Showing posts with label Vertica. Show all posts
Showing posts with label Vertica. Show all posts

Friday, May 29, 2015

How Tableau Software and Big Data Come Together: Strong Visualization Embedded on an Agile Analytics Engine

Transcript of a BriefingsDirect discussion on the interaction between a high-performance data analytics engine and insights presentation software that together gives users an unprecedented view into their businesses.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Download the transcript. Sponsor: HP.

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

Gardner
Our next big data innovation discussion interview highlights how Tableau Software and HP Vertica come together to provide visualization benefits for those seeking more than just big-data analysis. They're looking for ways to improve their businesses effectively and productively.

So, in order to learn more, we're joined by Paul Lilford, Global Director of Technology Partners for Tableau Software, based in Seattle. Welcome, Paul.

Paul Lilford: Thanks, Dana. It’s great to be here.

Gardner: We're also here with Steve Murfitt, Director of Technical Alliances at HP Vertica. Welcome, Steve.

Steve Murfitt: Thank you. Great to be here.
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Gardner: Why is the tag-team between Tableau and Vertica so popular. Every time I speak with some one using Vertica, they inevitably mention that they're delivering their visualizations through Tableau. This seems to be a strong match.

Lilford: We’re a great match primarily because Tableau’s mission is to help people see and understand data. We're made more powerful by getting to large data, and Vertica is one of the best at storing that. Their columnar format is a natural format for end users, because they don’t think about writing SQL and things like that. So, Tableau, as a face to Vertica, empowers business users to self serve and deliver on a depth of analytics that is unmatched in the market.

Lilford
Gardner: Now, we can add visualization to a batch report just as well as a real-time. streamed report. What is it about visualization that seems to be more popular in the higher-density data and a real-time analysis environment?

Lilford: The big thing there, Dana, is that batch visualization will always common. What’s a bigger deal is data discovery, the new reality for companies. It leads to becoming data driven in your organization, and making better-informed decisions, rather than taking a packaged report and trying to make a decision that maybe tells you how bad you were in the past or how good you might think you could be in the future. Now, you can actually have a conversation with your data and cycle back and forth between insights and decisions.

The combination of our two technologies allows users to do that in a seamless drag-and-drop environment. From a technical perspective, the more data you have, the deeper you can go. We’re not limiting a user to any kind of threshold. We're not saying, this is the way I wrote the report, therefore you can go consume it.

We’re saying, "Here is a whole bunch of data that may be a subject area or grouping of subject areas, and you're the finance professional or the HR professional. Go consume it and ask the questions you need answered." You're not going to an IT professional to say, "Write me this report and come back three months from now and give it to me." You’re having that conversation in real time in person, and that interactive nature of it is really the game changer. 

Win-win situation

Gardner:  And the ability for the big data analysis to be extended across as many consumer types in the organization as possible makes the underlying platform more valuable. So this, from HP's perspective must be a win-win. Steve?

Murfitt: It definitely is a win-win. When you have a fantastic database that performs really well, it's kind of uninteresting to show people just tables and columns. If you can have a product like Tableau and you can show how people can interact with that data, deliver on the promise of the tools, and try to do discovery, then you’re going to see the value of the platform.

Murfitt
Gardner: Let’s look to the future. We've recently heard about some new and interesting trends for increased volume of data with the Internet of Things, mobile, apps being more iterative and smaller, therefore, more data points.

As the complexity kicks in and the scale ramps up, what do you expect, Paul, for visualization technology and the interactivity that you mentioned? What do you think we're approaching? What are some of the newer aspects of visualization that makes this powerful, even as we seek to find more complexity?

Lilford: There are a couple of things. Hadoop, if you go back a year-and-a-half or so, has been moving from a cold-storage technology to more to a discovery layer. Some of the trends in visualization are predictive content being part of the everyday life.

Tableau democratizes business intelligence (BI) for the business user. We made it an everyday thing for the business user to do that. Predictive is in a place that's similar to where BI was a couple years ago, going to the data scientist to do it. Not that the data scientist's value wasn’t there, but it was becoming a bottleneck to doing things because you have to run it through a predictive model to give it to someone. I think that's changing.

So I think that predictive element is more and more part of the continuum here. You're going to see more forward-looking, more forecast-based, more regression-based, more statistical things brought into it. We’ll continue to innovate with some new visuals, but the standard visual is unstructured data.

This is the other big key, because 80 percent of the world's data is unstructured. How do you consume that content? Do you still structure it or can you consume it where it sits, as it sits, where it came in and how it is? Are there discoverers that can go do that?

You’re going to continue see those go. The biggest green fields in big data are predictive and unstructured. Having the right stores like Vertica to scale that is important, but also allowing anyone to do it is the other important part, because if you give it to a few technical professionals, you really restrict your ability to make decisions quickly.

Gardner: Another interesting aspect, when I speak to companies, is the way that they're looking at their company more as an analytics and data provider internally and externally. The United States Postal Service  view themselves in that fashion as an analytics entity, but also looking for business models, how to take data and analysis of data that they might be privy to and make that available as a new source of revenue.

I would think that visualization is something that you want to provide to a consumer of that data, whether they are internal or external. So we're all seeing the advent of data as a business for companies that may not have even consider that, but could.

Most important asset

Lilford: From our perspective, it's a given that it is a service. Data is the most important asset that most companies have. It’s where the value is. Becoming data driven isn’t just a tagline that we talk about or people talk about. If you want to make decisions and decisions that move your business, so being a data provider.

The best example I can maybe give you, Dana, is healthcare. I came from healthcare and when I started, there was a rule -- no social. You can't touch it. Now, you look at healthcare and nurses are tweeting with patients, "Don’t eat that sandwich. Don't do this."
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Data has become a way to lower medical costs in healthcare, which is the biggest expense. How do you do that? They use social and digital data to do that now, whereas five, seven years ago, we couldn't do it. It was a privacy thing. Now, it's a given part of government, of healthcare, of banking, of almost every vertical. How do I take this valuable asset I’ve got and turn it into some sort of product, market, or market advantage, whatever that is?

Gardner: Steve, anything more to offer on the advent or acceleration of the data-as-a-business phenomena?

Murfitt: If you look at what companies have been doing for such a long time, they have been using the tools to look at historical data to measure how they're doing against budget. As people start to make more data available, what they really want to do is compare themselves to their peers.
As people start to make more data available, what they really want to do is compare themselves to their peers.

If you're doing well against your budget, it doesn't mean to say you gaining or losing market share or how well you’re doing. So as more data is shared and more data is available, being able to compare to peers, to averages, to measure yourself not only internally, but externally, is going to help with people making their decisions.

Gardner: Now for those organizations out there that have been doing reports in a more of a traditional way that recognize the value of their data and the subsequent analysis, but are yet to dabble deeply into visualization, what are some good rules of the road for beginning a journey towards visualization?

