The creation and preparations for this historic event were driven by our marketing group and trusted advisors, not to mention half the company (well it seemed that way, I may be over exaggerating a little bit). We had our executives on hand to deliver the core messaging with some great insights, as well as folks from The Office of Technology and Planning. This was our first of many events which will enable HDS to share its strategy and technologies within an exclusive (for now anyway) invitation to the industries’ most prestigious analysts and bloggers.

There were many internal meetings to discuss our individual participation and also to cover each presenters’ topics, strategy materials, presentations, NDA clarification, blood type, first born, and dare I forget that I had to sign my name in secret ink (I am kidding, there was no secret ink).

All kidding aside, it was a great event.  Jack Domme (pictured above) was first to present, and he did a great job as always. (I will save the remaining details for my fellow colleagues and bloggers, who I am sure will do it better justice than I can).

One of the initiatives at the event was to decide who would be monitoring Twitter and responding. I of course volunteered, as did many of my peers and colleagues. The job was easy enough, as I am on Twitter (@FTWilkinson) as much as I can, and also I like to see instant feedback from our customers and peers in real-time.

As the event started off, the Twitter chatter was quiet, but started to pick up rather quickly with some tweets pointing out that HDS is the best kept secret…

• @seepij: If you thought #HDS were just into storage – like I did – hearing impressive insights on new technology coming #hdsday
• @CIOmatters: #HDS much more innovative and strategic than I realised -not content buying IP they build it and use/re-use it, ahead of the market #hdsday
• @nigelpoulton: Randy Demont saying customers are really pleased but telling HDS that they dont market well enough <– only for the last 10+ years! #hdsday
• @ekhnaser: Great message at #hdsday but y limited to 70 people? This should be heard by more partners customers influencers….

Ahem!…The presentations were great and overall were well received—at some level I think we shocked some folks by our candor as well as laying out our strategy and some possible future areas of focus and technologies (all covered under NDA, so no sharing in this blog forum). Nonetheless, the Summit was a great inaugural event that proved to those in attendance that we, HDS, do have a complete strategy, and a plan and vision for carrying them out.

So, YES, we do that!

For more content from HDS Analyst Day, visit our bit.ly bundle: http://bitly.com/u0mh27

In my last blog post, I wrote about the impact and requirements of a new class of applications on storage and computing infrastructures. As this massive wave of unstructured data is created, we need platforms that are specifically architected to efficiently ingest, store and analyze this data.

In the early 90s I had the privilege of working on the second version of the Teradata data warehouse appliance which was used by leading companies around the world to mine their structured data sets. The interfaces were SQL and we implemented an ACID compliant RDBMS while leveraging a scale-out shared nothing architecture to achieve scale and performance for TCP-D (decision support) type queries. Teradata allowed companies to achieve record “time to answer” results to their most pressing business intelligence problems. In the process, Teradata gave these select companies a significant competitive advantage and in many cases led to these companies dominating their various industry segments.

An example of this is Walmart, which invested heavily in this technology in the 1990s and even sued Amazon for hiring their data warehouse experts as it considered this knowledge a competitive weapon. Walmart was able to analyze supplier and customer patterns and figure out what product to put on which shelf, in which city, in which month in order to maximize its revenue. It is interesting to compare the relative growth rates of Walmart to Kmart, which I believe did not invest aggressively in this area in the 1990s!

What we’re witnessing is the second wave of this paradigm. The fundamental difference is that the data sets are a few orders of magnitude larger and they’re all unstructured or semi-structured.

Many organizations are realizing that there’s tremendous hidden value in this data and it needs to be harnessed to enable businesses to get new insight about their businesses and customers. Proof points are leading web companies such as Google, Amazon, Facebook, Yahoo!, eBay and others that are mining user activity and patterns to sell ads or products or both.

Because the size of these data sets is relatively large (TB’s to PB’s), it is impractical and expensive to copy the data over a network into a data warehouse in order to process. It is much faster and cheaper to move the processing to where the data is stored, which brings me to why ParaScale is an important bridge to the Information Cloud.

