Synthetic APIs Approach Improves Fragmented Data Acquisition for Thomson Reuters’ Content Sharing Platform

Transcript of a BriefingsDirect podcast on how Kapow Software helps a worldwide data company manage data acquisition in a cost-effective and consistent way.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Kapow Software, a Kofax company.

Dana Gardner: Hello, and welcome to a special BriefingsDirect discussion series on how innovative companies are dodging data complexity through the use of Synthetic APIs.

Gardner

Dana Gardner, Principal Analyst at Interarbor Solutions, is your host throughout this series of Kapow Software-sponsored BriefingsDirect use case discussions.

We'll see how from across many different industries and regions of the globe, inventive companies are able to get the best information delivered to those who can act on it with speed and at massive scale. The next innovator interview examines the improved data use benefits at Thomson Reuters in London.

Here to explain how improved information integration and delivery can be made into business success, we're joined by Pedro Saraiva, product manager for Content Shared Platforms and Rapid Sourcing at Thomson Reuters. Glad to have you with us.

Pedro Saraiva: Thank you very much. Pleased to meet you.

Gardner: Pedro, you first launched Thomson Reuters content-sharing platform over four years ago, I'm told, after joining the company in 1996. And the platform there now enables agile delivery of automated content-acquisition solutions across a range of content areas.

Saraiva: That's right.

Gardner: Tell me what that really means. What are you delivering and to whom?

Saraiva: It's actually very simple. We're a business that requires a lot of information, a lot of data because our business is information -- intelligence information, and we need to do that in a cost-efficient manner. Part of that requires us to have the best technology. When we started four years ago, one of the most obvious patterns that we found was that we had a lot of fragmentation of our content acquisition processes where they were based, who was doing them, and more importantly, what processes they were following or not following.

Saraiva

The opportunity that we immediately saw was to consolidate it all, not just around the central capability, but into an optimal capability, with real experts around it making it work and effectively creating a platform as a service (PaaS) for our internal experts in each content area to perform their tasks just as usual, but faster, better, more reliably, and more consistently.

Fundamentally, we are a platform for web-content acquisition. And that is part of our content-shared platform because it's all part of a bigger picture, where we take content from so many sources and many different kinds of sources, and not just web.

Gardner: So, your customers are essentially other organizations within Thomson Reuters. Is that correct?

Content management

Saraiva: That's right. I don't know the exact percentage, but I would guess that about half of what we do is content management, rather than site technology, per se. And a lot of those content management tasks are highly specialized because that's the only way we're going to add value. We're going to understand the content, where it comes from, what it means, and we are going to present it and structure it in the best possible way for our customers.

So, the needs of our internal groups and internal content teams are huge, very demanding, and very specialized. But they all have certain things in common. We found many of them were using Excel macros or some other technologies to perform their activities.

We tried to capture what was common, in spite of all that diversity, to leverage the best possible value from the technology that we have. But also, from our know-how, expertise, and best practices around how to source content, how to be compliant with the required rules, and producing consistent, high-quality data that we could trust, we could claim to our customers that they could trust our content because we know exactly what happened to it from beginning to the end.

Gardner: Just for the benefit of our listeners, Thomson Reuters is a large company. Tell us how large, and tell us some numbers around the number of different units within the company that you are providing this data to.

Saraiva: We are a large organization. We have about 50,000 employees worldwide in the majority of countries. For example, our news operations have reporters on the ground throughout the world.

We have all languages represented, both internally and in terms of our customers, and the content that we provide to our customers. We're a truly diverse organization.

It takes shape in the vast number of different teams we have specializing in one kind of content.

We have a huge number of individual groups organized around the types of customers that we serve. Are they global? Are they regional? Are they local? Are they large organizations? Are they small organizations? Are they hedge funds? Are they fund managers? Are they investment banks? Are they analysts? We have a variety of customers that we serve within each of our customer organizations around the world.

And that degree of specialty that I mentioned earlier, at some point, has to take shape. It takes shape in the vast number of different teams we have specializing in one kind of content. It may be, perhaps, just a language, French or Chinese. It may be fundamentals, versus real-time data. We have to have the expertise and the centers of excellence for each of those areas, so that we really understand the content.

Gardner: You had massive redundancy in how people would go about this task of getting information from the web. It probably was costly. When you decided that you wanted to create a platform and have a centralized approach to doing this, what were the decisions that you made around technology? What were some of the hurdles that you had to overcome?

Saraiva:  We were looking for a platform that we would be able to support and manage in a cost-effective manner. We were looking for something that we could trust and rely on. We were looking for something that our users could make sense of and actually be productive with. So, that was relatively simple.

The biggest challenge, in my opinion, from the start, was the fact that it's very hard to take a big organization with an inherently fragmented set of operating units and try to change it, because trying to introduce a single, central capability. It sounds great on paper, but when you start trying to persuade your users that there's value to them in in migrating their current processes, they'll be concerned that the change is not in their interest.

Demonstrating value

And there is a degree of psychology at work in trying to not only work with that reluctance that all businesses have to face, but also to influence it positively and try to demonstrate that value to our end users was far in excess to the threat that they perceived.

Gardner: I've heard someone refer to that as having insanely good products. That's going to change people's behavior. Is that what you've been able to accomplish?

Saraiva: Absolutely. I can think of examples that are truly amazing, in my opinion. One is about the agility that we've gained through the introduction of technology such as this one, and not just the user of that technology, but the optimal use of it. Some time ago, before RSA was used in some departments, we had important customers who had an urgent, desperate need for a piece of information that we happened not to have, for whatever reason. It happens all the time.

We tried to politely explain that it might take us a while, because it would have to go through a development team that traditionally build C++ components. They were a small team and they were very busy. They had other priorities. Ultimately, that little request, for us, was a small part of everything we were trying to do. For that customer, it was the most important thing.

The conversation to explain why it was going to take so long why we were not giving them the importance that they deserved was a difficult conversation to have. We wanted to be better than that. Today, you can build a robot quickly. You can do it and plug it into the architecture that we have so that the customer can very quickly see it appearing almost real time in their product. That's an amazing change.

But ultimately, most importantly, we needed the confidence that we could get our job done.