What might you consider in terms of how you set up your warehouse or you set up your analysis engine, and then make tools available to your constituencies? What are some good beginning concepts to consider?

Murfitt: One of the most important things is start small, prove it, and scale it from there. The days of boiling the ocean to try come up with analytics only to find out it didn’t work are over.

Organizations want to prove it, and one of the cool things about doing that visually is now the person who knows the data the best can show you what they're trying to do, rather than trying to push a requirement out to someone and ask "What is it you want?" Inevitably, something’s lost in translation when that happens or the requirement changes by the time it's delivered.

Real-time conversation

You now have a real-time, interactive, iterative conversation with both the data and business users. If you’re a technical professional, you can now focus on the infrastructure that supports the user, the governance, and security around it. You're not focused on the report object anymore. And that report object is expensive.

It doesn’t mean that for compliance things the financial reports go away, it means you've right sized that work effort. Now, the people who know the data the best deliver the data, and the people who support the infrastructure the best support that infrastructure and that delivery.

It’s a shift. Technologies today do scale Vertica as a great scalable database. Tableau is a great self-service tool. The combination of the two allows you to do this now. If you go back even seven years, it was a difficult thing. I built my career being a data warehouse BI guy. I was the guy writing reports and building databases for people, and it doesn’t scale. At some point, you’re a bottleneck for the people who need to do their job. I think that's the biggest single thing in it.

Gardner: Another big trend these days is people becoming more used to doing things from a mobile device. Maybe it’s a “phablet,” a tablet, or a smartphone. It’s hard to look at a spreadsheet on those things more than one or two cells at a time. So visualizations and exercising your analytics through a mobile tier seem to go hand in hand. What should we expect there? Isn't there a very natural affinity between mobile and analysis visualization?
Most visuals work better on a tablet. Right-sizing that for the phone is going to continue to happen.

Lilford: We have mobile apps today, but I think you're going to see a fast evolution in this. Most visuals work better on a tablet. Right-sizing that for the phone is going to continue to happen, scaling that with the right architecture behind it, because devices are limited in what they can hold themselves.

I think you'll see a portability element come to it, but at the same time, this is early days. Machines are generating data, and we're consuming it at a rate at which it's almost impossible to consume. Those devices themselves are going to be the game changer.

My kids use iPads, they know how to do it. There’s a whole new workforce in the making that knows this and things like this. Devices are just going to get better at supporting it. We're in the very early phases of it. I think we have a strong offering today, and it's only going to get stronger in the future.

Gardner: Steve, any thoughts about the interception between Vertica, big data, and the mobile visualization aspect of that?

Murfitt: The important thing is having the platform that can provide the performance. When you're on a mobile device, you still want the instant access, and you want it to be real-time access. This is the way the market is going. If you go with the old, more traditional platforms that can’t perform when you're in the office, they're not going to perform when you are remote.

It’s really about building the infrastructure, having the right technology to be able to deliver that performance and that response and interactivity to the device wherever they are.

Working together

Gardner: Before we close, I just wanted to delve a little bit more into the details of how HP Vertica and Tableau software work. Is this an OEM, a partnership, co-selling, co-marketing? How do you define it for those folks out there who either use one or the other or neither of you? How should they progress to making the best of a Vertica and Tableau together?

Lilford:  We're a technology partnership. It’s a co-selling relationship, and we do that by design. We're a best-in-breed technology. We do what we do better than anyone else. Vertica is one of the best databases and they do what they do better than anyone else. So the combination of the two, providing customers options to solve problems, the whole reason we partner is to solve customer issues.

We want to do it as best-in-breed. That’s a lot what the new stack technologies are about, it’s no longer a single vendor building a huge solution stack. It's the best database, with the best Hadoop storage, with the best visualization, with the best BI tools on top of it. That's where you're getting a better total cost of ownership (TCO) over all, because now you're not invested in one player that can deliver this. You're invested in the best of what they do and you're delivering in real-time for people.
It's the best database, with the best Hadoop storage, with the best visualization, with the best BI tools on top of it.

Gardner: Last question, Steve, about the degree of integration here. Is this something that end user organizations can do themselves, are there professional services organizations, what degree of integration between Vertica and Tableau visualization is customary.

Murfitt: Tableau connects very easily to Vertica. There is a dropdown on the database connector saying, "Connect to Vertica.” As long as they have the driver installed, it works. And the way their interface works, they can start query and getting value from the data straight away.

Gardner: Very good. I'm afraid we will have to leave it there. We've been learning about how Tableau software and HP Vertica come together to provide a strong visualization capability on top of a highly scaling, agile, in near-real time analytics engine. I'd like to thank our guests, Paul Lilford, Global Director of Technology Partners at Tableau Software in Seattle. Thank you, Paul.
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Lilford: Thank you.

Gardner: And we have been here with Steve Murfitt, Director of Technical Alliances at HP Vertica. Thank you, Steve.

Murfitt: Thank you.

Gardner: And a big thank you also to our audience for joining the discussion.

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. Get the mobile app for iOS or Android. Download the transcript. Sponsor: HP.

Transcript of a BriefingsDirect discussion on the interaction between a high-performance data analytics engine and insights presentation software that together gives users an unprecedented view into their businesses. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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Thursday, March 19, 2015

Axeda Builds Machine Cloud on HP Vertica to Deliver On-Demand Analysis Services

Transcript of a BriefingsDirect discussion on how to manage machine-to-machine communication for better big data collection and analysis.

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 Podcast Series. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing sponsored discussion on IT innovation and how it’s making an impact on people’s lives. Once again, we're focusing on how companies are adapting to the new style of IT to improve IT performance and deliver better user experiences, as well as better business results.
Gardner

Our next innovation case study interview highlights how Axeda, based in Foxboro,  Massachusetts, is creating a machine-to-machine (M2M) capability for analysis -- in other words, an Axeda Machine Cloud.

We're going to learn how they partner with HP in doing that. We're joined in our discussion today by Kevin Holbrook, the Senior Director of Advance Development at Axeda. Welcome, Kevin.

Kevin Holbrook: Thank you for having me.

Gardner: We have the whole Internet of Things (IoT) phenomenon. People are accepting more and more devices, end points, sensors, even things within the human body, delivering data out to applications and data pools. What do you do in terms of helping organizations start to come to grip with this M2M and Internet of Things data demand?
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Holbrook: It starts with the connectivity space. Our focus has largely been in OEMs, equipment manufacturers. These are people who have the "M" in the M2M or the "T" in the Internet of Things. They are manufacturing things.

The initial drivers to have a handle on those things are basic questions, such as, "Is this device on?" There are multi-million dollar machines that are currently deployed in the world where that question can’t be answered without a phone call.

Initial driver

That was the initial driver, the seed, if you will. We entered into that space from the remote-service angle. We deployed small-agent software to the edge to get the first measurements from those systems and get them pushed up to the cloud, so that users can interact with it.

Holbrook
That grew into remote accesstelnet sessions or remote desktop being able to physically get down there, debug, tweak, and look at the devices that are operating. From there, we grew into software distribution, or content distribution. That could be anything from firmware updates to physically distributing configuration and calibration files for the instrument. We're recently seeing an uptake in content distribution for things like digital signage or in-situ ads being displayed on consumer goods.

From there, we started aggregating data. We have about 1.5 million assets connected to our cloud now globally, and there is all kinds of data coming in. Some of it's very, very basic from a resource standpoint, looking at CPU consumption, disks space, available memory, things of that nature.

It goes all the way through to usage and diagnostics, so that you can get a very granular impression how this machine is operating. As you begin to aggregate this data, all sorts of challenges come out of it. HP has proven to be a great partner for starting to extract value.

We can certainly get to the data, we can connect the device, and we can aggregate that data to our partners or to the customer directly. Getting value from that data is a completely different proposition. Data for data’s sake is not high value.
From our perspective, Vertica represents an endpoint. We've carried the data, cared for the data, and made sure that the device was online, generating the right information and getting it into Vertica.

Gardner:  What is it that you're using Vertica for to do that? Are we creating applications, are we giving analysis as a service? How is this going to market for you?

Holbrook: From our perspective, Vertica represents an endpoint. We've carried the data, cared for the data, and made sure that the device was online, generating the right information and getting it into Vertica.

When we approach customers, were approaching it from a joint-sale perspective. We're the connectivity layer, the instrumentation, the business automation layer there, and we're getting it into Vertica ,so that can be the seed for applications for business intelligence (BI) and for analytics.

So, we are the lowest component in the stack when we walk into one of these engagements with Vertica. Then, it's up to them, on a customer-by-customer basis, to determine what applications to bring to the table. A lot of that is defined by the group within the organization that actually manages connectivity.

We find that there's a big difference between a service organization, which is focused primarily on keeping things up and running, versus a business unit that’s driving utilization metrics, trying to determine not only how things are used, but how it can influence their billing.

Business use

We've found that that's a place where Vertica has actually been quite a pop for us in talking to customers. They want to know not just the simple metrics of the machines' operation, but how that reflects the business use of it.

The entire market has shifted and continues to shift. I was somewhat taken aback only a couple of weeks ago, when I found out that you can no longer buy a jet engine. I thought this was a piece of hardware you purchased, as opposed to something that you may have rented and paid per use. And so [the model changes to leasing] as the machines get  bigger and bigger. We have GE and the Bureau of Engraving and Printing as customers.

We certainly have some very large machines connected to our cloud and we're finding that these folks are shifting away from the notion that one owns a machine and consumes it until it breaks or dies. Instead, one engages in an ongoing service model, in which you're paying for the use of that machine.

While we can generate that data and provide some degree of visibility and insight into that data, it takes a massive analytics platform to really get the granular patterns that would drive business decisions.

Gardner: It sounds like many of your customers have used this for some basic blocking and tackling about inventory and access and control, then moved up to a business metrics of how is it being used, how we're billing, audit trails, and that sort of thing. Now, we're starting to look at a whole new type of economy. It's a services economy, based on cloud interactivity, where we can give granular insights, and they can manage their business very, very tightly.
There's not only a ton of data being generated, but the regulatory and compliance requirements which dictate where you can even leave that data at rest.

Any thoughts about what's going to be required of your organization to maintain scale? The more use cases and the more success, of course, the more demand for larger data and even better analytics. How do you make sure that you don't run out of runway on this?

Holbrook: There are a couple of strategies we've taken, but before I dive into that, I'll say that the issue is further complicated by the issue of data homing. There's not only a ton of data being generated, but the regulatory and compliance requirements which dictate where you can even leave that data at rest. Just moving it around is one problem, and where it sits on a disk is a totally different problem. So we're trying to tackle all of these.

The first way to address the scale for us from an architectural perspective was to try to distribute the connectivity. In order for you to know that something's running, you need to hear from it. You might be able to reach out, what we call contactability, to say, "Tell me if you're still running." But, by and large, you know of a machine's existence and its operation by virtue of it telling you something. So even if a message is nothing more than "Hello, I'm here," you need to hear from this device.

From the connectivity standpoint, our goal is not to try to funnel all of this into a single pipe, but rather to find where to get a point of presence that is closest and that is reasonable. We’ve been doing this on our remote-access technology for years, trying to find the appropriate geographically distributed location to route data through, to provide as easy and seamless an experience as possible.

So that’s the first, as opposed to just ruthlessly federating all incoming data, distributing the connectivity infrastructure, as well as trying to get that data routed to its end consumer as quickly as possible.

We break down data from our perspective into three basic temporal categories. There's the current data, which is the value you would see reading a dial on the machine. There's recent data, which would tell you whether something is trending in a negative direction, say pressure going up. Then, there is the longer-term historical data. While we focus on the first two, we’d deliberately, to handle the scale problem, don't focus on the long-term historical data.

Recent data

I'll treat recent data as being anywhere from 7 to 120 days and beyond, depending on the data aggregation rates. We focus primarily on that. When you start to scale beyond that, where the real long tail of this is, we try to make sure that we have our partner in place to receive the data.

We don't want to be diving into two years of data to determine seasonal trending when we're attempting to collect data from 1.5 million assets and acting as quickly as possible to respond to error conditions at the edge.

Gardner: Kevin, what about the issue of latency? I imagine some of your customers have a very dire need to get analysis very rapidly on an ongoing streamed basis. Others might be more willing to wait and do it in a batch approach in terms of their analytics. How do you manage that, and what are some of the speeds and feeds about the best latency outcomes?

Holbrook: That’s a fantastic question. Everybody comes in and says we need a zero-latency solution. Of course, it took them about two-and-a-half seconds to say that.
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There's no such thing as real-time, certainly on the Internet. Just negotiating up the TCP stack and tearing it down to send one byte is going to take you time. Then, we send it over wires under the ocean, bounce it off a satellite, you name it. That's going to take time.

There are two components to it. One is accepting that near-real-time, which is effectively the transport latency, is the smallest amount of time it can take to physically go from point A to point B, absent having a dedicated fiber line from one location to the other. We can assume that on the Internet that's domestically somewhere in the one- to two-second range. Internationally, it's in the two- to three-second or beyond range, depending on the connectivity of the destination.

What we provide is an ability to produce real-time streams of data outbound. You could take from one asset, break up the information it generates, and stream it to multiple consumers in near-real-time in order to get the dashboard in the control center to properly reflect the state of the business. Or you can push it to a data warehouse in the back end, where it then can be chunked and ETLd into some other analytics tool.

For us, we try not to do the batch ETLing. We'd rather make sure that we handle what we're good at. We're fantastic at remote service, at automating responses, at connectivity and at expanding what we do. But we're never going to be a massive ETL, transforming and converting into somebody’s data model or trying to get deep analytics as a result of that.

Gardner: Was it part of this need for latency, familiarity, and agility that led into Vertica? What were some of the decisions that led to picking Vertica as a partner?

Several reasons

Holbrook: There were a few reasons. That was one of them. Also the fact that there's a massive set of offerings already on top of it. A lot of the other people when we considered this -- and I won't mention competitors that we looked at -- were more just a piece of the stack, as opposed to a place where solutions grew out of.

It wasn't just Vertica, but the ecosystem built on top of Vertica. Some of the vendors we looked at are currently in the partner zone, because they're now building their solutions on top of Vertica.

We looked at it as an entry point into an ecosystem and certainly the in-memory component, the fact that you're getting no disk reads for massive datasets was very attractive for us. We don’t want to go through that process. We've dealt with the struggles internally of trying to have a relational data model scale. That’s something that Vertica has absolutely solved.

Gardner: Now your platform includes application services, integration framework, and data management. Let’s hone in on the application services. How are developers interested in getting access to this? What are their demands in terms of being able to use analysis outcomes, outputs, and then bring that into an application environment that they need to fulfill their requirements to their users?
It wasn't just Vertica, but the ecosystem built on top of Vertica. Some of the vendors we looked at are currently in the partner zone, because they're now building their solutions on top of Vertica.

Holbrook: It breaks them down into two basic categories. The first is the aggregation and the collection of data, and the second is physical interaction with the device. So we focus on both about equally. When we look at what developers are doing, almost always it’s transforming the data coming in and reaching out to things like a customer relationship management (CRM) system. It's opening a ticket when a device has thrown a certain error code or integrating with a backend drop-ship distribution system in the event that some consumable has begun to run low.

In terms of interaction, it's been significant. On the data side, we primarily see that they're  extracting subsets of data for deeper analysis. Sometimes, this comes up in discrete data points. Frequently, this comes up in the transfer of files. So there is a certain granularity that you can survive. Coming down the fire-hose is discrete data points that you can react to, and there's a whole other order of magnitude of data that you can handle when it's shipped up in a bulk chunk.

A good example is one of the use cases we have with GE in their oil and gas division  where they have a certain flow of data that's always ongoing and giving key performance indicators (KPIs). But this is nowhere near the level of data that they're actually collecting. They have database servers that are co-resident with these massive gas pipeline generators.

So we provide them the vehicle for that granular data. Then, when a problem is detected automatically, they can say, "Give me far more granular data for the problem area." it could be five minutes before or five minutes since. This is then uploaded, and we hand off to somewhere else.

So when we find developers doing integration around the data in particular, it's usually when they're diving in more deeply based on some sort of threshold or trigger that has been encountered in the field.

Gardner: And lastly, Kevin, for other organizations that are looking to create data services and something like your Axeda Machine Cloud, are there any lessons learned that you could share when it comes to managing such complexity, scale, and the need for speed? What have you learned at a high level that you could share?

All about strategy

Holbrook: It’s all going to be about the data-collection strategy. You're going to walk into a customer or potential customer, and their default response is going to be, "Collect everything." That’s not inherently valuable. Just because you've collected it, doesn’t mean that you are going to get value from it. We find that, oftentimes, 90-95 percent of the data collected in the initial deployment is not used in any constructive way.

I would say focus on the data collection strategy. Scale of bad data is scale for scale’s sake. It doesn’t drive business value. Make sure that the folks who are actually going to be doing the analytics are in the room when you are doing your data collection strategy definition. when you're talking to the folks who are going to wire up sensors,  and when you're talking to the folks who are building the device.

Unfortunately, these are frequently within a larger business ,in particular, completely different groups of people that might report to completely different vice presidents. So you go to one group, and they have the connectivity guys. You talk about it and you wire everything up.
We find that, oftentimes, 90-95 percent of the data collected in the initial deployment is not used in any constructive way.

Then, six to eight months later, you walk into another room. They’ll say "What the heck is this? I can’t do anything with this. All I ever needed to know was the following metric." It wasn’t collected because the two hadn't stayed in touch. The success of deployed solutions and the reaction to scale challenges is going to be driven directly by that data-collection strategy. Invest the time upfront and then you'll have a much better experience in the back.

Gardner: Very good. I'm afraid we’ll have to leave it there. We've been learning how Axeda in Foxboro Massachusetts is providing services to its customers to the Axeda Machine Cloud and as a partner with HP as using their Vertica Analysis Platform to provide those insights.
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So I'd like to thank our guest. We've been joined by Kevin Holbrook, the Senior Director of Advanced Development at Axeda. Thank you, Kevin.

Holbrook: Thank you.

Gardner: And a big thank you to our audience for joining us for the special new style of IT discussion. 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 discussion on how to manage machine-to-machine communication for better big data collection and analysis. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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Wednesday, December 03, 2014

HP Launches Haven OnDemand to Deliver Big Data Services Suite in the Cloud

Transcript of a BriefingsDirect podcast on new offerings, announced this week at HP Discover in Barcelona, that provide on-demand, pay-as-you-go data analysis servcies.

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 Podcast Series. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing sponsored discussion on IT innovation and how it’s making an impact on people’s lives.

Gardner
Once again, we're focusing on how companies are adapting to the new style of IT to improve IT performance and deliver better user experiences, as well as better business results.

This time, we’re coming to you directly from the HP Discover 2014 Conference in Barcelona.

We’re here the week of December 1 to learn directly from IT and business leaders alike how big data changes everything … for IT, for businesses and governments, as well as for you and me.
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Our next panel discussion explores some very big news made here at Discover, the announcement of HP Haven OnDemand, a new set of big data in the cloud services.

Stay with us now as we unpack the details and implications of this debut of the cloud-based HP Vertica OnDemand and HP IDOL OnDemand components within the HP Haven OnDemand suite.

And we also welcome to our panel two early users of HP Haven OnDemand, and we will learn how they developed new sales force value and knowledge-intense mobile services from these new HP cloud offerings.

With that, I'd like to welcome our guests, Fernando Lucini, Chief Technology Officer for HP Big Data. Welcome, Fernando.

Fernando Lucini: Hi, Dana.

Gardner: We're here also with Howard Brown, Founder and CEO of RingDNA, based in Los Angeles. Welcome, Howard.

Howard Brown: Hello. Thanks for having me.

Gardner: And we're here with Neal Holley, Operations Director at GateWest New Media Ltd., based in Bristol, UK. Welcome, Neal.

Neal Holley: Hi, there.

Big picture

Gardner: Fernando, let's go first to you. We've heard quite a bit the last few days at Discover, and HP Software General Manager Robert Youngjohns has delivered the news Tuesday about HP OnDemand. Let's look at this from the big picture first. Why are data and analytics, combined with the cloud-hosting model and delivery model, such a good fit? Why is this an important milestone for the cloud?

Lucini: Thanks Dana. It's exciting in a number of ways, but let me take a quick step back. If you think about what we've launched, we recognized early that our customers, our partners, and developers out there were going to consume technologies in a new way. This is something that the industry all agreed on. We were just early birds in this and we recognized that it's all going to be about on-demand consumption, self-service, speed, elasticity, and all those nice things.

Lucini
So in some respects, the industry wants to consume things in this fashion. We recognize it, and then the next step for us is to think about the people and what they're going to do with these kinds of services.

You can think about it in two different ways. You have the people out there in the real world who are creating applications on top of very rich information, and that's the mobile apps that we all use. It's the applications to look at both human information, as well as business information, or very structured information, creating applications that do that. We have that persona and we really wanted to make sure that that developer had all the right tools in that model on-demand, self-service.

The other part of the equation is the world of the data warehouse, where we have very large amounts of information. We're traditionally applying analysis, but in this new generation, we need the tools that can do this at a bigger scale, can do it quicker, and can be more flexible. This is our Vertica technology and the same kind of on-demand, self-service needs are out there. So the second part of our answer to the question for industry is that we'll provide you an on-demand way to serve that particular purpose.

Brown
Today’s announcement comes from a number of good reasons. It provides the market with an answer to both of these peoples' needs. It does so in an incredibly elastic fashion and it does it with incredible richness. It has quite a unique degree of depth and variety.

If you look at the IDOL OnDemand functionality, there are new APIs that you can explore and use with the freemium model.

If you look at the Vertica OnDemand space, it allows you to manage whatever size warehouse you need in an incredibly elastic and transparent way, but still on-demand.

I hope that answers the question. There’s so much to tell. It’s such an exciting time for the industry, and being in HP, leading the charge, is pretty, pretty impressive and important.

Great importance

Gardner: Clearly, this isn't news just for one part of an IT organization. This seems to have a great importance for data scientists, IT operators, developers, even line of business users of business intelligence (BI).

Holley
So let's look at this a little bit from the perspective of the IT operator. This is something that's a cost issue in many respects and broadens the use of something like IDOL and Vertica to a much larger market. With it being in the cloud, you don't need to set up your data center and you don’t need to have those capital expenditures.

Let’s start at the top, where we're talking about this as a cloud model. Why does this broaden the market for data and analytics?

Lucini: Go back to this IT operator. This guy or gal has always wanted to provide their business with the tools. There was an element there where these guys want to provide the analysis capabilities, they want to have the ingestion and the features, but it’s a tough thing, as you very well put it. There is capital expenditure, maintenance, and training.

As the differentiator here, the move is that the acceleration is going to be immediate. Let’s use simple examples, I want to be able to take video and do face recognition, extract license plates, extract behaviors, or listen to voice and do something, I want to do that and I don’t want the burden of all the science that goes behind doing these things.
IT operators are going to be incredibly happy that they can provide the business with what the business needs at a lower cost and get outcomes quicker.

This IT operator is going to say, "No problem. Here’s the link. You pay this as you go. Enjoy." And that's as complex as it gets. So the acceleration is going to be immediate, which translates almost immediately to create more and more applications and doing more and more analysis, which is what we all want, at a lower cost point in shorter times.

IT operators are going to be incredibly happy that they can provide the business with what the business needs at a lower cost and get outcomes quicker.

Gardner: This should be of interest to large enterprises that might want to augment their current warehouse approach and strategy. It also sounds like for those organizations that may have been too small or didn’t have the budget to set up their own on-premises data warehouse, they now have an opportunity to walk right into a deep, powerful analytics capability.

Lucini: It democratizes the whole idea of analytics. You want to make it as democratic as possible. Size isn't necessarily important with regards to intelligence, interest, having something to say, or having something to analyze. It’s all about making it democratic, and the cloud really helps in that.

It's also about giving functionality that wasn't accessible to some of these guys. We're talking about very advanced analysis -- technologies for video, voice, or text analysis, let alone warehousing. It’s now available to everybody. They can go in there, test it out, play with it, see how valuable it is to them, and stop dreaming about the value, but make the value. Then, if that’s what they need, they can just start paying as they go and getting on with their lives.

General availability

Gardner: Let’s dig into a little of the details. HP announced Haven OnDemand on December 2, with general availability coming in Q1 2015, so pretty rapidly. Vertica, that’s the one that's coming up first and then IDOL OnDemand is currently available as a freemium model, as you mentioned, on an early access basis, but will be generally available in a few months later into 2015.

What else should we know about the pricing here? Why is this compelling not only as an OPEX versus a CAPEX, but with pricing that is very compelling and attractive.

Lucini: Indeed. In some respects, because you're removing the necessity to open the hardware and to scale it up, we're also providing economies of scale in what we're doing. In HP Cloud Services, we have an amazing cloud that we can go to elastically, and everybody gets advantage of this.

If you think about it, ultimately in one of these models, you get a lot of people come in, have a look, play, investigate, understand, and learn. Then, you get a smaller percentage that actually commit, do the greater applications, and run their warehouses.
You should be in a position where you understand exactly what you're using and what you are paying for it, and it should allow you to toggle back and forth on that need. It’s pretty cool.

It balances out and it allows us to have a lower price point. It also allows us to charge as we go. It allows us a pay-as-you-go model. It all works out. Over time, we'll understand more and more what people want. This is being done in a very collaborative fashion, listening to the market for on-demand.

In the very beginning, we have been very Net Promoter Score focused. I challenge anybody to get yourself a login, and you'll see the Net Promoter kick in.

All the analysis is very much linked to what you want to do, what’s important for you, what’s being used most, and what gives us the most economies. That drives us to be more competitive.

It’s very transparent. It’s very clean. You should be in a position where you understand exactly what you're using and what you are paying for it, and it should allow you to toggle back and forth on that need. It’s pretty cool.

Gardner: As for the actual cloud that this is running on, is there a choice with that or is this starting out on HP Helion Cloud, the HP public cloud. What's the roadmap for the public-cloud infrastructure that this operates on? 

Lucini: At the moment, this is running in HP Cloud Services, which is Helion based of course. It is all designed on top of Helion. So the roadmap for it in the next few courses will be that it will be deployed in any Helion implementation. As long as you have Helion, you can deploy the services underneath.

Of course, Helion is a flavor of OpenStack. So you have the ability to use this in other flavors of OpenStack, but we're principally focused on Helion. We're principally focused on the Public HP Cloud Services and the private Helion implementations with our colleagues from Enterprise Services.

No difference

In some respect in the next year it should be a choice for you to go public cloud for what you need to do. If you're a developer and you just want to create your own app, the private-versus-public doesn’t make a difference to you.

Corporate may want to use this inside a firewall. As you know, in HP we have some of the largest corporates out there. If you're one of these guys and have the need to have that privacy you can install Helion and run these services of top of Helion. Following the HP philosophy, it’s a matter of what the client requires and we'll achieve that.

Gardner: It sounds as if this has been made of, by, and for a hybrid model over time.

Lucini: Correct. Most of our big customers are hybrid, and we're delighted to serve them.

In the meantime, as they o go into a mode of using this stuff on Helion inside of the firewall, they'll still get all the elasticity that Helion provides them. They'll still get all the simplicity that REST and Web Services OnDemand provides them, and the flexibility that Vertica OnDemand provides them for scalability In some respects, there is no downside. There is absolutely no downside to anything that’s happening here. It’s just a matter of choice.
In terms of pricing, I think we're competitive. The features and functions are worth the spend.

Gardner: We'll get to our use cases and the examples of how this is being used shortly, but I just want to look at the competitive landscape. A big player out there, of course, in the public cloud is Amazon Web Services, and Amazon has what’s called http://aws.amazon.com/redshift/. It's their data warehouse in the cloud. How does what HP has announced compare and contrast to Redshift? Why is it a worthy competitor and is this price comparable?

Lucini: Of course, guys out there and everybody listening might know Vertica is a leading product in the analytics space and in the warehousing space. So we're coming  at this already as a leader proven inside the firewall.

You get all of the economies, flexibility, and features that Vertica provides; the Flex Zones, all of the optimizations, and the incredible scaling growth factors; and you get it in an on-demand package.

Just because we now have an on-demand version, these things don’t go away. It's quite the opposite. They're immediately available. In that respect, I think we have a strong proposal against Redshift, because you have all the features and functions, not only just the database itself. 

In terms of pricing, I think we're competitive. The features and functions are worth the spend. Our customer base, our history, and our legacy certainly prove that to be the case. Little by little, more and more of the features will seep in, and more customers will start to get comfortable with using it. We already have a few out there in beta land.

We're going to compete. Because of the features, the Flex Zones and other things, we'll carve our own space as well.

What is the differentiator?

Gardner: One of the things that seems unique to me, Fernando, is the IDOL OnDemand being so broad in terms of the types of media, content, information, and data that can now be brought into what’s essentially the type of analytics engine you would only think of for structured information. So it's the best of the structured analytics and high-performance environment, with that breadth and depth of the various types of content. Is that a differentiator in your opinion?

Lucini: Absolutely. I call it everything on-demand. As you notice, I tend not to differentiate between BOD and IOD. The whole philosophy was that we deal with unstructured, structured, and semi-structured information every day to build what we need for our businesses. So why should we see this differently?

If I happen to have an image, it's an image. If I happen to have a file, it's a file. If I happen to have an Excel sheet, it's an Excel sheet. All of these things are materially important. So let’s give our application developer and our data analyst a way to consume all this.

We have the connectors in the cloud, ways for you to suck information into the platform. We have the ability for you to index them and analyze them. We have some protected APIs for you to have a play around with.
It's as broad in analytics as possible. At the same time, it's still market leading in every single one of those APIs.

We have text-mining APIs. Obviously, this is a platform for us. So even though we're using the word Vertica and IDOL, underneath IDOL OnDemand, we have Vertica powering some of our features for user management. All our billing and other APIs are coming up.

It's all about giving the application developer all the tools. What the data is, isn't necessarily important. What's important is that they can process it, use it, extract as much value from it as possible, and make their business successful.

So you are absolutely right. It's as broad data-wise as possible. It's as broad in analytics as possible. At the same time, it's still market leading in every single one of those APIs, which is pretty cool stuff.   
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Gardner: Now, when you're able to bring all sorts of information and media together, when you're able to tap web services, social media, when you're able to create a sentiment engine and a search engine capability, you're really starting to develop intelligence in new ways.

It seems to me, you can gain insight into markets, prospects, competition, customer inclinations, and directions. It's really about bringing more of a data-driven aspect to a business in ways that had really been sort of an art before, something that was not always by experience, but was by gut instinct.

Before we go to our use cases, how are we really changing a business environment here? Are we talking about a data-driven approach? Are we giving the type of tools that will move a marketing organization, for example, from guesswork into a scientific approach to how they make decisions?

Testing instincts

Lucini: You put it very nicely. We're moving into a world where we're allowing instincts to be tested, and tested quickly. In the past, we had a lot of clever professionals in the marketing world making educated guesses about what’s going on, what I like and don’t like, what you like and don’t like, or what’s popular and what’s not.

We're opening the door for businesses to take data, take a sample of it or take it all, it's their choice, whatever that may be, and in whatever varieties they come, to test out their theories, to see if this theory is correct.

I used to call it the CIO conundrum, where the CIO thinks they've got something and it becomes very difficult for them to prove if they do or don’t, and then they question the results when they get them.

We want them to be able to test this out. If they have an opportunity with their voice data and they think there's massive value in the voice data and they want to cross-correlate it to the social presence, do it, and let the data speak for itself.
It's very exciting stuff, because there is a real change in the industry, and we all have to adapt to it.

It's now no longer difficult. Just go into the platform, put the voice in there, put the text in there, use the analytics tools, give us our enterprise resource planning (ERP) warehouse. We'll do the queries and we'll create what we call combinations -- which is everything coming together as one -- and test the value.

Now, it no longer matters that this is not a very large project with very large budget. It will prove out the case. We have a next generation of proving things out and being capable of proving things out.

That might lead you to a very interesting onsite project with our tools, where you're inside a firewall, but you have proven it out. Or it might take you to a very interesting on-demand implementation. Either way you perform the testing or the proving or the thinking in a much more practical way.

It's very exciting stuff, because there is a real change in the industry, and we all have to adapt to it.

Gardner: It is very exciting. Let's learn how some people have been using this already to change their business. Let's go first to RingDNA. Howard Brown, tell us a little bit about your company, what you do, and then how you've been using Haven OnDemand from HP?

Brown: Thank you. RingDNA is a comprehensive sales acceleration platform that allows companies to create high-performance sales teams by combining powerful communications tools with prospect or customer DNA. That's a combination of marketing data, social data, customer relationship management (CRM) data, and account history, and pulling that all together to allow a sales rep to perform sales faster.

Data for inside sales

Gardner: It’s almost as if you're putting the tools of a data scientist in the hands of a salesperson without them having to be a scientist, to get all sorts of information to make the best call on a call in real-time on an inside sales basis.

Brown: You've got it. It's applying a scientific approach to sales. It's taking all of the data that exists out there which can be truly overwhelming, prioritizing it, and making it contextual to make sales much more effective.

Gardner: And this cuts across communications, as well as data, applications, and web services. Is that correct?

Brown: Absolutely. We apply both a theory-testing model and set of communication tools. When a RingDNA customer walks in in the morning, they know exactly who they should be calling, who they should be emailing or texting, and prioritizing the messages so that they know exactly who to call, how to reach out to them,  and what to say.
What HP IDOL OnDemand has provided us is the ability to test all kinds of theories, because every business we work with tends to have a different theory of what a hot prospect may be.

What’s so exciting is that you can start to understand buyer intent from marketing data from past interactions with your customers. We can look at voice transcripts and sentiment analysis and have a whole new way of determining who the right prospect is, how we should be contacting them, and with what messages.

Gardner: So it's up to your organization to take the best of technology, data, and analytics and empower those inside salespeople. It sounds like it's been up to HP to take the best of its technology in the cloud model and analysis to empower you. How, in fact, has HP empowered RingDNA with your early access use of HP Haven OnDemand?

Brown:  It's been truly game-changing. You nailed it when you talked abut taking business information and human information and combining those two. What HP IDOL OnDemand has provided us is the ability to test all kinds of theories, because every business we work with tends to have a different theory of what a hot prospect may be.

They can simply and easily test those theories using RingDNA and HP IDOL OnDemand. If there are buying signals, like someone visiting a website and downloading a whitepaper in combination with other factors, such as that person viewing web pages or maybe tweeting about their product or service, we can look at that buyer’s sentiment through HP IDOL OnDemand.

We're taking a bunch of this data, processing it through IDOL, and making our reps that much more productive and that much more powerful.

Gardner: One of the things you're doing is you are joining and bringing together very disparate data and information and tidbits of analysis. Is HP IDOL OnDemand doing that for you? Are you doing that? How do you make those joins that bring all that information together? Is the cloud the key to doing that?

Cloud is key

Brown: The cloud certainly is the key. We couldn’t deliver the type of product and service we do today without the cloud. RingDNA is all about accelerating a sales team’s ability to close deals. The last thing you want is to negatively impact those teams.

The cloud model means we can quickly implement a RingDNA process within an organization, bring in all that contextual data, bring in all that metadata, and make that rep that much more productive without negatively impacting their workflow.That’s critical to any business today.

It’s one thing to be able to deliver information. It’s another thing to be able to deliver information and insight without negatively impacting the business. Let's face it, in this  day and age, we can’t afford to slow down. With tools like IDOL OnDemand and RingDNA, you’re not slowing down teams. You're actually accelerating them beyond what you ever thought was possible.

Gardner: Fernando, as you're listening to Howard, is there anything about the way that RingDNA is using Haven OnDemand that you think highlights some specific benefits or values here. Are they a poster child for a certain type of way in which you can use Haven OnDemand?
With IDOL OnDemand coming on stream, we’ve found that we had a whole world of options opened up to us.

Lucini: Certainly they understand that they need to use tools to solve their problems and they go ahead and do it. In that respect, it’s great to see. There are a bunch of things we could learn as an industry from them in terms of seeing the opportunity of mixing two pieces of data, how these things collide, and how we get them to customers. I would challenge anybody to check them out because ultimately the end result is key, and I think everybody would be impressed.

Gardner: Let’s go to our next example. We're also joined by GateWest and Neal Holley. Neal, tell us a little bit about GateWest, what you do, and how you’ve been using HP Haven OnDemand?

Holley: We're HP Autonomy partners and have been since about 2002. During that time, we have deployed and maintained many IDOL-based systems. We provide a lot of support services to our clients on an annual basis. We also provide user interfaces to the core engine, our internal development team.

As well as enterprise search, we also specialize in knowledge management (KM). We have a couple of products addressing the management of knowledge, particularly within law firms, and recently we launched an application for the iTunes App Store providing mobile access to IDOL OnDemand, and we see this part of our strategy of what we’ve termed Mobile KM.

Gardner: Tell me a bit more about the iTunes App Store app. What is it called, and how did you use IDOL OnDemand to build it?

Holley: The app is called KnowGate and it was developed in direct response to the offering of IDOL OnDemand. Over the years, we’ve found that IDOL on-premise had a large cost of entry. Obviously, with IDOL OnDemand coming on stream, we’ve found that we had a whole world of options opened up to us. We were very surprised how straightforward it was to take the standard tools for producing the iPhone apps and iPad apps and interface them with IDOL OnDemand.

Great performer

It’s given us that opportunity to bring the technology that we've worked with for so many years and found to be such a great performer and hold the audience that we’ve always wanted to bring it to. The offering has allowed us to do that through its low cost of entry. As Fernando said, it’s democratizing the tools of the very large corporates that we've traditionally worked for.

Gardner: Help me to better understand this. There is no easier way to adopt a technology than to download it for a few dollars from the app store and instantly fire it up on your mobile device. If I were to download that app today, what would I be able to do with it? Who is the typical user? What is the function that that they would gather from it?

Holley: The typical user is predominantly a business user. The first instance is that you would be able to access your KM, your valuable documents or your key information that you need whether in a law firm, or whether it's engineering specifications or your latest contracts.

That’s the first element of it. The second element is being able to actually capture knowledge while on the move and being able to take information from an email or take a photograph of a document, OCR it, and then be able to ingest that into IDOL OnDemand and share it with the rest of your organization.
So it really opens up that kind of ability, and of course, once it’s shared it becomes valuable.

So it really opens up that kind of ability, and of course, once it’s shared it becomes valuable.

Gardner: Very interesting. Fernando, we're seeing with GateWest, this joining of the cloud model with the mobile model. How is that accelerating the use of analytics? That is to say, an application that can gather data and information and extend it to the cloud and then the cloud can create an analytics value and then send it back to that mobile device? How are you seeing that as a powerful new way of broadening the use and value of analytics in general?

Lucini: If you think about it, mobility is everywhere. We all create mobility and mobility apps for everything you have. I'm sure you guys walk around with a mobile device.

We have to be very clear that all of our consumers, even if it's enterprise-consumers versus consumer-consumers, all become little data analysts. We're all much better versed on information than we ever were.

Now you see 18 year-old kids or 20 year-old kids coming out of university and their ability to manage information in their devices, in their environment, is incredible. You no longer have a situation where you can associate analytics from mobile.

Mobile apps are mostly about analytics with some description, certainly about adding value to the data that a user asks you to create it. When I say "create it," I mean create it indirectly, create it by the motion on your wrist, versus you directly writing something down. So you get these two sources of data.

