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Longtime IT industry analyst Dana Gardner is a creative thought leader on enterprise software, SOA, cloud-based strategies, and IT architecture strategies. He is a prolific blogger, podcaster and Twitterer. Follow him at


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How fast analytics changes the game and expands the market for big data value

Posted By Dana L Gardner, Monday, October 05, 2015

The next BriefingsDirect big-data thought leadership discussion highlights how fast analytics -- or getting to a big data analysis value in far less time than before -- expands the market for advanced data infrastructure to gain business insights.

We'll learn how bringing analytics to a cloud services model also allows smaller and less data-architecture-experienced firms to benefit from the latest in big-data capabilities. And we'll explore how Dasher Technologies is helping to usher in this democratization of big data value to more players in less time.

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

To share how a fast ramp-up for big data as a service has evolved, we're joined by Justin Harrigan, Data Architecture Strategist at Dasher Technologies, as well as Chris Saso, Senior Vice President of Technology at Dasher Technologies in Campbell, California. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Justin, how have big-data practices changed over the past five years to set the stage for rapid leveraging of big-data capabilities?

Harrigan: Back in 2010, we saw big data become mainstream. Hadoop became a household name in the IT industry, doing scale-out architectures. Linux databases were becoming common practice. Moving away from traditional legacy, smaller, slower databases allowed this whole new world of analytics to open up to previously untapped resources within companies. So data that people had just been sitting on could now be used for actionable insights.


Fast forward to 2015, and we've seen big data become more approachable. Five years ago, only the largest organizations or companies that were specifically designed to leverage big-data architectures could do so. The smaller guys had maybe a couple of hundred or even tens of terabytes, and it required too much expertise or too much time and investment to get a big-data infrastructure up and running.

Today, we have approachable analytics, analytics as a service, hardened architectures that are almost turnkey with back-end hardware, database support, and applications -- all integrating seamlessly. As a result, the user on the front end, who is actually interacting with the data and making insights, is able to do so with very little overhead, very little upkeep, and is able to turn that data into business-impact data, where they can make decisions for the company.

Gardner: Justin, how big of an impact has this had? How many more types of companies or verticals have been enabled to start exploring advanced, cutting-edge, big-data capabilities? Is this a 20 percent increase? Perhaps almost any organization that wants to can start doing this.

Tipping point

Harrigan: The tipping point is when you outgrow your current solutions for data analytics. Data analytics is nothing new. We've been doing it for more than 50 years with databases. It’s just a matter of how big you can get, how much data you can put in one spot, and then run some sort of query against it and get a timely report that doesn’t take a week to come back or that doesn't time out on a traditional database.


Almost every company nowadays is growing so rapidly with the type of data they have. It doesn’t matter if you're an architecture firm, a marketing company, or a large enterprise getting information from all your smaller remote sites, everyone is compiling data to create better business decisions or create a system that makes their products run faster.

For people dipping their toes in the water for their first larger dataset analytics, there's a whole host of avenues available to them. They can go to some online providers, scale up a database in a couple of minutes, and be running.

They can download free trials. HP Vertica has a community edition, for example, and they can load it on a single server, up to terabytes, and start running there. And it’s significantly faster than traditional SQL.

It’s much more approachable. There are many different flavors and formats to start with, and people are realizing that. I wouldn’t even use the term big data anymore; big data is almost the norm.

Gardner: I suppose maybe the better term is any data, anytime.

Harrigan: Any data, anytime, anywhere, for anybody.

Gardner: I suppose another change over the past several years has been an emphasis away from batch processing, where you might do things at an infrequent or occasional basis, to this concept that’s more applicable to a cloud or an as-a-service model, where it’s streaming, continuous, and then you start reducing the latency down to getting close to real time.

Are we starting to see more and more companies being able to compress their feedback, and start to use data more rapidly as a result of this shift over the past five years or so?

Harrigan: It’s important to address the term big data. It’s almost like an umbrella, almost like the way people use cloud. With big data, you think large datasets, but you mentioned speed and agility. The ability to have real-time analytics is something that's becoming more prevalent and the ability to not just run a batch process for 18 hours on petabytes of data, but having a chart or a graph or some sort of report in real time. Interacting with it and making decisions on the spot is becoming mainstream.

We did a blog post on this not long ago, talking about how instead of big data, we should talk about the data pipe. That’s data ingest or fast data, typically OLTP data, that needs to run in memory or on hardware that's extremely fast to create a data stream that can ingest all the different points, sensors, or machine data that’s coming in.

Smarter analysis

Then we've talked about smarter analytic data that required some sort of number-crunching dataset on data that was relevant, not data that was real-time, but still fairly new, call it seven days or older and up to a year. And then, there's the data lake, which essentially is your data repository for historical data crunching.

Those are three areas you need to address when you talk about big data. The ability to consume that data as a service is now being made available by a whole host of companies in very different niches.

It doesn’t matter if it’s log data or sensor data, there's probably a service you can enable to start having data come in, ingest it, and make real-time decisions without having to stand up your own infrastructure.

Gardner: Of course, when organizations try to do more of these advanced things that can be so beneficial to their business, they have to take into consideration the technology, their skills, their culture -- people, process and technology, right?

Chris, tell us a bit about Dasher Technologies and how you're helping organizations do more with big-data capabilities, how you address this holistically, and this whole approach of people, process and technology.

Dasher has built up our team to be able to have a set of solutions that can help people solve these kinds of problems.

Saso: Dasher was founded in 1999 by Laurie Dasher. To give you an idea of who we are, we're a little over 65 employees now, and the size of our business is somewhere around $100 million.

We started by specializing in solving major data-center infrastructure challenges that folks had by actually applying the people, process and technology mantra. We started in the data center, addressing people’s scale out, server, storage, and networking types of problems. Over the past five or six years, we've been spending our energy, strategy, and time on the big areas around mobility, security, and of course, big data.

As a matter of fact, Justin and I were recently working on a project with a client around combining both mobility information and big data. It’s a retail client. They want to be able to send information to a customer that might be walking through a store, maybe send a coupon or things like that. So, as Justin was just talking about, you need fast information and making actionable things happen with that data quickly. You're combining something around mobility with big data.

Dasher has built up our team to be able to have a set of solutions that can help people solve these kinds of problems.

Gardner: Justin, let’s flesh that out a little bit around mobility. When people are using a mobile device, they're creating data that, through apps, can be shared back to a carrier, as well as application hosts and the application writers. So we have streams of data now about user experience and activities.

We also can deliver data and insights out to people in the other direction in that real-time of fashion, a closed loop, regardless of where they are. They don’t have to be at their desk, they don’t have to be looking at a specific business-intelligence (BI) application for example. So how has mobility changed the game in the past five years?

Capturing data

Harrigan: Dana, it’s funny you brought up the two different ways to capture data. Devices can be both used as a sensor point or as a way to interact with data. I remember seeing a podcast you did with HP Vertica and GUESS regarding how they interacted with their database on iPads.

In regards to interacting with data, it has become not only useful to data analysts or data scientists, but we can push that down into a format so lower-level folks who aren't so technical. With a fancy application in front of them, they can use the data as well to make decisions for companies and actually benefit the company.

You give that data to someone in a store, at GUESS for example, who can benefit by understanding where in the store to put jeans to impact sales. That’s huge. Rather than giving them a quarterly report and stuff that's outdated for the season, they can do it that same day and see what other sites are doing.

On the flip side, mobile devices are now sensors. A mobile device is constantly pinging access points over wi-fi. We can capture that data and, through a MAC address as an unique identifier, follow someone as they move through a store or throughout a city. Then, when they return, that person’s data is captured into a database and it becomes historical. They can track them through their device.

Read more on tackling big data analytics
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It allows a whole new world of opportunities in terms of the way retailers interact with where they place merchandise, the way they interact with how they staff stores to make sure they have the proper amount of people for the certain time, what weather impact has on the store.

Lastly, as Chris mentioned, how do we interact with people on devices by pushing them data that's relevant as they move throughout their day?

The next generation of big data is not just capturing data and using it in reports, but taking that data in real time and possibly pushing it back out to the person who needs it most. In the retail scenario, that's the end users, possibly giving them a coupon as they're standing in front of something on a shelf that is relevant and something they will use.

Gardner: So we're not just talking about democratization of analytics in terms of the types of organizations, but now we're even talking about the types of individuals within those organizations.

Do you have any examples of some Dasher’s clients that have been able to exploit these advances and occurrences with mobile and cloud working in tandem, and how that's produced some sort of a business benefit?

Business impact

Harrigan: A good example of a client who leveraged a large dataset is One Kings Lane. They were having difficulty updating the website their users were interacting with because it’s a flash shopping website, where the information changes daily, and you have to be able to update it very quickly. Traditional technologies were causing a business impact and slowing things down.