The ParaScale platform will allow organizations to efficiently ingest, store and mine unstructured and semi-structured big data sets at scale and all in one place using a scale-out shared nothing architecture. This technology provides an elegant and flexible method to combine storage and compute in the same stack and achieve performance, efficiencies and capabilities required to solve this class of problems. The journey from the Content Cloud to the Information Cloud requires an array of technologies and we think the ParaScale technology is a key element required to bring this vision to reality.

Want to read more about our Cloud Roadmap? Visit our bit.ly bundle here: http://bitly.com/pCt5Gk

They all have their impact to how we run business, and that is the easy part to discern from big data issues. What about insight?  How will we better utilize our data objects, object stores and their associated metadata to really help drive real opportunities and insight? This was part of the premise of business intelligence solutions, and what they were going to deliver, right?

Not so much!

Seriously Big Data

There are many reasons to take big data issues seriously, not to mention how to best manage it and integrate it, leverage it, and back it all up. Those are challenges all in themselves.  For me the greater issue is: how will we interact with the data and make decisions based upon factors that can have an immediate impact to our business?  Sure, integration, mobility, backup and exponential data growth are important and they do relate to how we can gain better insight for making more accurate business decisions, but the fact is, if we have not figured out how to manage the data growth by now, we are in serious trouble.  Data consolidation, converged infrastructures and scalable architectures are the beginning to solving how best to approach big data impacts, but what do you do with the data, how will you leverage its insights, and what will you use to extract the insight of the data?

Data mashup dashboards and business dashboards are not necessarily new concepts, and while there are some that have made impacts to the market, none of them   have breached beyond basic information without leaving you feeling like you forgot something (like that feeling you get when you leave the house in the morning).

Analytics are becoming more relevant in unstructured data, just as they did for structured data. This is such a big market for enterprise vendors such as IBM, EMC, NetApp and Hitachi, that we are all hard at work building better solutions to help this effort. But that does not mean all solutions will be created equal, nor does it mean that they will offer identical or similar approaches. The end result will be end user adoption, usability and customization.

The question I often ask myself is:”what is really needed in an architecture that will lend itself to provide greater clarity and insight of the data?”  This is a complex problem, and one with many different variants for the answer.  I believe that we are facing a dynamic shift in how solutions will be designed in the future with a heavier emphasis on truly understanding how businesses run and keying in on the requirements to help business make more intelligent decisions.  Throughout the past twenty years we have focused on helping to improve processes, IO, smarter applications and the underlining hardware technologies. I feel, however, the next step is determining the best way to marry hardware and software functions more tightly with more internal hand-off.  As an example of this, Michael Hay has written a post describing the essence of autopoiesis, the marriage and tighter integration between hardware and software functions.  This is a start. But how?

There is no doubt that our systems and solutions have become much more intelligent—as well as more complex—but at the same time they have become less dynamically integrated with no common connector to share information in a true usable way. This can be leveraged for greater insight to what is happening within the systems and applications, being able to report data into a business dashboard (which can be used as the single pane of glass view of the data and its relevance to the business).

What Are We Searching For? What Will We Discover?

If I want to understand a particular event, such as a sales engagement, what would I like to know about it?  Is there information already residing within my data stores?  What type of data?  Could I also have instant access to social media data and what kind of insight can I gain to assist my decisions going forward?

What I am talking about is how can we have a true 360 degree view of the data that we have access to, as well as data that we need to have access to?  While we are in the midst of a big data revolution, the issue is how can we find what we are searching for when the results are collected from various, and perhaps disparate, data silos presented in a correlated view?  This would enable data to be displayed with relevancy across data streams and data types.  As an example:  If I perform a search for John Dowe, the results returned should show all John’s activity related to the constructed query.  In this case, the results may show some relevant emails, documents, John’s network or server activity, log file data  (he was tying to gain access to a SharePoint site which he does not have access to), some voice mails and perhaps some security surveillance video of him while on the company campus and let’s add in his social network activities, to see if he has breached corporate security by discussing corporate secrets (Yes big brother is watching).  Alone, the data parts are not very interesting by themselves, but If we can tie the data together, it can show that John was up to something very nefarious due to the email correspondence to a competitor, voice mails that captured a conversation between John and a corporate spy, security footage with audio capturing me in the parking lot exchanging a sealed envelope.

