I am a Mainframer: Panel Discussion with Len Santalucia, Todd Schofield, and Jay Desai

In today’s episode of the “I Am A Mainframer” podcast, Steven Dickens sits down with Len Santalucia, Todd Schofield, and Jay Desai.  The panel discusses their journey with the mainframe, moving and understanding data, and where they see the Mainframe going in the future.

Steven Dickens: Hello, glad to have you with us. My name is Steven Dickens and I’m the host of the I Am A Mainframer podcast. We’re shaking it up a little bit today, where we’ve got a panel of revered guests joining me on the podcast. I think you’re going to enjoy this session we have planned for you today. The Open Mainframe Project is part of the Linux Foundation Collaborative Project structure. We’re a project that’s designed to build a community to gather around the mainframe technology. Our objective is to drive forward this platform. Without further ado, I’ll introduce some of our guests, I’m really looking forward to today’s conversation. I’ve got Jay, Todd, and Len on the call with me, so if we can go around and do some introductions. I’m keen to get you talking and for the listeners out there not to be hearing my voice and to be hearing from you as guests. Len, if we could start with you if you could position your role and use that as a way to kick us off here.

Len Santalucia: Certainly, my name is Len Santalucia. I am the chair of the governing board of the Linux Foundation Open Mainframe Project, also I am the CTO and business development manager for a Vicom Infinity, a premier IBM business partner. A funny thing happened, I met these two fellows you’re about to hear and I thought the message that they have would be very good for this podcast. That I know Steven runs frequently and I think you’re going to enjoy it because it really shows another great way to leverage the powerful IBM Z Mainframe. So, let me turn it back over to you, Steve.

Steven Dickens: Fantastic Len, I don’t know whether we’ve ever done this podcast together, but it’s long overdue for me having you on here as a guest.

Len Santalucia: Well, I’m glad to be here today. Thank you.

Steven Dickens: Fantastic. Todd, if we could go to you next and if you could just position your role for us.

Todd Scofield: Yeah, I’m Todd Scofield. I’m actually the founder and co-director of the Data-Intensive Discovery Initiative at The University at Buffalo. Prior to that, I was an entrepreneur in the sales automation space, way prior to salesforce.com. How I ended up on this podcast is because I had an insurance problem many years ago where I had to try to figure out how I could move my insurance from New Jersey to Texas. It created a data discovery problem that turned into understanding data profiling, which turns us in today where we have this massive problem about understanding data.

Steven Dickens: That sounds interesting Todd, and thanks for the headlights into that as part of your introduction. We’ll certainly get into that in the body of the call, no doubt they sound some rich topics for me to come back to. But, let’s stay on introductions for a moment. Jay, if you could just position your role on where you fit, that’ll be fantastic for the listeners.

Jay Desai: Sure, Steve. My name is Jay Desai, and I’m one of the co-founders of XtremeData, we’re a company based in Illinois. Prior to XtremeData, I was a co-founder of a data warehouse consultancy called Knightsbridge Solutions. We started that in Chicago and grew to about 750 people and the company was ultimately acquired by HP. I’ve been in the data space for more than 25 years helping enterprise customers solve very large data challenge issues.

Steven Dickens: So, if we can spend some time at just really getting our listeners calibrated, get a perspective of where you guys have come from and what’s been your personal journey. The podcast is called the, I Am A Mainframer podcast and what I try and do on these calls is really just get behind the LinkedIn profile. Get behind the job title on the business card and really get a bit of the personal story so, let’s shake this up. Jay, you went last on introduction so maybe you can go first on giving us a couple of minutes on your personal journey and how you’ve ended up on this podcast today.

Jay Desai: Sure. Like I said, I have a background in data. It started long ago, I think you and I were talking just before this podcast started. I was born in Kenya, raised there, grew up in England, started working there, came to the US in 1988. I started working in consulting and started my own company in Knightsbridge and then XtremeData. The journey has been focused on data from the very onset for me and specifically within data, I have been focused on Steven, large complex data issues that enterprises have. Whether back in the 90s the challenges were assembling data, organizing data to be able to do proper business intelligence support, reporting, analytics and so forth. Today, companies are preparing for the AI and ML era. What we’ve been focused on is solving those issues to help companies do this on a very large scale.