They were able to leverage a really fast columnar database to make these changes and actually grow the inventory, grow the site, and have updates happen in almost real time, so that there was no impact or downtime when they needed to make these changes. That's a real-world example of when big data had the direct impact on the business line.

Gardner: Chris, tell us a little bit about how Dasher works with Hewlett Packard Enterprise technologies, and perhaps even some other HP partners like GoodData, when it comes to providing analytics as a service?

Once Vertica . . . has done the analysis, you have to report on that and make it in a nice human-readable form or human-consumable form.

Saso: HP has been a longtime partner from the very beginning, actually when we started the company. We were a partner of Vertica before HP purchased them back in 2011.

We started working with Vertica around big data, and Justin was one of our leads in that area at the time. We've grown that business and in other business units within HP to combine solutions, Vertica, big data, and hardware, as Justin was just talking about. You brought up the applications that are analyzing this big data. So we're partners in the ecosystem that help people analyze the data.

Once HP Vertica, or what have you, has done the analysis, you have to report on that and make it in a nice human-readable form or human-consumable form. We’ve built out our ecosystem at Dasher to have not only the analytics piece, but also the reporting piece.

Gardner: And on the as a service side, do you work with GoodData at all or are you familiar with them?

Saso: Justin, maybe you can talk a little bit about that. You've worked with them more I think on their projects.

Optimizing the environment

Harrigan: GoodData is a large consumer of Vertica and they actually leverage it for their back-end analytics platform for the service that they offer. Dasher has been working with GoodData over the past year to optimize the environment that they run on.

Vertica has different deployment scenarios, and you can actually deploy it in a virtual-machine (VM) environment or on bare-metal. And we did an analysis to see if there was a return on investment (ROI) on moving from a virtualized environment running on OpenStack to a bare-metal environment. Through a six-month proof of concept (POC), we leveraged HP Labs in Houston. We had a four-node system setup with multiple terabytes of data.

We saw 4:1 increase in performance in moving from a VM with the same resources to a bare-metal machine. That’s going to have a significant impact on the way they move data in their environment in the future and how they adjust to customers with larger datasets.

Gardner: When we think about optimizing the architecture and environment for big data, are there any other surprises or perhaps counter-intuitive things that have come up, maybe even converged infrastructure for smaller organizations that want to get in fast and don’t want to be too concerned with the architecture underlying the analytics applications?

That’s going to have a significant impact on the way they move data in their environment in the future and how they adjust to customers with larger datasets.

Harrigan: There's a tendency now with so many free solutions out there to pick a free solution, something that gets the job done now, something that grows the business rapidly, but to forget about what businesses will need three years down the road, if it's going to grow, if it’s going to survive.

There are a lot of startups out there that are able to build a big data infrastructure, scale it to 5,000 nodes, and then they reach a limit. There are network limits on how fast the switch can move data between nodes, constantly pushing the limits of 10 Gbyte, 40 Gyte and soon 100 Gbyte networks to keep those infrastructures up.

Depending on what architecture you choose, you may be limited in the number of nodes you can go to. So there are solutions out there that can process a million transactions per second with 100 nodes, and then there are solutions that can process a million transactions per second with 20 nodes, but may cost slightly more.

If you think long-term, if you start in the cloud, you want to be able to move out of the cloud. If you start with an open ecosystem, you want to make sure that your hardware refresh is not going to cost so much that the company can’t afford it three years down the road. One of the areas we help consult with, when picking different architectures, is thinking long-term. Don't think six weeks down the road, how are we going to get our service up and running? Think, okay, we have a significant client install base, how we are going to grow the business from three to five years and five to 10 years?

Gardner: Given that you have quite a few different types of clients, and the idea of optimizing architecture for the long-term seems to be important, I know with smaller companies there’s that temptation to just run with whatever you get going quickly.

What other lessons can we learn from that long-term view when it comes to skills, security, something more than the speeds and feeds aspects of thinking long term about big data?

Numerous regulations

Harrigan: Think about where your data is going to reside and the requirements and regulations that you may run into. There are a million different regulations we have to do now with HIPAA, ITAR, and money transaction processes in a company. So if you ever perceive that need, make sure you're in an ecosystem that supports it. The temptation for smaller companies is just to go cloud, but who owns that data if you go under, or who owns that data when you get audited?

Another problem is encryption. If you're going to start gaining larger customers once you have a proven technology or a proven service, they're going to want to make sure that you're compliant for all their regulations, not just your regulations that your company is enforcing.

There's logging that they're required to have, and there is going to be encryption and protocols and the ability to do audits on anyone who is accessing the data.

Gardner: On this topic of optimizing, when you do it right, when you think about the long term, how do you know you have that right? Are there some metrics of success? Are there some key performance indicators (KPIs) or ROIs that one should look to so they know that they're not erring on the side of going too commercial or too open source or thinking short term only? Maybe some examples of what one should be looking for and how to measure that.

If you implement a system and it costs you $10 million to run and your ROI is $5 million, you've made a bad decision.

Harrigan: That’s going to be largely subjective to each business. Obviously if you're just going to use a rule of thumb, it shouldn't cost you more money than it makes you. If you implement a system and it costs you $10 million to run and your ROI is $5 million, you've made a bad decision.

The two factors are the value to the business. If you're a large enterprise and you implement big data, and it gives you the ability to make decisions and quantify those decisions, then you can put a number to that and see how much value that big-data system is creating. For example, a new marketing campaign or something you're doing with your remote sites or your retail branches and it’s quantifiable and it’s having an impact on the business.

The other way to judge it is impact on business. So, for ad serving companies, the way they make money is ad impressions, and the more ad impressions they can view, for the least cost in their environment, the higher return they're going to make. The delta is between the infrastructure costs and the top line that they get to report to all their investors.

If they can do 56 billion ad impressions in a day, and you can double that by switching architectures, that’s probably a good investment. But if you can only improve it by 10 percent by switching architectures, it’s probably too much work for what it’s worth.

Read more on tackling big data analytics
Learn how the future is all about fast data
Find out how big data trends affect your business

Gardner: One last area on this optimization idea. We've seen, of course, organizations subjectively make decisions about whether to do this on-premises, maybe either virtualized or on bare metal. They will do their cost-benefit analysis. Others are looking at cloud and as a service model.

Over time, we expect to have a hybrid capability, and as you mentioned, if you think ahead that if you start in the cloud and move private, or if you start private you want to be able to move to the cloud, we're seeing the likelihood of more of that being able to move back and forth.

Thinking about that, do you expect that companies will be able to do that? Where does that make the most sense when it comes to data? Is there a type of analysis that you might want to do in a cloud environment primarily, but other types of things you might do private? How do we start to think about breaking out where on the spectrum of hybrid cloud set of options one should be considering for different types of big-data activity?

Either-or decision

Harrigan: In the large data analytics world, it’s almost an either-or decision at this time. I don’t know what it will look like in the future.

Workloads that lend themselves extremely well to the cloud are inconsistent, maybe seasonal, where 90 percent of your business happens in December. Seasonal workloads like that lend themselves extremely well to the cloud.

Or, if your business is just starting out, and you don't know if you're going to need a full 400-node cluster to run whatever platform or analytics platform you choose, and the hardware sits idle for 50 percent of the time, or you don’t get full utilization. Those companies need a cloud architecture, because they can scale up and scale down based on needs.

Companies that benefit from on-premise are ones that can see significant savings by not using cloud and paying someone else to run their environment. Those companies typically pin the CPU usage meter at 100 percent, as much as they can, and then add nodes to add more capacity.

The best advice I could give is, if you start in the cloud or you start on bare metal, make sure you have agility and you're able to move workloads around. If you choose one sort of architecture that only works in the cloud and you are scaling up and you have to do a rip and replace scenario just to get out of the cloud and move to on-premise, that’s going to be significant business impact.

One of the reasons I like HP Vertica is that it has a cloud instance that can run on a public cloud. That same instance, that same architecture runs just as well on bare metal, only faster.

Gardner: Chris, last word to you. For those organizations out there struggling with big data, trying to figure out the best path, trying to think long term, and from an architectural and strategic point of view, what should they consider when coming to an organization like Dasher? Where is your sweet spot in terms of working with these organizations? How should they best consider how to take advantage of what you have to offer?

Saso: Every organization is different, and this is one area where that's true. When people are just looking for servers, they're pretty much all the same. But when you're actually trying to figure out your strategy for how you are going to use big-data analytics, every company, big or small, probably does have a slightly different thing they are trying to solve.

That's where we would sit down with that client and really listen and understand, are they trying to solve a speed issue with their data, are they trying to solve massive amounts of data and trying to find the needle in a haystack, the golden egg, golden nugget in there? Each of those approaches certainly has a different answer to it.