Gardner: So, how did the Kapow platform come to your attention? What was the story behind your adoption of this?

Saraiva: We spent some time looking at the technologies available. We spoke with a number of other customers and other people we knew. We did our own research, including a little bit of the shotgun kind of research that you tend to do on the Internet, trying to find what's available. Very quickly, we had a short list of five technologies or so.

All of them promised to be great, but ultimately, they had to pass the acid test, which was evaluation in terms of our technical operations experts. Is this something that we are able to run? And also in terms of the capabilities we were expecting. They were quite demanding, because we had a variety of users that we needed to cater to.

But ultimately, most importantly, we needed the confidence that we could get our job done. If we are going to invest in a given technology, we want to know that it can be used to solve a given kind of problem without too much fuss, complexity, or delay, because if that doesn't happen, you have a problem. You have only partially achieved the promise, and you will forever be chasing alternatives to fill that gap.

Kapow absolutely gives us that kind of confidence. Our developers, who at first had a little bit of skepticism about the ability of a tool to be so amazing, tried it. After the first robot, typically, their reaction was "Wow." They love it, because they know they can do their job. And that's what we all want. We want to be able to do our jobs. Our customers want to use our products to do their jobs. We're all in the same kind of game. We just need to be very, very good at what we do. Kapow gave us that.

Gardner: Approximately how long have you been using Kapow? Do you have any metrics that might give an indication of what benefits are there? Maybe it's reduced number of developer hours or rapid use for creating robots that can get you the information you want. Any sense of the benefits?

Critically important

Saraiva: Perhaps, the most interesting examples are those about web sources that were critically important to us, and that until we were able to leverage Kapow, we just couldn't automate sensibly.

It was not even a matter of it taking a long time. We were not able to do it. With Kapow, it was a straightforward process. We just click, follow the process that really mirrors a complex workflow in the flow chart that we designed, and the job is done.

In terms of the rapid development of the solutions, it was at least a reduction from several months to weeks. And this is typical. You have cases where it's much faster. You have cases where it's slower, because there are complex, high-risk automation processes that we need to take some time to test. But the development process is shortened dramatically.

Gardner: We were recently at the Kapow User Summit. We've been hearing about newer versions, the Kapow platform 9.2. Is there anything in particular that you've heard here so far that has piqued your interest? Something you might be able to apply to some of these problems right away?

Saraiva: A lot of what we've been doing and focusing on over the last four years was around a pattern whereby we have data flowing into the company, being processed and transformed. We're adding our value, and it's flowing out to our customers. There is, however, another type of web sourcing and acquisition that we're now beginning to work with which is more interactive. It's more about the unpredictable, unplanned need for information on demand.

The main advantage of a cloud-based service running Kapow would be in freeing us from the hassle of having to manage our own infrastructure.

There, interestingly, we have the problem of integrating the button that produces that fetch for data into the end-user workflows. That was something that was not possible with previous versions of Kapow or not straightforward. We would have to build our own interfaces, our own queues, and our own API to interface with the robo-server.

Now, with Kapplets it all looks very, very straightforward because we can easily see that we could have an arbitrary optimized workflow solution or tool for some of our users that happens to embed a Kapplet that allows a user to perform research on demand, perhaps on the customer, perhaps on a company for the kind of data that we wouldn't traditionally be acquiring data on a constant fixed basis.

Gardner: Looking to the future about deployments, we heard the possibility of a cloud version of Kapow. How would you prefer to move in the future on deployments? It sounds as if the direction of bridging organizational boundaries continues for you, maybe delivering this to mobile devices specifically having a cloud-based Kapow set of platform services would make sense.

Saraiva: Over time, things keep changing.  Although currently, we run a relatively standard, low-scale infrastructure, it's always a cost, an overhead, and an extra worry that you have to configure networks.

Security

And you have to worry about security. You have to ensure that things are being monitored and that you respond to alarms and so on. In theory, if we were able to get exactly the same service that we now have internally based in the cloud, we could scale it much more transparently without much planning. That would definitely give us an advantage.

So, right now, I'm beginning to think about that precise question. For the next few years, are we going to have just hosted infrastructure at our premises, or are we going to begin leveraging the cloud properly, because then we can focus on what we really want which is to get value out of robots.
 
Gardner: I'm afraid we're about out of time, but quickly, now that you've been doing this for some time, do you any advice that you might offer to others who are grappling with similar issues around multiple data sources, not being able to use APIs, needing a synthetic API approach, what lessons have you learned that you might be able to share?

I've been amazed at what is possible with technologies such as Kapow.

Saraiva: I suppose the most important message I would want to share is about confidence in technology. When I started this, I had worked for years in technology, many of those years in web technology, some complex web technology. And yet, when I started thinking about web content acquisition, I didn't really think it could be done very well.

I thought this is going to be a challenge, which is partly the reason why I was interested in it. And I've been amazed at what is possible with technologies such as Kapow. So, my message would be don't worry that technology such as Kapow will not be able to do the job for you. Don't fear that you will be better off using your own bespoke C++ based solution. Go for it, because it really works. Go for it and make the most of it, because you will need it with so much data, especially on the Internet. You have to have that.

Gardner: I’m afraid we’ll have to leave it there. We've been talking about how Thomson Reuters in London has improved information integration and delivery using Kapow technology and a Synthetic APIs approach to gain significant business benefits.

Please join me in thanking our guest, Pedro Saraiva, product manager for Content Shared Platforms and Rapid Sourcing at Thomson Reuters. Thanks for being on BriefingsDirect.

And thanks to our audience for joining this special discussion, coming to you from the recent 2013 Kapow.wow user conference in Redwood Shores, California.

I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host throughout this series of Kapow Software-sponsored BriefingsDirect discussions. Thanks for listening, and come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: Kapow Software, a Kofax company.

Transcript of a BriefingsDirect podcast on how Kapow Software helps a worldwide data company manage data acquisition in a cost-effective and consistent way. Copyright Interarbor Solutions, LLC, 2005-2013. All rights reserved.

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T-Mobile Swaps Manual Cloud Provisioning for Services Portal, Gains Lifecycle Approach to Cloud Across Multiple Platforms and Data Centers

Transcript of a BriefingsDirect podcast on how a major telecom company has improved its IT performance to deliver better experiences and payoffs for its businesses and end users alike.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Dana Gardner: Hello, and welcome to the next edition of the HP Discover Performance Podcast Series. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your moderator for this ongoing discussion of IT innovation and how it’s making an impact on people’s lives.

Gardner

Once again, we're focusing on how IT leaders are improving their services' performance to deliver better experiences and payoffs for businesses and end users alike, and this time we're coming to you directly from the HP Discover 2013 Conference in Las Vegas. [Disclosure: HP is a sponsor of BriefingsDirect podcasts.]

Our next innovation case study interview highlights how wireless services provider T-Mobile US, Inc. improved how it delivers cloud- and data-access services to its enterprise customers. We'll see how T-Mobile walked back use of manual cloud provisioning services and delivered a centralized service portal to manage and deploy infrastructure better and also improve their service offerings across multiple platforms and across multiple data centers.

To learn more about how T-Mobile enabled a lifecycle approach to delivering advanced cloud services, please join me in welcoming our guest, Daniel Spurling, Director of IT Infrastructure at T-Mobile US, Inc. Welcome.

Daniel Spurling: Thanks, Dana.

Gardner: Tell me about the trends that are driving your business now. We know T-Mobile as a mobile provider, but is this speed, is this competition? What are some of the big top-of-mind issues for you and your market?

Spurling: To answer that question, I'm going to frame up a little history and go into where T-Mobile has come from in the last few years and what has driven some of that business shift in our space.

As many know, in 2011 AT&T attempted to acquire T-Mobile. When that dissolved, there was a heavy recognition that we needed to drive greater innovation on our business side. We had received a generous donation, we’ll call it, of $4 billion dollars and a lot a spectrum. We drove a lot of innovation on our network side, on the RF side, but the IT side also had to evolve.

We, as an IT group, were looking at where we needed to start evolving within the infrastructure space, we recognized that manual processes are a very rudimentary way of delivering servers or compute storage, etc. This was not going to meet the agility needs that our business was exhibiting. So we started on this path of driving a significant cultural shift, and mindset shift as well as the actual technological shift in the infrastructure space, with cloud as one of the core anchor points within that.

Gardner: When you decided that cloud was the right model to gain this agility, what were some of the problems that you faced in terms of getting there?

Not a surprise

Spurling: When you talk about cloud, you have to define what cloud is. We recognize that cloud is almost like a progression of where we've been going within IT. It is not like it is a surprise.

Spurling

We've been trying to figure out how to enable more self-service. We've been trying to figure out how to drive greater automation. We've been trying to figure out how to utilize those ubiquitous network access points, the ubiquitous services, external or internal of the company, but in a more standardized and consolidated fashion.

It wasn't so much that we were surprised and said, "Oh, we need to go cloud." It was more on the lines of we recognized that we needed to double down our efforts in those key tenets within cloud. For T-Mobile, those key tenets really were how we drive greater standardization consolidation to enable greater automation and then to provide self-service capabilities to our customers.

Gardner: Were there particular types or sets of applications that you identified as being the first and foremost to go into this new model?

Spurling: That's a great question. A lot of people look at the applications, as either an application play or an infrastructure play, because of the ecosystem that existed when the cloud ecosystem was kind of birthing, a year-and-a-half ago, two years ago. We started more on the infrastructure side. So we looked at it and said, "How do we enable the application growth that you are talking about? How do we enable that from an infrastructure perspective?"

We recognized that we needed to double down our efforts in those key tenets within cloud.

And we saw that we needed to focus more on the infrastructure side and enable our partners within our IT teams -- our development partners, our application support partners, etc. -- to be able to transform the application stacks to be more cloud-capable and cloud-aware.

We started giving them the self-service capability on the infrastructure side, started on that infrastructure-as-a-service (IaaS) type capability, and then expanded into the platform-as-a-service (PaaS) capability across our database, application, and presentation layers.

Gardner: The good news with cloud is that you do away with manual processes and you have self-service and automation. The bad news is that you have self-service and automation, and they can get very complex and unwieldy, and like with virtual machines (VMs), sometimes there is a sprawl issue. How did you go about this in such a way that you didn’t suffer in terms of these new automation capabilities?

Spurling: I'm going to break it into two parts. Look at the complexity of an IT organization today, especially for a company of T-Mobile's size. T-mobile has 46,000 employees, around 43 million customers. It's not a small entity. The complexity that we have in the IT space mirrors that large complexity that we have in the business space.

Tough choices

We recognized on the infrastructure side, as well as in the application, test and support sides, that we cannot automate everything. We had to really drive heavy consolidation and standardization. We had to make some tough choices about the stuff that we were -- for lack of a better term -- going to pare off our infrastructure tree: different operating systems, different hardware platforms, and data centers that we were going to shut down.

We had to drive that heavy rationalization across all of the towers within our IT space, in order to enable the automation you talked about, without creating a significant amount of complexity.

On the sprawl question though, we made a conscious decision that we were going to allow or permit some level of sprawl, because of the business agility that was gained.

When you look at server sprawl, there are concerns around licensing, computer utilization, and stranding resources or assets. There are a lot of concerns around sprawl, but when you look at how much business benefit we got from enabling that agility or that speed to deliver and speed to market, the minimal amount of sprawl that was incurred was worth it from a business perspective.

You have to continue to deliver for your customers, but you need to prioritize what you are doing in that maintenance space.

We still try to manage it. We still make sure that we're utilizing our compute storage data centers, etc., as efficiently as possible, but we've almost back-burnered the sprawl issue in favor of enabling business.

Gardner: So with multiple platforms -- Windows, Linux, AIX, Unix -- and multiple data centers across large geographies, how can you do that without a larger staff? Do you find the centralization possible or is it really pie in the sky?

Spurling: It’s a bit of both. When you look at how much work there is to enable an automation solution, you almost have to be -- and my team hates it when I use the term -- ambidextrous. On one hand, you have to continue to deliver for your customers, but you need to prioritize what you are doing in that maintenance space and shave off a bit to invest in the innovation space.