Is that interesting?  Absolutely!

How Do We Get There?

The Search is NOT over!

Search is a term which is too widely used and not fully understood for its potential abilities.  Whether you are searching data as part of an eDiscovery process, corporate governance or simply to find relevance around a certain subject, search is the tool we want to leverage and has the results we want to gain insight from to make decisions based on.  Search is just the beginning, but to be fair, its just search, and what we need is more data and data types to help us see a true picture of what we are looking at.

Starry Night by Van Gogh is a beautiful painting depicting a wondrous night sky.  But what if you only could see the Rhone river? What would we know about the rest of the painting?  Would we know that the artist wanted us to know that it is evening?  Would we know that there are other aspects and objects?  Of course not, we would only see what we have access to.  Much like search and discovery of data, we only know what we know. Nothing more.

There are often discussions around what it would take to get there, and the answer I hear quite often is that it is too hard and too costly. To get to where business needs us to be, we need to think about how we can develop THE NEXT smarter architectures and infrastructures leveraging open source solutions and common connectors, while leveraging PCIe, smarter controllers, FPGA’s, SSDS and imbed software functions more closely to the data streams.

Sounds hard right?  Yes, but not impossible.  We have spent the last two decades adding more and more complexity and infrastructure to handle large amounts of data we generate, but we fell short of true integration across the data center and business applications. I do agree that there are technical obstacles to overcome in order to have a better integration, and how we can push search functions and its associated IO down to the hardware layer to minimize the IO across the infrastructure.  This is where HDS shines, as we have the best scientists, engineers and resources that are working and solving these problems.  Let’s not forget that Hitachi develops some of the worlds best technology solutions and core IP that it utilized in almost every facet of life and is leveraged in some of the technology you have in your data center. We know a thing or two about taking the overtly complex and making it simple.

While I cannot go into great detail about our current endeavors around our research and development efforts, rest assured that we have been working to decipher the necessary technologies to bring THE NEXT—whatever it may be.

Check back soon for my next blog, which will discuss next generation discovery needs and data correlation.

I joined HDS through the acquisition of ParaScale, a startup I founded to focus on solving what the industry is now calling the Big Data problem.  When I visit customers, I notice a growing percentage are faced with a very challenging data problem.  The data in its original form is unstructured as expected, but it’s not your typical “human generated data”. Human generated data is what I refer to as file based data such as documents, spreadsheets, presentations, medical records, or block based data such as transactions, customer records, sales and financials records that are usually stored in relational databases. These traditional data sets are adequately served with high performance SAN/NAS systems, which have excellent random I/O characteristics and can handle massive amounts of structured and unstructured content.

I noticed that this new workload and the corresponding data being produced was fundamentally different from traditional enterprise workloads.  This data was being generated by hundreds to thousands of machines, was rarely updated, often appended, and was fundamentally streaming in nature. I called this category “Machine Generated Data” and people have started to embrace this term over the last few years. We started looking at this problem as early as 2004.

Imagine a web company that stores and processes log files from one hundred thousand web servers to generate optimized advertisements to monetize its freemium business model.  Prime examples of such business models are Google, Facebook or Yahoo. Imagine a bio-informatics company sequencing millions of genomes in hopes of finding patterns in disease, or a security company scanning lots of high-def video and analyzing it looking for a specific person or object, or millions of sensors generating data that needs to be processed and analyzed.

Besides the workload being different for Machine Generated Data (MGD), what else is different? Notice that with MGD, companies store this data in order to be able to analyze it shortly after storing it.  The faster they can analyze the data, the better the payoff usually. Think of ad placement by analyzing log files or video analytics to find the right guy in the video just in time, or analyzing genome data for a cancer patient who doesn’t have a lot of time, or finding inefficiencies or trends in financial markets or looking for oil in all the wrong places.