Steven Dickens: Fantastic. What’s been the mainframe as part of that journey, Jay? I’m just trying to pull through how those technology platforms have been part of that narrative.

Jay Desai: Sure. What is very exciting to us basically is Steven, that most of the customers we’ve worked with in history have been very large enterprise customers. These customers have massive investments in the mainframe environment and most of the critical data, the real high dollar value data, in the business world today. It originates on the mainframe and at XtremeData as we are perfecting our software. We recently boarded everything to the mainframe, including scalable tests, Poughkeepsie that we’ve done. We are very excited about what the solution can do on the mainframe environment. We’re looking at a very large set of opportunities where we bring to bear a very high speed and analytic abilities to the mainframe community at large in the world. We’ll go through more details as we go through this, but that’s the journey to mainframe Steve.

Steven Dickens: Okay. What’s been your exposure to the platform? It’s interesting when I talk to people in leadership roles, how many of them have got a history as a third shift tape operator.

Jay Desai: Very much so Steve. It started… I think I mentioned to you earlier, my first earliest job in England coming out of university was working for the Prudential Assurance Company. Prudential was one of the preeminent large IBM mainframe customers back in the 80s, so that was the start basically. Working in a very, very large enterprise, in financial services, that had a very deep implementation of mainframe systems, whether it’s policy claims, so on and so forth. I started there and my role there basically was in the networking area, so it was the SNA networking Steve and Metro capacity planning and implementations there off. That was my role at Prudential for most likely four years.

Steven Dickens: Fantastic. So, been with the platform and been around this space for a while Jay, good to hear.

Steven Dickens: It’s fantastic how people always have got a perspective of where they started their career and what they were first on from a platform point of view. That you can tech spot the real mainframers if they can rattle off the first terminals, they were on. Todd, if we could just do the same if you can just give us a little bit of an insight for the listeners into your journey and how you’ve come to where you are today.

Todd Scofield: Well Steve,  I was the absolute opposite of Jay. I started working with Digital Equipment Corporation as my first job, 30 plus years ago and I was doing everything to beat back the IBM mainframe.

Steven Dickens: So, it’s poacher turned gamekeeper for you Todd, this is cool.

Todd Scofield: Yeah. What happened to me when I first started was the PC. Actually, the first job I had was using IBM’s mainframes as an intern with a database called NOMAD developed by a company called National CSS. Which was a group of engineers at IBM that said, “We need to use this technology beyond batch capabilities.” They created a timesharing company, which today is now called The Cloud, that I was hired to help the VP of sales to create new endeavors for a new IBM, not IBM, new mini-computer. That ultimately turned out to be the IBM 4300 series and I was trying to help them sell this device, which turned out to be a failed moment at the time. That was my introduction to IBM and the mainframe.

Steven Dickens: You’ve got a history of trying to compete against us and failing which is always good to talk about.

Todd Scofield: The net is, I ended up figuring out data profiling through getting insurance for cars and that kind of stuff. When I moved from New Jersey to Texas and I had Liberty Mutual as my provider, I ended up sitting in front of a customer service rep who told me, “We need to move data from one system to another.” I sat there for an hour and a half and watched her cut and paste stuff where they didn’t know what the data was and that was my first-

Steven Dickens: I saw the challenge firsthand of how back in the day people were moving data from point A to point B and system to system.

Todd Scofield: Right. That’s my exposure and that’s when I got exposed to the first data profiling technology that was there. Was from a company called Evoke and that technology is now actually part of Informatica. But the thing is that you can’t. The way it’s being used today, it’s just being used by an individual. Now, if you can actually expose the whole dataset right up front, like what you can do with Xtreme,  all of a sudden all these data scientists and others can know what’s going to happen next for them.

Steven Dickens: Okay. We’ll come back to that Todd, there are some interesting points there that I want to pull up on and maybe try and foster a conversation with Jay and yourself on that particular topic. Len, if you can maybe give the listeners just a little bit about your personal story. I’ve probably heard this a hundred times, but every time you tell it, there’s a little anecdote in there I’ve not heard before. So, I’m keen to hear you tell it again and for many of our listeners, this is going to be a fascinating story. If you can give us a perspective, Len, on your personal, I Am A Mainframer journey, that’d be excellent.

Len Santalucia: Certainly. I graduated from a place called Binghamton University, which is in the same town where IBM incorporated in 1924 and when I was growing up, I was part of an IBM family being a third generation. IBM has been in my family since 1936 and… My grandpa then my dad than me. When I got out of college I tried like heck to go to work at IBM but there was a hiring freeze in IBM Endicott and I went to work for someplace that Todd’s familiar with Digital Equipment Corporation. I used to sit across the table from my grandfather and at the family get-togethers and he’d always look around real proud of the family and I was the firstborn. I was at the other end and he was at the head and he’d say, “Leonardo, when the heck are going to come and work for us? And I’d go, Pa, when the heck are you’re going to hire me?” Back and forth.

Len Santalucia: I got in after a few years finally, because I always wanted to work for IBM. But it took me a while because I did first Digital, then Burrows, then I finally got him in, in 1978. 1978 to 2008 almost 2009, almost a full 31 years and I retired when an opportunity came along because I was on that retirement plan that IBM had back then. That pension which you sometimes don’t hear too much about anymore and came to work for us and I’ve been here over another decade. Still doing the same thing I love to do and that’s working with the IBM mainframe, selling it, installing it, maintaining it, teaching about it, all kinds of things and seen a lot of things in those years. One of the things that I got involved with was something known as OpenEdition in February 1993 which I did for a while. Trying to get people to come from Sun servers and other systems over to the mainframes form of UNIX built into MVS called OpenEdition.

Len Santalucia: Eventually it became known as UNIX System Services, as we know it today, but after doing it for a while, it was a good experience. But I decided to take on a role to teach system engineers that were coming on from colleges and helping form their careers. It was a new hire program and I enjoyed doing very much but as time went on, all of a sudden I started getting these calls from people saying, “Len, we’d like you to come back and learn this thing called Linux. I said, it sounds like UNIX. I said I’ve already tried, therefore I was like, now I really like working with these young people. Let the young people do it, I’ve banged my head against that wall for a long time. They said, no, we need you because you know the wall street customers were making inroads, this is totally different. It’s not that you’re going to find it to be better.”

Len Santalucia: One thing led to another and they made me an offer I could not refuse, so 1999 I took them on, I was the first person in the company to take on this role, getting Linux out to the wall street customers. Either on x86 or Power or Z, it wasn’t always just.  It wasn’t pure mainframe, it was the Linux impact team. Great experience, learned a lot about other architectures that I never worked on before besides the mainframe. But eventually, as time went on, I really found out that I made a good move, it was very exciting, it was pioneering. We were doing all kinds of things that we never would have been able to do and in such an emerging business like this. All of a sudden the mainframes started taking off on wall street with Linux and Virtualization with z/VM and eventually KVM in the middle 2000s and then the rest is history. As we know, a lot of Linux runs across the world with the Linux on the mainframe, and I eventually became US Air’s most frequent traveler. In the world that had 7 million miles on US Air.

Len Santalucia: People wanted to know what this kid was doing on Wall Street with Linux so successfully and I said, “I don’t know, just talking to people.” But it was very exciting and used to sell a heck of a lot of blades too. There were some of these customers that were buying 40, 50,000 blades at a time with all this Linux Virtualization and a lot on Z. Because it could consolidate the blades, and because some people were putting too many in and they were burning up the place. I remember one time I came out of a subway hall, looked across the street to the customer I was going to go look at. They had yellow tape all across the front of it because they had caused a fire underneath the streets. They overloaded the circuits and then I get a call from that customer saying, “We need to talk to you about this consolidation. I said I told you should have done it a long time ago. They were friends of mine. He says, no, we’ve got to do it now. We know Len, don’t give us a hard time to come and talk to me, will you?”

Len Santalucia: It’s just been a lot, and then my kid,  heard me talk about it so much. I got a subscription from the kids to the New York times because we decided to move to live in Manhattan for a while after they moved out of the house. My wife didn’t have a good time.  It was labeled, New York times, Linux, L-E-N-U-X Santalucia, instead of Len Santalucia. It’s just been one heck of a ride.

Steven Dickens: Len, there’s always one anecdote that I’ve not heard in that story and Linux was the one. I knew there was another story but there had to be something in there that I’ve not heard.

Len Santalucia: I’m not kidding about it. I use it all the time when I talk to people if I remember.

Steven Dickens: That’s awesome. Guys in those personal journeys, there’s a lot of interesting nuggets. But I think for the next part of our discussion, I maybe want to go back to something you mentioned Todd, around this whole data challenge. I think there’s some fertile ground for us there to maybe spend five minutes or so. Jay, maybe give us your perspective, what’s the Xtreme’s data view of that data challenge and then maybe we’ll come to Todd, to get your angle on it as well. Start with you Jay.

Jay Desai: Sure.

Todd Scofield: Just before Jay, speaks there is the, understanding the data is absolutely core to us moving forward as a world. Jay, go ahead.

Jay Desai: Sure. Exactly, Steven, I think our view basically is that when you look at every… Today, the term people use base is, data is the new oil, data is the new resource and so forth as an example. In general, we think very much about the case, right? But if you step back and look at the big picture and basically say that if data is a new resource, it’s replacing oil and gold as an example, obviously it is a substitute. Then you look at what’s going on, I want to use a couple of examples Steve, to illustrate the point. If you look at industries, the aviation industry is a good example of it, where aircraft… A hundred started to be made a hundred years ago. Today, there are hundreds of thousands that are flying every day, as an example and the choice of the material they use to make the aircraft was aluminum by and large.

Jay Desai: It was done because it is light and all the properties and so forth a hundred years ago. Now, if you looked at a hundred years ago and you look at today and basically said, “If a hundred years ago all the way from sourcing bauxite and processing it and improving it if the quality of aluminum hadn’t improved. Today, every minute there’ll be a wing dropping off from an aircraft.” So, the aluminum industry has gone through taking a raw material, raw resources like aluminum, to really purify it and deliver it in such a way that you can actually fly and nothing breaks. Similarly, the pharma industry, the food industry, every industrial area of the world today, when you look at it, they’ve gone through this process. Where they actually take what they have, they put rigorous processes behind it to improve the quality and the value that they get in the end.

Jay Desai: If I turn to information as an example and information businesses. I think the first 50 years of our journey, I guess we have a big resource and today it’s more abundant. But there is no such thing going on right now, so the metal bit basically is that everything looks like projects. Cottage industries, if you like with data as an example, I have, fiefdoms, silos you hear all these terms. Our view, Steven, is that there are many challenges companies have with data. The biggest challenge that they have obviously today is securing the data, they have to protect the data, and the second biggest challenge is quality. The quality problem is so big that it costs. IBM has studied that it can cost anywhere up to, $1 trillion globally is what it’s costing. We think that these things can be tackled in a more industrial way now and that’s how we approach tackling the issues around data. In a more industrial way where the customers can look at it holistically, across every surface area where they’re deploying solutions as an example.

Jay Desai: Behind it, if you look at this, you also hear today with machine learning and AI, organizations are preparing to launch those initiatives and a lot of them are starved because the quality of the data is not right. Now to fix the quality, you employ a lot of processes, a lot of capabilities, and you hear the term data wrangling. It’s very common to hear everyone describe data wrangling and say, “80 percent of the time is spent in data wrangling.” The journey from actually taking a resource or actually getting the value that AI audio in a deep learning journey, we think there are industrial approaches needed for data security, curation, so on and so forth. That’s the way organizations should start thinking, that’s the way we think about it, Steven.