Read more on tackling big data analytics
Learn how the future is all about fast data
Find out how big data trends affect your business

So coming with your business problem and also what you would like to see as a result -- we would like to see x-number of increase in our customer satisfaction number or x-number of increase in revenue or something like that -- helps us define the metric that we can then help design toward.

Listen to the podcast. Find it on iTunes. Get the mobile app. Read a full transcript or  download a copy. Sponsor: Hewlett Packard Enterprise.

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How content in context within apps and process strengthens marketing muscle

Posted By Dana L Gardner, Tuesday, September 15, 2015

The next BriefingsDirect discussion explores the changing role and impact of content marketing, using the IT industry as a prime example. Just as companies now communicate with their consumers and prospects in much different ways -- with higher emphasis on social interactions, user feedback, big data analysis, and even more content to drive conversations -- so too the IT industry has abruptly changed.

There's more movement to cloud models, to mobile applications, to leveraging data at every chance -- and they are also facing lower-margin subscription business models. The margin for error is shrinking in the IT industry. If any industry is the poster child for how to deal with rapid change on all fronts, including marketing, it's surely the global information technology market.

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

To examine how the IT industry is adjusting to the dynamic nature of marketing, we're joined by Lora Kratchounova, the Founder and Principal at Scratch Marketing and Media in Cambridge, Mass. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Lora, you and I have been talking about marketing for years now. We're in an interesting field, and it’s been such a dynamic time. I have some interesting ideas about where technology is going and where marketing is intercepting, and how they are both changing.

So, let’s start at a high level. Content marketing has proven to be very successful, and you and I have had a hand in this. Creating compelling stories, narratives about what’s going on, and how people can learn from peers as they go through problems and solve them, has become a mainstay in marketing. From your perspective, why is content marketing so important? Why has it been so successful?

Kratchounova: There are couple of reasons for that. The pace of change is tremendous now. People are trying to get their bearings on what’s going on in their markets, and a lot of times, they need to get educated. What has changed with social media now, information is a lot more immediate and transparent, and you can get it from many more sources than the just online presence of a company, for example.


The top-down modeling in the marketing is changing. We used to rely on companies to tell us how to think about the world, and now we can form our own opinions. As we realize that the customer is in the driver’s seat, they educate themselves, and they make the right decisions about how to go about change, companies are realizing that they need to feed into that flow and be part of that discussion. So content marketing has been so successful, because you become an educator, not just selling to people, and especially in IT.

Gardner: And I think people have become much more accustomed to conversations, rather than just a one-direction information flow. "We're the seller and we're going to tell you what it is." Now, people want to relate. They want to hear what others have to think. It’s much more of an actual conversation.

Ongoing conversation

Kratchounova: Exactly. Look at any IT domain. It’s interesting when we look at who is influencing and who the main voices in it are, who the voices that people consider experts are. You pretty much consistently see reporters, journalists, and the analysts folks like you, but then we see that there are a lot of C-level executives from IT companies who are becoming that kind of a voice as well.

That just points to the need for that ongoing conversation, the need for sharing at all levels of the buyer funnel. Once people have bought into a selection, they need to make sure of adoption, and they are maximizing the investment.

So the conversation is very important, and the immediacy of having access to folks and having the ability to exchange a few thoughts on Twitter or LinkedIn has changed the dynamic completely. So it’s absolutely about conversations and storytelling, but it's still mapped to the buyer’s funnel.

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People are still educating and still looking at options for a change or for replacement, one or the other, until they select the people they want to work with. And it’s usually people in brands. It's not just that they want to work with this company, but the people behind it. We're moving more to a people economy.

Gardner: As you point out, you can get to the real source of the knowledge nowadays. Publishing is available to anybody whether they're tweeting, blogging, posting on Facebook, or putting something up on their company website. Anybody who has something to say can say it. It can get indexed and it can be made available to anybody who wants to hear about that particular topic.

The ability to publish is great, and it democratizes the means of how we communicate with each other and educate each other, but yet you still have to earn it.

Most people now don’t just sit back and wait for information to reach them. They're proactive. They go out, they start to search, they do hashtag searches on Twitter, and they can do Google or Bing on web.

It’s much more of, "I know something; I'm putting it out there." And there's another case of someone saying, "I need to know something; I am seeking it." They come together on their own. The content makes that possible. The better the content, the better the likelihood that those in a need to know and those in a need to tell come together.

Kratchounova: Exactly, but I think you hit on something very important. Everybody can publish, and a lot of people are publishing. Yet, we're interested in a love for your people, falling in love for your people, and what they have to say.

The ability to publish is great, and it democratizes the means of how we communicate with each other and educate each other, but yet you still have to earn it. This is very important. People who really are influential are usually domain experts and they're there to help other people. That’s the other aspect of it that both companies and their marketing teams and their executives need to think about. You have to actively participate and show your expertise, it doesn’t come for granted.

Important of curation

Gardner: And there's another aspect to greasing the skids between the knowledge and the acquirer of the knowledge, and that is content curation. There are people who point at things, give it credence, and say that it's a good thing, you should read it; or that’s a bad thing, don’t waste your time -- and that helps refine this.

Kratchounova: It’s pretty exciting.

Gardner: There are machines doing the same thing. There are algorithms, there's indexing, there's both human and machine aspects of winnowing down the good stuff and providing it to people in a need to know, and that’s when we are going to get more powerful.

Kratchounova: Great. I'm sure you know about Narrative Science. I've had a professional crush on this company for few years now. They take data, turn it into storytelling, and they think this is phenomenal. Obviously, that’s not going to replace some of the human storytelling that needs to happen, but some of the data storytelling will come from technology. This is one particular application where marketing and technology come together to bring something completely new into life.

Gardner: So we can get knowledge through expertise or we can get knowledge through experience, someone who has gone through it already and is willing to share that with you. If you're acquiring IT, it’s super important to avail yourself of everything, because it changes so rapidly and the costs are high.

IT depends on the IT buyer, because we can’t necessarily lump them together and ask how the IT buyer goes about it. There are people with different needs, and it depends on their role.

If you make a big mistake in how you're designing a data center, you're out millions of dollars, your products don’t work, and your front office are going to come screaming down on you. You have to make the big decisions and you have to make them correctly in IT. It’s not just a service to the business; it is the business.

So, let’s think about the IT industry in particular, and then think about how content marketing as we’ve discussed is powerful. How do IT people acquire content marketing? Do they get it through websites, emails, or tweets? Is it delivered to them at a webinar that they opt into? How does content marketing reach somebody who's an IT buyer?

Kratchounova: IT depends on the IT buyer, because we can’t necessarily lump them together and ask how the IT buyer goes about it. There are people with different needs, and it depends on their role. If you're CIO or CTO, there is a different mix of channels and sources you use. If you're on the dev or on the ops side and looking for specific solutions, you're going into completely different channels.

For example, if you're a DevOps professional, you're maybe on Stack Overflow and you might be seeking advice from other folks. You might be on GitHub and sharing open-source code and getting feedback on that.

If you're a CIO or CTO, what we have found working with number of different companies, be that global companies or maybe companies that are growing, is that they do seek their peers to validate what the peers are going through. One of the best things that companies can do, when they try to talk to the C-level, is expose some of those connections that they already have from their customers. Make sure that the customers are part of the discussion, and they can chime in.

Another important source of information for the C level in IT would be folks like you, analysts, and strategic system integrators like Accenture and Deloitte, because these folks are exposed to the kinds of challenges that a CIO or CTO would go through. So they have a lot to bring to the table in terms of risk mitigation, optimal deployment, and maximization of the investment in IT. Making those connections and sharing those experiences we have seen work really, really well.

Let me just throw this in as well. The other thing we have seen is that the C level is still going on Google. They're still doing the searches. We have compelling data, across the board, that in any B2B complex enterprise environment folks are self-educating as well. So it’s not a question of either/or; it’s what’s the right mix for each company depending on channels, depending on where people sit.

Spectrum of content

Gardner: So there is a spectrum of content, some highly technical and defined, on places like GitHub that are germane to a technologist. Then, there is that spectrum up from there to a higher level toward peer review of products and peer review of solutions. Then, there are more business topics about what is strategic, what’s the forward direction, how do I understand at an architectural-level decision processes, and where can I go for more information to find out what’s coming down the pike and then put it in place.

Kratchounova: Think about Spiceworks. They're probably at five million IT professionals at this point, and the community is there for a reason. So again, with each particular, there isn’t one size fits all. One thing that we always recommend to folks is that if you’re looking to develop an influential strategy and approach IT, it really depends on what domains you span.

You find that even if you're doing mobile application development, the folks who were really influential and set the standards of that stage are somewhat different from the folks who are concerned with security in mobile app development. So there isn’t necessarily one pool of influencers that you need to go then to develop a relationship and understand what’s in their mind. It really depends on your domain.

