations, hands-on breakout sessions and a panel discussion on big data, the event was an overwhelming success.

As a pretext to the panel discussion on big data I moderated, I referenced The Economist’s Kenneth Cukier’s example of Google Translate as an example of how the scale and depth of data distinguishes big data from the data analytics of yesterday. Jürgen Krebs, director of Business Development and Marketing of HDS Germany, and Bob Plumridge, chief technology officer of HDS EMEA, joined me on the panel to discuss their views and real world examples of big data. We also discussed how object storage impacts big data, how the role of IT leaders will evolve as big data drives business decisions, as well as the impact of SAP HANA adoption.

You can view the Hitachi Information Forum panel discussion below.

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In the past 2 months, I have been involved with several big data conferences and speaking opportunities with customers and analysts from all over the world. I see some clear and actionable economic conditions around big data projects that are consistent with actual customer work that I have been involved with over the last 2-3 years. In short, the costs required to build out new big data infrastructures are:

1. Not always aligned to the business value of the analytic results
2. Start off with low-cost systems, then they become economically unsustainable as the growth accelerates
3. Can be too high compared to the (undefined) business value

HDS sponsored a Big Data Research survey in the UK recently, and a couple of conclusions that came from this work were very interesting to me. 2 questions in this survey had to do with big data costs and value.

1. What are the barriers preventing you from adopting big data solutions? (in a sample 200 respondents 58% said cost and 41% said unclear on ROI)
2. What are the major challenges to big data analytics adoption? (in a sample 200 respondents 51% said aligning IT costs to business budgets and growth)

This survey supports my observation that we have an alignment issue around the costs and economic value for big data infrastructure.

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Flash is undeniably a hot-topic in today’s IT landscape and flash storage deployments are growing rapidly across a number of important application environments.  Flash storage is on the minds of customers, analysts, the press and vendors of all shapes and sizes, as everyone is looking for ways to capitalize on this important new storage option.

This focus means that it’s become rather noisy in the market with claims of superiority and the finding of the flash “Holy Grail” ringing out near constantly.  This in turn leads to performance claims where benchmarks are contorted in every conceivable way possible to eke out new claims of performance leadership.  All of this noise can make it harder on a customer to separate fact from fiction and truly understand what approach can best help them address their current needs and future directions.  Much like the scene in Indiana Jones and the Last Crusade where there are a number of possible “Grails” in front of Dr. Jones, there are just as many flash options.

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This is my first blog since recently joining Hitachi Data Systems as senior product marketing manager for data protection, and I’d like to share why I decided to make this career move, and why I’m really excited to be blogging for HDS.

Many technology marketers like to draw an analogy between data in the IT world and blood in the human body. It makes sense because, if you lose your data / blood, or if it stops flowing, the organization / body will likely die.

This analogy, however, fails to recognize one important difference between IT and nature: the human body has a fantastic ability to regulate the amount of blood it contains; as new blood cells are created, old ones die. The alternative would be reminiscent of several horror and sci-fi movies you may have seen.

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Two weeks ago I spoke at a CIO forum in Australia, sharing the stage with IDC on the topic of trends and directions with big data. Last week, same topic but in Thailand. Next week, same topic but in London.

I will probably take this topic of ‘big data economics’ and break it down into some bite-size (or blog-size) messages that I have seen over the years. I have observed and measured Hadoop and Azure storage infrastructure costs for about 3 years now. Back in early 2010, I don’t even know if it was called big data, since we have had this type of analytics and data warehousing function for years. What has changed, and what analysts and surveys keep showing, is a rapid acceleration of the amount of data, the variety of data and the impact of machine-to-machine generated data. So this is where we can start on some economic concepts, as well as some simple points. Here is a summary of my observations: