The Future of Business Intelligence as a Service with GoodData and HP Vertica

Transcript of a BriefingsDirect discussion on how GoodData helps customers gain new insights into their businesses with on-demand data analytics.

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

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 discussion on IT innovation and how it’s making an impact on people’s lives.

Gardner

Our next big data case study interview highlights how GoodData expands the realms and possibilities for delivering business intelligence (BI) and data warehousing as a service. We'll learn how they're exploring new technologies to make that more seamless across more data types for more types of users.

With that, we welcome Jeff Morris, Vice President of Marketing at GoodData in San Francisco. Welcome, Jeff. 

Jeff Morris: Thanks very much, Dana.

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Gardner: We are also here with Chris Selland, Vice President for Business Development at HP Vertica. Welcome, Chris.

Chris Selland: Thanks, Dana. Great to be here with you both.

Gardner: First, Jeff, for those who might not be that familiar, tell us about GoodData, what you do and why it's different.

Morris: GoodData is an analytics platform as a service (PaaS). We cover the full spectrum end-to-end use case of creating an analytic infrastructure as a service and delivering that to our customers.

Morris

We take on the challenges of collecting the data, whatever it is, structured and unstructured. We use a variety of technologies as appropriate, as we do that. We warehouse it in our multitenant, massively scalable data warehouse that happens to be powered by HP Vertica.

We then combine and integrate it into whatever the customer’s particular key performance indicators (KPIs) are. We present that in aggregate in our extensible analytics engine and then present it to the end users through desired dashboards, reports, or discoverable analytics.

Our business is set up such that about half of our business operates on an internal use case, typically a sales and marketing and social analytic kind of use case. The other half of our business, we call "Powered by GoodData." and those customers are embedding the GoodData technology in their own products. So we have a number of companies creating these customer-facing data products that ultimately generate new streams of revenue for their business.

40,000 customers

We've been at this since 2007. We're serving about 40,000 customers at this point and enjoying somewhere around 2.4 million data uploads a week. We've built out the service such that it's massively scalable. We deliver incredibly fast time to market. Last quarter, about two thirds of our deployments were delivered within 16 weeks or less.

One of the divisions of HP, in fact, deployed GoodData in less than six weeks. They are giving their first set of KPIs and delivering that value to them. What’s making us different in the marketplace right now is that we're eliminating all of the headaches associated with creating your own big data lake-style BI infrastructure and environment.

What we end up doing is affording you the time to focus on the analytics and the results that you gain from them—without having to manage the back-end operations.

Gardner: What’s interesting to me is that you mentioned PaaS for BI. Instead of developing applications and then having a production environment that’s seamlessly available to you, you're creating analytic applications on datasets that are contributed to your platform. Is that right?

Morris: Yes, indeed. The datasets themselves also tend to be born in the cloud. As I said, the types of applications that we're building typically focus on sales and marketing and social, and e-commerce related data, all of which are very, very popular, cloud-based data sources. And you can imagine they're growing like crazy.

We see a leaning in our customer base of integrating some on-premise information, typically from their legacy systems, and then marrying that up with the Salesforce, or the market data or social information that they want to integrate and build a full view of their customers -- or a full exposure of what their own applications are doing.

What we end up doing is affording you the time to focus on the analytics and the results that you gain from them—without having to manage the backend operations.

Gardner: So, you're really providing an excellent example of how HP Vertica is a cloud-borne analytics platform and implementation. That’s kind of interesting.

But I wonder whether any of your clients, maybe not so much in the media, but some of the more traditional verticals like healthcare, retail, or government, are trying to do this across a hybrid model. For example, they're doing some BI and they have warehouses on-premises or maybe other hosting models, but they also want to start to dabble in moving this to the cloud and taking advantage of what the cloud does best. Are we now on the vanguard of hybrid BI?

Morris: We're getting there, and there are certainly some industries are more cloud friendly than others right now. Interestingly, the healthcare space is starting to, but they're still nascent. The financial services industry is still nascent. They're very protective of their information. But retailers, e-commerce organizations, technology ISVs, and digital media agencies have adopted the cloud-based model very aggressively.

We're seeing a terrific growth and expansion there and we do see use cases right now where we're beginning to park the cloud-based environment alongside your more traditional analytics environments to create that hybrid effect. Often, those customers are recognizing that the speed at which data is growing in the cloud is driving them to look for a solution like ours.

Gardner: Chris, how unique is GoodData in terms of being all cloud moving toward hybrid, and does this really provide a poster child, in a sense, for Vertica as a service?

Special relationship

Selland: GoodData is certainly a very special partner and a very special relationship for us. As you said, Vertica is fundamentally a software platform that was purpose-built for big data that is absolutely cloud-enabled. But GoodData is the best representation of the partner who has taken our platform and then rolled out service offerings that are specifically designed to solve specific problems. It's also very flexible and adaptable.

Selland

So, it’s a special partnership and relationship. It's a great proof point for the fact that the HP Vertica platform absolutely was designed to be running in the cloud for those customers who want to do it.

As Jeff said, though, it really varies greatly by industry. A large majority of the customers in our customer advisory board (CAB), which tend to be some of our largest customers and some pretty well-known industries, were saying how they will never put their data in the cloud.

Never is a very long time, but at the same time, there are other industries that are adopting it very rapidly. So there is a rate of change that’s going on in the industry. It varies by size of company, by the type of competitive environment, and by the type of data. And yes, there is a lot of hybridization going on out there. We're seeing more of the hybridization in existing organizations that are migrating to the cloud. There's a lot of new breed companies who started in the cloud and have every intent of staying there.

But there's a lot of dynamism in this industry, a lot of change, and this is a partnership that is a true win-win. As I said, it's a very special relationship for both companies.

Gardner: Jeff, given that we have such variability, vertical by vertical, company by company, green-field versus an established company will behave differently vis-à-vis their architecture and their IT implementation. You need to be ready for any and all of that, and I suppose Vertica does as well.

We're triple clustering each set of instances of our vertical warehouses, so they are always reliable and redundant.

We're hearing also more than just HP Vertica here. We're talking about Haven, which includes Hadoop, Autonomy, security and applications. Is there a path that you see whereby you can try to be as many things to as many types of customer and vertical industries?

I'm thinking about Hadoop, security, and bringing some of the more enterprise-caliber KPIs and SLAs, so that some of those folks that are hesitant to move at least some their data in some ways to the cloud would move in that direction. Is that a vision for you? Maybe you could explain where you see this going on a hybrid basis.

Morris: Absolutely. The HP Haven-style architecture is a vision in a direction that we are going. We do use Hadoop right now for special use cases of expanding and providing structure, creating structure out of unstructured information for a number of our customers, and then moving that into our Vertica-based warehouse.

The beauty of Vertica in the cloud is the way we have set this up and this also helps address both the security and the reliability issues that might be a thought of as issues in the cloud. We're triple clustering each set of instances of our vertical warehouses, so they are always reliable and redundant.

Daily updates

We, like the biggest enterprises out there, are vigilantly maintaining our network. We update our network on behalf of our customers on a daily basis, as necessary. We roll out and maintain a very standardized operating environment, including an open stack-based operating environment, so that customers never need to even care about what versions of the SSL libraries exist or what versions of the VPN exist.

We're taking care of all of that really deep networking and things that the most stalwart enterprise-style IT architects are concerned about. We have to do that, too, and we have to do it at scale for this multi-tenant kind of use-case.

As I said, the architecture itself is very Haven-like, it just happens to be exclusively in the cloud -- which we find interesting and unique for us. As for the Hadoop piece, we don’t use Autonomy yet, but there are some interesting use cases that we are exploring there. We use Vertica in a couple of places in our architecture, not only that central data warehouse, but we also use it as a high-performance storage vehicle for our analytic data marts.

So when our customers are pushing a lot of information through our system, we're tapping into Vertica’s horsepower in two spots. Then, our analytic engine can ingest and deal with those massive amounts of data as we start to present it to customers.

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On the Haven architecture side, we're a wonderful example of where Haven ends up in the cloud. For the applications themselves, the kind of things that customers are creating, might be these hybrid styles where they're drawing legacy information in from their existing on-premise systems. Then, they're gathering up, as I said before, their sales and marketing information and their social information.

The one that we see as a wonderful green field for us is capturing social information. We have our own social analytic maturity model that we describe to customers and partners on how to capitalize on your campaigns and how to maximize your exposure through every single social channel you can think of.

We're very proficient at that, and that's what's really driving the immense sizes of data that our customers are asking for right now. Where we used to talk in tens of terabytes for a big system, we're now talking in the world of hundreds, multiple hundreds of terabytes, for a system. Case by case by case, we're seeing this really take off.

Gardner: It's fine to talk about this as an abstraction, but it's really useful to hear some examples. Do you have any companies, either named or unnamed, that provide a great use case example of PaaS, for BI apps that take advantage of some of the attributes of HP Haven and Vertica?

Where we used to talk in tens of terabytes for a big system, we're now talking in the world of hundreds, multiple hundreds of terabytes, for a system.

Morris: One of our oldest and most dear customers is Zendesk. They have a very successful customer-support application in the cloud. They provide both a freemium model and degrees of for-fee products to their customers.

And the number one reason why their customers upgrade from freemium to general and then general to the gold level of product is the analytics that they're supplying inside of there. They very recently announced a whole series of data products themselves, all powered by GoodData, as the embedded analytic environment within Zendesk.

We have another customer, Service Channel which is a wonderful example of marrying together two very disparate user communities. Service Channel is a facility’s management enterprise resource planning (ERP) application. They bring together the facility managers of your favorite brick-and-mortar retailers with the suppliers who provide those retail facilities service, janitorial services, air-conditioning guy, the plumbers.

Disparate customers

Marrying disparate types of customers, they create their own data products as well, where they are integrating third-party information like weather data. They score their customers, both the retailers as well as the suppliers, and benchmark them against each other. They compare how well one vendor provides service to another vendor and they also compare how much one of the retailers spends on maintaining their space.

Of course, Apple gets incredibly high marks. RadioShack, right now, as they transition their stores, not so much. Service Channel knew this information long before the industry did, because they're watching spend. They, too, are starting to create almost a bidding network.

When they integrated their weather data into the environment, they started tracking and saying, "Apple would like to gain first right of refusal on the services that they need." So if Apple’s air conditioning goes out, the service provider comes in and fixes the air-conditioning sooner than Best Buy and all of their competitors. And they'll bid up for that. So they've created almost a marketplace. As I said before, these data products are really quite an advantage for us.

Gardner: Looking a bit to the future, we've heard the interest in moving from predictive to prescriptive analytics. It seems to me that that’s really a factor of the quality of the data in getting data from different sources and bring it together, something you can do in a cloud more easily or more efficiently than server by server, or cluster by cluster.

We feel like we're creating a central location where analysts, data scientists, and our regular IT can all come together and build a variety of analytic applications.

What kind of services should we envision as the analytics as a business model unfolds in the cloud and you can start to do joins across different types of data for an industry, rather than just an enterprise? Is there an opportunity to get that prescriptive value as a provider with the past capability? It sounds very exciting and interesting. What's coming next?

Morris: Most definitely, we're seeing a number of great opportunities, and many are created and developed by the technologies we've chosen as our platform. We love the idea of creating not only predictive, but prescriptive, types of applications in use cases on top of the GoodData environment. We have customers that are doing that right now and we expect to see them continue to do that.

What I think will become really interesting is when the GoodData community starts to share their analytic experiences or their analytic product with each other. We feel like we're creating a central location where analysts, data scientists, and our regular IT can all come together and build a variety of analytic applications, because the data lives in the same place. The data lives in one central location, and that’s an unusual thing. In most of the industry your data is still siloed. Either you keep it to yourself on-premise or your vendors keep it to themselves in the cloud and on-premise.

But we become this melting pot of information and of data that can be analytically evaluated and processed. We love the fact that Vertica has its own built-in analytic functions right in the database itself. We love the fact that they run our predictive language without any other issue and we see our customers beginning to build off of that capability.

My last point about the power of that central location and the power of GoodData is that our whole goal is to free time for those data scientists and those IT people to actually perform analytics and get out of the business of maintaining the systems that make analytics available, so that you can focus on the real intellectual capital that you want to be creating.

Identifying trends

Gardner: So, Chris, to cap this off, I think we've identified some trends. We have PaaS for BI. We have hybrid BI. We have cloud data joins and ecosystems that create a higher value abstraction from data. Any thoughts about how this comes together, and does this fit into the vision that you have at HP Vertica and that you're seeing in other parts of your business?

Selland: We're very much only at the front end of the big data analytics revolution. I ultimately don’t think we are going to be using the term "big data" in 10 years.

I often compare big data today to eBusiness 10, 12 years ago. Nobody uses that term anymore, but that was when everything was going online, and now everything is online, and the whole world has changed. The same thing is happening with analytics today.

With a hundred times more data we can actually get 10,000 times more insight. And that's true, but it's not just the amount of data; it's the ability to cross-correlate. That's the whole vision of what Jeff was just talking about that GoodData is trying to do.

We're very much only at the front end of the big data/analytics revolution. I ultimately don’t think we are going to be using the term "big data" in 10 years.

It's the vision of Haven, to bring in all types of data and to be able to look at it more holistically. One of my favorite examples, just to make that concrete, is that there is an airline we were talking to. They were having a customer service issue. They were having a lot of their passengers tweeting angrily about them, and they were trying to analyze the social media data to figure out how to make this stop and how to respond.

In a totally separate part of the organization, they had a predictive maintenance project, almost an Internet-of-things (IoT) type of project, going on. They were looking at data coming off the fleet, and trying to do better job of keeping their flights on time.

If you think about this, you say, "Duh." There was a correlation between the fact that they were having service problems and that the flights were late with the fact that the passengers were angry. Suddenly, they realized that maybe by focusing less on the social data in this case, or looking at that as the symptom as opposed to cause, they were able to solve the problem much more effectively. That's a very, very simple example.

I cite that because it makes real for people that it's when you really start cross-correlating data you wouldn't normally think belong together -- social data and maintenance data, for example -- you get true insights. It's almost a silly simple example, but those types of examples we're going to see much more. The more of this we can do, the more power we are going to get. I think that the front end of the revolution is here.

Gardner: And then those insights become empirical, and not just intuitive or based on someone's observation. You have hard evidence.

Selland: Correct, exactly.

Gardner: All right. I'm afraid we have to leave it there. We have been learning about how GoodData delivers a platform as a service around business intelligence, built on HP Vertica, in the cloud. I'd like to thank our guests, Jeff Morris, the Vice President of Marketing at GoodData, and Chris Selland, Vice President for Business Development at HP Vertica.

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And I'd like to thank our audience as well for joining us for this 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 do 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 Enterprise.

Transcript of a Briefings Direct discussion on how GoodData is helping its customers gain new insights into their businesses with data analytics. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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Big Data’s Big Payoff Arrives as Customer Experience Insights Drive New Business Advantages

Transcript of a BriefingsDirect podcast on how analyzing chatter on social sites can lead to big gains for companies.

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 how data is analyzed and used to advance the way we all work and live.

Once again, we’re showcasing how thought leaders and innovative companies worldwide are capturing myriad knowledge, gaining ever deeper analysis, and rapidly and securely making those insights available to more people on their own terms.

Our next big data payoff discussion focuses on the fast developing field of social customer relationship management or Social CRM. We’ll examine now how the power of big data technology can be applied successfully to understanding such complex concepts as consumer sentiment and intent and to vastly improve user experience management.

Gardner

We’ll learn how customer analytics services provider Attensity has used natural-language processing (NLP) technology and HP Vertica capabilities to effectively listen to the social web to gain valuable insights and actionable intelligence.

To learn how, please join me now in welcoming our guests, Howard Lau, Chairman and CEO of Attensity. Welcome, Howard.

Howard Lau: Good morning. How are you?

Gardner: Good. We’re also here with Chris Selland, Vice President of Marketing and Business Development at HP Vertica. Welcome, Chris.

Chris Selland: Thanks, Dana. Great to be here.

JetBlue Case Study

NY-based JetBlue Airways created a new airline market category based on value, service, and style

Goals:

• Provide a unique flying experience that truly satisfies each individual customer and improves services quality
• Better understand and meet customer needs, as amenities such as its individual TVs and spacious leather seats are no longer enough to set them apart from the competition

Solution:

• Attensity Analyze, powered by HP HAVEn with HP Vertica Analytics Engine

Results:

• Instituted Customer Bill of Rights
• More clearly understand what customers need and are able to make improvements and be proactive
• Track complaints by plane’s tail number, allowing the customer service organization to see which planes have the most and fewest  issues

See more at:
http://www.attensity.com/2014/04/02/jetblue-airways/

Gardner:  Howard, let’s start with you. Sellers and marketers worldwide have always wanted to know what their customers are anticipating or what they want next. I guess we could go back hundreds of years with these questions.

But as someone said recently, it seems that the ability to know what customers want and how to respond to them rapidly has changed more in the last 5 years than in the past 500. Do you agree with that? And why is that the case? What’s so new and different?

Lau: Absolutely. What has happened and emerged in the past 10 years or so, especially in the world of Twitter -- Twitter has been around since 2006 -- is that consumers are finding a voice to express their opinions about companies, products and brands. They can express their voice immediately through social channels.

That’s one of the new emerging things where, not only are they finding their voice online, but they’re also realizing that they’re able to amplify that voice by connecting with their friends and their followers.

Gardner: Why is that making such a big difference in how we know what customers  want? I understand that the social part is new and innovative, but how is this changing marketing?

Controlling the conversation

Lau: The way things have happened before is that companies, as they engage with consumers, controlled the conversation. Whether you fill in an online form or you call an 800 number for customer service or purchase, you’re greeted initially with an automated prompt, and the whole prompt system navigates your engagement.

Lau

What makes Social CRM so unique and empowering for consumers is that, for the first time, it’s transferring the control and ownership of the conversation to the consumer, the customer. What that means is that the customer now controls what they want to talk about, where they want to talk about it, and what channel they want to use to communicate their needs or issues.

They don’t want to do it in a predefined form, where you check off boxes or answer specific prompts. They want to express their interests more organically and use the company’s branded channels on Facebook and Twitter and non-branded channels on industry forums and communities. That’s what’s key about Social CRM and that’s what’s so unique about this new generation of products to analyze the social web.

Gardner: Let’s go to Chris Selland. Chris, HP Vertica is dealing with a lot of organizations that are trying to do new and innovative things with marketing. Do you also agree that marketing and what we can do have shifted just dramatically in the last five years? Has it really changed the game?.

Selland: Absolutely. There’s been a very dramatic shift in the last five years in marketing. That’s driven, not exclusively, but certainly heavily, by what’s been going on in the social-media world -- Twitter and other channels, Facebook, LinkedIn, and so forth.

Selland

It has had two impacts. First, it has amplified the voice of the customer. I always remember that commercial about I will tell two friends and she will tell two friends, and so on. Customer voice has always had an impact, but the impact of customer voice these days is dramatically amplified by social media.

The other thing that’s really changed the game entirely is that now organizations that are seeking to understand their customers can no longer exclusively rely on internal data, and by internal data I mean things like customer relationship management (CRM).

In the past, when I, as a marketer, or any customer-facing exec running support or something else, wanted to understand my customer relationships, as long as we have had computers and applications had been able to look at something like my CRM system to see when my customer called the call center or when they bought something. Or I can view my transaction logs with them.

But what I haven't been able to look at and analyze is what they are doing when they’re not interacting with me, when they are interacting with the world, or when my customer is tweeting or on Facebook. Obviously, there is a very rich vein of data there. There is also a lot of noise to screen through, but if you do it right, there is potentially a very rich vein of data to help enhance relationships.

As I said, companies can choose to ignore that, but generally that would be strategically disadvantageous to do. Most companies recognize that there's a tremendous amount of data out there that doesn’t belong to me and that’s not necessarily all about me, but I can certainly use it to understand my present and future customers better.

If you interview a typical consumer, when are you more truthful, when you are interacting directly with the company or when you are actually tweeting or making recommendations to your friends or liking something on Facebook, a lot of the real information is outside of the walls of traditional IT. That’s what’s really changed things dramatically as well.

Quite a challenge

Gardner: Of course, that’s also provided quite a challenge when the information is in the form of sentiment or intent that we see through social interactions. It's more difficult to attain that and assess it.

Let’s go back to Howard. What are some of the challenges when it comes to getting information, maybe through NLP in order to extend it into this analysis capability?

Lau: When people go online in a social realm, they don’t think about their intent. They just express themselves. So the challenge is letting people communicate the way they choose to communicate and then try to figure out and infer what is their intent and their sentiment.

Trying to determine that is what we do using NLP in an effort to understand what the chatter is about and what the sentiment is about that chatter.

When you get down to what people are talking about, you have to understand from which domain they’re talking.

Gardner: In doing so, have you developed limits in terms of what you can do with the technology? It seems like this is a fairly a vast amount of information?

Lau: It's vast, and it's also very domain specific. There’s different terminology based on the domain. For example, in the hospitality and travel industry, when you use the word “service,” service means the service you are getting from the hotel or from the airline.

But when you use word “service” in the telecommunications space, that means something totally different. It means, your service plan, how many minutes you have, do you have text, and so forth.

So when you get down to what people are talking about, you have to understand from which domain they’re talking, infer their meaning and understand their sentiments.

Gardner: So there is a difficult issue in terms of language issues and then there are also technology issues around scale and depth, but let’s stick to the ones about NLP. What is it that Attensity does in order to solve that problem?

Ingesting data

Lau: First thing is that we ingest a tremendous amount of data. Most of it is social, but we also ingest company’s internal emails, customer notes, employee notes, and online surveys.

Then, we analyze it and annotate it. Part of the annotation is trying to explain the meaning of a sentence or a sentence fragment. The way we do annotations is driven by our proprietary NLP technology.

One of the first things we do is figure out who is this person and what he’s talking about. We’re trying to find the right industry domain that they are talking about and then distill that into the actual meaning -- the intent, as well as the sentiment.

Gardner: Howard, tell me a little bit more about how your relationship with HP has evolved. You have been working with Vertica for a while. Tell us a little bit about why Vertica was of interest to you as you’re trying to accomplish your goals with NLP.

Lau: With the annotations, we generate a lot of intelligence, a lot of metadata. Prior to our relationship with HP, we basically serviced the online surveys and certain internal notes and customer notes for corporations. As we embraced social, we had an explosion of content and annotations.

We’re trying to find the right industry domain that they are talking about and then distill that into the actual meaning -- the intent, as well as the sentiment.

For us, our relationship with HP was indispensable. HAVEn is not just a product; it's a platform. And it's a platform that scales well, not just handling the process of injecting large amounts of data, but also creating stores, a large store for us, as well as customer stores for each of our clients.

There’s absolutely no way we could have scaled our solution to address the continuing growth of the social realm without this relationship and partnership we have with HP and on the HAVEn platform.

Gardner: Just to be clear, HAVEn, of course, includes quite a few things. Maybe you could just help us understand which elements of HAVEn you’re using and which ones are the most beneficial to you?

Lau: First, it's Vertica. We use Vertica for every customer we have for analytical tools. Vertica sits behind that. Then, for managing the whole ingestion and the storage of the documents that we get from the social space, we use Hadoop and HBase from Hadoop. That’s how we embraced the HAVEn platform.

Gardner: Chris Selland, what is it about the Attensity use case that you think demonstrates some unique characteristics of Vertica and perhaps even more elements of HAVEn?

Complementary nature

Selland: First of all, it demonstrates the complementary nature of Vertica and Hadoop. The Vertica platform has been built to do very high-performance analytics on very large volumes of data. That’s really what we’re all about.

Obviously, Hadoop is also built to scale for very large volumes of data, and so we have bidirectional integration, actually huge integration and increasing convergence with Hadoop. Attensity is doing a great job of showing that.

Then, as we were talking about, it’s just the massive volumes of data that they’re managing. When you’re in the realm of the social world, again, it's not just the volume. I always say that big data is not just big, but it's the velocity, the variety, the ability to ingest very fast, and interpret, analyze, and produce results very fast. That’s really what the Vertica engine is all about, and it’s doing that with very high performance.

It's a very important market segment for us, and it's great to have partners. Vertica is a platform. We rely on our partners to provide solutions to run our platforms. It's social CRM and social analytics and all the kinds of solutions we’re looking to highlight. We love it when we have great partners like Attensity bringing those to market, being successful, and making our joint customers successful.

The Vertica platform has been built to do very high-performance analytics on very large volumes of data. That’s really what we’re all about.

Gardner: Of course, Howard, your customers are probably not so much concerned about what’s going on underneath the hood, whether it's Vertica, HAVEn, or Hadoop. They’re interested in getting results. I’d like to go back to that Social CRM aspect of our discussion and help people understand why that can be so beneficial, which then of course makes it clear why the technology that supports it is so important.

Can you give us any examples, Howard, of where people have used Social CRM, where they have leveraged NLP and Attensity and what that’s done for them in real business terms?

Lau: Absolutely. Some of the industries we service include industries such as telecommunications, hospitality, travel, consumer electronics, financial services, and eCommerce. We provide the services, the tools for our customers and they implement them for very different use cases based on their priorities.

One of the leading prepaid mobile phone providers use Attensity’s deep semantic approach to analyze sentiment about their service and alert the brand management teams to their unique voice of the customer (VoC). 

Attensity effectively measures the overall experience for each brand taking into account their different products and services to determine the accurate wants and needs of the customer. Their whole return-on-investment (ROI) story is how can they use what’s going on in the social realm to manage their install base and minimize customer churn.

Focusing on that, they were able to achieve a 25 percent reduction in customer churn. Now, in the mobile telco space, that directly translates into a 25 percent increase in revenue. Keep in mind that this company is somewhere between half a billion to one billion dollars in revenue. That’s a very sizable return on investment.

We also have other cases where we have an insurance company in the financial services space, and they focus on fraud detection. They use our technology, not only in social space, but also reviewing claims. They were able to reduce workers’ compensation pretty dramatically, to a tune of over $25 million annually, just using our technology, and using our NLP to analyze the data and then figure out which ones they could go after to manage their fraud cost.

Looking toward the future

Gardner: Where do we go next with this, Howard? We have a capability to deal with large data and the variety of data. We certainly have a great treasure trove of information available from the social media and social web. Combining that with the traditional datasets in CRM, where do you go next? Are you looking for even more datasets and what do you have your eye on?

Lau: Getting more datasets is always helpful. The more you get, the more complete your analysis is, but the view right now is just analyzing big data. We are finding that, within that big data, there are tremendous amounts of individual voices. So the goal is to figure out where these individual voices are and how to build relationships with ones that are important to you.

I’m going to go back to a book that Malcolm Gladwell wrote way back called The Tipping Point. He talks about mavens and the influence of mavens. In the social chatter, there are all these people that have outside influence on other people. The next step in applying our NLP technology in the social realm is uncovering these mavens, so that companies can build relationships with these outside influencers. So that’s one of the next things that we’re really excited about.

Gardner: Tell us also where you are going in terms of services for business. Obviously we have talked about marketing, but are their other aspects -- maybe product development? How deeply does this extend into how it can influence a business, not just on the selling and marketing, but perhaps even knowing where their business should be going, a strategy level?

Having an analytical store where you can do what-if scenarios after the fact is incredibly useful for them.

Lau: When people hear about social, the first thing they do is listen, but there is a whole model for how people adopt business solutions in the social realm. We have a model we call LARA, and it stands for Listen, Analyze, Relate, and Act.

The first thing that a lot of companies do is become aware that they need to pay attention to what’s being discussed socially. So they put out these listening posts and they use us to ingest all this information and analyze it for them. The benefit of that is sentiment analysis on companies, on brands, and products. They want this type of sentiment in real time, and we’re able to deliver it in real time.

The next thing companies want to do is analyze the data they have accumulated, and it's for variety of different use cases. I mentioned fraud detection and customer churn. They also want to surface emerging trends. Having an analytical store where you can do what-if scenarios after the fact is incredibly useful for them.

Once they have the store of customer data and they’ve analyzed and segmented their customers, they want to define how they want to relate to the customers, in aggregate or in smaller segments.

The last and final thing they want to do as part of the whole consumer experience is figure out how to engage with the ones that are important to them.

As an example, if someone tweets that they like this phone, that’s great  sentiment. But if somebody else tweets that they don’t like the service they’re getting from this mobile phone provider, if that mobile phone provider is an Attensity customer, we actually take that tweet, route it into their customer-care organization, route it to the proper person, and respond to someone in the social realm.

This ability to kind of close that loop, from a person just tweeting generally to his friends about an experience, and then actually getting the customer to hear them and respond to them is incredibly powerful for organizations.

Following the path

Gardner: For companies that see the value here pretty readily, what steps should they take in order to be in the position to follow that path, that LARA path? Do they need to gather this data themselves? Should they try to ramp up how social media interactions are focused on their products or services? Are there any steps that companies should take in order to better leverage something like Attensity, that’s built on something like Vertica, to get these really powerful insights? Howard?

Lau: That’s part of the value that we bring. All the customer needs to do is recognize that social is important for them. We’re not just talking about corporations that are in the B2C space, but also in the B2B. Once they have that recognition, we’ll handle it for them afterwards.

Part of our products and services offering is that we ingest all this data for them, whether from the social sphere or in the companies emails or customer service notes. We ingest all that information, and they're all defined by one common trait, which is that they are unstructured data. We apply our NLP technology to provide an understanding of the big stream of data and then we create the analytical store for them.

All companies need to do is recognize the importance of wanting to hear their customers, listen to the customers, and ultimately, engage with them socially. They just have to have that motivation, and we will work with them as a partner to realize that solution for them.

Part of our products and services offering is that we ingest all this data for them, whether from the social sphere or in the companies emails or customer service notes.

Gardner: Chris Selland, I’m thinking that organizations that are sophisticated about this will go to a company like Attensity and get some great value, but eventually they’re going to want to try to get that holistic view of analysis. That means that, not only would they leverage what services and insights that Attensity could provide to them, but they’re going to want to share and correlate and integrate that with what they have going on internally and across many other systems.

Is there something about HAVEn that we should bring out for them in terms of open standards and integration capabilities that allows, over time, for more and more of these different data activities to relate to one another, so that we do get a whole greater than the sum of the parts?

Selland: HAVEn certainly provides a very broad platform of which, as we mentioned, Vertica is obviously a key part, the V in the middle. Yes is the short answer. The solutions ultimately need to be part of a much broader data architecture and strategy around how to leverage all sorts of different types of data, that’s not even necessarily customer data.

Just to give you an example and to make that tangible, there was an airline that I was engaged with not too long ago, probably about a year-and-a-half ago at this point. I can’t name them, but it's a well-known airline, and it was one that didn’t have a particularly good reputation for customer service.

They were working on their social-media strategy and trying to figure out how to make customers who were tweeting unhappily that they hated the airline say nicer things -- so how to analyze and respond more quickly.

What they quickly discovered was the reason so many of these customers were angry and saying they hated the airline was that their flight wasn’t on time. What they also realized was they had an awful lot of data on their maintenance operation, and sensor data from the planes, and so on from their fleet.

Predictive maintenance

They saw that by maybe doing a better job of predictive maintenance, keeping their flights on time, and keeping their fleets better maintained, they would actually have much more impact on customer satisfaction than responding to the tweet from the customer who was stranded, which kind of makes sense, if you think about it.

I just bring that example out because that’s an example of data that has nothing to do with the customer. It might be a sensor on an engine, or it might be a performance data of some sort, but it's related obviously to customer satisfaction.

So ultimately, yes, there needs to be a data infrastructure and a data strategy that spans the different solutions. It's not to say you don’t absolutely still need Social CRM solutions and all sorts of different solutions, predictive maintenance solutions and operational, financial analytic solutions, but ultimately the data infrastructure needs to be unified.

That’s really where this is going next. In many leading organizations that’s where it's going already, which is, these solutions absolutely play a key role, but they can’t be 24/7. So there needs to be an infrastructure and a strategy behind them that is very, very holistic.

What he’s driving towards is a world where it's really the Internet of Things, where everything is wired to the Internet and they broadcast messages or communicate messages related to their purpose and their focus. 

We're talking about the competitive bar moving here, and that’s the direction that the competitive bar is going to continue to move in.

Gardner: Howard, do you have any reaction to what Chris has said in terms of seeing a value of a holistic data architecture, not only from what Attensity can do, but extending it across many aspects of business?

Lau: I totally agree with what Chris just said. What he’s driving towards is a world where it's really the Internet of Things, where everything is wired to the Internet and they broadcast messages or communicate messages related to their purpose and their focus. 

Where we provide our value is that before we get to the world of Internet of Things, there is the Internet of People. People need to express themselves the way they normally do. Where we add value is trying to understand, distill the customers in a person’s voice, and have that complement the future of the Internet of Things.

I totally agree that having an integrated architecture, integrated approach to data management, big data management is crucial going forward.

Gardner: Very good. I’m afraid we’ll have to leave it there. You’ve been listening to a thoughtful discussion on the power of big-data technology and how it's being applied successfully to understanding such complex concepts as consumer sentiment and Social CRM.

And we have seen how analytics services provider Attensity has used NLP technology and HP Vertica and HAVEn capabilities to effectively listen to the social web to gain these valuable insights and then also develop actionable intelligence.

This discussion marks the latest episode in the ongoing HP Big Data Podcast Series, where leading-edge adopters of data-driven business strategies share their success stories and where the transformation nature of big data takes center stage.

So please join me now in thanking our guests. We’ve been here with Howard Lau, Chairman and CEO of Attensity. Thank you, Howard.

Lau: Dana, thank you very much for having me today.

Gardner: And we’ve also been here with Chris Selland, Vice President of Marketing and Business Development at HP Vertica. Thanks so much, Chris.

Selland: Thanks so much, Dana, and thank you, Howard, as well.

Gardner: To learn more about how businesses anywhere can best capture knowledge, deep analysis, and rapidly and securely make those insights available to more people on their own terms, please visit the HP HAVEn Resource Center at hp.com/haven.

I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your moderator for this ongoing sponsored journey into how data is analyzed and used to advance the way we work and live. Thanks so much for listening, and come back next time for the next episode in the HP Big Data Podcast Series.

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

Transcript of a BriefingsDirect podcast on how analyzing chatter on social sites can lead to big gains for companies. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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HP Experts Analyze and Explain the HAVEn Big Data News From HP Discover Conference

Transcript of a BriefingsDirect podcast on how HP's new HAVEn Initiative puts the full power and breadth of big data in the hands of companies.

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.]

We're here in the week of June 10 and we are now joined by our co-host, Chief Evangelist at HP Software, Paul Muller. Welcome, Paul.

Paul Muller: Dana, I'm surprised your voice is holding out after this week.

Gardner: Right, it’s been quite busy. There has been a lot said about big data in the last year and HP has made an announcement for a broader vision for businesses that gained actionable intelligence from literally a universe of potential sources and data types.

We're now joined by two additional HP executives to explore the implication and business values from the HAVEn news at Discover. Please join me now in welcoming our guests. First is Chris Selland, Vice President of Marketing at HP Vertica. Welcome, Chris.

Chris Selland: Thanks Dana, it’s great to be here. It's great to work with you again, Paul, and I'm really looking forward to this.

Gardner: And we're joined by Tom Norton, Vice President for Big Data Technology Services at HP. Welcome, Tom.

Tom Norton: Hello, Dana.