You're going to have to make some capital investments, and maybe some resource investments as well, to drive that innovation the next step forward. But you almost have to do it within the space that you are coexisting in that maintains and innovates at the same time, because you can't drop one in favor of the other.

We did have to make some tradeoffs on the maintenance side, in order to take some qualified and some bright resources that we are excited about in our burgeoning cloud future, and then invest those resources to continue driving us forward in the technological and also cultural space. We made a significant cultural change too.

Gardner: That was going to be my next question. When it comes to making these transitions in technology, platform, and approach, I often hear companies say they have a lagging cultural shift as well. What did that involve in terms of your internal IT department making that shift more of a service bureau supporting your business like a business within a business?

Buggy whips

Spurling: A lot of times when you talk about evolution in either business context or kind of an academic context, you hear the story about the buggy whip. The buggy whip, back in the day, was something that everybody knew. About 125 years ago, everybody probably knew someone who made buggy whips or who sold buggy whips. Today, no one knows anybody who makes or sells buggy whips.

The buggy whip industry went away, but a brand-new industry emerged in the automobile space. In the same context. the old IT way of manually building servers, provisioning storage, and loading applications may be going away, but there is a brand-new environment that's been created in a higher value space.

As to the cultural shift you talked about, we had to make significant investments in our leadership to be able to help set a vision, show our employees where that vision intersected with their personal careers and how they continue to move on.

Then, you lead and help them to do that kind of emotional change. I'm not a server builder anymore. I'm now a consultant with the business on delivering a value, I'm now an automation engineer, or I'm now delivering future value and looking at new products that we can drive further automation into. That cultural change is ongoing, and it’s certainly not done.

Gardner: And given that this transition and transformation is fairly broad in terms of its impact, you don’t just buy this out of a box with your professional services. How did the combination of people, process, technology and outside your knowledge come together?

With those tools, with HP professional services, and with our own internal team members, we created a tactical team that went out there and "attacked cloud."

Spurling: When we started down the path, we had a lot of people in our teams who were really excited about making IT better. T-Mobile is full of people who are dedicated and excited about making T-Mobile the best wireless company out there. They're starting to change the conversation to make T-Mobile the best company that is enabling people to get access to the Internet, to their friends, to data, etc.

So the people were excited to jump on, but we still had a knowledge gap. We knew that, from a leadership perspective, we weren’t going to get the time to market that we wanted, by training our resources, helping them learn and make mistakes. We had to rely on professional services. So we partnered with HP very heavily to drive greater, instant-on services in our cloud solution.

On the technology side, we have everybody under the sun from a tooling perspective, but we do have a significant investment in HP software. We made a decision to move forward with the HP Cloud Suite. Pieces like HP Operations Orchestration (HPOO) or Cloud Service Automation (CSA), and building out those platforms to be the overarching cloud solution that, for lack of a better term, created that federation of loosely coupled systems that enabled cloud delivery.

With those tools, with HP professional services, and with our own internal team members, we created a tactical team that went out there and "attacked cloud," delivered that, and continues to deliver that now.

Paybacks

Gardner: Before we close out, and it might be too early in your journey to measure this, but are there any paybacks? Can you look at results, either business, technological, or financial from going to a cloud model, provisioning with that automation, advancing the technology, making those cultural hurdles? What do you get for it?

Spurling: I could talk for hours on this one question. When you break out all of the advances that we've made internally and all the business benefits that have been realized, you can break them into so many different categories, in green-dollar and blue-dollar saves, in resource saves, etc. I’ll highlight a few.

When we look at the cloud opportunity and the agility that has been gained, the ability to deliver things in an almost immediate fashion, one of the byproducts that we may not exactly have intended was that our internal customers have demanded in the past a lot of complexity or a lot of significant specific systems.

When we said, you can get that significant system, whatever it is, in a couple of weeks or you can get this cloud solution that delivers 95 percent of what you ask in a couple of hours, almost always those things that we thought were hard requirements melted away. The customer said, "You know what, I'm okay with this 95-percent deal because it gets me to my business objective faster."

Because of the investments we made in standardization and automation, our cloud portfolio, we were able to build out that capacity in record time.

Though we as IT thought you had to have that complexity, we're realizing now that that complexity may not have been required all along, because we are able to deliver so quickly. The byproduct of that is that we're seeing massive amounts of standardization that we could never have thought would organically be possible.

From an agility perspective, there's time to market. We had a significant launch with the iPhone, a big event in T-Mobile’s history, probably one of the largest launches that we've had. That required a significant amount of investment in our back-end systems because of the load that was put in our activations and payment inside our systems.

Because of the investments we made in standardization and automation, our cloud portfolio, we were able to build out that capacity in record time, in days versus what would have taken in weeks or months two years previously. We were able to support our business with very little lead time, and the results were very impressive for us as a business. So those two areas, that standardization and consolidation and that rapid ability to deliver on business objectives, are the two key ones that we take away.

Gardner: Daniel, let’s close out on the future. When you look to unforeseen events in your business, it could be mergers, acquisitions, changes in the market, new products, new applications, do you feel that the investments you’ve made in cloud also puts you in a position to be able to move rapidly? What future direction do you have in mind for your cloud trajectory?

Spurling: As I said in the beginning, we're just starting with cloud. That’s not fair to say. We are just continuing with cloud. We've done it in the past. We've used mainframes to distribute it.

Just one step

We’ve done application hosting with the Internet craze into software as a service (SaaS), that we now are seeing PaaS external to our internal organizations. We're seeing software to find everything starting to have a role. And there is a really interesting play that says, there is no end. Cloud is just one step in continuing to evolve IT to be more of a business partner.

That's really how we are looking at it. We're making great strides in that space. You talked about new applications or business mergers, etc. In every single area, we're setting ourselves up to be closer to the business, to move that self-service capability. I'm not just talking about a webpage. I am talking about being able to consume an IT service as a business leader in a simple way. We're moving that closer-and-closer to the business and we are being less and less of a gatekeeper for technology, which is super-exciting for us to see in the organization.