Steven Dickens: Okay. That’s a lot to pick up on there, Jay. Just if you could maybe elaborate and go next level down, what role do you see the mainframe playing in that type of discussion? I think there wouldn’t be any argument from our listeners around that data wrangling and some issues that people would see. But obviously we’ve got a mainframe specific crowd as our listener audience here. What role do you see the mainframe, I suppose playing in that whole landscape?

Jay Desai: Absolutely. In fact, I think the mainframe plays perhaps the most critical of roles here. If you look at financial services or other industries as an example, the business’s most critical data enterprises have is in the mainframe. Whether it is transaction data, credit card data, banking data, healthcare data, whatever you want to say. All the data is arising in the mainframe and much of the data basically is analyzed on the mainframe, but then it leaves the mainframe as well as an example. People have to go, they take the data elsewhere. We look at this and say that in the mainstream environment right there where the systems of records exist, that is an opportunity to start to industrialize. To start to tackle issues right there on the z/OS mainframe environment as an example and that’s the capability that we’ve got in place now.

Jay Desai: This is to help companies take the data that’s coming up, the business-critical systems they have, whether they are in DB2 or Oracle or any other… IMS, VCM, whatever they have and be able to essentially analyze and curate and prepare the data right there in the mainframe. They don’t have to extend the data out to some other environments and wait for it. There are several benefits here, one is that you’re able to use a mainframe for a lot more where the data is originating and you can then put the data back to use for a ML, AI, right there in the mainframe. When companies are looking at AI and ML today, they’re looking to infuse artificial intelligence capabilities in business processes and these business processes are right there running on the main premise for example. Why pull things out, go someplace else, bring it back and you can actually just expand the mainframe and do it right there. Steven, does that give you a perspective?

Steven Dickens: It really does and I think our listeners are going to be appreciative of what you’re saying there Jay, which I would summarize as don’t try to move and transport that data to another platform. And as you eloquently put it, infuse that data management onto the platform itself rather than do an ETL type activity to move it off the mainframe and do that work elsewhere.

Jay Desai: Exactly, all the new data, the critical data is being generated on the mainframe so do it right there. You don’t have to go anywhere else and it increases security, it has a lot of benefits behind it for large enterprises, yes.

Steven Dickens: That much comes through. I’m conscious of time here and we’ve got an audience of listeners that really are starting out on their career. I think we’ve got some veterans here and myself included, but what I’d like to do is get your perspective of. If you were talking to yourself back as you were leaving college, maybe age 21 or 22 what would you be saying based on your years of experience? Maybe we can go to go run the horde, Todd, if you want to kick us off, what would you be saying to your younger self as you were starting a career in IT, based on your experience?

Todd Scofield: Well, I’m a sales guy and I am absolutely positive to tell my son when he comes back from Italy on his vacation is to say that, “You need to go get exposed to the IBM mainframe in selling that technology because it’ll be a great career.”

Steven Dickens: I think that’s solid advice and maybe yourself, Len, what would your advice be to your younger self?

Len Santalucia: I believe very much in the IBM mainframe because it is basically invisible to the world, but at the same time it runs the world. What I usually do when I’m in these academic initiative sessions with many of the colleges and universities that I go to, to present about the mainframe. I level set them in this way, I say “Turn off Google, Facebook, Twitter, Instagram, and any of these other types of social media. You might not be very happy, but the world would continue to survive. Turn off all the mainframes in the world and you better go find shelter very quickly because mainframes would fall out of this… Excuse me, airplanes would fall out of the sky. You wouldn’t be able to do any kind of financial transactions. You couldn’t use your credit cards, you couldn’t get medical help, you couldn’t do just about anything because all of that runs and depends upon the mainframe.”

