Big data, risk, and predictive analysis drive use of cloud-based ITSM, says panel

This BriefingsDirect IT operations innovation panel discussion focuses on the changing role of IT service management (ITSM) in a hybrid computing world.

As IT systems, resources, assets, and information are more scattered across more enterprise locations and devices -- as well as across various cloud service environments -- how can IT leaders hope to know where their "stuff" is, who’s using it, how to secure it, and then accurately pay for it?

Better than ever, it turns out. Advanced software asset management (SAM) methods can efficiently enforce compliance, reduce audit risk, cut costs, and enhance end-user productivity -- even as the complexity of IT itself increases.

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We'll hear from four IT leaders about how they have improved ITSM despite such challenges, and we'll learn how the increased use of big data and analytics when applied to ITSM improves IT assets inventory control and management. We'll also hear how a service brokering role can also be used to great competitive advantage, thanks to ITSM-generated information.

To learn more about how ITSM solves multiple problems for IT, we're joined by Charl Joubert, a change and configuration management expert based in Pretoria, South Africa; Julien Kuijper, an expert in asset and license management based in Paris; Patrick Bailly, IT Quality and Process Director at Steria, also based in Paris, and Edward Jackson, Operational System Support Manager at Redcentric, based in Harrogate, UK. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Let’s talk about modern SAM, software asset management. There seems to be a lot going on with getting more information about software and how it’s distributed and used. Julien, tell us how you're seeing organizations deal with this issue.

Kuijper: SAM has to square quite a complicated circle. One is compliance in a company, compliance with regard to software installation and usage, and also ensuring that while doing this, we must ensure that the software that is entering a company isn't dangerous. It's things like not letting a virus come in, opening threats or complications. Those are three very technical and very factual environments.

Kuijper

But, you also want to please your end-user. If you don’t please your end-user and you don’t give them the ability to work, they're going to be frustrated. They're going to complain about IT. It’s already a complicated enough.

You have to square that circle by implementing the correct processes first, while giving the correct information around how to behave in the end-to-end software lifecycle.

Gardner: And asset management when it comes to software is not small, there are some very big numbers -- and costs -- involved.

Kuijper: It’s actually a very inconvenient truth. An audit from a publisher or a vendor can easily reach 7 or 8 digits, and a typical company has between 10 and 50 publishers. So, at 7 digits per publisher, you can easily do the math. That’s typically the financial risk.

You also have a big reputation risk. If you don’t pay for software and you are caught, you end up being in the press. You don’t want your company, your branding, to be at that level of exposure.

You have to bring this risk to the attention of IT leaders at the CIO level, but they don’t really want to hear that, because it costs a lot. When they hear this risk, they can't avoid investment, and the investment can be quite large as well.

Typically, if this investment is reaching five percent of your overall yearly software spending, you're on the right level. It’s a big number, but still it’s worth investing.

But you have to compare this investment with regard to your overall software spending. Typically, if this investment is reaching five percent of your overall yearly software spending, you're on the right level. It’s a big number, but still it’s worth investing.

Coming with this message to IT management and getting the ear of a person who is interested in the topic and then getting the investment authorization, you've gone through half the journey. Implementation afterward will be defining your processes, finding the right tool, implementing it, and running it.

Gardner: When it comes to value to the end-user, by having an understood, clearly-defined process in place allows them to get to the software they want, make sure they can use it, and look for it on a sanctioned list, for example. While some end-users might see this as a hurdle, I think it enables them eventually to get the tools they need when they need them.

Smart communication

Kuijper: Right. At the beginning, every end-user will see all those SAM processes as a burden or a complication. So you have to invest a lot in communication, smart communication, with your company and make people understand that it’s everyone’s responsibility to be [software license] compliant and also that it can help in recovering money.

If you do this in a smart way, and the process has a delivery time not longer than three days, then you're good. You have to ensure, of course, that you have a software catalog that is up-to-date, with an easy access to your main titles. All those points from the end-to-end software lifecycle are implemented -- from software tool, then software delivery, then software re-usage, software, and also disposal. When all this is lean, then you’ve made your journey. Then, the software lifecycle process will not be seen any more as a pain, but it will be seen as a business-enabler.

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Gardner: Now, asset management doesn’t just cover the realm of software. It includes hardware, and in a network environment, that can be very large numbers of equipment and devices, endpoints as well as network equipment.
Edward at Redcentric, tell us about how you see the management of assets through the lens of a network.

Jackson: We have more than 10,000 devices in management from a multitude of vendors and we use asset management in terms of portfolio management, managing the models, the versions, and the software.

Jackson

We also have a configuration management tool that takes the configurations of these devices and runs them against compliance. We can run them against a gold or a silver build. We can also run them against security flaws. It gives us an end-to-end management.

All of this feeds into our ITSM product and then also it feeds into things like the configuration management data base (CMDB). So we have a complete end-to-end knowledge of the software, the hardware, and the services that we're giving the customer.

Gardner: Knowing yourself and your organization allows for that lifecycle benefit that Julien referred to. Eventually, that gives you the freedom to manage and extend those benefits into things like helpdesk support, even IT operations, where the performance can be maintained better.

Jackson: Yes, that's 360-degree management from hardware being delivered on-site, to being discovered, being automatically populated into the multitude of support and operational systems that we use, and then into the ITSM side.

If you don’t get it right from the start and you don’t have the correct models defined for example a Cisco device or the correct OS version on that device, one perhaps where it has security flaws, then you run the risk of deploying a vulnerable service to the customer.