Gardner: So if you're a marketer and you recognize that quality content is super important, you need to have a spectrum of content. It needs to be some content that would be germane to a technologist that’s highly detailed, a how-to type. You need to have peer review and stories, case studies, testimonial type content where the customer is telling what they’ve done, why it benefited them, and what you can learn from that.

You also need to have higher-level discussions with experts to help people chart the next course, the strategic level. So content needs to come across a spectrum, and we recognize that the way in which people get that content might be through search. It might be through web, e-mail, webinars, webcasts, reading certain online sites, listening to certain Twitter feeds or groups, or having a select group of people that you follow. All of that happens.

But what’s interesting to me, Lora, is that all has to do with the web. But what we're seeing in IT is a rapid movement toward mobile apps, rather than just the web. And in many cases, they're starting to overtake the web as to where people spend their time. I'm sure you're using a smartphone and you have mobile apps. You're not going on the web to find a cab; you’re going to the Uber app to find a cab.

If you're looking for a restaurant review, you’re not necessarily going on the web and doing a search. You’re going into a specific app on Yelp, OpenTable, or somewhere else to find out where your restaurants are and you’re going into Google Maps to find out how to get there.

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So more-and-more, we're seeing, on the consumer side, people using mobile apps for more of their processes, for their inquiry, for their actual productivity. Then, on the enterprise side, the business-to-employee (B2E) side, we're seeing people using cloud services.

We're moving more toward mobile applications, cloud services, an API-driven world that leverages big data and analytics in order to put context into process. It's all about user experiences, and mobile delivers the best. How then does content continue to reach people? Do we lose the ability to deliver content when they are in apps?

Different perspective

Kratchounova: I have a different perspective on what you're describing. I don’t know that we are moving to a mobile app experience necessarily. When we think about the apps and the examples you gave -- Yelp or Uber -- yes, they're best-of-breed applications that we use because these are the most frequently used applications.

But what you're seeing is actually a digital transformation. Digital no longer means the web, as we know it, going online through your computer. You're actually navigating on a mobile device. So it’s this digital transformation that’s happening, and the trend that we're seeing is aggregation.

It’s not about one individual app, but it’s more about what is the Flipboard within the enterprise. You're seeing that sort of aggregation bubbling up to the top because information overload is a huge problem. People can’t prioritize anymore. They can’t toggle among those different applications and companies.

For example, one of our clients, not to necessarily add a plug for them, actually is very germane to the discussion. does exactly that.

Once you understand, then you understand what a partner is trying to do. Why are they are here, what’s the context, what’s the most logical next step or the optimal next step?

In those kinds of environments, what we're finding and where I totally agree with you, is the ability to read and understand context, so that you can support the user, be that an employee with internal work experience, or external customers, to support them to get the job done.

The role of content is actually merging with big data, because big data is helping us to understand context and say, "What do we serve this person here?" On the marketing side, and the lingo side it’s more about ongoing customer journeys. Think about the same thing on the employee side, ongoing employee journeys or partner journeys.

Once you understand, then you understand what a partner is trying to do. Why are they are here, what’s the context, what’s the most logical next step or the optimal next step? Now, content becomes both an ability for people to find something, but also for marketers or product development folks. I think those functions are emerging as well to deliver the right content in the right format so that the user can get the job done. That’s my perspective on that.

Gardner: There's no disagreement from me on this issue of context to process, context to location, context to need for knowledge all being much more granular and powerful going forward. What I am concerned about is that, when I talk to developers, the vast majority of them are much more interested in a mobile-first, cloud-first world.

They're not much interested in building what we used to think of as big honking applications in the enterprise. They're much more interested in how to bring services -- and microservices -- together in context to provide a better productive outcome and how to leverage low-cost services in APIs and from any cloud.

Discovering inference

So, to me, it becomes, on one hand, all the more important to have the ability to deliver content contextually into these processes, but at the same time these processes are becoming fragmented. They're going across hybrid-cloud environments, they include both what we call cloud and SaaS, and I'm not sure where the marketer now can get enough inference to support the injection of content appropriately.

The ways that it’s been done now is usually through the web where we have links, and we have code, and we can do cookies. It’s sort of like, it’s Web 1.0 mechanisms by which marketers are injecting content, but we are moving not only pass Web 2.0, we're into Web 3.0  cloud platform. To me this is a big question mark.

Kratchounova: It is a question mark. I don’t know that there is going to be one mode of delivering what we're talking about or one approach or one framework. I'll give you one example. Look at how web content management has changed. It used to be about managing pages and updating content. Now, web content management is becoming the Marketing Command Center, if you look at a web content management system like Sitefinity, for example.

Now, marketers can deal with the customer through his own mobile and on the web, so they can inject the content that needs to happen there. The reason they can do this now is because there is this ability, the analytics that come from all of these customer interactions of you, actually creating cohorts of people as they're going through your web experience or online experience. You know why they're there and what’s the optimal path for them to get where they need to be.

You're seeing this ability to distribute content to post content to people, but in a much more contextual way. So, there is going to be a pull and push, but the push is getting a lot smarter and very contextual.

So, you're seeing this ability to distribute content to post content to people, but in a much more contextual way. So, there is going to be a pull and push, but the push is getting a lot smarter and very contextual.

Gardner: So it’s incumbent upon us who are examining this marketing evolution in the context of the IT industry to create that spectrum of content to make it valuable, to make it appropriate and not too commercial or crass, but useful. And at the same time now, think about how to get this in front of right people at the right time.

It seems to me that if I'm an IT company, and more and more of my services, whether it’s a B2B, B2C, B2E, or all of the above, I need to be thinking about ways that I'm going to communicate with my existing universe or market and move them toward new products and services as they need them in context of their process.

Think about this in a B2C environment in retail, where I am walking through Wal-Mart. I have my smartphone and, as I turn the corner, they know that now I am interested in home goods, and they are going to start to incentivize me to buy something. That’s kind of an understood mechanism by which my location and the fact that I turned a corner and made a decision provides an inference that then they can react to with content or information.

But take that now to the B2B environment where I'm in a business setting. I'm in procurement, I'm in product development, or I'm looking for a supply chain efficiency. I want to move into a new geographic location and I need to find the means to do that. All of those things are also like turning a corner in a Wal-Mart, except you're in a business application using cloud services, using a mobile device and apps.

If I'm an IT vendor, I'm going to want to have content or information that I can bring to that situation, perhaps even through an example of what other people have done when they face that same process crossroads. So the content can be more important and more germane. These are multi-million-dollar decisions in some cases.

Don’t you think that big companies should be starting to make content with the idea that it’s going to become part of their application services, part of their cloud delivery services, and that they need to use big data and analytics to know when to inject it?

Understanding context

Kratchounova: I absolutely agree. I think that difference between the example you just gave for Wal-Mart and a B2B environment is that, in Wal-Mart, you don’t need to understand so much about who the person is, what their role is, whether they work at an accounting firm or whether they are a physician, for example.

In a B2B environment you do need to understand context, and context is the location or the point where they are in their journey, whatever that journey maybe, and their role as well, because different people do have different decisions to make.

It’s a little bit more complex to bring context in a B2B environment, but it’s absolutely essential. You used the word inference. We always get enamored by the concept of the big data and guess what, once the machines are there, they're going to analyze everything and it's going to be this perfect world of marketing where everyone is aligned. 

Just look at the history of marketing. We don’t know ourselves as people. We individually don’t know ourselves as well, let alone someone else getting to know us that well. Inference is very important, but it’s going to be a balance between inferring what the person needs and allowing the person to customize this experience as well. So it’s going to come both ways.

Some people still believe that it’s a relationship-based world and, therefore, there's no need for a digital experience for their customers or for their potential buyers, which is actually never the case.

Some people going to one extreme or the other. Some people still believe that it’s a relationship-based world and, therefore, there's no need for a digital experience for their customers or for their potential buyers, which is actually never the case. Other people believe that it’s all digital; therefore they don’t need to touch them in any other way, which is rarely the case, especially in IT. 

Gardner: I also suggest to you that the data is more readily available, because I, as an employer, as a corporation, control what’s going on. I know what that employee is doing. I know what apps they're using. I know what data they're seeking. 

They're going to provide a feed of data back to you about what’s going on, on those apps from your very own employees.

What I'm suggesting then, as we begin to think about closing out this fascinating conversation, is that you need to have content, stories, and customers lined up, so that you can uncover their path to truth, their path to value, and have that content context-ready. Not only you are going to be using it in webinars, webcasts, podcasts, blogs, but pretty soon, if my hypothesis is correct, you're going to be using that content in the context of process and inside of applications in cloud services and on mobile devices.

Way of the future

Kratchounova: Maybe this is an opportunity, because it is the way of the future, and some people are more mature and others are less mature, but maybe we can bring other people into the discussion and see what other folks in the field think about where the content is going, how to contextualize and how to deliver it. One of the biggest question is how do we scale this. You can still do a meaningful experience or create a meaningful experience one-on-one, but it’s hard to recreate that even if your customers are 200, 500, or even 5,000 within the IT space. 