Len Santalucia: That is how important it is and if you’re thinking about a career as opposed to just a job, this is the place to look and that’s how I do it and it kind of hits him between the eyes. I remember one kid came up to me, he said, “Is that really true? I said, yeah.” It was at Virginia Commonwealth University, Steven, and right outside the doorway in the student union building where I was presenting to the students was a Wells Fargo ATM. Literally the door opened and there it was standing.

Len Santalucia: I said, “Have you ever used that Wells Fargo terminal over there? He says, yeah, I use it all the time because sometimes I don’t have enough money with me. I’m on my way to the snack bar or I’m going to the bookstore to get something I need. I go, what if you couldn’t use that? He says I don’t know where I would get my money. I said, guess what that runs on? No way Len. I go, yes it does, it runs on the mainframe.” That’s how I approach that I talk a lot with these students and that is why I volunteer so much of my time to the IBM Z academic initiative going around 12 schools.

Steven Dickens: Jay, what would… That’s fantastic Len, and I know you’re a strong advocate for the platform and I think the community certainly appreciates your advocacy and what you do. I think the essential nature of the platform is lost, I think on a lot of people, so we need to spread that word. Jay, finally go to you, what would be your advice to your younger self?

Jay Desai: I think it would be that some of the biggest databases that run the world today are run on IBM mainframe, they don’t run anyplace else. That world is running on the mainframe and if I want to work with data, the most critical data is on the IBM mainframe. Industry upon industry, upon industry, the data sits on the IBM mainframe and ultimately a natural order is taking place with data as well. I would look at this, if I were a young person coming out of college, you want to work with the most critical data in an enterprise, it is on the mainframe. If you want to develop analytic capabilities for the most critical data, it will be on the mainframe. What you have is an opportunity to basically place yourself in a very unique quadrant by looking at the industry.

Jay Desai: The second thing Steven, is that historically people… Because mainframe has a long history and I started talking about terminals and what have you. Today, all that is completely invisible. The mainframe is no longer mainframe, it is behind a cloud and nobody even sees it, the infrastructure is invisible to anybody except you get a lot of capacity. You’ve got a lot of capabilities and a lot of innovation if I’m a technical person and I’m looking at innovation around security, blockchain and all of those things. The mainframe is leading the charge, it’s not the generalized commodity world that is out there today. I would look at it from the perspective of advanced technology, critical data, the really important things to the business. If I want to work in that area, it’s a mainframe, that’s what I would do as a graduate coming out of college, go for the mainframe.

Steven Dickens: That’s fantastic and I’d certainly agree with Jay. Well, I’m conscious to keep us on time here gents. We always like to wrap the call and the show for our listeners by talking about what we see going forward. I always ask this question as a good way to wrap up our time together, which is where do you see things two, three, five years out into the future? Maybe we’ll start with you Len and just try and keep us on time here and keep it quick. If you could just give us your projections going forward. Where do you see us three years from now?

Len Santalucia: I believe that people are going to quickly learn how this open, hybrid multi-cloud infrastructure known as the IBM Z mainframe is going to be able to help. Especially because of the requirements and regulations in and around security and also availability, resiliency, and protection of assets. This is right up the alley of the mainframe since it’s inception on April 7th, 1964 it has continually paid attention to that, designed it in from the ground up. Once we get to the right people and explain this and help educate them, it’s really a bunch of education that needs to be done here. Over the next three years or five years, I see this as very important to the world, not just to the platform, that’s how I view it.

Steven Dickens: Yeah, I’d agree with Len. Todd, where would you see your projections? Where would you see us three to five years out?

Todd Scofield: Having been an outsider of IBM in the past, since my mother went to high school with Ken Olsen, who founded Digital Equipment Corporation. Is coming around to the mainframe technology has been, I don’t call it uplifting is the wrong term, right. But, how it comes back to driving XtremData’s technology on the IBM mainframe is pretty outstanding.