Thinking about scale

Gardner: Looking at the different types of tools and approaches, this goes beyond thinking about assets alone. We're thinking also about scale. Tell us about your organization, and why the scale and ability to manage so many devices and information is important?

Jackson: Being a managed service provider (MSP), we have about 1,000 external customers, and each one of those has a tailored service, ranging from voice, storage, to data, and cloud. So we need to be able to manage these services that are contained within the 10,000 plus devices that we have.

We need to understand the service end-to-end. So there’s quite bit of service level management in there. It all ties down to having the correct kind of vendor, the correct kind of service mapping, and information needs to be accurate in the configuration items (CIs), so support can utilize this information.

If we have an incident that is automatically generated on the management platforms, it goes into the ITSM platform. We can create an effective customer list within, say, five minutes of the network outage and then email or SMS the customer pretty much directly.

We need to understand the service end-to-end. So there’s quite bit of service level management in there.

There’s more ways of doing it, but it’s all due to having a tight control on the assets that are out there in the field, having an asset management tool that can actually control that, and being able to understand the topology of the network and where everything lies. This gives us the ability to create relationships between these devices and have hierarchical logical and physical entities.

Gardner: You have confidence that you work with tools and platforms that can handle that scale?

Jackson: All the tools that we have are pretty much carrier-grade. So we can scale a lot more than the 10,000 devices that we currently have. If you set it up and plan it right, it doesn’t really matter how many devices you have in management. You have to have the right processes and structure to be able to manage them.

Gardner: We've talked about software, hardware, and networks. Nowadays, cloud services, microservices, and APIs are also a big part of the mix. IT consumes them, they make value from them, and they extend that value into the organization.

Let’s go to Patrick at Steria. How are you seeing in your organization an evolution of ITSM into a service brokering role? And does the current generation of ITSM tools and platforms give you a road to that service brokering capacity?

Extending services

Bailly: What’s needed for becoming a service broker that is we need to offer the ability to extend the current service that we have to the services that are available today in the cloud.

Bailly

To do that, we need to extend the capability of our framework. Today, our framework has been designed in order to run the operation on behalf of our customers, to run the operation on the customer side, or the operation on our data center, but more or less, traditionally IT. The current ITSM framework is able to do that.

What we're facing is that we have customers who want to add short-term [cloud capacity]. We need to offer that capability. What's very important is to offer one interface toward the customers, and to integrate across several service providers at the same time.

Gardner: Tell us a bit about Steria. You're a large organization, 20,000 employees, and in multiple countries.

Bailly: We're an IT service provider, and we manage different kinds of services from infrastructure management, application management, business process outsourcing, system integration, etc., all over Europe. Today, we're leveraging the capabilities that we have today in India and in Poland.

Gardner: Now, we've looked at what ITSM does. We haven’t dug into too much about where it’s going next in terms of what analysis of this data can bring to the table.

Charl, tell us, please, about how you see the use of analytics improving what you've been doing in your setting. How do baseline results from ITSM, the tools we have been talking about, improve when you start to analyze that data, index it, cleanse it, and get at the real underlying information that can then be turned into business benefits?

Joubert: Looking at inadequacies of your processes is really the start of all of this. The moment you start scratching at the vast amount of information you have, you start seeing the errors of your ways, and ways and opportunities to correct them.

Joubert

It's really an exciting time in ITSM. We now have the ability to start mining this magnitude of information that’s being locked inside attachments in all of these ITSM solutions. We can now start indexing all that unstructured data and using it. It’s a fantastic time to be in IT.

Gardner: Give me an example of where you've seen this at work -- maybe a helpdesk environment. How can you immediately get benefits from starting to analyze systems and IT information?

Million interactions

Joubert: In the service desk I'm involved in, we have about a total of a million interactions over the past few years. What we've done with big data is index the categorization of all these interactions.

With tools from HP, Smart Analytics and Smart Ticketing, we're able to predict the categorization of these interactions to a accuracy of about 84 percent at the moment. This assists the service desk agents to more accurately get the correct information to the correct service teams the first time, with fewer errors in escalation, which in turn leads to greater customer satisfaction.

Gardner: Julien, where does the analysis of what you're doing with software asset management, for example, play a role? Where do you see it going?

Kuijper: SAM is already quite complex on-premise and we all know today that the IT world is moving to the cloud, and this is the next challenge of SAM, because the whole point of the cloud is that you don’t know where your systems are.

However, the licensing models, as they are today, refer to CPU, to on-premise, to physical assets. Understanding how you can adapt your licensing model to this new concept -- not that new anymore now -- this new concept of cloud is something to which even the software publishers and vendors have not really adapted their model.

This is the next challenge of SAM, because the whole point of the cloud is that you don’t know where your systems are.

You also have to face some vendors or publishers who are not willing to adapt their model, especially to be able to audit specific customers and get more revenue. So, on one hand, you have to implement the right processes and the right tools, which are now going to navigate in a very complex environment, very difficult to scan, very difficult to analyze. At the same time, you have to update all your contracts, and sometime, this will not be possible.

Some vendors will have a very easy licensing model if you are implementing their software in their own cloud environment, but in another cloud environment, in a competitor, they might make this journey quite complicated for you.

So this will be complex and will be resolved by correct data to analyze and also some legal workforce and purchasing workforce to try to adapt the contracts.

Gardner: In many ways right now, we never really own software. We only lease it or borrow it and we're charged in a variety of ways. But soon we'll to be going more to that pay-as-you-use, pay-as-you-consume model. What about the underlying information associated with those services? Would logs go along with your cloud services? Should you be able to access that so that you can analyze it in the context of your other IT infrastructure?