Gardner: You also have to remember that people's connections to apps, cloud services and context-aware processes are only going to increase. The Internet of Things and new classes of devices like the Apple Watch are expanding the end points and ways to connect to them. One of the things that’s important with the Apple Watch functionally is that it’s very good at alerts and notifications. It can also detect a lot of context of what you're doing physically and your location, and it can relate, because it integrates to your phone, with what you're doing with applications and cloud services.

Wouldn’t it be interesting if you're wearing an Apple Watch or equivalent, you're in a business setting, and you come up against a problem that you might not even know yet, but all of these services working together are going to say, "That person is going to be facing a problem; they are going to need to make a decision. Let’s put some information, content, and use cases together for them that will help them as they face that situation to make a better decision." That’s the kind of role I think we're heading toward. 

Before we sign off, Lora, tell me more about Scratch Marketing and Media, what you do and why that’s related to this discussion we have had today.

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Kratchounova: Scratch Marketing and Media is an integrated marketing agency. We help B2B technology companies with market growth. Sometimes that means helping the sales folks within IT companies and sometimes it means working with the marketing folks on things like content marketing programs, PR, and all its relations, and influence their relations in social media.

Gardner: And how could they find out more information about Scratch Marketing Media?

Kratchounova: You can go online at

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How HTC centralizes storage management to gain visibility and IT disaster avoidance

Posted By Dana L Gardner, Wednesday, September 09, 2015

The next BriefingsDirect storage management innovation case study discussion highlights how communications cooperative HTC centralizes storage management to gain powerful visibility, reduce costs, and implement IT disaster avoidance capabilities.

We’ll learn more about how HTC lowers total storage utilization cost while bringing in a common management view to improve problem resolution, automate resources allocation, and more fully gain compliance -- as well as set the stage for broader virtualization and business continuity benefits.

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

To learn how HTC attains total storage management, we sat down with Philip Sellers, Senior System Administrator at HTC in Myrtle Beach, South Carolina. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Tell us about HTC.


Sellers: HTC is the largest telephone cooperative in the nation. We serve the Myrtle Beach and surrounding South Carolina area. We started out as a telephone company, but at this point, we're a full-line telecommunications company, doing cable TV, internet security, home automation, and through our partnership with AT and T, we also do wireless service. 

Gardner: Now, you are not HTC, the handset maker from Asia; you are an entirely different company.

Sellers: A completely different company, although we do sell a few of those handsets with our wireless division.

Gardner: You told me when we talked earlier that you are a reluctant storage administrator. You started out as a VMware in virtualization admin. How did you get from one to the other, and why is it important for your organization?

Common story

Sellers: It’s probably a common story in a lot of shops. As VMware became more prolific in our environment, the line started to blur between networking and VMware, and storage and VMware. So I was pulled more into those directions as the primary VMware admin for our company. That gave me the opportunity to dig in and start to learn an area of IT that was new to me.

Gardner: Philip, tell us a little bit about the scale: how many virtual machines (VMs), how many employees, what sort of a size organization are you?

Sellers: We have 700 or so employees at this point, and almost that number of VMs that we're managing. We have a couple of different storage platforms today with the HP EVA and HP 3PAR StoreServ in-house.

We also use lots of other things. We have HP StoreOnce for backup and HP StoreVirtual for some of our smaller needs, such as remote offices. 

Gardner: What kind of storage workloads are we dealing with here? Is this all of the apps across the company? What set of IT workloads are you addressing? 

One of the great benefits we've realized with VMware is the ability to have a good test and development platform to mirror what we have in production.

Sellers: The group that I'm a part of is actually the internal IT group. So we're running line-of-business applications, not the things that our customers are delivered service across, but the things that run our business to take orders, support financial operations, and those sorts of things.

And we're running a mixture of test and dev and production. One of the great benefits we've realized with VMware is the ability to have a good test and development platform to mirror what we have in production. So it runs the gamut for internal IT.

Gardner: When you start to think about progressing to a better utilization and the rationalization of storage, rather than have overlapping or disjointed storage capabilities, what sort of philosophy do you have about storage? How do you think that you can make the whole greater than sum of the parts and get those utilization benefits over time?

Deeper insight

Sellers: It’s something that I learned back in my virtualization days. For me, it’s huge to have visibility into what’s going to in your storage. One of the benefits of our transition to HP 3PAR storage is that we've been able to realize much deeper levels of insight into what’s going on inside of the arrays.

You know, as we were making that switch, we evaluated other third parties, ultimately deciding on the mid-range 7000 3PAR series for our environment and for our needs. That visibility has been key for us.

But it’s also come with a set of challenges, because we now have multiple storage consoles that we need to manage from. We have different places that we need to check. One of the keys for us is having somewhere where we can see it all, or get a better idea of the entire environment from an end-to-end perspective.

One of the other huge benefits that we've realized is some level of disaster avoidance.

That’s one of the things we learned from our VMware days. We were flying blind early on, and that caused us problems and potential problems, because we didn’t know something was going on. One of our main goals is establishing good visibility into our storage environment.

Gardner: So, it’s not just enough to modernize your storage and improve your storage capabilities, but at the same time you really need to address the management issues and consolidate management. In doing so, what have been some of the payoffs that you can recall? How has this helped your organization better provide IT services internally?

Sellers: From a performance standpoint, our former primary storage platform was not great at telling us how close we were to the edge of our performance capabilities. We never knew exactly what was going to cause a problem or the unpredictability of virtual workloads in particular. We never knew where we were going to have issues.

Being able to see into that has allowed us to prevent help desk cost for slow services, for problems that maybe we didn’t even know were going on initially. One of the other huge benefits that we've realized is new levels of disaster avoidance.

Gardner: And what do you mean by that, rather than disaster recovery (DR), which is taking care of business after we have had some terrible thing happen? How do you head that off?

Disaster avoidance

Sellers: I know that’s not an industry term, but that’s what I like to call it, because in our environment, we have two data centers that are fairly close together. What we've implemented is the HP 3PAR StoreServ metro storage clustering feature, which they call peer persistence, but it's VMware’s metro storage clustering. We've also done that with Windows clustering as well.

We have two sets of 3PARs in different data centers, and they act as one. So, they replicate synchronously between the two locations and they fail-over "automagically." I don’t know how else to say it. It just seamlessly fails-over between the two sites.

For our environment, we were at a particularly vulnerable state if we lost a data array, because so many things were pointing at it. Now if we lose a single data array it’s not a big deal. It fails-over and it continues running.

Gardner: And when you say vulnerable, I think you're talking about hurricanes?

Sellers: A lot of times we plan for those large natural disasters, but sometimes it’s the small ones that get us like UPS maintenance or something as simple as a power outage. Maybe your generator doesn’t kick in in time. Sometimes, that can be a disaster of almost the same scale as a hurricane to your business operations -- just from something simple.

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Gardner: So the storage management capability has provided "automagically," as you say, this disaster avoidance. That’s a pretty important metric. Do you have any idea of the value of that to your business, and maybe start to put that in dollar terms? It seems a pretty profound difference.

Sellers: I can’t necessarily put it into dollar terms. That’s not the world that I work in, but I know that anytime there is downtime to our customer relationship advisers, and the people in the field, that’s bad for business.

So we're avoiding those kinds of situations as best we can. We could lose an entire data center site and, with technology built into the VMware layer and into the HP 3PAR layer, it will come back up. It may be reboot of a server, but we try to do everything we can to avoid disaster situations today, rather than just plan for needing to fail a data center over to "site B," and go through all of that testing.

Gardner: Let’s get down to some more brass tacks on actual storage utilization benefits. Any thoughts or recollections about what this means in terms of utilization, so  no more worries about running out of storage base or capacity?

Seeing benefits

Sellers: Yeah, the HP 3PAR platform has been really great inside of our environment because we realize the marketing term of the "two-to-one thin provisioning." We're seeing that benefit.

When I looked at the console before I came here, we were seeing around a 2.3 to 1 compaction, and that’s without deduplication and some of the other newer technologies that are capable in the 3PAR platform. We may be able to realize better than that in the future.

Gardner: We've talked about disaster avoidance. We've recognized some significant savings in the provisioning and utilization. Let’s go back to management. What sort of benefits are you getting now with a more holistic approach and how does that help, perhaps on a data lifecycle basis?

Sellers: One of the ways that we're approaching that set of problems is with storage resource management software. We've traditionally used a piece of software called Storage Essentials, which HP makes. It’s heterogeneous storage-management software, so it can look at all of our different arrays and looks at our backup arrays and our primary storage arrays, as well as our back-up environment, and pulls all that information together.

We've been able to leverage that from a reporting standpoint to be able to view and pinpoint growth to see how see things are running from a dashboard view.