But it's certainly now such a rich space. Let me give you an example. You can take what's coming out of the back of a device, which is probably machine-driven, all the stuff that really the machine produces. You can put that in Vertica OnDemand and that will be your warehouse for doing the analysis on that: What am I doing, when, how, for how long, all that kind of jazz.

Creating context

At the same time, I'm producing the information directly from my mind. I'm creating context, I'm writing, I'm speaking, or I'm recording, whatever the case may be. Now, IDOL OnDemand can deal with that.

Anybody creating a mobile app is not going to want to have a hard server-based infrastructure, because the whole point of mobility is that it is distributed. It is a distributed computing model.

Those are kind of solutions that are on demand, in the cloud, elastic, pay-as-you-go kind of things. They're perfect for this generation, whether it's enterprise or not. The kind of partners we have are guys who understand that their intelligence and the value they add is not necessarily that they know a tool, but that they are the experts in their space and they know how to balance Vertica OnDemand.

I have my machine or business information and I need to do something important with that. I have my human information and anything in between, and it's the understanding of how this information adds values to people’s lives and how they execute them that’s he key.
The beauty of our OnDemand infrastructure is that it was created for everyone. It was created for our customers and it was created for ourselves.

So it's a really important moment. Mobile is the linchpin of much of what's going on around this that makes sense. If you look at any company today, there's no chance that they won't have a mobile intent.

At the same time, we have a lot of hackathons in OnDemand. I can tell you that 90 percent of the products that are created as a result of hackathons are mobile. It kind of speaks for itself.

Gardner: I know. The combination of the cloud-delivery model, analysis on demand, or as a service and the mobile device is just creating entirely new opportunities to add value as a consumer and as a company. It's really flipping many businesses around.

Let’s look at a particular business when we think about the impact of this new series of models and how they interact. I'm thinking about the IT organization in a company, in an enterprise.

With HP Software having a very broad portfolio of applications, many of which are designed and geared towards those IT organizations and developer organizations in companies, how can Haven OnDemand with that analysis-as-a-service capability be brought to bear on other HP software applications focused on IT organizations?

Lucini: The beauty of our OnDemand infrastructure is that it was created for everyone. It was created for our customers and it was created for ourselves. Not to unveil too many wonderful things, but there will be a number of announcements of our own tools, which will be powered by OnDemand. And we made a distinction of what is on demand versus what we call core. It’s our language to speak about our internal use versus our external use.

Organizational tools

These are tools that help the IT organizations.We have tools for backup, where the on-demand model will add great flexibility to what the IT operators can do with the information and how they can serve the legal compliance and partner infrastructures.

We have uses of OnDemand for a wider HP software family where they provide analytics, both for security as well as operational systems, and things like that. So it's a very democratic tool. We recognize that the world of information pivots on two things, and that’s why we created a platform.

It pivots on our ability to incredibly scale up and analyze structured information and semi-structured information. That’s why we have a Vertica core engine. We recognize that human beings create information and so we have our IDOL infrastructure.

And it's these two things together that every single one of our internal partners, IT, our own software product that tender to IT as well as external customers only to leverage this product. And then in some cases it goes very heavily one way, or very heavily another, you have a very, very strong warehouse.
All of our internal partners look at us and say that they're coming at it either from very human or from very machine, or actually in most cases, both.

You always have that road-map of possibility to get you to the other side, either more heavily toward IDOL or Vertica. You can really start, for example, with a Vertica OnDemand warehousing cloud, make it super-flexible, and put information in Flex Zones, really massage that data, don’t be upset by schemas,  and then work as you go, and scale up.

At the same time, think of what if you need some enrichment, what if you need to take some information that’s coming in and asking to say take in your social feed. So I need to take a voice feed and text information, classify it, and put it into my Flex Zones. That is available, and in the opposite direction, it’s exactly the same.

All of our internal partners look at us and say that they're coming at it either from very human or from very machine, or actually in most cases, both. This is the roadmap to get them to take advantage of both in the same platform. So you can see, it's very, very compelling for our internal partners to use, and we are delighted to serve them.

Gardner: I'm seeing a great deal of flexibility on the applicability of this. We've seen from RingDNA how this can help an inside sales organization do things they just could never have done before.

We have seen from GateWest how this is essential to bringing knowledge management and document management to a whole new level by combining the best of cloud and mobile devices.

Then, as you're now saying, we're only scratching the surface about how IT organizations can use the cloud and the analytics as a service for improving their application lifecycle management, their business service management, or their application development test. So it's really an exciting time.

I'm afraid we are about out of time for today’s discussion, but there's a lot more that people can learn at hp.com in terms of Haven OnDemand. Let’s just end with one more peek into the future. Fernando, what might we expect next? Where do you think Haven OnDemand will go in the near future in terms of a new type of business value?

Disrupting markets

Lucini: Let me just say that we're going to disrupt a bunch of markets. We're going to be looking to take over some markets out there that have been very traditionally on premise and we're going to try to democratize it. You can guess that we're going to take the world of video and voice and we are going to make that very democratic.

There are going to be lots of interesting things coming out where we're going to allow our customers to create their own APIs and extend the platform themselves. So there is a lot of that to look forward to.
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We'll also be extending our Vertica OnDemand presence, getting more-and-more customers in there and getting more modes, using more of our Vertica technology to add functionality in a REST kind of way, in a web-service kind of way to the on-demand picture, and adding more and more APIs just to reflect the richness of a platform. So it's clear to everyone that this is only the beginning of an amazing story. So there are quite a lot of APIs, but there are many, many more to come. So there is quite a lot to look forward to.
There are going to be lots of interesting things coming out where we're going to allow our customers to create their own APIs and extend the platform themselves.

Gardner: Well, very good. I'm afraid we will have to leave it there. We've been talking about some very big news made here at HP Discover in Barcelona, the announcement of HP Haven OnDemand, a new set of big data in the cloud services.

We've learned a lot about the details and implications of this debut of the cloud-based HP Vertica OnDemand and HP IDOL OnDemand components within HP Haven. And we've delved also into two early users of HP Haven OnDemand and how they created new inside sales force value and a new knowledge management mobile service.

So please join me in thanking our guests, Fernando Lucini, Chief Technology Officer for HP Big Data; Howard Brown, the Founder and CEO for RingDNA, and Neal Holley, Operations Director at GateWest. And thanks our audience as well for joining us for this special New Style of IT discussion, coming to you directly from the HP Discover 2014 Conference in Barcelona.

We’ve explored solid evidence from early enterprise adopters of how big data changes everything … for IT, for businesses and governments, as well as for you and me.

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 new offerings, announced this week at HP Discover in Barcelona, that provide on-demand, pay-as-you-go data analysis servcies. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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