For us specifically, we're recognizing that the investments we made in our PaaS plays as well as test automation as well as some of the dev platforms. We're seeing those start to have payoffs in the fact that we're developing cloudware applications that are now scalable in a way that we've never seen before, without massive human invention.

So we're able to tell our business, "Go ahead and have a great marketing idea, and let’s move it forward. Let’s try that thing out. If it doesn't work, it’s not going to hurt IT. It's not going to take 18 months to deliver that." We're seeing IT able to respond about as fast as the business wants to go.

In every single area, we're setting ourselves up to be closer to the business, to move that self-service capability.

We are not there yet today. It’s a continuing journey, but that’s our trajectory in the next 6 to 12 months, and then who knows what’s going to happen, and we are excited to see.

Gardner: Well, great, I'm afraid we have to leave it there. We've been learning about how wireless services provider T-Mobile US, Inc. improved how it delivers cloud and data and applications to its enterprise customers, and we've seen how T-Mobile walked back the use of manual cloud provisioning and in order to move to a more advanced and automated approach and that has delivered some very impressive results.

So join me in thanking our guest, Daniel Spurling, Director of IT Infrastructure at T-Mobile US. Thanks so much.

Spurling: Thanks, Dana. It’s my pleasure.

Gardner: I'd like to thank our audience as well for joining us for this special HP Discover Performance podcast coming to you directly from the HP Discover 2013 Conference in Las Vegas.

I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HP sponsored discussions. Thanks again for joining, and come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Transcript of a BriefingsDirect podcast on how a major telecom company has improved their IT performance to deliver better experiences and payoffs for their businesses and end users alike. Copyright Interarbor Solutions, LLC, 2005-2013. All rights reserved.

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HP Vertica General Manager Sets Sights on Next Generation of Anywhere Analytics Platform

Transcript of a BriefingsDirect podcast on how HP Vertica is evolving to meet the needs of enterprises as data continues to grow.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Dana Gardner: Hello, and welcome to the next edition of the HP Discover Performance Podcast Series. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your moderator for this ongoing discussion of IT innovation and how it’s making an impact on people’s lives.

Gardner

Once again, we’re focusing on how IT leaders are improving their business performance for better access, use and analysis of their data and information. This time we’re coming to you directly from the HP Vertica Big Data Conference in Boston and we're delighted to welcome the General Manager of HP Vertica to his debut on BriefingsDirect.

Please join me in welcoming Colin Mahony, General Manager at HP Vertica. Good to have you with us, Colin. [Follow Colin on Twitter.] [Disclosure: HP is a sponsor of BriefingsDirect podcasts.]

Colin Mahony: Thanks, Dana. It’s great to be here. I appreciate you having me.

Gardner: Well, it's been well over two years since HP acquired Vertica and, as we begin the inaugural 2013 Big Data Conference, how would you best characterize how Vertica has evolved since its founding back in 2005?

Mahony: Oh, wow. We’ve evolved quite a bit. It’s been a busy couple of years here, certainly post the acquisition. But I think at a high level, we’ve really shifted and expanded from being an MPP column store, very narrowly-focused database company, really into an analytic platform company.

With that comes several developments, obviously on the product side, but also as an organization, going through that maturation in terms of being able to operate at a global scale across the spectrum of what you would expect an analytics provider to offer.

Gardner: And how do you characterize the difference between a store and a platform? Are there many ecosystem players or is this an organic evolution of your capabilities or both?

Mahony: It’s both, the ecosystem and the tools that you interact with. And of course, we support a very rich and vibrant ecosystem of business-intelligencve (BI) tools, extract, transform and load (ETL) tools, and other types of management tools. Not just the ecosystem around it, but also looking within our own products.

Mahony

So it's adding a lot of the capabilities like backup and recovery, additional analytics capabilities beyond just standard SQL with the SDKs that Vertica supports, the ability to run both the procedural and the other types of code within the product, being able to express things like MapReduce beyond what a traditional database system would do.

Since the founding of the company, we've tried to take the best part of the database world and the best parts of the SQL world, but address the most challenging issues that traditional databases have had. So whether it is scalability or it’s being able to run things beyond SQL or it’s just the performance, those are all the things that we have taken into account while we built Vertica, and I think we have always been on the fast track to a platform.

We knew it would be a journey and we knew that building a product and a platform from the bottom up is not an easy thing, but we also knew that once we got there, once we sort of crossed that chasm, if you will, then all those decisions that made in the beginning about this product and building an engine from the bottom up would pay off.

Platform modularity

For probably the last year, that's where we’ve been. Right now, we're seeing that it’s easy to add functionality to the platform because of the modularity of the platform, and we can add that functionality without giving up any of the performance.

For me, it’s probably the most exciting time. Being part of HP offers us so many things that make it a lot easier to become a platform, not only on the development side, but a much greater ecosystem, a global scale, being able to support customers globally 24/7.

Gardner: This is a large conference. I'm pretty impressed with the attendance, but for our audience, this might be an introduction. Tell our listeners and readers a bit more about yourself and your background?

Mahony: I've been with Vertica since the beginning. In fact, long before Vertica, my background has always been databases. I've always loved computer science, and had a minor in computer science in my undergraduate degree. In my first job out of school, I was taking databases -- it's one of our competitors now, so I won't name them -- but I was using their database, and working with civilian US Government clients, and getting a lot of information published up to the web in the earliest days of the web.

I had a couple of other roles, but they were always very technology focused. Then I got my MBA on the business side and went into venture capital for seven years. That's where I met Mike Stonebraker, the founder of Vertica.

Those are all the things that we have taken into account while we built Vertica, and I think we have always been on the fast track to a platform.

I just loved the idea, everything I knew about databases and the challenges of traditional database and everything I knew about the new world order of information -- at the time we didn’t even talk about the term big data -- it just seemed to align really well.

So I decided to leave the dark side of venture capital and I jumped into something that I have been incredibly passionate about. If you look at that lifecycle even my own background with Vertica and where we’ve come, it’s just been a great. The timing was great and as always it takes a lot more than just great technology and great people.

There is definitely a lot of luck and timing, and I had the fortune of stepping into the right market at the right time, being part of a great team, and learning from a lot of great people along the way.