We've been able to leverage that from a reporting standpoint to be able to view and pinpoint growth to see how see things are running from a dashboard view. Over the last six months or so, I've been working in an early-release program for a product called HP Storage Operations Management.

This software is the next iteration of Storage Essentials. It’s got a much more approachable and modern user interface, which brings up and aggregates our total environment so that we can get a full picture of what’s going on there. Then, we can drill down and see at specific levels how things are performing, what our utilization trend is, or how much time we have until a device or a storage pool is full.

Those are things that keep us out of the really dangerous situations in getting down to a time where you're in a mission critical season, maybe the holidays or something where it’s heavy sales, and you run out of disk space and you can’t get your procurement cycle to get storage quickly enough.

Those things are just as dangerous as the hurricane that we were talking about earlier from a business operations perspective. Tools like this help us to manage and see what’s going on in the environment and help us plan and act proactively.

Gardner: I could really see why your philosophy is visibility and management oversight. It comes back again and again as a huge force multiplier benefit. 

Room to grow

Sellers: Absolutely. There's a saying that ignorance is bliss. When you're flying blind, that’s true, until it catches up with you, and it eventually overtakes you. We have lots and lots of room to grow and capabilities where we're at today. This new version of management storage resource management product has lots of great potential, too.

It’s an initial release. So, it’s got somewhat limited support for different storage families and that kind of thing, but they're working to bring in additional support and make it all that the previous product was, and much more -- and that’s visible from the initial release.

So we're excited about seeing where that can help us, particularly because one of the switches in this new product is that it’s not just a collect, an analytics reporting system. It’s a dashboard system where it takes that analytics and brings it back to a dashboard to let you drill down in to it and see it real clearly in near-real-time. I won’t say in real-time, but within whatever amount of time you configure.

Gardner: How about your future business activities? How well you can support them? I know that media is a fast-changing business. Do you feel confident now that when your superiors in your organization come to you and say, "We need this," that you're in a better position to hop-to quickly? Is there a sense of confidence that you can take on market change better?

We feel confident that we have room to grow and that we can do so in shorter terms.

Sellers: I certainly believe so. We've been able to adapt and change more quickly because of changes that we've made with VMware, with HP 3PAR. We feel confident that we have room to grow and that we can do so in shorter terms. We've been able to try and look at new things like VDI deployments to help us with compliance-type issues, where we're under regulations and have to patch and have to ensure that our systems are secure.

And so we are looking at things like that now that we were afraid to put on to primary storage in the past. It's something where we think we have a good mix today for the future.

Gardner: What advice might you might provide others who would be approaching a disparate storage environment? And maybe share your philosophy about visibility and anticipation being better than reaction. Maybe they are also seeking disaster avoidance, rather than disaster recovery. For those folks that are not quite as far along in this journey as you are, what might you suggest for them to be thinking about -- or that you wish you knew about earlier?

Sellers: There is definitely some low hanging fruit, and that’s what visibility will bring to you -- the ability to handle some of that low-hanging fruit. If you have a situation where your storage team is siloed away from your server team, bringing something in that can see both of those sides and map together that whole environment is a real easy way to identify inefficiency.

Those are LUNs that maybe are provisioned -- but not in use. There is no I/O on them. That’s a dollar amount immediately reclaimed. Finding VMs and things with visibility. These tools can look in to the VMware environment where you can see that you have lots and lots of VMs that are shut down.

There are easy things that you can do to start that process, no matter what your storage platform is. I think that’s a universal thing. If you have something that can gain you visibility in to the environment there are some easy things and easy wins that you can bring back.

Further improvements

Gardner: And those of course provide grist for the mill of further improvements and further budget to accomplish even more.

Sellers: Absolutely. If you want to make a storage platform switch or if you want to do other improvements and gain more efficiency, this gives you a little bit of extra room, some wiggle room, to make those things reality. We spent an awful lot of our budget just in keeping the lights on, keeping things up and running. Anytime you can gain some wiggle room from that budget, it certainly allows you the ability to look at innovation.

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

Posted By Dana L Gardner, Wednesday, September 02, 2015

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.

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

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.


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.


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.


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.


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.

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Tags:  BriefingsDirect  Charl Joubert  Dana Gardner  Edward Jackson  HP  HP DISCOVER  Interarbor Solutions  ITSM  Julien Kuijper  Patrick Bailly  service desk 

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

Posted By Dana L Gardner, Monday, August 24, 2015

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: 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?

Learn more about Rolta AdvizeX Solutions
For the Retail Industry

And for Healthcare Companies

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?


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
For the Retail Industry

And for Healthcare Companies

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.

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

Posted By Dana L Gardner, Tuesday, August 18, 2015

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.


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.


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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Gain access to the free HP Vertica Community Edition

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.

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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HP pursues big data opportunity with updated products, services, developer program

Posted By Dana L Gardner, Tuesday, August 11, 2015

HP today at its Big Data Conference in Boston unveiled a series of new products, services, and programs designed to help organizations better leverage data and analytics.

The company announced:

  • A new release of HP Vertica, called Excavator, that feature data streaming and advanced log file text search to power high-speed analytics on Internet of Things (IoT) data.
  • Broader support for and contributions to open source technologies, including optimized Hadoop performance, integration with the Apache Kafka distributed messaging system, and advancements in Distributed R predictive analytics.
  • The HP Haven Startup Accelerator program, which provides early-stage companies with fast, affordable access to both HP Big Data and Application Delivery Management software and services.

"Big data helps us make more sense of it all, a byproduct of what we do everyday," said Robert Youngjohns, Executive Vice President and General Manager at HP Software, in a keynote address. "Big data solves everyday problems like security, inventory, and empowers workers ... and you can now exploit 100 percent of your data."

Youngjohns said that HP at its HP Protect show in a few weeks will announce how to better mine IT systems analytics to make enterprises more secure.

"Big data changes the game in the idea economy," said Colin Mahony, Senior Vice President and General Manager of Big Data at HP. "Big data is core to all apps, but customized composite analytic applications are coming."

The key question for enterprises is, how can you embed analytics into the role you play in your organization?, said Mahony, adding that, enterprises need to spin up new apps constantly to identify and analyze via context, which they cannot get from packaged apps.

"Developers are the new heroes of the idea economy," said Mahony. "Through our Haven and Haven OnDemand platforms, we are empowering these heroes to transform their business through data, by allowing them to harness the value of all forms of information, rapidly connect and apply open source, and quickly access the tools they need to build winning businesses." [Disclosure: HP Enterprise is a sponsor of BriefingsDirect podcasts.]

Also addressing the keynote audience was recent Turing Award winner Mike Stonebraker, CTO and co-founder of Tamr. He said that the development of the column store database was the most disruptive thing I ever did. "It transformed the market," he said, and lead to the Vertica big data platform that HP acquired in 2011.

But he cautioned against getting caught up in marketing buzz over data science substance. "I’ve been doing big data forever. The buzzword is meaningless, it’s about solving data volume, velocity, and variety problems."

"Analytics is moving to complex analytics, using machine learning and statistics, so you need to get smart at data science," said Stonebraker.


Capabilities in the new version of Vertica, codenamed "Excavator," include:

  • Data-streaming analytics offering native support for the Apache Kafka open-source distributed messaging system to enable organizations to quickly ingest and analyze high-speed streaming data, including IoT, in near real time. This capability delivers actionable insight for a wide range of use cases, including manufacturing process control, supply-chain optimization, healthcare monitoring, financial risk management, and fraud detection.
  • Advanced machine log text search to enable organizations to collect and index large log file data sets generated by systems and business applications, helping IT organizations quickly identify and predict application failures and cyber-attacks, and investigate authorized and unauthorized access.

HP also released a series of solutions designed to enable organizations to combine the innovation of open source with the enterprise-scale, reliability, and security. These include:

"Developers are the new heroes of the Idea Economy."

  • HP Vertica for SQL on Hadoop native file support, bringing significant increase in performance on popular Hadoop formats like ORC and Parquet. Specifically, HP worked collaboratively with Hortonworks to develop a new high-performance access layer that enables SQL queries to run directly on ORC files, resulting in a 5x increase in execution times.
  • HP Vertica Flex Zone Table Library with which HP has open sourced its innovative Flex Table "schema on-need" technology to the global developer community. With this move, organizations will be able to fully harness virtually any form of semi-structured data to meet their unique needs.
  • HP announced its commitment to integrate Vertica with Apache Spark. This will enable accelerated data transfer between Vertica and Spark, allowing organizations to take full advantage of their Spark-based deployments. This future capability will enable the developer community to build their models in Spark and run them in Vertica for high-speed and sophisticated analytics.