Gardner: Let’s go to Chris first. Fairly recently, only critical data was given this high-falutin' treatment for analysis, warehousing, applying business intelligence (BI) tools, making sure that it was backed up and treated almost as if it were a cherished child.

But almost overnight, the savvy businesses, those who are looking for business results, are more interested in all the data or more information of any kind so that they can run their businesses and find inferences in the areas that they maybe didn’t understand or didn’t even know about.

So what do you think has happened? Why have we moved from this BI-as-sacred ivory tower approach to now more pedestrian?

Competitive issue

Selland: First-and-foremost, it’s really that it’s become a competitive issue. Competitiveness issue might be a better way to say it. Just about every company will pay attention to their customers.

Selland

You can tell senior management that this data is important. We're going to analyze it and give you insights about it, but you start realizing that we have an opportunity to grow our business or we're losing business, because we're not doing a good enough job, or we have an opportunity to do better job with data.

Social media has been the tip of the arrow here, because just about all industries all of a sudden realize that there is all data out there floating around. Our customers are actually talking to each other and talking about us, and what are we doing about that? That’s brought a lot of attention above and beyond the CIO and made this an issue that the CMO, the CFO, the COO, the CEO start to care about.

We’ll drill down on this, as we go through the discussion today. Big data is about far more than social media, but I do think social media gets a lot of the credit for making companies pay a lot more attention. It's, "Wait a minute. There is all this data, and we really need to be doing something with this."

Gardner: Paul Muller, as you travel around the world and speak with businesses and governments, are you seeing a shift in the way that people perceive of data as an asset or have they shifted their thinking about how they want to exploit it?

Muller: At the risk of reaching for the third rail here, which is the kind of a San Francisco West Coast joke, in the conversations that I'm having consistently around the globe, executives, both CIOs, but also non-IT executives, are realizing that big data is probably not the most helpful phrase. It’s not the size of the data that matters, but it’s what you do with it.

Muller

It’s about finding the connections between different data sets to help you improve competitiveness, help you improve efficiency if you are in the public sector, help you to detect fraud pattern. It's about what you do with the data in that connected intelligence that matters.

To make that work, it’s about not just the volume of data. That certainly helps, not having to throw out my data or overly summarize it. Having high-fidelity data absolutely helps, but it’s also the variety of data. Less than 15 percent of what we deal with on a daily basis is in structured form.

Most of the people I meet are still dealing with information in rows and columns, because traditionally that’s what a computer has understood. They’ve not built the unstructured things like video, audio, images, and for that matter social, as Chris just mentioned.

Finally, it’s about timeliness. Nobody wants to might be making tomorrow’s decision with last week’s data, if that makes sense. In other words, with a lot of the decisions we have to make, it’s usually done through a revision mirror, which is not helpful, if you're trying to operate today’s thoughts as well.

Variety of systems

Gardner: Chris, it seems as if we have more interest, more business activities, and more constituencies within businesses looking for inputs that help them make decisions or analysis. But we’ve got a variety of systems. We’ve got relational databases, flat files, and all sorts of social APIs that we can draw on.

How do you make sense of this? Is there a common thread now? Is there a way for us to think about data beyond the traditional IT definition of data, and what does that mean for actually then getting access and managing it?

Selland: To pick up on what Paul was saying. I have a love-hate relationship with the term "big data." The love part is the fact that it really has been adopted. People gravitate to it and are starting to realize that there is something here they need to pay attention to. And that’s not just IT.

It’s funny because if you go to something like Wikipedia and you look for the origins of the term "big data," you’ll actually find that in IT circles, we've been talking about big data for about a dozen years. There are probably five or six different people. There is a discussion on Quora, you can look it up if you are interested in the creation of the term which was about a dozen years ago.

As a matter of fact, this is the problem that Vertica was created to solve. It was that, as this big data thing became real, which it is now, traditional databases would be unable to handle it. So the good news is that there has been a recognition in business circles outside the CIO -- the CMO, the COO, and the CFO -- that has just started to happen in the last 18 to 24 months, in a big way.

The love part is that people are paying attention to big data. The hate part is that it’s much more than “big”.

The love part is that people are paying attention to big data. The hate part is that it’s much more than “big”.

I like the Doug Laney definition of big data. Doug is an analyst who is now at Gartner Group, although when he coined term, he was actually at another firm. He said it is the 3Vs -- volume, velocity, and variety. Volume is a part of it and it’s certainly about big.

But as Paul was just talking about, there is also a tremendous variety these days. We've already talked a little bit about social media, but the fact that people equate "social media" with "big data" is another pet peeve of mine.

Social media is driving big data, but it’s only a very small part of it. But it’s an important part, because it’s what’s brought a lot of that other attention. You're looking at audio, video, and all of this user-created content and such, and there is such a variety. Then, of course, it’s coming in so fast. Then, we’d like to sometimes add the forth V, which is value. How is this all going to make money for me? What do we do about this strategically as a business.

So there is just a lot going on here and this is really what’s driven the HAVEn initiative and the HAVEn strategy. We have this tremendous portfolio of assets here at HP from software to hardware to services and HAVEn is about putting that portfolio behind these different analytic engines – Vertica, IDOL, Logger, and Hadoop - that complement each other and their ability to integrate and build solutions.

Broad strategy

So how do we bring this together under a single broad strategy to help companies and global enterprises get their hands around all of this, because it’s a lot more than big? Big data is great. It’s great that the term is taken off, but it’s a lot bigger than that.

Gardner: All right. Before we go into the HAVEn announcement, I’d like to remind our readers and listeners that there is a lot of information available, if they search online for HP, HAVEn, or HP Discover 2013. But before we go there, let’s go to Tom Norton.

We've been talking about data, big data, the movement and shift in the market, and we also find ourselves talking about platforms and certain types of data format and technologies, but there is more than that. It seems that if we're going to change these organizations so that they use data more effectively, we need to go beyond the technology. Give me an idea from the technology services' perspective of what also needs to be considered when we go about these shifts in the market.

Norton: When you think about a data platform, that’s not new. Both Paul and Chris mentioned that data platforms and data analysis have been around for years, but this is a shift. It is different in a number of ways: We mentioned velocity, volume, and variety, but there is also a demand, as Chris mentioned, to have this access to information faster.

Norton

The traditional systems or platforms that IT is used to providing are now becoming legacy. In other words, they're not providing the type of service level to meet the workload demands of the organization. So IT is faced with the challenge of how to transform that BI environment to more of a data refinement model or a big data ecosystem, if you want to still hang on to big data as a term.

IT is challenged there, and the goal overall is to be able to provide that service level that Paul mentioned to be able to support through timeliness, and the type of actions the business wants to take. So the business is now demanding an action from IT.

The ability to respond quickly to this platform transformation is what we want to help our customers do from our technology services' perspective. How can we speed the maturity or speed the transformation of those traditional BI systems which are more sequential and more structured to be able to deal with the demands of the business to have relevant and refined information available to them at the time they need it, whether it’d be 1.5 seconds or 15 hours.

The business needs the information to be able to compete and IT needs to be able to adapt, to have that kind of flexible, secure, and high-performing platform that can deal with the different complexities of raw data that’s available to them today.

Gardner: Tom, on other programs, we’ve talked about application modernization and application transformation. We're following a similar trajectory with data. We're bringing in more data types, but we don’t necessarily want to assimilate them into a common warehouse or format. We're looking to do integration with the data, do hybrid activities with the data, buy-and-sell data, or barter it. It’s really transformed data.

It used to be that the way data came about was as a refuge from the application. So is the role of services for managing the data continuum and lifecycle similar to what we did with applications over the past 10 years?

Similar to cloud

Norton: I think it's similar It’s actually very similar to cloud in some ways, when you think of a platform which enables a service. When you consider the models that people are looking at today concerning cloud, there is a maturity reality that goes with it. We start with a platform and then you start looking at the service-level catalogs, automation, and security, and then you look at the presentation layers.

Data platforms are exactly the same. You have to take what was the very singular service that was offered and start looking at more complex content. So you have to consider data sources, which could come from many different places. You have to consider data source from a cloud, from a traditional BI system, or from other data sources within the organization.

Acquiring data in that context has to be considered. Then, as was mentioned earlier, you have to consider that processing and the service levels for processing of that raw material to produce refined information that’s useful.

And that’s very similar to when you start thinking about what cloud would do. Like the performance from a presentation perspective of how quickly the environment is able to deliver an app, is very similar in terms of presenting information that can be useful to the business. Then you have to look at the presentation format.

You have to consider data source from a cloud, from a traditional BI system, or from other data sources within the organization.

We've had discussions about mobile users, for example, on how social media not only produces information, but there are expectations from mobile users today of how they can get access to it. Considering that format, it's very similar to what we've done in terms of applications and very similar to the approach that you need to take. When you look at a cloud platform, you have to look at that.

Data is unique in that it is both the platform and the service. It’s slightly different than cloud at least in that way, where you're presenting services from that. Data is unique because there is a specialized platform that needs to be integrated, but you have to consider the information service that’s presented and approach it like you would in application. It’s a really interesting approach and an interesting transformation for IT.

Gardner: Chris Selland, let’s get back to the news of the day of the HAVEn initiative, the HAVEn vision. Tell us in a nutshell what it is, what it includes, and then we can talk about what it means.

Selland: I talked about the tip of the spear before. In this case the tip of the spear are our analytic engines, our analytic platforms, the Vertica Analytics Platform, Autonomy IDOL, ArcSight Logger. HAVEn is about taking this entire HP portfolio and then combining those with the power of Hadoop.

We have been talking about our open partnership. There are a number of Hadoop distributions, and we support them all. It's taking that software platform, running it on HP’s Converged Infrastructure, wrapping HP’s services around it, and then enabling our customers, our consultants of course, our channel partners, our systems integrators, and our resellers to build these next-generation analytic-enabled solutions and big-data analytic enabled solutions that customers need.

I keep talking about big data is in a classic crossing-the-chasm moment -- for those of you who have read the book, and while I don't want to do a primer on the book, it’s basically about when the attention around this topic starts to shift, and of course IT still remains very much at the center, but now it becomes a business-enabler.

Changing the business

It’s when technology starts to change the business, and that’s what’s going on right now. When you're talking to businesspeople, you can't talk about platforms and you can’t talk about speeds and feeds. When you say Hadoop to a businessperson they usually say, "God bless you," these days.

You have to talk about customer analytics. You have to talk about preventing fraud. You have to talk about being able to operationally be more effective, more profitable, and all of those things that drive the business. It really becomes more-and-more a solutions discussion.

HAVEn is the HP platform that provides our customers, our partners, and of course, our consultants, when our customers choose to have us do it for them, the ability to deliver these solutions. They're big-data solutions, analytic-enabled solutions. They're the solutions that companies, organizations, and global enterprises need to take their businesses forward and to make their customers more satisfied to become more profitable. That's what HAVEn is all about, the fundamental story behind the HAVEn initiative.

Gardner: It’s very interesting and fascinating to think about these working in some sort of concert. When I first looked at the announcement and heard the presentations, I thought, "Oh ArcSight. Isn’t that an odd man out? Isn't that an outlier?

Why, in your understanding, would having great insights to all the data from your system be something relevant to alter the data that you're driving from your applications, your outside data sources, your customer interactions, the social media, the whole kit and caboodle. Help me understand better why ArcSight is actually a good partner?

Even though social media has been the tip of the spear here for business attention around big data, it’s much, much bigger than that.

Selland: It really goes back to what I said earlier, that even though social media has been the tip of the spear here for business attention around big data, it’s much, much bigger than that. One of the terms that people are starting to hear now, and you're going to hear a lot more about, is the "Internet of things."

There are various third-party estimates out there that within the next few years, there are going to be about 150 sensors per person worldwide, and that number is going to keep growing. Think about all the things that go on in your car, on a factory floor, in a supply chain.

We tend to think about the fact that everybody is walking around with a computer in their pocket these days, a smartphone, but that’s not just communicating with you. It’s communicating with the network to provide quality of service, to monitor what’s going on, to obviously manage your calls and your downloads, and everything else.

There's so much data flowing around out there. The Logger Engine essentially reads and interprets and connects to all of these different sources, various types of machines, system log files, and real-time data as well. It’s not just about being able to interpret social media. It’s being able to pull in all of these different data types.

As the internet of things grows, and the sensors go everywhere, McKinsey estimates that, just to give a tangible example, a typical jet engine throws off about two terabytes per hour of data. What do you do with all that data? How do you manage that data?

Internet of things

Think about all of our IT systems, all of our physical systems, all of our network systems. Think about all these sensors that are in this Internet of things. It’s becoming huge and the ability to process this data from machines, systems, and log files is a huge, huge part of this.

Gardner: Paul Muller, we understand now that we can bring Hadoop benefits to Autonomy's breadth and depth of information, unstructured information to Vertica, speed and ability to do analytics very rapidly and efficiently to ArcSight with machine and other data. How do you take this out to an enterprise, a C-class group of people, and make them understand that you are, in fact, giving them some tools that really weren’t available before, and certainly weren’t cobbled together in such a way? How do you put this in business terms so they can get just how powerful this really is?

Muller: Dana, did you just say Hadoop?

Gardner: I did.

Muller: Bless you.

Selland: Well played.

Muller: Had to be done, Chris. That’s ultimately the question. Let me just give you an example that we talk about and that I share with people quite frequently, and it usually generates a bit of a smirk. We’ve all been on the telephone and called a company or a public service, where you've been told by the machine that the call will be monitored for quality of service purposes. And I am sure we’re all thinking, "Gosh, if only."

The scary part is that all those calls are recorded. They're not only recorded, but they're recorded digitally. In other words, they're recorded to a computer. Much like the airline example that Chris just gave, almost all of that data is habitually thrown away, unless there is an exception to the rule.

What we're able to do with the HAVEn announcement is combine those concepts into one integrated platform.

If there is a problem with the flight or if there is some complaint about the call that escalates the senior management, they may eventually look at it. But think about how much information, how much valuable insight is thrown away on a daily basis across a company, across the country, across the planet. What we've aimed to do with HAVEn is liberate that information for us to find that connected intelligence.

In order to do that, we get back to this key concept that you need to be able to integrate telemetry from your IT systems. What’s happening inside them today? For example, if somebody to send an email to somebody outside of the company, that typically will spawn a question that asks who they send that email to? Was there an attachment there? Is it a piece of sensitive information or not? Typically that would require a person to look at it.

Finally, it's to be able to correlate patterns of activity that are relevant to think about revenue, earnings, or whatever that might be. What we're able to do with the HAVEn announcement is combine those concepts into one integrated platform. The power of that would be something like in that call center example. We can use autonomy technology to listen to the call, to understand people's emotions, and whether they’ve said, "If you don't solve this problem, I'm never going to buy from you again."

Take that nugget of information, marry that to things like whether they are a high net worth customer, what their spending patterns have been, whether they're socially active, are they more likely to tell people about their bad experience, and correlate that all in real-time to help give you insight. That's the sort of being the HAVEn can do it, and that's a real world application that we're trying to communicate in business.

Norton: I want to echo that. I have one more example of what Paul has just indicated. Take healthcare, for example. We're working with the healthcare providers. There are some three-tier healthcare providers. A major healthcare organization could have as many as 50 different business units. These separate business units have their own requirements for information that they want to feed to hospital systems.

Centralized structure

So you have a centralized organizational IT structure. You have a requirement of a business unit within the organization that has its own processing requirement, and then you have hospital systems that buy and share information with the business unit.

Think about three-tiered structure and you think of some of the component pieces that HAVEn brings to that. You have IT which can manage some of those central systems that becomes that data lake or data repository, collecting years and years of historical healthcare information from the hospital systems, from the business units, but also from the global healthcare environment that could be available globally.

IT provides this ecosystem around the data repository that needs to be secured, and and that data pool needs to be governed.

Then, you combine that with information that's coming publicly and needs to be secured. You have those corner pieces which are natural to the Hadoop distributed system inside that data lake that keeps that repository of healthcare information.

The business unit has a requirement because it wants to be able to feed information to the healthcare providers or the hospital systems, and to collect from them as well. Their expectations of IT is that they may need instant response. They may need a response from a medical provider in seconds, or they may look at reporting on changes in healthcare in certain environmental situations that are creating change in healthcare. So they might get daily reporting or they might have half-day reporting.

That's what's driving IT, because they need that very flexible and responsive data repository.

Within HAVEn, you look at Vertica, to drive that immediate satisfaction of that query that comes from the hospital system. Combine that with Hadoop and combine that with the kind of data-governance models that Autonomy brings. Then, look at security policies around the sensors from patients that are being sent to that hospital system. That combination is a very powerful equation. It's going to enable that business to be very successful in terms of how it handles information and how it produces it.

When we start looking at that integration of those components, that's what's driving IT, because they need that very flexible and responsive data repository that can provide that type of insight that the hospital systems need from that from the business unit that's driving the healthcare IT organization itself.

Those are the fits even in a large enterprise, where you can take that platform and apply it in an industry sense, and it makes complete sense for that industry overall.

Gardner: Chris Selland, I think about what companies, governments, and verticals like healthcare, the leaders and innovators in those areas, can do with this. It could really radically change how they conduct their businesses, not by gut, not by instinct, not by just raw talent, but by empirical evidence that can be then reestablished and retested time after time. It strikes me that it's a fundamentally different value that HP is bringing to the market.

HP has, of course, been a very large company with a long heritage, but are we really stepping outside of the traditional role that HP has played? It sounds as if HP is becoming a business-services company, not a technology services company. Correct me if I'm wrong.

Bridging the gap

Selland: Yes and no. First of all, we do need to acknowledge that there is a need to bridge the gap between the IT organization and the business organization, and enable them to talk the same language and solve problems together.

First of all, IT has to become more of an enabler. Second, and I mentioned this earlier and I really want to play this up, it's absolutely an opportunity for our partners. HP has a number of assets, but one of our greatest assets is HP's partner network -- our partner ecosystem, our global systems integrators, our technology partners, even our services providers, our training providers, all of the companies that work in and around the global HP.