This problem as it turns out is really about large scale data storage and mining of unstructured and semi-structured data to gather information and insight from a company’s vast datasets.  It’s nothing short of information processing, repurposing, and data transformations to uncover hidden patterns in data that will ultimately lead to better decisions.

So what are the high level requirements to build a system to solve this problem?

1. Scale – the system has to scale. But scale to what and in which dimensions. I suppose it has to scale in capacity to be able to store petabytes or exabytes of data. But it also has to ingest this data pretty fast, since there are lots of concurrent streams of machine generated data that are coming through the pipe. So the pipe can’t be the bottleneck either.

What role does system management play in this equation? If you have lots and lots of data, you can’t afford to hire lots of storage admins to sit around and manage this data; it costs too much! So what is one to do? The system should be self healing and self managing, right? It should handle most of the mundane data management tasks automatically instead of relying on people.  Ideally all people do is add new hardware or replace failed disks or power supplies.

2. Processing – once you store petabytes of data into a storage system, how do you analyze it? You can’t very well load a few petabytes over the network into a compute farm, can you? How long would that take? Isn’t the network the bottleneck then? What if we were able to move the processing or the program to the data and run it there instead. The program is pretty small compared to the data set size and so you at least take the network out of the equation to a large degree.  Therefore, the platform should allow in-place data processing or analytics.

3. Cost – when you store lots and lots of data, your TCO should be reasonable, so using commodity components as much as possible, virtualization, and automation will go a long way.

So when  you look around and you read and hear about people talk about or working on gfs, bigtable, hadoop , hdfs, cassandra, riak, couchbase, mongodb, hbase, zookeeper, flume, mahout, pig, hive, etc, etc, …  you know what I’m thinking about.

The information revolution is upon us and its pace is accelerating.

Stay tuned…

To that end, my co-authors and I will provide a series of tutorials on distributed object stores (DOS), essentially providing a short course on the subject with the occasional odd topic interspersed periodically.

Where it makes sense, articles will be split along business and technical lines, as the two topics often will have completely different audiences. In some cases this division will result in wholly separate articles, but generally both should fit in a single article.

It’s always challenging to get the right level of technical detail with a diverse audience. Generally we’ll bias toward simplicity, as there’s an inverse relationship between how technical an article is and the number of possible readers. So, the typical article will strive to hit a moderate technical level, hopefully avoiding the arcane. However, if there’s sufficient interest in a particular topic, we’ll go back later and add greater technical and mathematical rigor to those specific areas of interest.

I’ll try to construct the series in such a way that each topic builds successively upon the previous. However, as this will be my first attempt at creating such a course I’m sure to get some topics out of sequence. Fortunately, web pages  – with their ability to readily point to other content outside the page displayed – allow for non-linear reading in a manner far superior to what is possible in print.

Here is the Topic Index as I see it at this juncture. A single topic might span several articles connected together. As with source code, it’s generally better to write several small modules that connect together rather than a single large source file that tries to do everything.

This method of relatively short articles also allows me to later go back and insert new articles within a given topic as either new things occur to me, in response to feedback about a given topic, or additions as the state of technology changes.

Topic Index

1. What is Structured and Unstructured data, and why do we care about the difference?

2. What is an Object?

3. What is an Object Store?

4. What is a Distributed Object Store?

5. When would I use an Object Store versus other forms of Storage?

6. Industry Implications of Object Stores

a. Traditional Storage Vendors vs. Cloud Vendors Approach to Object Stores

7. Basic elements of an Object Store ecosystem

8. Distributed Object Store Blueprint

9. Architectural Considerations of an Object Store

10. A Comparison of Object Store Implementations

11. The Life and Times of an Object

a. The Birth of an Object

b. Data Ingest

c. Life inside the Object Store

d. Where does an Object live?

e. How is Data Protection achieved?

f. Object Mobility

i. Duplication and Replication

g. Why is Tape Backup not required?

h. Basics of Data Unavailability and Data Loss (DUDL)

i. Fundamentals of Self Healing

j. Read / Write: What makes it fast, or slow?