This is our first user conference. It’s ironic that we've never had one before, but I think also this is a testament to that scale I was referring to with what HP can bring. We have wanted a user conference since the beginning. Obviously, it takes some critical mass to get there which we now have, but also it takes the support of an organization that knows how to do these conferences and understand the value of them.

So it's just wonderful to be here. It’s wonderful to see all of these partners, customers, employees and friends of Vertica and HP here in Boston, of course Vertica’s hometown, so truly exciting.

Gardner: You mentioned the marketplace and the timing. I have to go back to that because in 2005, while scale and performance were very important. This whole notion of big data being so prevalent in the market really hadn't happened yet. What’s the state of the union, if you will, with this marketplace? Do more and more IT functions and business functions begin and end with Big data? It seems to be at the center of so many things.

Exponential growth

Mahony: It is. To go back to the founding of Vertica, I remember when Mike Stonebraker was giving the early presentations on the need for it. He talked a lot about the exponential growth of data and how that was outpacing any laws like Moore’s law or other hardware laws. So much information was being created, there was no way that just using more paralyzed hardware was going to be able to address the issue.

The state of the union back then was, just as you said, there was no such thing as big data, but I think Mike, as a visionary, knew what was going to happen in the industry. And it has happened.

It wasn’t a long time ago, but I remember that I was trying to find our first sample dataset that was over a terabyte and we had a difficult time finding it. When we would talk to the early customers, they looked at us like we were crazy when we were asking about a terabyte.

We have an easy time now finding terabytes of data. The state of the union today is that what's driving so much around big data is that you have obviously the volume, variety, and velocity that we talk about often, but what's really driving those three things is human information, whether it's social media, tweets, or expressive content that’s just so prevalent right now, as well machine information.

If you look at the traditional structured database market by any number, it’s a small percentage of the amount of data that’s out there. The strength of Vertica, and really the strength of HP overall, is that we have the best assets for the unstructured human information in Autonomy, as well as the best assets when it comes to machine information and large data.

When we would talk to the early customers, they looked at us like we were crazy when we were asking about a terabyte.

That has some structure. It’s semi-structured information, but it’s not your traditional transaction system. The power of all of that data comes together when you can have an engine that applies some structure to it and then is able to deliver the analytics that the organization needs. It's both IT as well as line of business, and even this new category we often talk about, which is the data scientist.

One of the great things about this show here is that we’ve got Billy Beane of Moneyball fame as our keynote speaker. The reason that we wanted Billy to come speak here is that Moneyball is exactly what’s happening right now in the world when it comes to big data.

You have the data scientist or the statistician, you have the line of business folks, and you have IT. They all have a part to play in the success of how information is used in companies. By bringing them together and by making the software that much easier for them to come together and solve these problems, you can create very real and differentiated value within organization.

So Moneyball is exactly what’s happening, certainly in corporate America, but also in government and in many other institutions that want to leverage information to be more efficient and create a competitive advantage.

Gardner: Before we delve into the latest and greatest with Vertica, let’s put some context around this. It’s only been a few months since the HP Discover 2013 Conference in Las Vegas where the HAVEn Initiative was announced. This puts Vertica in a very prominent place among other HP properties, technologies, platforms and approaches to solving this big data issue. Recap for us, if you would, what HAVEn is and why Vertica formed such an important pillar for this larger HP initiative?

Big-data lake

Mahony: What companies are looking for is this notion of the big-data lake. To me, it can mean many different things, but at the end of the day, companies want to take all the information assets that they have and they want to put them into a safe place, but a place where access to that information can be used by many different constituencies, whether it's IT, line of business, or data scientist.

So the notion of having a safe place, a harbor, or a port is what we announced as HP HAVEn, which is HP’s big data platform. It is primarily for analytics, but it can be used for just about anything when it comes to information and data.

What's so important about information right now is that there are different constituencies in the companies that want to take the information. First of all they want to capture all the information, not just structured, not just unstructured, but 100 percent of their information.

They want to get it to a place where they can leverage it and use it for a lot of different use cases, but the first part is get that information into the right place. For us, that is one of three components of HAVEn, which is the connectors.

We have over 700 connectors as part of HAVEn coming from Autonomy, coming from our Enterprise Security Group, the ArcSight core Logger and those connectors. That can be human information, extreme log information, or traditional database structured information.

They're driven by vast volumes of information and they close the loop, meaning that the experiences that are happening with an application.

Step one is the connectors to get these components. Step two is to put that data into the best engine for that data. Vertica obviously is one component, but you also have the Autonomy IDOL Engine, you have the ArcSight Logger engine, and also open-source technologies like Hadoop, which is actually the HP HAVEn. So we’ve got a place to put the information.

Step three is any N number of applications. What I'm seeing happening in the industry right now is just like we went from mainframe to client-server, and client-server to LAN, we're in a period now where applications are being developed. They're certainly web-based and distributed, but they're also analytical in nature.

They're driven by vast volumes of information and they close the loop, meaning that the experiences that are happening with an application, if you're driving a car, or whatever it might be, information is being passed, closed loop, back to a system that can then optimize the experience. That is creating a new class of applications.

For that new class of applications, you need the platform to be able to drive those. What we're bringing together in HAVEn is Hadoop, Autonomy, Vertica, Enterprise Security, core assets, and the N number of applications.

At Discover, we announced some of our own internal applications, which are powered by the HAVEn platforms. We announced our HP Analytics offering, which is built using Hadoop, Vertica, Enterprise Security, and Autonomy assets.

About community

We're making some of our own applications, but this is about the community and getting people to be able to build new set of applications that can use these components to really change how people are interacting with their data.

That’s HAVEn, and I am always careful to point out to people that HAVEn itself is not a product, but it's a platform and it’s a broader platform than the one that is just Vertica, Autonomy, or Enterprise Security. It’s a platform where 1+1+1+1+1, instead of equaling 5, should equal 8 or 10 or 12, and that's the goal. Of course, it's also a roadmap into areas that each of these components are working on to bring those closer together. So it’s exciting.

Gardner: Let’s look a bit more specifically at Vertica and try to factor why it’s differentiated in the market, but then also get a sense of where it’s going.