We can't know every nuance of every business at HP. So the HAVEn initiative is very much about enabling our partners to create the solutions we're creating. We're using our own platform to create solutions for the core audiences that we serve, which in many cases, are things like IT management solutions or security solutions which are being featured and will continue to be featured.

We're going to need to get into all of these different nuances of all of these different industries. How do these companies and organizations compete with each other in particular verticals? We can’t possibly know all of that. So we're very reliant on our partners.

The great news is we have, we have what I believe, is the world's greatest partner network and this is very much about enabling those partners and those solutions. In many cases, those solutions will be delivered by partners and that’s what the solutions are all about as well.

We have what I believe, is the world's greatest partner network and this is very much about enabling those partners and those solutions.

Gardner: Just to drill down on that a bit, if there are these technologies that are available to these ecosystems within verticals and attacking different business problems, what's the next step with HAVEn? Now that we put together the various platforms, given the whole is greater than the sum of the parts in terms of a business value, what's the vision beyond that to making these usable, exploitable?

Are there APIs and tools or is that something also that you are going to look to the partners for, or both? How does it work in terms of the go to market?

Selland: There absolutely are APIs and tools. We need to prime the pump, to some degree, with building and creating some of our own solutions to show what can be done in the markets we serve, which we're doing, and we also we have partners on board already.

If you look at the HAVEn announcements, you'll see partners like Avnet and Accenture and other partners that are already adopting and building HAVEn-based solutions. In many cases, we've started delivering to customers already.

It's really a matter of showing what can be done, building what can be built, and delivering them. I mentioned earlier the crossing-the-chasm moment we're having. The other thing that happens, when you get into this market, is you're moving from its being purely a CIO decision to where the business starts getting involved.

Great ROI

There is great return on investment (ROI), there's this big data analytic solution we're going to enable, and we are going to build to deliver better customer loyalty. We are going to better customer retention and lower churn. The first thing I need to say is, "Okay, show me the numbers, show me the money." Those are Jerry Maguire terms, and the best way to do that is show examples of other companies that have done it.

So you run into a situation where you need to be able to show who is doing it, how they're doing it, and how they're making money with it. You've got to get that early momentum, but we're already in the process of getting it, and we've already got partners on board. So we're really excited.

Gardner: Tom Norton, what are your thoughts about my observation that this takes HP to a different plane in terms of the level of value it can bring to a business, and then perhaps some additional thoughts based on what Chris said in terms of how this fits into a value chain?

Norton: You can take two separate perspectives, but you can't separate them. In order for my group, TS, to be able to help IT transform, IT has to be aligned to that business decision anyway, or they have to be aligned to the business requirements and the workloads that business may be presenting.

For me to help to build an integration plan or to build a design for a data platform like this transformation of a data platform, I have to have some idea of what the workload requirements may be from the business. I have to know if the business is trying to do something that's going to require an immediate type of satisfaction, or they are going to do something that can be done in more of a batch format.

I have to have some idea of what the workload requirements may be from the business.

Those expectations of a business in terms of when they want to be presented with that business aligned information, that's going to determine short term and midterm what IT needs to do.

You can't separate those two, especially when we're starting to drive and accelerate the kind of format and the kind of workloads that businesses may need. You may get requirements from 20 different businesses and each business may have 10 different business requirements that they have in terms of the presentation of information.

So how can we get to the point where we can separate from the business, the view of what IT is doing? The business shouldn't need to know about Hadoop, as Chris mentioned earlier. They shouldn't need to know how Hadoop is integrated with Vertica, integrated with Autonomy, or how the three are combined and secured, but they should have an expectation that they're going to get the information that they need at the time they need it.

We really can't design a platform, unless we know that spectrum, and how we can create a road map for how to resolve that and how to mature it. So we have to know that, and the second part is going to be, as you've mentioned before, from how the business needs to access it.

Flexible technology

If the business is going to a more distributed, a remote, or a mobile type of workforce or mobile access, our design requirements for IT have to be for the infrastructure. The technology has to be flexible enough to deliver information to those consumption formats.

If you're dealing with finance, for example, and you're going to have a sales force selling capital investments to their largest investors, a $100 million a year investors, the expectation of those salespeople to that investment model is that they can provide their customers -- probably the most important customers that that finance organization has -- information within 15-30 minutes. That's the time that the salesperson is talking to them about what may be happening with their portfolio.

Think about how complex that can be. You have to access social media, as was brought up earlier, and be able to get information on Twitter feed so that they can provide a meaning-based analysis on how this stock portfolio is being reflected in the market.

To get that in that time frame of 0-30 minutes requires a different design, than someone who is going to look at market reporting trends over a 24-hour period and present that each morning. So it’s very important that we have that alignment between technology and business, and unless we can understand both, we're not going to be able to drive that road map in the direction that's going to satisfy the business requirements.

Gardner: Paul Muller, when we think about the value to the business, and we recognize that IT is in the middle between when data is analyzed and inferences are gathered, acting on those inferences and putting them into place perhaps goes back in through IT.

It seems to me that HP is in a unique situation now by pulling together these different data analysis types.

There are applications that need to be addressed. There are mobile devices that need to be reached. It seems to me that HP is in a unique situation now by pulling together these different data analysis types, making it available in a holistic context, but also being a provider of the means to then be actionable, to create applications, to populate applications, and to allow IT to be the traffic cop on this two-way street or multi-way street.

Tell me how HP is differentiated. Given what we've now seen with the HP Discover announcements with cloud, with converged infrastructure and with HAVEn, give us a bit more of an understanding of how HP is uniquely positioned?

Muller: Dana, you made such a great point. Insight without action is a bit like saying that you have a strategy without execution. In other words, it’s pretty close to hallucination, right?

The ability to take that insight and then reflect that into your business rapidly is critical. I have a point of view that says that almost every enterprise is defined by software these days. In other words, when you make an insight and you want to make a change, you're changing the size. If you are Mercedes, you're changing one of the 100 million lines of code in your typical S class. Some of the major based around the planet now hire more programmers than Microsoft has working on Windows today.

Most companies are defined by software. So when they do get in an insight, they need to rapidly reflect that insight in the form of a new application or a new service, it’s typically going to require IT.

Absolutely critical

Your ability to quickly take that insight and turn that into something a customer can see, touch, and smell is absolutely critical, and using technique like Agile delivery, increasing automation levels, DevOps approaches, are all critical to being able to execute to get to that.

I would like to come back up to Chris’ response to just touch on a conversation I had with a CIO last week, where he said to me, "Paul, my problem is actually not about big data. It’s great, and we’ve got it, but I still can’t work out what to do with it. We should have a conversation about innovation in the profits of big data." So, Chris, do you want to maybe take Dana’s question?

Selland: It’s really, first of all, our focus. It's not just big data, but helping our customers be successful in leveraging big data is a core focus and a core pillar of HP strategy. So first of all it’s focus.

Second of all, it’s breadth. I talked about this earlier, so I don’t want to repeat myself too much. The software, hardware, and converged cloud assets, capabilities of services, and of course their service’s portfolio -- all of the resources that the global HP brings to bear -- are focused on big data.

And it’s also the uniqueness. Obviously, being an HP Software Executive, I'm most familiar with the software. If you really look at it, nobody, none of HP’s competitors, has anything like Vertica. None of HP’s competitors have anything like IDOL. None of HP’s competitors has anything like ArcSight Logger. None of HP's competitors has the ability to bring those assets together and get them interoperating with each other and get them solving problems and building solutions.

Your ability to quickly take that insight and turn that into something a customer can see, touch, and smell is absolutely critical.

Then, you take our partner channel, wrap it around that, and you combine it with the power of open-source industry initiatives like Hadoop. HP has very much openness of the core of everything we're doing. We have all sorts of partners helping and supporting us around here.

I haven’t even talked about technology partners, BI, or visualization partners. We're partnering with all of the major Hadoop distribution. So there is just tremendous breadth and depth of resources focused on the problem. At the end of the day, it really is about execution, because that’s the other thing that I talked about earlier, customers. They want to hear big ideas and they want to know how technology helps them get there, but they also want to see proof points.

Muller: Let’s just start from that. Chris, maybe we'll finish on a slightly controversial note here, but it’s worth talking about. Then, maybe this is potentially a good segue to Tom. I met with a CIO again. I was speaking to some of our listeners and met with some CIOs in South Africa a couple of weeks back. This head of manufacturing turned to me and said, "You know, Paul, I understand big data technology is there, I understand. I can pretty much ingest this. At least the potential is there that I can.

"What I'm not sure is, in my industry, how does it matter to me? Don’t just talk to me about technology. How can I turn that into a justifiable business case that the business will want to invest in?" And it kind of struck me that the technology in some respect is slightly ahead of our customer’s ability to think of themselves as innovators rather than as infrastructure managers.

Part of the problem

Selland: You certainly just defined part of the problem. There is no one-size-fits-all big-data-in-a-box solution, because the answer to that question is something that you really need to have a significant understanding of the business and it’s really a consultative question, right?