Edward, any thoughts as a managed services environment and a management of networks provider. Do you see that as you provide more services that you are providing insight or ITSM metadata along with the services?

IaaS to SaaS

Jackson: Over the past five or six years, the services that we offered pretty much started as infrastructure as a service (IaaS), but it’s now very much a software-as-a-service (SaaS) offering, managed OS, and everything up the technology stack into managed applications.

It's gotten to a point now that we are taking on the managing of bespoke applications that customers wanted to hand over to Redcentric. So not only do we have to understand the technology and the operating systems that go on these platforms in the cloud, but we also have to understand the bespoke software that’s sitting on them and all the necessary dependencies for that.

The more that we invest into cloud technologies, the more complex the service that we offer our customers becomes. We have a multitude of management systems that can monitor all the different elements of this and then piece them together in a service-level model (SLM) perspective. So you get SLM and you get service assurance on top of that.

Gardner: We've recently  heard about HP's IDOL OnDemand and Vertica OnDemand, as part of the Haven OnDemand. They're bringing these analytics capabilities to cloud services, APIs as well. As I understand it, they're going to be applying them to more IT operations issues. So it’s quite possible that we'll start to see a mash up, if you will, between a cloud service, but also the underlying IT information associated with that service.

Let’s go back to Patrick at Steria. Any thoughts about where this combination of ITSM within a cloud environment develops? How do you see it going?

Bailly: The system today exists for traditional IT, and we also have to have the tooling for designing and consuming cloud services. We are running HP Service Manager for traditional IT, legacy IT, and we are running HP Cloud Service Automation (CSA) for managing and operating in the cloud.

We’d like to have a unique way for reconciling the catalog of services that are in Service Manager with the catalog of services that are in CSA, and we would need to have a single, unique portal for doing that.

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What we're expecting with HP Propel is to offer the capabilities to aggregate services that are coming from various sources and to extend that by also offering them. When we're serving this live, we need to offer some additional features like collaboration, incident management, access to the knowledge base, collaboration between service desk and end user, collaboration between end users, etc.

There's also another important point and that is service integration. As a service provider, we will have to deliver and control the services that are delivered by some partners and by some cloud service providers.

In order to do that, we need to have strong integration, not only partnership, but also strong integration. And that integration should be multiple point, meaning that, as soon as we're able to integrate a service provider with this, that integration will be de facto available for our other customers. We're expecting that from HP Propel.

And it’s not only an integration for provisioning service, but it’s also an integration for running the other processes, collaboration, incident management, etc.

Gardner: Patrick mentioned HP Propel, do any of you also have some experience with that or are looking at it to solve other problems?

Single view

Joubert: We're definitely looking at it to give a single view for all our end users. There are various supportive partners in the area where I work. The end user really wants one place to ask for fixing a broken light, to fixing a broken PC, to installing software. It's ease of use that they're looking for. So yes, we are definitely looking at Propel.

Gardner: Let’s take another look to the future. We've heard quite a bit about the Internet of Things (IoT) -- more devices, more inputs, and more data. Do you think that’s something that’s going to be an issue for ITSM, or is that something separate? Do you view that the infrastructure that’s being created for ITSM lends itself to something like managing the IoT and more devices on a network?

Kuijper: For me, as asset management experts and software asset management experts, we have to draw a line somewhere and say, "There is this IoT, and there is some data that we have to say we don’t want to analyze." There are things that are here on the Internet. That’s fine, but too much engineering around that might be over-killing the processes.

We also have to be very careful about false good ideas. I personally think that bring your own device (BYOD) is a false good idea. It brings tremendous issues with regards to who takes care of an asset that is personally owned by a person in a corporate environment, who deals with IT.

Today, it’s perfect. I bring the computer that I'm used to in the office. Tomorrow, it’s broken. Who is going to fix it? When I buy software for this machine, who is going to pay for it and who's going to be responsible for non-compliance?

We also have to be very careful about false good ideas. I personally think that bring your own device is a false good idea.

A CIO might think it’s very intelligent and very advanced to allow people to use what they're used to, but the legal issues behind it are quite complicated. I would say this is a false good idea.

Gardner: Edward, you mentioned that at Redcentric, scale doesn’t concern you. You're pretty confident that the systems that you can access can handle almost any scale. How about that IoT? Even if it shouldn’t be in the purview legally or in terms of the role of IT, it does seem like the systems that have been developed for ITSM are applicable to this issue. Any thoughts about more and more devices on a network?

Jackson: In terms of the scale of things, if the elements are in your control and you have some structure and management around them. You don’t need to be overly concerned. We certainly don’t keep anything in our systems their shouldn’t be in there or doesn’t need to be.

Going forward, things like big data and smart analytics layered on top would give us a massive benefit in how we could deliver our service, and more importantly, how we can manage the service.

Once you have your processes is in place, and can understand the necessity of those processes, you have the structure, and you have the kind of management platform that your sure is going to handle the data, then you can basically leverage things like big data, smart analytics, and data mining to enable you to offer a sophisticated level of support that perhaps your competitors can’t.

Esoteric activity

Gardner: It's occurred to me that the data and the management of that ITSM data is central to any of these major challenges, whether it’s big data, cloud service brokering, management of assets for legal or jurisdiction compliance. ITSM has become much more prominent, and is in the position to solve many more problems.

I'd like to end our conversation with your thoughts along those lines. Charl, ITSM, is it more important than ever? How has it become central?

Joubert: Absolutely. With the advent of big data, we suddenly have the tools to start mining this information and using it to our benefit to give better service to our end-users.

With the advent of big data, we suddenly have the tools to start mining this information and using it to our benefit to give better service to our end users.