12. De-duplication and Object Stores

13. The Road Ahead – The Evolution of Object Stores going forward

Please let me know your thoughts along the way.

This is my first official blog with Hitachi Data Systems.  I started with HDS on of all days, Valentines Day 2011!  This reminded me of what I love: of course my wife and children, but also what I have chosen as a career.  I mean, who can’t get excited everyday when you have the unique opportunity to try and make the world (or at least technology) a better place?

Some background on me: I have been in the technology industry for over 15 years.  I am a Master Architect, both hardware and software, and have worked in sales, pre-sales and strategy roles for most of my career.

For the past 10+ years my main focus has been around search and eDiscovery, and I have had the pleasure to work with some of the industry’s top thought leaders and developers, who actually created this space for archiving and search in 1999. I have helped create this market, and along with some others, we have watched our “child” grow into what it is today, which is far bigger than most thought it would be: hungry for better, faster, richer search capabilities and faster insight to data. I have worked for some of the largest providers of search and eDiscovery solutions and it has been a joy ride all the way.

Like I said, I started on Valentines Day of this year and I think it is apropos, that I have the fortunate opportunity to work for the best IT Solutions company in the world!  I get to do what I love and that is to create new concepts and technology, and bring new ideas for Content Cloud solutions for HDS.  Who wouldn’t be excited?!

Speaking of exciting…

Big Data Problems

IT is not a mystery in that at some point we were going to run into issues of having too much data which led to too much overly complex architectures, networking latency issues and backup issues—just to name a few.  The reality is this is not a new problem, but one that has always existed; just look back at the first Disk Pak that you bought, 10mb and we thought that was enough capacity. When that space ran out we bought a larger one, and thought that was going to be enough capacity!  We simply added capacity as needed and along with that we grew our complexities for backup, recovery, replication, networking, etc. You get the picture.

As the decades flew by we had grown to the point of an information overload and grew data beyond our wildest imaginations.  While archiving content gave us some relief for email and file data, it also added an extra capability that allowed the ingest data to be indexed, making the data searchable.  This was the beginning of the next era for search and eDiscovery.  

Fast forward a few years and now we see that there are hundreds of companies who offer search and archiving solutions—perhaps you have one of those solutions in your enterprise today.

I am not telling you something that you don’t already know, but I will tell you that Big Data impacts your ability for greater insight to your data.

Why do we keep data?

Why don’t we expunge that data when its usefulness expires?

Well, we know that data comes in all types and forms, and while some have a high relevance to your business—such as a financial record or contract–we also keep data for historical reference.

So What’s The Issue Here?

The reality is that we have tired to address the big data issue with options like archiving, data de-duplication, application retirement, consolidation, file planning, etc.  Please don’t get me wrong, we need these technologies in order to hold the Big Data beast at bay, and these technologies also offer the baseline for building the next generation architectures.  Since data has grown exponentially over the past several years, we have tired our best to contain data in its place while trying to adjust our architectures for the next great thing.

So Where Are We Today?

We have complicated and rigid architectures which may not lend themselves to take advantage of Cloud solutions. We have complicated applications and integration issues. We have a ton of meta-data, objects and even some archiving solutions in place—perhaps too many and not easily integrated together. We’ll even throw in some business intelligence solutions.

So What Do We Have?

A big mess!

Maybe it’s just me, and perhaps I see things from  too simplistic a view point, but the data we create should serve the business and allow us to make better business decisions that react to the markets with more agility.  As Michael Hay and my friends over at the Techno-Musings blog preach, data should not hold us hostage and cause such pains. To me, that is what is important: how can we make business’ run better and more efficiently while reducing risk and reducing the size complexity of IT, while maximizing new technology to gain better insight to the data?  This is what I love, and it is my passion (just ask anyone who knows me).

I do have a point to all of this, I promise!