You’ve got to have a broad enough portfolio to know that you’ve got the confidence and the assets to eventually solve the problem, but at the same time start with understanding the problem, the industry, and solutions. This is where our service is, and this is where our partner ecosystem comes into play. And having the breadth of the portfolio of software/hardware and cloud services to be able to deliver on it is really what’s it’s all about, but there is no one-size-fits-all answer to the question we just asked.

Gardner: Tom Norton, when we think about the observation that the technology is getting a bit out in front of what the businesses understand they can do with it, it sounds like a really good opportunity for a technology consultant and a technology services organization to come in. It sounds as if you have to bring together disparate parts of companies.

We talked about developers. If the people are allowing for analytics to develop wonderful insights, but they’ve never really dealt with the App Dev people, and the App Dev people have never really dealt with the BI people, what do we need to do to try to bring them together? In your company, how would you go about bringing them together so that as insights develop, new ways of delivering those insights to more people and more situations are possible? I guess we're talking about cultural shifts here?

There is no one-size-fits-all big-data-in-a-box solution.

Norton: HP actually has, from a services' perspective, a unique approach to this. You've seen it before in the cloud and you've seen it before in the days of IT transformation, where we started looking at that transformation experience.

HP has developed these workshops over time. They bring IT together with the business to help IT build a plan for how it's going to address the business needs and pull out from the business what the business requirements of IT will be.

It’s no different, now that we're in the data world. Through our services' groups within HP, we have the ability from an information management and analytics approach to work with companies to understand the business value that they're trying to drive with information, and ideally try to understand what data is available to them today that is going to provide that business aligned information.

Through the Big Data Discovery Experience workshops, we're able to ask, "What is the business I am capable of doing with the data they have available to them today, and how can that be enhanced with alternative data sources that may fall outside of the organization today?"

As we mentioned earlier, it’s that idea of what can be done. What's the art of the possible here that is going to provide value to the organization? Through services we can take that all the way down, then say, now once you have got the idea, that says I’ve got a road map for analytical value and the management of the information that we have, and we could have made available to the businesses.

Then, you can align that, as I mentioned before, through IT strategies where you do the same thing. You align the business to IT and ask how IT is going to be able to enable those actions that the business wants to take on that information.

Entire lifecycle

So there's an entire lifecycle of raw material data to business-aligned and business-valued information through a service’s approach, through a consultative approach, that HP is able to bring to our customers.

That’s unique, because we have the ability through that upfront strategy from business value of information to the collection and refinement of raw materials and meeting in the middle in this big data ecosystem. HP can supply that from end to end, all the way from software to hardware to services, very unique.

Muller: I’ve got to summarize this by saying that the great part about HAVEn is that you can pretty much answer any question you could think of. The challenge is whether you can think of smart questions to ask.

Gardner: I think that’s exactly the position that businesses want to be in -- to be able to think about what the questions are to then propel their businesses forward.

Selland: Let me give you a tangible example that I was reading about not long ago in The Wall Street Journal. They were talking about how the airline industry is starting to pay attention to social media. Paul talked before about intersections. What do we mean by intersections?

The great part about HAVEn is that you can pretty much answer any question you could think of. The challenge is whether you can think of smart questions to ask.

This article in The Wall Street Journal was talking about how airlines are starting to pay attention to social media, because customers are tweeting when they're stuck at the airport. My flight is delayed, and I am upset. I'm going to be late to go visit my grandmother -- or something like that.

So somebody tweets. Paul tweets "I'm stuck at the airport, my flight is delayed and I am going to be late to grandma’s house." What can you really do about that besides respond back and say, "Oh, I'm sorry. Maybe I can offer you a discount next time," or something like that? But it doesn’t do anything to solve the problem.

Think about the airline industry, customer loyalty programs or frequent-flyer programs. Frequent-flyer programs were among the first customer loyalty problems. They have all this traditional data, as well which some might call customer relationship management (CRM). In the airline industry, they call it reservation systems.

I gave the example before about a jet engine throwing off two terabytes of data per hour. By the way, on any flight that I'm on, I want that to be pretty boring data that just says all systems are go, because that’s what you want.

At the same time, you don’t want to throw it away, because what if there are blips, or what if there are trends? What if I can figure out a way to use that to do a better job of doing predictive maintenance on my jets?

Better job

By doing a better job of predictive maintenance on my jets, I keep my flights on time. By keeping my flights on time, then I do a better job of keeping my customers satisfied. By keeping my customers more satisfied, I keep them more loyal. By keeping my customers more loyal, I make more money.

So all of this stuff starts to come together. You think about the fact there is a relationship between these two terabytes per hour of sensor data that’s coming off the sensors on the engine, and the upset customers, and social media tweeting in the airport. But if you look at the stuff in a stove-piped fashion, we don’t get any of that.

That’s just one example, and I use that example, because most of us are businesspeople and get stuck in airports from time-to-time. We can all relate to it, but there’s a variant of that kind of example in any and every industry.

How do we start to bring this stuff together? This stuff does not sit in a single database and it’s not a single type of structure and it’s coming in all over the place. How do I make sense of it?

As Paul said very well, ask smart questions, figure out the big picture, and ultimately make my organization more successful, more competitive, and really get to the results I want to get to. But really, it’s a much, much bigger set of questions than just "My database is getting really big. Yesterday, I had this many terabytes and I am adding more terabytes a day." It’s a lot bigger than that.

HAVEn gives us that platform model, which is scalable, flexible, secure, and integrated.

We need to think bigger and you need to work with an organization that has the breadth of resources and the breadth not just inside the organization but within our partnerships to be able to do that. HP has got the unmatched capability to do that, in my view, and that’s why this HAVEn initiative is so very exciting and why we have such great expectations from this.

Gardner: What really jumped out of me in listening to the announcements was that so often in technology we get products and services that allow us to do things faster, better, cheaper, all of which is very important. But what’s quite new here, and different with HAVEn is that we're able to now start enabling organizations to do things they simply could not have done before or in any other way.

It’s really opening up to me a new chapter in business services enablement, both internal services and, external benefits, and external services. So last word to each of quickly on why this HAVEn announcement is something that’s unique and is really more than just a technology announcement. Let’s start quickly with you, Tom Norton.

Norton: I think it’s interesting, because we just talked before about integration. Customers with data as complex as it can be, you need models. HAVEn gives us that platform model, which is scalable, flexible, secure, and integrated. It's what the customers need to be able to react quickly, what IT needs to be able to stay relevant, and what the business needs to know they are going to have a predictable and responsive platform that they can base their analytics on. It’s an answer to a very difficult question and very impactful.

Gardner: Paul Muller, why does this go beyond the faster, better, cheaper variety of announcements?

Fundamental difference

Muller: It’s the ability to bring together a set of technologies that allow you to look at all the data all of the time in real-time. I think that that’s the fundamental difference. As I said, shifting the discussion from why can’t we do it to what do we need to do next is an exciting possibility.

Gardner: Last word to you, Chris Selland, why is this going beyond repaving cow paths and charting new territory?

Selland: I just gave a long answer. So I'll give a short one. It’s really about the future, the competitiveness of the business, and IT becoming an enabler for that. It’s about the CIO, really having a chance to play a key role in driving the strategy of the business, and that’s what all CIOs want to do.

Is this big-data thing real? We think it’s very real and we think you're going to see more-and-more examples.

We have these inflection points in the marketplace, the last one was like 12 years ago, when the whole e-business thing came along. And, while I just used a competitor's tag line, it changed everything. The web did change everything. It forced businesses to adapt, but it also enabled the lot of businesses to change how they do business, and they did.

Now, we're at another one, a very critical inflection point. It really does change everything, and there is still some skepticism out there. Is this big-data thing real? We think it’s very real and we think you're going to see more-and-more examples. We're working with customers today or showing some of those examples how it really does change everything.

Gardner: Great. I am afraid we'll have to leave it there. We've been exploring the vision and implications of the HAVEn news that’s been delivered here at Discover and we are learning more about HP strategy for businesses to gain actionable intelligence from a universe of sources and data types. So if you want more information on HAVEn, you can find it online by searching under HP Discover 2013 or HP HAVEn.

I'd like to now wrap up by thanking our co-host, Chief Evangelist at HP Software, Paul Muller. Thanks again so much, Paul.

Muller: It’s not the size; it’s how you use it, when it comes to big data, mate.

Gardner: Also a big thank you to Chris Selland, Vice President of Marketing at HP Vertica. Thank you, Chris.

Selland: It’s great to be here, thanks.

Gardner: And lastly, a thank you to Tom Norton, Vice President of Big Data Technology Services at HP. Thank you, Tom.

Norton: Thank you very much, Dana; it’s been a pleasure.

Gardner: Great. And also of course the biggest thank to our audience for joining us for this special HP Discover Performance podcast coming to you 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 discussion.

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's new HAVEn Initiative puts the power of big data in the hands of companies. Copyright Interarbor Solutions, LLC, 2005-2013. All rights reserved.

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