Kuijper: ITSM is definitely core to any IT environment, because ITSM is the way to put the correct price tag behind a service. We have service charging and service costing. If you don’t do that correctly, then you basically don’t tell the truth to your customer or to your end user.

If you mix this with the IoT and the possibility to have anything with an IP address available on the network, then you enter into more philosophical thoughts. In a corporate environment, let’s assume you have a tag on your car keys that helps you to find them, and that is linked on the Internet. Those gizmos are happening today.

This brings some personal life information into your corporate environment. What does the corporate environment do about this? The brand of your car is on your car tag. They will know that you bought a brand new car. They will know all this information which is personal. So we have to think about ethics as well.

So drawing a line of what the corporate environment will take care and what is private will be essential in this IOT. When you have your mobile phone, is it personal, it is business? Drawing a line will be very important.

Gardner: But at least we will have the means to draw that line and then enforce the drawing of that line.

Kuijper: Right. Totally correct.

Gardner: Edward, the role of ITSM, bigger than ever or not so much?

Bigger than ever

Jackson: I think it’s bigger than ever. It’s the front end of your business, and the back-end of your business its what the customers see. It’s how you deliver your service, and if you haven’t got it right, then you are not going to be able to deliver the service that a customer expects.

You might have the best products in the world, but if your ITSM systems and your ITSM team aren’t doing what they're supposed to be doing then you know it’s not going to be any good, and the customers are going to say that.

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Gardner: And lastly to Steria, and Patrick, the role of ITSM, bigger than ever? How do you view it?

Bailly: For me, the role of IT Service Management (ITSM) won't change. We did ITSM in the past and we still continue to have that in the future. In order to deliver any service,  we need to have the detailed configuration of the service. We will have to run processes and not have the service change. What will change in the future is the diversity of service providers that we use.

As a service provider, we'll have to walk with a lot of other service providers. So the SLA will be more complex to manage for service management. It will be critical. For the customer, you will have to not only manage — but to govern — that service even if it is provided by lot of service providers.

Gardner: So the complexity goes up, and therefore the need to manage that complexity also needs to go up.

Bailly: What is also very important in license management in the cloud is that very often the return on investment (ROI) of the cloud adoption has ignored or minimized the impact of software cost. When you tell your customers, internal or external, that this xyz cloud offer will cost them that amount of money, you will most likely have to add up 20-30 percent because of the impact of the software cost afterward.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy. Sponsor: HP Enterprise.

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Rolta AdvizeX experts on hastening big data analytics in healthcare and retail

The next BriefingsDirect big data innovation case study interview highlights how Rolta AdvizeX in Independence, Ohio creates analytics-driven solutions in the healthcare and retail industries.

We'll also delve into how the right balance between open-source and commercial IT products helps in creating a big-data capability, and we'll further explore how converged infrastructure solutions are hastening the path to big-data business value and cloud deployment options.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy.

To learn more about how big data can be harnessed for analysis benefits in healthcare and retail, please join me in welcoming our guests, Dennis Faucher, Enterprise Architect at Rolta AdvizeX, and Raajan Narayanan, Data Scientist at Rolta AdvizeX. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Dennis, what makes big data so beneficial and so impactful for the healthcare and retail sectors?

Faucher

Faucher: What we're finding at Rolta AdvizeX is that our customers in healthcare and retail have always had a lot of data to make business decisions, but what they're finding now is that they want to make real-time decisions -- but they've never been able to do that. There was too much data, it took too long to process, and maybe the best they could do was get weekly or maybe monthly information to improve their businesses.

We're finding that the most successful healthcare and retail organizations are now making real-time decisions based upon the data that's coming in every second to their organization.

Gardner: So it's more, faster, and deeper, but is there anything specific about healthcare, for example? What are some top trends that are driving that?

Two sides of healthcare

Faucher: You have two sides of healthcare, even if it's a not-for-profit organization. Of course, they're looking for better care for their patients. In the research arms of hospitals, the research arms of pharmaceutical companies, and even on the payer side, the insurance companies, there is a lot of research being done into better healthcare for the patient, both to increase people's health, as well as to reduce long-term costs. So you have that side, which is better health for patients.

On the flip side, which is somewhat related to that, is how to provide customers with new services and new healthcare, which can be very, very expensive. How can they do that in a cost-effective manner?

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So it's either accessing research more cost-effectively or looking at their entire pipeline with big data to reduce cost, whether it's providing care or creating new drugs for their patients.

Gardner: And, of course, retail is such a dynamic industry right now. Things are changing very rapidly. They're probably interested in knowing what's going on as soon as possible, maybe even starting to get proactive in terms of what they can anticipate in order to solve their issues.

Faucher: There are also two sides to retail as well. One is the traditional question of, How can I replenish my outlets in real time? How can I get product to the shelf before it runs out? Then, there's also the traditional side of the cross-sell, up-sell, and what am I selling in a shopping cart, to try to get the best mix of products within a shopping cart that will maximize my profitability for each customer.

Those are the types of decisions our customers in retail have been making for the last 30-50 years, but now they have even more data to help them with that. It's not just the typical sales data that they're getting from the registers or from online, but now we can go into social media as well and get sentiment analysis for customers to see what products they're really interested in to help with stocking those shelves, either the virtual shelves or the physical shelves.

So it's either accessing research more cost-effectively or looking at their entire pipeline with big data to reduce cost.

The second side, besides just merchandising and that market-basket analysis, is new channels for consumers. What are the new channels? If I'm a traditional brick-and-mortar retailer, what are the new channels that I want to get into to expand my customer base, rather than just the person who can physically walk in, but across many, many channels?