One of the things that strikes me about the market nowadays is that there seems to be a sense of tradeoffs going on when organizations are trying to pick their data engine or their platform. They have a set of value on one side, but it’s opposed by value on the other. They can’t have everything. One size does not fit all.

So how are you at Vertica able to help people deal with these tradeoffs that they're facing when it comes to a next-generation data platform?

Vertica was founded on the premise that one size does not fit all.

Mahony: Before I explain the tradeoffs, I couldn’t agree with you more, Dana. In fact, Vertica was founded on the premise that one size does not fit all. Using a single OLTP transactional database to do everything, including analytics, just doesn't make a lot of sense.

If you think about the areas that the people have to trade off, usually it’s scale for performance or analytics functionality for performance. One of things that I've spent a lot of time looking at is, especially over the last couple of years, is just some of the alternative platforms, not just for analytics, but for all of the different data needs.

You can take something like Hadoop as an example. Hadoop really is a distributed file system and has capabilities to run rudimentary analytics and transform processed data. But I think what people love about Hadoop is that it's really easy to load data into Hadoop. You don't have to define the schema or anything.

Instead of schema on write or load time, it’s schema on read time. People like that. They also like at least the perception that it is free and the scalability of it. On the database side, what people love about the database is that you're going to get really good performance, because the data is structured. If you're using a NexGen MPP platform like Vertica, you'll get the performance of the scalability.

So what we’re trying to do and what we've always done a pretty good job of at Vertica is look at the things that would make sense for Vertica to do. We look at expanding the platform in ways that, number one, we have the expertise and the capability to do, not only from the development standpoint, but from the support standpoint. And number two, we have the ability to create something differentiated. If we don't, or it’s not core, then we won’t do it, sticking to the purity of one size doesn’t fit all.

Hadoop-like

We've been doing a lot of work in areas like making it easier to get the data into the platform, doing more with it, making it seem much more like a Hadoop-like environment. You can look at our past releases and see that there's been a lot of work done on that and we continue to make those investments.

One thing has been consistent at Vertica since the beginning. What we focus on is to make it really easy for people to get information onto the platform. Then, we make sure we continue to deliver new capabilities, performance, and functionality within the platform.

We make sure we’re enabling our customers and partners to deploy Vertica anywhere and everywhere, whether it’s cloud appliances, software, or the like. Those are the three tenets of the company. It’s all around this notion of making data matter and help people make better decisions that lead to better outcomes with superior information.

There's so much that can be done in this space, but I think the key for us is to focus on the things that we know we do really well. The good news is that it's such a large space with so many demands that we know we can make a huge impact without trying to take on the world. We know we can make a huge impact in what we’re doing.

I think you'll continue to see some interesting developments along the lines of what I'm describing, and it's very much in line with where we've been.

No matter what on-ramp they take, they tend to find a lot of the other capabilities once they get on.

Gardner: While we're at the user conference, there are some great use cases and some examples. It's one of my favorite points of communication that it's always better to show than to tell.

Of the various user organizations and use cases here, are there are any that jump at you personally when you think about what Vertica started out as and what it became? Are there any ways that some users are putting this to work to really capture, "This is what we intended, and this is what we went through those paces to allow, to encourage, and to now see the fruits of?"

So, from all of the happenings here with the conference, what sort of gets your blood flowing?

Mahony: One thing I've certainly noticed over the years with our customers is that the shiny object of why a customer chooses Vertica may look very different across our customers. For some, it's the price. For some, it's the performance and the scale, massive volumes. For some it's a particular analytic function or several pattern matching capabilities. And for others, it's something entirely different.

But what's so exciting, especially about this conference, is that no matter what on-ramp they take, they tend to find a lot of the other capabilities once they get on. Hopefully, here at the conference, we're going to accelerate some of that just by getting our customers and our partners together in an environment where they can share stories.

Partners and customers

In fact, if you look at the agenda for the conference, it's very light on Vertica presentations. It's very heavy on partner and customer presentations, because this is the time that we want our partners and our customers to learn from each other. We want them to talk about how they are using it.

To answer your question directly, what gets me most jazzed up is when a customer is taking advantage of nearly everything that we do. Again, it's a cycle. It's not something that can happen immediately.

There are so many customers here that have been with us for four or five years and had just been great partners for the Vertica organization in terms of the feature we are developing and the direction that we are taking the product. They tend to be the ones who are using just about every feature in the product. So it gets me really excited.

I have got a customer that's got massive volumes of information, lot of diversity in the information, many different lines of business constituents who are accessing the information, data scientists, DBAs, programmers, different people who are creating applications and keeping the system up and through all that change in the organization.

Sometimes it's not only change in the organization, but potentially change in the industry and changing the way that people are interacting with data and may be changing healthcare outcomes, or drastically improving the quality of mobile phone service or other types of services.

It is about the connection between our customers and our partners, so that they can talk to each other.

So there isn't any one customer of whom I'd say, "You have to go see these guys." The reality is that you should see all of our customers and hear what they have to say. For me, that's the most important part of this conference.

It is about the connection between our customers and our partners, so that they can talk to each other. We can just be a fly on the wall and listen to some of the things that they're saying, good, bad, or ugly -- hopefully very good. But we can even hear things that they want us to improve. That's an important part of any company, certainly a software company, and that's what we're hoping to get out of it. For our customers and partners, they're going to get a lot of out of this just by talking to each other.

Gardner: Colin, what about the notion of business transformation. We've been hearing about this for 30 years. It's been big part of the academic work in business schools. Process re-engineering has evolved into balanced scorecards, and the flavor of the day is about how to change the nature of companies.

But it strikes me that this whole greater than the sum of the parts that you alluded to earlier, where data and analytics is made more available across easier applications to morph that, is inside the company that can then access more types of information across the boundaries of the organization into supply chain and ecosystems.

Getting more detailed information in real time about the customers and the marketplace probably has as much or more of a opportunity to transform businesses than just about anything else that's happened, with the possible exception of the Internet itself, over the past 20 years.

More than technology

So without going too much into a hype curve, the interest of the incredible amount of attention paid to big data in the past few years is about more than the technology. It's really about an empirical data-driven approach, a cultural shift if you will, within businesses. How you have been seeing that manifest itself here at the conference?