The challenge, as I see it, is not giving you more solutions and adding more complexities, as some companies are trying to sell you, but rather the opposite.  How can we look to technology to help solve some basic issues with regards to Big Data?

We have all been promised at one time or another that technology will unlock the value in IT. Well, I am here to tell you that we are almost there, but we still have work to do.

While there are many challenges to address, there are a few which make it to the top of the list:

1. Objects and Meta-data: In order for greater insight, we know that we need to do a much better job at exploring and expanding meta-data capabilities. And while we are at it, a better way to standardize meta-data abstraction and find relationships between dissociative meta data.
2. Search: we need to think bigger than just search.  Analytics need to be more tightly integrated with the data and meta-data. I call it “Content Analytics,” since that is really what we need.  If all the data and its associated meta-data can be made to be more intelligent—and yes we are talking way beyond meta-data tagging here, and yes I am leaving out all the other parts to this like (data dictionaries, indices, BI, etc.) because they are understood, at least for now—then we will be able to provide a much richer set of data and meta-data. This could lend itself to a more refined result, giving us greater insight. But how do we get there?
3. Open Frameworks and API’s:  If we really want to think about the impact that big data has today and its strong hold on our data centers and architecture, then we need to think of a better way for data to communicate with business’.  Part of this can be addressed with the adoption and implementation of such open source solutions like OpenSearch, which allows for the sharing of data through a common and structured format for data to be shared and even extended with formats like RSS and Atom just to name a few.

I Told You I Had A Point…..Here It Is!

When I look at the different variants and the many ways in which meta-data is utilized today, and how we are just learning how to maximize its potential, we are still tied to the old architectures, and thus tied to a less spectacular way to gain insight of our data. Being able to leverage that analysis will help us make better business decisions.  I am not talking about Business Intelligence solutions, although I do ponder that perhaps if data and its meta-data were able to leverage a common open source set of API’s, then we may have something here. Perhaps we could then be the well for all intelligent meta-data and be the providers and or helpers who other solutions can leverage for meta-data analytics.  In order to shrink our architectures and squeeze out more benefits from our data, we need to think about a new approach to meta-data management and what we could expect from next generation solutions.

In Michael Hay’s recent blog post Interacting with Cloud Stores, Michael articulates precisely what is needed around a common set of open sourced APIs, which can be leveraged throughout the enterprise across all applications, storage, data objects and meta-data in order to get us to be able to link and share meta-data across not only the enterprise, but with external meta-data from social media networks (and even from public cloud solutions). Meta-data is the Holy Grail, and the more we can leverage, embed unique markers and add intelligence into our meta-data, the more we can begin to reap the benefits of clearer insight to that data.

As you can probably tell, my blog will take on the challenge of looking at Big Data in many different ways, but most importantly around Content Cloud, Search and eDiscovery.

I look forward to your thoughts and comments!

On this blog we will focus on the area of data storage commonly referred to as “unstructured data.” We have all seen the industry charts that show the growth of unstructured data to be dramatic, growing to exabytes by 2015. The storage systems designed for this class of data are evolving to meet the challenges associated with trying to manage and keep track of such massive amounts of data.
Perhaps the most important distinction is that these systems are really software applications that use storage, but aren’t intrinsic to the storage subsystem. As a software solution, the storage application itself can perform functions particular to the data content as there’s a greater knowledge of the nature of the customer data than is possible in a pure disk storage subsystem. This increased knowledge allows for a greater set of actions to be taken based upon the data content, rather than on more generic qualities such as disk segment boundaries. A simple example is the ability for an application or storage administrator to select which specific objects or files it would like to have replicated, when and where. A more complex example involves performing arbitrarily complex analytics on the data that effectively transforms data to information.