There are so many channels now that retailers can sell to. There is, of course, their online store, but there may be some unique channels, like Twitter and Facebook adding a "buy" button. Maybe they can place products within a virtual environment, within a game, for customers to buy. There are many different areas to add channels for purchase and to be able to find out real-time what are people buying, where they're buying, and also what they're likely to buy. Big data really helps with those areas in retail.

Gardner: Raajan, there are clearly some compelling reasons for looking at just these two specific vertical industries to get better data and be more data-driven. The desire must be there, even the cost efficiencies are more compelling than just a few years ago. What’s the hurdle? What prevents them from getting to this goal of proactive, and to the insights that Dennis just described?

Main challenge

Narayanan: One of the main challenges that organizations have is to use the current infrastructure for analytics. The three Vs: velocity, variety and the volume of data serve up a few challenges for organizations in terms of how much data I can store, where do I store it, and do I have the current infrastructure to do that?

Narayanan

In a traditional business, versus the new flash areas, how do you best access the data? How fast you need to access the data is one of the challenges that organizations have.

In addition, there are lots of analytics tools out there. The ecosystem is growing by the day. There are a few hundred offerings out there and they are all excellent platforms to use. So the choice of what kind of analytics I need for the set purpose is the bigger challenge. To identify the right tool and the right platform that would serve my organization needs would be one of the challenges.

The third challenge would be to have the workforce or the expertise to build these analytics or have organizations to address these challenges from an analytical standpoint. This is one of the key challenges that organizations have.

Gardner: Dennis, as an enterprise architect at Rolta AdvizeX, you must work with clients who come at this data issue compartmentalized. Perhaps marketing did it one way; R and D did it another; supply chain and internal business operations may have done it a different way. But it seems to me that we need to find more of a general, comprehensive approach to big data analytics that would apply to all of those organizations.

We work with a company, look at everything they're doing, and set a roadmap for the next three years to meet their short-term and long-term goals.

Is there some of that going on, where people are looking not just for a one-off solution different for each facet of their company, but perhaps something more comprehensive, particularly as we think about more volume coming with the Internet of Things (IoT) and more data coming in through more mobile use? How do we get people to think about big-data infrastructure, rather than big-data applications?

Faucher: There are so many solutions around data analytics, business intelligence (BI), big data, and data warehouse. Many of them work, and our customers unfortunately have many of them and they have created these silos of information, where they really aren’t getting the benefits that they had hoped for.

What we're doing with customers from an enterprise architecture standpoint is looking at the organization holistically. We have a process called Advizer, where we work with a company, look at everything they're doing, and set a roadmap for the next three years to meet their short-term and long-term goals.

And what we find when we do our interviews with the business people and the IT people at companies is that their goals as an organization are pretty clear, because they've been set by the head of the organization, either the CEO or the chief scientist, or the chief medical director in healthcare. They have very clear goals, but IT is not aligned to those goals and it’s not aligned holistically.


Not organized

There could be skunk works that are bringing up some big-data initiatives. There could be some corporate-sponsored big data, but they're just not organized. All it takes is for us to get the business owners and the IT owners in a room for a few hours to a few days, where we can all agree on that single path to meet all needs, to simplify their big data initiatives, but also get the time to value much faster.

That’s been very helpful to our customers, to have an organization like Rolta AdvizeX come in as an impartial third-party and facilitate the coming together of business and IT. Many times, as short as a month, we have the three-year strategy that they need to realize the benefits of big data for their organization.

Gardner: Dennis, please take a moment to tell us a little bit more about AdvizeX and Rolta.

We don’t lead with products. We develop solutions and strategy for our customers.

Faucher: Rolta AdviseX, is an international systems integrator. Our US headquarters is in Independence, Ohio, just outside of Cleveland. Our international headquarters are in Mumbai, India.

As a systems integrator, we lead with our consultants and our technologists to build solutions for our customers. We don’t lead with products. We develop solutions and strategy for our customers.

There are four areas where we find our customers get the greatest value from Rolta AdvizeX. At the highest level are our advisory services, which I mentioned, which set a three-year roadmap for areas like big data, mobility, or cloud.

The second area is the application side. We have very strong application people at any level for Microsoft, SAP, and Oracle. We've been helping customers for years in those areas.

The third of the four areas is infrastructure. As our customers are looking to simplify and automate their private cloud, as well as to go to public cloud and software as a service (SaaS), how do they integrate all of that, automate it, and make sure they're meeting compliance.

The fourth area, which has provided a lot of value for our customers, is managed services. How do I expand my IT organization to a 7x24 organization when I'm really not allowed to hire more staff? What if I could have some external resources taking my organization from a single shift to three shifts, managing my IT 7x24, making sure it’s secure, making sure it’s patched, and making sure it’s reliable?

Those are the four major areas that we deliver as a systems integrator for our customers.

Data scientists

Gardner: Raajan, we've heard from Dennis about how to look at this from an enterprise architecture perspective, taking the bigger picture into account, but what about data scientists? I hear frequently in big data discussions that companies, in this case in healthcare and retail, need to bring that data scientist function into their organizations more fully. This isn't to put down the data analysts or business analysts. What is it about being a data scientist that is now so important? Why, at this point, would you want to have data scientists in your organization?

Narayanan: One of the key functions of a data scientist is to be able to look at data proactively. In a traditional sense, a data analyst's job is reflective. They look at transactional data in a traditional manner, which is quite reflective. Bringing in a data scientist or a data-scientist function can help you build predictive models on existing data. You need a lot of statistical modeling and a lot of the other statistical tools that will help you get there.

This function has been in organizations for a while, but it’s more formalized these days. You need a data scientist in an organization to perform more of the predictive functions than the traditional reporting functions.

We're seeing that in the open-source, big-data tools as well. Customers have embraced open-source big-data tools rapidly.

Gardner: So, we've established that big data is important. It’s huge for certain verticals, healthcare and retail among them. Organizations want to get to it fast. They should be thinking generally, for the long term. They should be thinking about larger volumes and more velocity, and they need to start thinking as data scientists in order to get out in front of trends rather than be reactive to them.

So with that, Dennis, what’s the role of open source when one is thinking about that architecture and that platform? As a systems integrator and as enterprise architect, what do you see as the relationship between going to open source and taking advantage of that, which many organizations I know are doing, but also looking at how to get the best results quickly for the best overall value? Where does the rubber hit the road best with open source versus commercial?

Faucher: That’s an excellent question and one that many of our customers have been grappling with as there are so many fantastic open-source, big-data platforms out there that were written by Yahoo, Facebook, and Google for their own use, yet written open source for anyone to use.

I see a little bit of an analogy to Linux back in 1993, when it really started to hit the market. Linux was a free alternative to Unix. Customers were embracing it rapidly trying to figure out how it could fit in, because Linux had a much different cost model than proprietary Unix.

We're seeing that in the open-source, big-data tools as well. Customers have embraced open-source big-data tools rapidly. These tools are free, but just like Linux back then, the tools are coming out without established support organizations. Red Hat emerged to support the Linux open-source world and say that they would help support you, answer your phone calls, and hold your hand if you needed help.

Now we're seeing who are going to be the corporate sponsors of some of these open-source big data tools for customers who may not have thousands of engineers on staff to support open source. Open-source tools definitely have their place. They're very good for storing the reams and reams, terabytes, petabytes, and more of data out there, and to search in a batch manner, not real time, as I was speaking about before.

Real-time analytics

Some of our customers are looking for real-time analytics, not just batch. In batch, you ask a question and will get the answer back eventually, which many of the open-source, big-data tools are really meant for. How can I store a lot of data inexpensively that I may need access to at some point?

We're seeing that our customers have this mix of open-source, big-data tools, as well as commercial big-data tools.

I recently participated in a customer panel where some of the largest dot-coms talked about what they're doing with open source versus commercial tools. They were saying that the open-source tools was where they may have stored their data lake, but they were using commercial tools to access that data in real time.

They were saying that if you need real-time access, you need a big-data tool that takes in data in parallel and also retrieves it in a parallel manner, and the best tools to do that are still in the commercial realm. So they have both open source for storage and closed source for retrieval to get the real-time answers that they need to run their business.

Gardner: And are there any particular platforms on the commercial side that you're working with, particularly on that streaming, real-time, at volume, at scale equation?

Learn more about Rolta AdvizeX Solutions
For the Retail Industry
And for Healthcare Companies

Faucher: What we see on our side with the partners that we work with is that HP Vertica is the king of that parallel query. It’s extremely fast to get data in and get data out, as well as it was built on columnar, which is a different way to store data than relational is. It was really meant to get those unexpected queries. Who knows what the query is going to be? Whatever it is, we'll be able to respond to it.

Another very popular platform has been SAP HANA, mostly for our SAP customers who need an in-memory columnar database to get real-time data access information. Raajan works with these tools on a daily basis and can probably provide more detail on that, as well as some of the customer examples that we've had.

Gardner: Raajan, please, if you have some insight into what’s working in these verticals and any examples of how organizations are getting their big data payoff, I'd be very curious to hear that.

Narayanan: One of the biggest challenges is to be able to discover the data in the shortest amount of time, and I mean discovery in the sense that I get data into the systems, and how fast I can get some meaningful insights.

Works two ways

It works two ways. One is to get the data into the system, aggregate it into your current environment, transform it so that data is harmonious across all the data sources that provide it, and then also to provide analytics over that.

In a traditional sense, I'll collect tons and tons of data. It goes through reams and reams of storage. Do I need all that data? That's the question that has to be answered. Data discovery is becoming a science as we speak. When I get the data, I need to see if this data is useful, and if so, how do I process it.

These systems, as Dennis alluded to, Vertica and SAP HANA, enable that data discovery right from the get-go. When I get data in, I can just write simple queries. I don't need a new form of analytic expertise. I can use traditional SQL to query on this data. Once I've done that, then if I find the data useful, I can send it into storage and do a little bit more robust analytics over that, which can be predictive or reporting in nature.

A few customers see a lot of value in data discovery. The whole equation of getting in Hadoop as a data lake is fantastic, and these platforms play very well with the Hadoop technologies out there.

Once you get data into these platforms, they provide analytic capabilities that go above and beyond what a lot of the open-source platforms provide.

Once you get data into these platforms, they provide analytic capabilities that go above and beyond what a lot of the open-source platforms provide. I'm not saying that open source platforms don’t perform these functions, but there are lots of tools out there that you need to line up in sequence for them to perform what Vertica or SAP HANA will do. The use cases are pretty different, but nevertheless, these platforms actually enable lot of these functions.

Gardner: Raajan, earlier in our discussion you mentioned the importance of skills and being able to hire enough people to do the job. Is that also an issue in making a decision between an open-source and a commercial approach?

Narayanan: Absolutely. With open source, there are a lot of code bases out there that needs to be learned. So there is a learning curve within organizations.

Traditionally, organizations rely more on the reporting function. So they have a lot of the SQL functions within the organization. To retrain them is something that an organization would have to think about. Then, even to staff for new technologies is something that an organization would have to cater for in the future. So it’s something that an organization would have to plan in their roadmap for big-data growth.

Gardner: Dennis, we can back at the speed and value and getting your big data apparatus up and running, perhaps think about it holistically across multiple departments in your organization, and anticipate even larger scale over time, necessitating a path to growth. Tell us a little bit about what's going on in the market with converged infrastructure, where we're looking at very tight integration between hardware and software, between servers that are supporting workloads, usually virtualized, as well as storage also usually virtualized.

For big data, the storage equation is not trivial. It’s an integral part of being able to deliver those performance requirements and key performance indicators (KPIs). Tell us a bit about why converged infrastructure makes sense and where you're seeing it deployed?

Three options

Faucher: What we're seeing with our customers in 2015 is that they have three options for where to run their applications. They have what we call best-of-breed, which is what they've done forever. They buy some servers from someone, some storage from someone else, some networking from someone else, and some software from someone else. They put it together, and it’s very time-consuming to implement it and support it.

They also have the option of going converged, which is buying the entire stack -- the server, the storage, and the networking -- from a single organization, which will both factory integrate it, load their software for them, show up, plug it in, and you are in production in less than 30 days.

The third option, of course, is going to cloud, whether that’s infrastructure as a service (IaaS) or SaaS, which can also provide quick time to value.

For most of our customers now, there are certain workloads that they are just not ready to run in IaaS or SaaS, either because of cost, security, or compliance reasons. For those workloads that they have decided are not ready for Saas, IaaS, or platform as a service (PaaS) yet, they need to put something in their own data center. About 90 percent of the time, they're going with converged.

Our customers’ data centers are getting so much bigger and more complex that they just cannot maintain all of the moving parts.

Beside the fact that it’s faster to implement, and easier to support, our customers’ data centers are getting so much bigger and more complex that they just cannot maintain all of the moving parts. Thousands of virtual machines and hundreds of servers and all the patching needs to happen, and keeping track of interoperability between server A, network B, and storage C. The converged takes that all away from them and just pushes it to the organizations they bought it from.

Now, they can just focus on their application and their users which is what they always wanted to focus on and not have to focus on the infrastructure and keeping the infrastructure running.

So converged infrastructure has really taken off very, very quickly with our customers. I would say even faster than I would have expected. So it's either converged -- they're buying servers and storage and networking from one company, which both pre-installs it at a factory and maintains it long-term -- or hyper-converged, where all of the server and storage and networking is actually done in software on industry-standard hardware.

For private cloud, a large majority of our customers are going with converged for the pieces that are not going to public cloud.

Gardner: So 90 percent; that’s pretty impressive. I'm curious if that’s the rate of adoption for converged, what sort of rate of adoption are you seeing on the hyper-converged side where it’s as you say software-defined throughout?

Looking at hyper-converged

Faucher: It’s interesting. All of our customers are looking at hyper-converged right now to figure out where it is it fits for them. The thing about hyper-converged, where it’s just industry standard servers that I'm virtualizing for my servers and storage and networking, is where does hyper-converged fit? Sometimes, it definitely has a much lower entry point. So they'll look at it and say, "Is that right for my tier-1 data center? Maybe I need something that starts bigger and scales bigger in my tier-1 data center."

Hyper-converged may be a better fit for tier-2 data centers, or possibly in remote locations. Maybe in doctor's offices or my remote retail branches, they go with hyper-converged, which is a smaller unit, but also very easy to support, which is great for those remote locations.

You also have to think that hyper-converged, although very easy to procure and deploy, when you grow it, you only grow it in one size block. It’s like this block that can run 200 virtual machines, but when I add, I have to add 200 at a time, versus a smaller granularity.

So it’s important to make the correct decision. We spend a lot of time with our customers helping them figure out the right strategy. If we've decided that converged is right, is it converged or is it hyper-converged for the application? Now, as I said, it typically breaks down to for those tier 1 data centers it’s converged, but for those tier 2 data centers or those remote locations, it’s more likely hyper-converged.

But some of the vendors that provide cloud, hyper-converged and converged, have come up with some great solutions for rapid scalability.

Gardner: Again, putting on your enterprise architect hat, given that we have many times unpredictable loads on that volume and even velocity for big data, is there an added value, a benefit, of going converged and perhaps ultimately hyper-converged in terms of adapting to demand or being fit for purpose, trying to anticipate growth, but not have to put too much capital upfront and perhaps miss where the hockey puck is going to be type of thinking?

What is it about converged and hyper-converged that allow us to adapt to the IoT trend in healthcare, in retail, where traditional architecture, traditional siloed approaches would maybe handicap us?

Faucher: For some of these workloads, we just don’t know how they're going to scale or how quickly. We see that specifically with new applications. Maybe we're trying a new channel, possibly a new retail channel, and we don’t know how it’s going to scale. Of course, we don’t want to fail by not scaling high enough and turning our customers away.

But some of the vendors that provide cloud, hyper-converged and converged, have come up with some great solutions for rapid scalability. A successful solution for our customers has been something called flexible capacity. That’s where you've decided to go private cloud instead of public for some good reasons, but you wish that your private cloud could scale as rapidly as the public cloud, and also that your payments for your private cloud could scale just like a public cloud could.

Typically, when customers purchase for a private cloud, they're doing a traditional capital expense. So they just spend the money when they have it, and maybe in three or five years they spend more. Or they do a lease payment and they have a certain lease payment every month.