Startup Accelerator

HP also unveiled The HP Haven Startup Accelerator, a new program designed to support and expand HP's ecosystem of developers and innovators by making HP Big Data and Application Delivery Management software products accessible to early-stage companies. The program removes traditional barriers for organizations looking to leverage analytics and data to build powerful, differentiated applications. Qualified participants will benefit from the following program components:

  • Free use of the community versions of HP IDOL and HP Vertica with expanded capacity.
  • Premium version of HP IDOL and HP Vertica with attractive pricing
  • HP Application Delivery Management tools including HP Agile Manager, HP LeanFT, and HP LoadRunner.

HP also announced an innovative framework of technology and proven best practices to accelerate the development of next-generation analytical applications. This framework extends the HP Haven Big Data Platform with quick-start visualization, syndicated data feeds, open on-premise and cloud-based APIs. This enables HP Professional Services and HP partners to quickly deliver a range of solutions, such as voice of customer, smart cities, and fraud detection.

"HP is uniquely positioned to help businesses and developers thrive in this new world."

Optimized platforms for big data include traditional HP ProLiant DL380 clusters, purpose-built Apollo 4510, 4530 and 4200 compute and storage servers, and HP's innovative asymmetric Big Data Reference Architecture. These systems enable customers to optimize their big data workloads, delivering the power of Haven to their business.

Availability Planned availability for new HP Haven Big Data offerings and services is set for fall of 2015.

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How eCommerce sites harvest big data across multiple clouds

Posted By Dana L Gardner, Monday, August 10, 2015

The next BriefingsDirect big data innovation thought leadership interview highlights how a consultant helps large ecommerce organizations better manage their big data architectures across cloud environments.

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 is best architected for the largest web applications, BriefingsDirect sat down with Jimmy Mohsin, Principal Software Architect at Norjimm LLC, a consultancy based in Princeton, New Jersey. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: How are large web applications deciding on the right big data architecture? 

Mohsin: There's a lot of interest in trying to deal with large data volumes, not only large data volumes, but also data that changes rapidly. Now, there are many companies that have very large datasets, some in terabytes, some in petabytes and then they're getting live feeds.

The data is there and it’s changing rapidly. The traditional databases sometimes can’t handle that problem, especially if you're using that database as a warehouse and you're reporting against it.

Basically, we have kind of a moving-target situation. With HP Vertica, what we've seen is the ability to solve that problem in at least some of the cases that I've come across, and I can talk about specific use cases in that regard.

Input/output issues

Gardner: Before we get into a specific use case, I'm interested particularly in some of these input/output issues. People are trying to decide how to move the data around. They're toying with cloud. They're trying to bring data for more types of traditional repositories. And, as you say, they're facing new types of data problems with streaming and real-time feeds.

How do you see them beginning this process when they have to handle so many variables? Is it something that’s an IT architecture, or enterprise architecture, or data architecture? Who's responsible for this, given that it’s now a rather holistic problem?

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Mohsin: In my present project, we ran into that. The problem is that many companies don't even have a well defined data-architecture team. Some of them do. You'll find a lot of companies with an enterprise-architect role and you'll have some companies with a haphazard definition of an architectural group.


Net-net, at least at this point, unless companies are more structured, it becomes a management issue in the sense that someone at the leadership level needs to know who has what domain knowledge and then form the appropriate team to skin this cat.

I know of a recent situation where we had to build a team of four people, and only one was an architect. But we built a virtual team of four people who were able to assemble and collate all the repositories that spanned 15 years and four different technology flavors, and then come up with an approach that resulted in a single repository in HP Vertica.

So there are no easy answers yet, because organizations just aren't uniformly structured.

Gardner: Well, I imagine they'll be adapting, just like we all are, to the new realities. In the meantime, tell me about a specific use case that demonstrates the intensity of scale and velocity, and how at least one architecture has been deployed to manage that?

Mohsin: One of my present projects deals with one of the world's largest retailers. It's eCommerce, online selling. One of the things they do, in addition to their transactions of buying and selling, is email campaign management. That means staying in touch with the customer on the basis of their purchases, their interests, and their profiles.

One of the things we do is see what a certain customer’s buying preferences have been over the past 90 days. Knowing that and the customer’s profile, we can try to predict what their buying patterns will be. So we send them a very tailored message in that regard. In this project, we're dealing with about 150 to 160 million emails a day. So this is definitely big data.

Here we have online information coming into one warehouse as to what's happening in the world of buying and selling. Then, behind the scenes, while that information is being sent to the warehouse, we're trying to do these email campaigns.

This is where the problem becomes fairly complicated. We tried traditional relational database management systems (RDBMS), and they kind of worked, but we ran into a slew of speed and performance issues. That's really where the big-data world was really beneficial. We were able to address that problem in about a seven-month project that we ran.

Gardner: And this was using HP Vertica?

Large organization

Mohsin: We did an evaluation. We looked at a few databases, and the corporate choice was Vertica. We saw that there is a whole bunch of big-data vendors. The issue is that many of the vendors don't have any large organizations behind them, and Vertica does. The company management felt that this was a new big database, but HP was behind it, and the fact that they also use HP hardware helped a lot.

They chose Vertica. The team I was managing did a proof of concept (POC) and we were able to demonstrate that Vertica would be able to handle the reporting that is tied to the email campaign management. We ran a 90 day POC, and the results were so positive that there was an interest in going live. We went live in about another 90 days, following a 90-day POC.

Gardner: I understand that Vertica is quite versatile. I've heard of a number of ways in which it's used technically. But this email campaign problem almost sounds like a transactional issue, a complex event processing issue, or a transfer agent scaling issue. How does big data, Vertica, and analytics come to bear on this particular problem?

Mohsin: It's exactly what you say it is. As we are reporting and pushing out the campaigns, new information is coming in every half hour, sometimes even more frequently. There's a live feed that's updating the warehouse. While the warehouse is being updated, we want to report against it in real time and keep our campaigns going.

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The key point is that we can't really stop any of these processes. The customers who are managing the campaigns want to see information very frequently. We can’t even predict when they would want their information. At the same time, the transactional systems are sending us live feeds.

The problem we ran into with the traditional RDBMS is that the reporting didn't function when the live feeds were underway. We couldn't run our back-end email campaign reports when new data was coming in.

One of the benefits Vertica has, due to its basic architecture and its columnar design is that it's better positioned to do that. This is what we were able to demonstrate in the live POC, and nobody was going to take our word for it.

The end user said, "Take few of our largest clients. Take some of our clients that have a lot of transactions. Prove that the reports will work for those clients." That's what we did in 30 days. Then, we extended it, and then in 90 days, we demonstrated the whole thing end to end. Following that was the go-live.

Gardner: You had to solve that problem of the live feeds, the rapidity of information. Rather going to a stop, batch process, analyze, repeat, you've gained a solution to your problem.

But at the same time, it seems like you're getting data into an environment where you can analyze it and perhaps extract other forms of analysis, in addition to solving your email, eCommerce trajectory issues. It seems to me that you're now going to have the opportunity to add a new dimension of analysis to what's going on and perhaps we find these transactions more toward a customer inference benefit.

More than a database

Mohsin: One of the things internally that I like to say is that Vertica isn't just a big database, it’s more than just a database. It's really a platform, because you have distributed all, you are publishing other tools. When we adopted it and went live with this technology, we first solved the feeds and speeds problem, but now we're very much positioned to use some of the capabilities that exist in Vertica.

We had Distributed R being one of them, Inference Analysis being another one, so that we can build intelligent reports. To date, we've been building those outside the RDBMS. RDBMS has no role in that. With Vertica, I call it more of a data platform. So we definitely will go there, but that would be our second phase.

As the system starts to function and deliver on the key use cases, the next stage would be to build more sophisticated reports. We definitely have the requirements and now we have the ability to deliver.

Gardner: Perhaps you could add visualization capabilities to that. You could make a data pool available to more of the constituents within this organization so that they could innovate and do experiments. That’s a very powerful stuff indeed.

Is there anything else you can tell us for other organizations that might be facing similar issues around real-time feeds and the need to analyze and react, now that you have been through this on this particular project. Are there any lessons learned for others.

One of the issues in big data at least today is that you can’t find a whole slew of clients who have already gone live and who are in production.

If you're facing transactional issues and you haven't thought about a big-data platform as part of that solution, what do you offer to them in terms of maybe lighting a light bulb in their mind about looking for alternatives to traditional middleware.

Mohsin: Like so many people try to do, we tried to see if anyone else had done this. One of the issues in big data at least today is that you can’t find a whole slew of clients who have already gone live and who are in production.

There are lots of people in development, and some are live, but in our space, we couldn't find anyone who was live. We solved that issue via a quick-hit POC. The big lesson there was that we scoped the POC right. We didn’t want to do too much and we didn’t want to do too little. So that was a good lesson learned.