Steven Dickens: Yeah, I’d agree and maybe if we look to wrap here Jay, what would be your three to five-year projection?

Jay Desai: I think the very things that the mainframe has stood for, for the last 30, 40, 50 years in terms of customer data protection and so forth. I think that obviously we are in an era where privacy protection is number one, security is number one. So, you never hear about breaches on the mainframe you hear about breaches every place else as an example. I think this is already starting to dominate, it will become the issue for brands to protect themselves. I think the mainframe has proven itself to be a very, very, very strong platform, the investments that IBM has made and continues to make just make it even stronger and easier for enterprises to adopt. Data protection, privacy protection, are very, very important, second is this industrialization. Where you avoid moving it around for unnecessary reasons and whatever, you can just industrialize and get the value of the data but as fast as it’s feasible very, very quickly. Industrialized approaches to cleaning up the data, protecting the data, machine learning, so on and so forth, is what we see coming up.

Steven Dickens: Fantastic. Well, guys, it’s been fantastic to have you with us today. I think we’ve had an extended version of the I Am A Mainframer podcast, so I’d like to thank you all for your participation. Click and subscribe, if you’ve liked the conversation today. I think we’re going to have some exciting podcasts coming through in the coming months as we go through 2020. So, click and subscribe to the links below and we’ll see you soon on the I Am A Mainframer podcast from the Open Mainframe Project.

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Jay Desai, Co-founder/SVP, XtremeData
Jay has been in data analytics for 25+ years.  He is currently a cofounder/SVP at XtremeData focused on strategy and growth.  Prior to that, he was a founder of Knightsbridge Solutions (acquired by HP), a Chicago based DW/BI consultancy that specialized in helping Fortune 500 companies architect and implement large-scale data warehouses.  As a Practice Leader, Jay served a number of clients over a span of 14 years including Bank of America, Citi, JPMorgan, Capital One, Fannie Mae, Freddie Mac, Wells Fargo, Visa, MasterCard, TransUnion and more.  Prior to that, Jay was a Principal at Technology Solution Company, where he helped IBM implement industry-leading customer services systems at Whirlpool and other customers.  Jay started his career working in IBM Mainframe environment at Prudential Assurance in the UK.  Jay is a graduate in Telecommunication from the University of Essex, UK. 

Len Santalucia, CTO an Business Development, Vicom Infinity
Len Santalucia has been in the IT industry since 1973. He is presently the CTO and the Business Development Manager for Vicom Infinity, Inc and Chairperson of the Linux Foundation Open Mainframe Project. Len is also a 30 year IBM veteran. Len was a Senior Consulting Certified Sales Specialist for IBM Americas Linux Impact Team and a Certified IBM eServer Systems and IT Specialist. He was also the first member of the elite IBM Linux Impact Team formed in 2000. In this role, he specialized in Linux, Open Systems, Large Scale Virtualization, Object Oriented, LAN, and Web 2.0 technologies for the Financial Services and Banking Sector. Len is a key liaison in providing IBM customer requirements into the IBM STG hardware lab and IBM Software Group. He is also frequently sought after for presenting IBM’s STG Hardware, Software, Linux, Virtualization, and OSS Strategy and Directions at the IBM Executive Briefing Centers, IBM Linux Center of Competence, IBM Palisades Conference Center, IBM Hawthorne Research Center, IBM Top Gun Classes, Technical Conferences, Consultant Conferences, as well as to individual customers all over the world.

About our host:
Steven Dickens, Global Sales, IBM
Steven is an IBMer responsible for global sales of IBM’s premier Linux and Open Source server platform, LinuxONE. In this role he; advocates, evangelizes and champions the adoption of LinuxONE in the Cloud, Managed Service Provider, Linux, and Open Source market segments. Steven has spent 20-years plus in the Information Technology industry, with considerable experience in both hardware and software platforms with senior roles in: Offering Management, Marketing Specialist Sales and Client Management for organizations such as CA (now Broadcom), HP and latterly IBM.