With flexible capacity, I can have more installed in my private cloud than I'm paying for. Let’s say, there is 100 percent there, but I'm only paying for 80 percent. That way, if there's an unexpected demand for whatever reason, I can turn on another 5, 10, 15, or 20 percent immediately without having to issue a PO first, which might takes 60 days in my organization, then place the order, wait 30 days for more to show up, and then meet the demand.

Flexible capacity

Now I can have more on site than I'm paying for, and when I need it I just turn it on and I pay a bill, just like I would if I were running in the public cloud. That’s what is called flexible capacity.

Another options is the ability to do cloud bursting. Let’s say I'm okay with public cloud for certain application workloads -- IaaS, for example -- but what I found is that I have a very efficient private cloud and I can actually run much more cost-effectively in my private cloud than I can in public, but I'm okay with public cloud in certain situations.

Well, if a burst comes, I can actually extend my application beyond private to public to take on this new workload. Then, I can place an order to expand my private cloud andwait for the new backing equipment to show up. That takes maybe 30 days. When it shows up, I set it up, I expand my on-site capability and then I just turn off the public cloud.

The most expensive use of public cloud many times is just turning it on and never turning it off. It’s really most cost-effective for short-term utilization, whether it’s new applications or development or disaster recovery (DR). Those are the most cost-effective fuses of public cloud.

Gardner: As a data scientist, you're probably more concerned with what the systems are doing and how they are doing it, but is there a benefit from your perspective of going with converged infrastructure or hyper-converged infrastructure solutions? Whether it’s bursting or reacting to a market demand within your organization, what is it about converged infrastructure that’s attractive for you as a data scientist?

One of the biggest challenges would be to have a system that will allow an organization to go to market soonest.

Narayanan: One of the biggest challenges would be to have a system that will allow an organization to go to market soonest. With the big-data platform, there are lots of moving parts in terms of network. In a traditional Hadoop technology, there are like three copies of data, and you need to scale that across various systems so that you have high availability. Big-data organizations that are engaging big data are looking at high availability as one of the key requirements, which means that anytime a node goes down, you need to have the data available for analysis and query.

From a data scientist standpoint, stability or the availability of data is a key requirement. The data scientists, when they build your models and analytic views, are churning through tons and tons of data, and it requires tremendous system horsepower and also network capabilities that pulls data from various sources.

Learn more about Rolta AdvizeX Solutions
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With the converged infrastructure, you get that advantage. Everything is in a single box. You have it just out there, and it is very scalable. For a data scientist, it’s like a dream come true for the analytic needs. 

Gardner: I'm afraid we are coming up towards the end of our time. Let’s look at metrics of success. How do you know you are doing this well? Do you have any examples, Dennis or Raajan, of organizations that have thought about the platform, the right relationship between commercial and open source, that have examined their options on deployment models, including converged and hyper-converged, and what is it that they get back? How would you know that you are doing this right? Any thoughts about these business or technology metrics of success?

New application

Faucher: I have a quick one that I see all the time. Our customers today measure how long it takes to get a new business application out the door. Almost every one of our customers has a measurement around that. How quickly can we get a business application out the door and functional, so that we can act upon it?

Most of the time it can be three months or six months, yet they really want to get these new applications out the door in a week, just constant improvement to their applications to help either their patients or to help their customers out or get into new channels.

What we're finding is they already have a metric that says, today it takes us three months to get a new application out the door. Let’s change that. Let’s really look at the way we are doing things -- people, process and IT end-to-end -- typically where they are helped through something like an Advizer, and let’s look at all the pieces of the process, look at it all from an ITIL standpoint or an ITSM standpoint and ask how can we improve the process.

There are tons of data sources out there. The biggest challenge would be to integrate all that in the fastest amount of time and make sure that value is realized at the soonest.

And then let’s implement the solution and measure it. Let’s have constant improvement to take that three months down to one month, and down to possibly one week, if it’s a standardized enough application.

So for me, from a business standpoint, it’s the fastest time to value for new applications, new research, how quickly can I get those out the door better than I am doing today.

Narayanan: From a technical standpoint Dana, it’s how much data I can aggregate at the fastest. There are tons of data sources out there. The biggest challenge would be to integrate all that in the fastest amount of time and make sure that value is realized at the soonest. With the given platform, any platform that allows for that would definitely serve the purpose for the analytic needs.

Gardner: Listening to you both, it almost sounds as if you're taking what you can do with big data analytics and applying it to how you do big data analytics, is there some of that going on?

Faucher: Absolutely. It’s interesting, when we go out and meet with customers, when we do workshops and gather data from our customers, even when we do Advizers and we capture data from our customers, we use that. We take all identifying customer information out of it, but we use that to help our customers by saying that of the 2,000 customers that we do business with every year, this is what we are seeing. With these other customers, this is where we have seen them be successful, and we use that data to be able to help our customers be more successful faster.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy. Sponsor: HP.

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The future of business intelligence as a service with GoodData and HP Vertica

The next BriefingsDirect big data innovation 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 -- all in the cloud.

Listen to the podcast. Find it on iTunes. Get the mobile app for iOS or Android. Read a full transcript or download a copy.

To learn the ups and downs of BIaaS, we welcome Jeff Morris, Vice President of Marketing at GoodData in San Francisco, and Chris Selland, Vice President for Business Development at HP Vertica. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: 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: You're creating analytic applications on datasets that are easily contributed to your platform.

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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 providing an excellent example of how HP Vertica forms a cloud-borne analytics platform. Are any of your clients doing this both on-premises 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?

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: There's more than just HP Vertica. There's HP 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?

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: Do you have any companies, either named or unnamed, that provide a great use case example of BI as a service?

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: What's coming next?

Morris: 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.

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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.

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