A second important function for this class of storage software is the ability to simply keep track of where all the data resides. It’s a lot easier to store a petabyte than it is to store, catalog, and keep track of a billion objects over their lifetime. As the time horizon for stored data approaches decades, it’s a given that the associated storage and server hardware will change generations multiple times. The software application that fronts these systems needs to be built to withstand these changes without requiring forklift upgrades. Therefore, it’s important that all hardware elements are sufficiently abstracted in the storage software to accommodate change. Likewise, the ability the test the veracity of stored data is needed as backup for data at this size is impractical.
On this blog we’ll talk about these and other issues particular to unstructured data and how it relates to what’s happening in the industry at large, as well as specific customer segments.
This blog will have four contributors: Cameron Bahar, Frank Wilkinson, Shmuel Shottan, and myself, Robert Primmer. Below are biographies for Cameron, Frank and Shmuel.

Cameron Bahar

Chief Product Strategist, Scale Out Storage Platforms & Big Data Analytics
Cameron Bahar leads the technology direction and strategy on scale-out storage platforms, distributed file systems and Big Data analytics at Hitachi Data Systems, bringing over 20 years of systems software development and deep expertise in distributed operating systems, parallel databases and data center management.
Cameron joined HDS through the acquisition of ParaScale where he was the founder, CTO and VP of engineering. At ParaScale, he developed and released a private cloud storage and computing software platform for the enterprise to address Big Data workloads.
Earlier, Cameron led design, deployment, and operation of Scale8′s distributed Internet storage service that provided storage for digital content owners including MSN and Viacom/MTV. At the HP Enterprise Systems Technology Lab, he developed system software for disk volume management, data security, and utility data center management. At Teradata, Cameron developed extensions to UNIX to allow the massive parallel processing required by the Teradata database, the world’s largest and fastest distributed RDBMS. Cameron started his career at Locus Computing, a pioneer in distributed operating systems, single-system image clustering, and distributed file systems.
Cameron holds a BS, summa cum laude, and an MS with honors, in Electrical Engineering from UC Irvine. Cameron holds 4 patents in scalable distributed storage systems, virtual file systems, and high availability systems.

Frank T. Wilkinson

Content Services Strategist, Office of Technology and Planning
As the content strategist in the Office of Technology and Planning, Frank brings over 15 years’ experience in the field of IT and software development and expertise in HDDS and HNAS. He is an industry expert in the areas of knowledge and content management, information management within the financial services, healthcare, media & entertainment, retail and SLED.
He has held many IT certifications, including being a Master Architect. Prior to joining HDS, Frank was the Business Strategist for HP Software, Information Management Division and spent five years developing and deploying information management and content management solutions. Prior to HP, he enjoyed stints at the EMC TSG group’s information management practice and K-Vault Software (Symantec Enterprise Vault). Frank has been an active speaker for industry led panels and discussion on the topics of eDiscovery, information management, content management and next generation solutions facing the enterprise.

Shmuel Shottan

SVP, CTO BlueArc, Part of Hitachi Data Systems
As CTO of BlueArc technology, Shmuel is responsible for developing and advancing BlueArc product innovation. Previously Shmuel served as senior vice president of Product Development of BlueArc. He joined BlueArc in 2001.
Shmuel has over 30 years’ experience in the research and development of hardware and software, and in engineering management for firms ranging from start-ups to Fortune 500 companies. Prior to BlueArc, he was senior vice president of Engineering and chief strategy officer for Snap Appliance. Earlier, Shmuel held executive positions at Quantum Technology Ventures and Parallan Computer. Previously he held senior engineering positions at AST Computers and ICL North America.
Shmuel holds B.S. degrees from the Technion – Israel Institute of Technology in electrical engineering and computer science.

As for me, I am senior technologist and senior director of product management in the file content and services division at HDS where I am responsible for devising technology solutions that will be incorporated into future enterprise and cloud product/service offerings, with in-depth knowledge and expertise with Hitachi Content Platform (HCP).
I have 25 years’ experience in technology, working in R&D and Product Management organizations with Cisco Systems, EMC, HDS and several start-ups. I am a member of ACM and IEEE and belong to the IEEE Computer Society.
We all look forward to bringing you insights on this dynamic topic of data content. If you have particular issues you’d like us to address, please let us know in the comments.