Mahony: It's an enormous opportunity for business transformation and definitely the  whole is greater than the sum of the parts. What makes companies really successful with information is not trying to boil the ocean, not trying to do a traditional enterprise data warehouse project that's going to take 24 months, if you're lucky, 36 most likely.

They’ll end up with some monolithic inflexible platform that will probably be outdated by the time it gets deployed. What is making a lot of companies successful is they find a particular use, they find a problem area that they want to drill down on, and they mobilize to do it.

For that, they need a solution that is quickly deployed, but also has that capability to become something much larger. Whether it's Vertica, Talend, or any of the other portfolios that we offer, we strive to make sure that somebody can get up and running quickly, whether it's Autonomy and human information analytics, Vertica and machine data or other types of transactional structured data.

The most important thing is that you find that business case, you focus on it, and prove very quickly. There's something we refer to as “Time to Terabyte,” which is less than a month, typically for Vertica. You get a return on investment (ROI) in less than a month for the investments that you made. If you prove that out, then everybody in the organization is happy, the line of business, the technology folks in IT, even the statisticians, data scientists.

It's not just about faster speeds and feeds. It's about fundamentally stepping back and asking how we're running this business.

From there, you start expanding the project, and that's exactly how we win most of our customers. We very rarely go in and say, "Buy an enterprise license for our product across the company." We certainly do those, but more typically we get into a business unit, we find the acute pain, and we solve that problem.

What they're betting on is the ability for us to expand and for them to expand in this platform. That's why we are, on the one hand, all about the platform and the integration, but on the other hand, not about to lose the flexibility and the modularity of what we do, because that's also a huge differentiator for HP's portfolio.

I think that this is a wonderful time in the world of business transformation, and I think, unlike what has been talked about for the last 30 years, you now have the data that can back it up and prove it in real-time to the organization.

That's the big difference. You gave the balanced scorecard as an example. If you look at the balance scorecard methodology, you can take that methodology and drill down into a thousand fields of detail and be able to get that information in real time. That's the opportunity here, and that's I think why this market is so huge.

It's not just about faster speeds and feeds. It's about fundamentally stepping back and asking how we're running this business. What assets, especially information assets, do we have that could dramatically boost the productivity to the same extent that computers, when they were first introduced, boosted productivity. That's the goal that everybody is looking for when it comes to information.

Cloud and hybrid

Gardner: For our last item today, I wonder if we could take out our crystal ball apparatus and try to do a little blue-sky thinking. One of the other big trends these days of course is cloud computing and hybrid models for the distribution of workloads for applications, but also for data. I'm wondering, as we go down this journey over the next year or two, how do big data and cloud computing come together?

There's this notion of an analytics platform-as-a-service (PaaS) deploy for developers, but now maybe more for data scientists and for those that are doing BI and other analytic chores. How do you foresee some of this whole greater than the sum of the parts extending beyond the technical capabilities into the deployment models and what is that portend, for  additional paybacks or payoffs?

Mahony: As I mentioned in terms of the three things that we are focused on, number one is make it easy to get data into the platform. Number two is do a lot more with the platform, so that there is better analytic capabilities, better pattern matching, and better analytics packs on top of it.

Number three is make sure you can deploy Vertica everywhere, and in the everywhere and anywhere categories, the cloud is certainly the first name that comes to mind. That is absolutely the future of computing. In some ways, I guess, it's the past, but it's interesting how the past repeats itself.

All these activities that are happening up on the cloud are generating a lot of information, information that will be analyzed, I'm sure, in many different ways.

We do run Vertica on hosted environments like Amazon cloud. We're in a private beta on the HP Cloud Service. So there are definitely offerings and developments that that has been underway here at Vertica for a while.

We embrace that, and to us, it's not mutually exclusive. What you described in the hybrid environment where you can run certain things locally. You can burst up to the cloud to do other workloads, especially if you're looking to pull some quick processing power and storage. That's going to be the future and that's the way, just like any other utilities, that we're going to consume some of these capabilities.

This is one of the strengths of a company the size and scale of HP. We have these offerings, whether it's software only, appliance, or cloud. We have the ability to deliver however the customer wants it, and we can also provide not only the flexible technologies, but the flexible business capabilities to make that happen with a lot of ease.

It's an exciting time. If you look at the pillars of the HP, we have cloud, mobility, big data, and security. All four of those pillars tie well into one another, because they're all related. Of course, all these activities that are happening up on the cloud are generating a lot of information, information that will be analyzed, I'm sure, in many different ways.

So it's something that kind of feeds on itself, the same way the mobility does. All of that is a good thing for the analytic space, wherever it is. The final thing I would say is that  the most important thing about analytics is that you do want it embedded into the various applications, just like when you are driving a car, you just want the GPS system to tell you where you are going.

Analytics is the same. You want it within the context of whatever it is that you are doing. Given that so many things are going to be served off the cloud, it's natural that that's the place that will host some of the analytics as well.

So it's an incredibly exciting time, and we're looking forward to having many more of these User Conferences and are certainly going to enjoy the rest of the show this week.

Gardner: Well great. I'm afraid we will have to leave it there. We've been learning more about the ongoing evolution of the HP Vertica platform and its capabilities, and we've developed better understanding about Vertica's growing role and making among the most challenging big data analytic chores more successful and impactful.

So, join me in extending a huge thank you to our special guest Colin Mahony, General Manager at HP Vertica. Thanks so much.

Mahony: Thank you, Dana. [Follow Colin on Twitter.]

Gardner: And also thank you to our audience for joining us for this special HP Discover Performance podcast, coming to you from the HP Vertica Big Data Conference in Boston.

I'm Dana Gardner, Principal Analyst at Interarbor Solutions; your host for this ongoing series of HP sponsored discussions. Thanks again for listening and come back next time.

Listen to the podcast. Find it on iTunes. Download the transcript. Sponsor: HP.

Transcript of a BriefingsDirect podcast on how HP Vertica is evolving to meet the needs of enterprises as data continues to grow. Copyright Interarbor Solutions, LLC, 2005-2013. All rights reserved.

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