The other big thing is the data-migration question. Maybe, to some extent, this problem will never be solved. It's not so easy to pull data out of legacy database systems. Very few of them will give you good tools to migrate away from them. They all want you to stay. So we had to write our own tooling. We scoured the market for it, but we couldn’t find too many options out there.

Understand your data

So a huge lesson learned was, if you really want to do this, if you want to move to big data, get a handle on understanding your data. Make sure you have the domain experts in-house. Make sure you have the tooling in place, however rudimentary it might be, to be able to pull the data out of your existing database. Once you have it in the file system, Vertica can take it in minutes. That’s not the problem. The problem is getting it out.

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We continue to grapple with that and we have made product enhancement recommendations. But in fairness to Vertica, this is really not something that Vertica can do much about, because this is more in the legacy database space.

Gardner: I've heard quite a few people say that, given the velocity with which they are seeing people move to the cloud, that obviously isn't part of their problem, as the data is already in the cloud. It's in the standardized architecture that that cloud is built around, if there is a platform-as-a-service (PaaS) capability, then getting at the data isn't so much of a problem, or am I not reading that correctly?

There is still a lingering fear of the cloud. People will tell you that the cloud is not secure.

Mohsin: No, you're reading that correctly. The problem we have is that a lot of companies are still not in the cloud. There is still a lingering fear of the cloud. People will tell you that the cloud is not secure. If you have customer information, if you have personalized data, many organizations don't want to put it in the cloud.

Slowly, they are moving in that direction. If we were all there, I would completely agree with you, but since we still have so many on-premise deployments, we're still in a hybrid mode -- some is on-prem, some is in the cloud.

Gardner: I just bring it up because it gives yet another reason to seriously consider cloud. It’s a benefit that is actually quite powerful -- the data access and ability to do joins and bring datasets together because they're all in the same cloud.

Mohsin: I fundamentally agree with you. I fundamentally believe in the cloud and that it really should be the way to go. Going through our very recent go-live, there is no way we could have the same elasticity in an on-prem is deployment that we can have in a cloud. I can pick up the phone, call a cloud provider, and have another machine the next day. I can't do that if it’s on-premise.

Again, a simple question of moving all the assets into the cloud, at least in some organizations, will take several months, if not years.

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How Localytics uses big data to improve mobile app development and marketing

Posted By Dana L Gardner, Wednesday, August 05, 2015

The next BriefingsDirect big data innovation case study interview investigates how Localytics uses data and associated analytics to help providers of mobile applications improve their applications -- and also allow them to better understand the uses for their apps and dynamic customer demands.

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 helps mobile application developers better their products and services, please join Andrew Rollins, Founder and Chief Software Architect at Localytics, based in Boston. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Tell us about your organization. You founded it to do what?

Rollins: We founded in 2008, two other guys and I. We set out initially to make mobile apps. If you remember back in 2008, this is when the iPhone App Store launched. So there was a lot of excitement around mobile apps at that time.


We initially started looking at different concepts for apps, but then, over a period of a couple months, discovered that there really weren't a whole lot of services out there for mobile apps. It was basically a very bare ecosystem, kind of like the Wild, Wild West. [Register for the upcoming HP Big Data Conference in Boston on Aug. 10-13.]

We ended up focusing on whether there was a services play in this industry and we settled on analytics, which we then called Localytics. The analogy we like to use is, at the time it was a little bit of a gold rush, and we want to sell the pickaxes. So that’s what we did.

Gardner: That makes a great deal of sense, and it has certainly turned into a gold rush. For those folks who do the mining, creating applications, what is it that they need to know?

Analytics and marketing

Rollins: That’s a good question. Here's a little back story on what we do. We do analytics, but we also do marketing. We're a full-service solution, where you can measure how your application is performing out in the wild. You can see what your users are doing. You can do anything from funnel analysis to engagement analysis, things like that.

From there, we also transition into the marketing side of things, where you can manage your push notifications, your in/out messaging.

For people who are making mobile apps, often they want to look at key metrics and then how to drive those metrics. That means a lot of A/B testing, funnel analysis, and engagement analysis.

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It means not only analyzing these things, but making meaningful interactions, reaching out to customers via push notifications, getting them back in the app when they are not using the app, identifying points of drop-off, and messaging them at the right time to get them back in.

An example would be an e-commerce app. You've abandoned the shopping cart. Let’s get you back in the application via some sort of messaging. Doing all of that, measuring the return on investment (ROI) on that, measuring your acquisition channels, measuring what your users are doing, and creating that feedback loop is what we advocate mobile app developers do.

Gardner: You're able to do data-driven marketing in a way that may not have been very accessible before, because everything that’s done with the app is digital and measurable. There are logs, servers -- and so somewhere there's going to be a trail. It’s not so much marketing as it is science. We've always thought of marketing as perhaps an art and less of a science. How do you see this changing the very nature of marketing?

Everything ultimately that you are doing really does need to be data-driven. It's very hard to work off just intuition alone.

Rollins: Everything ultimately that you are doing really does need to be data-driven. It's very hard to work off of just intuition alone. So that's the art and science. You come out with your initial hypothesis, and that’s a little bit more on the craft or art side, where you're using your intuition to guide you on where to start.

From there, you have to use the data to iterate. I'm going to try this, this, and this, and then see which works out. That would be like a typical multivariate kind of testing.

Determine what works out of all these concepts that you're trying, and then you iterate on that. That's where measuring anything you do, any kind of interaction you have with your user, and then using that as feedback to then inform the next interaction is what you have to be doing.

Gardner: And this is also a bit revolutionary when it comes to software development. It wasn't that long ago that the waterfall approach to development might leave years between iterations. Now, we're thinking about constantly updating, iterating, getting a feedback loop, and condensing the latency of that feedback loop so that we really can react as close to real-time as possible.

What is it about mobile apps that's allowed for a whole different approach to this notion of connectedness and feedback loops to an app audience?

Mobile apps are different

Rollins: This brings up a good point. A lot of people ask why we have a mobile app analytics company. Why did we do that? Why is typical web analytics not good enough? It kind of speaks to something that you're talking about. Mobile apps are a little bit different than the regular web, in the sense that you do have a cycle that you can push apps out on.

You release to, let’s say, the iPhone App Store. It might take a couple of weeks before your app goes out there. So you have to be really careful about what you're publishing, because your turnaround time is not that of the web. [Register for the upcoming HP Big Data Conference in Boston on Aug. 10-13.]

However, there are certain interactions you can have, like on the messaging side, where you have an ability to instantly go back and forth. Mobile apps are a different kind of market. It requires a little different understanding than the traditional approach.

... We consume the data in a real-time pipeline. We're not doing background batch processing that you might see in something like Hadoop. We're doing a lot of real-time pipeline stuff, such that you can see results within a minute or two of it being uploaded from a device. That's largely where HP Vertica comes in, and why we ended up using Vertica, because of its real-time nature. It’s about the scale.

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Gardner: If I understand correctly, you have access to the data from all these devices, you are crunching that, and you're offering reports and services back to your customers. Do they look to you as also a platform provider or just a data-service provider? How do the actual hosting and support services for these marketing capabilities come about?

Rollins: We tend to cater more toward the high end. A lot of our customers are large app publishers that have an ongoing application, let’s say a shopping application or news application.

In that sense, when we bring people on board, oftentimes they tend to be larger companies that aren’t necessarily technically savvy yet about mobile, because it's still new for some people. We do offer a lot of onboarding services to make sure they integrate their application correctly, measure it correctly, and are looking at the right metrics for their industry, as compared to other apps in that industry.

Then, we keep that relationship open as they go along and as they see data. We iterate on that with them. Because of the newness of the industry it does require education.

Gardner: And where is HP Vertica running for you? Do you run it on your own data center? Are you using cloud? Is there a hybrid? Do you have some other model?

Running in the cloud

Rollins: We run it in the cloud. We are running on Amazon Web Services (AWS). We've thought a lot about whether we should run it in a separate data center, so that we can dictate the hardware, but presently we are running it in AWS.

Gardner: Let’s talk about what you can do when you do this correctly. Because you have a capacity to handle scale, you've developed speed, and you understand the requirements in the market, what are your customers getting from the ability to do all this?

Rollins: It really depends on the customer. Something like an e-commerce app is going to look heavily at things like where users are dropping off and what's preventing them from making that purchase.

Another application, like news, which I mentioned, will look at something different, usually something more along the lines of engagement. How long are they reading an article for? That matters to them, so that they can give those numbers to advertisers.

So the answer to that largely depends on who you are and what your app is. Something like an e-commerce app is going to look heavily at things like where users are dropping off and what's preventing them from making that purchase.

Something like an e-commerce app is going to look heavily at things like where users are dropping off and what's preventing them from making that purchase.

Gardner: I suppose another benefit of developing these insights, as specific and germane as they might be to each client, is the ability to draw different types of data in. Clearly, there's the data from the App Store and from the app itself, but if we could join that data with some other external datasets, we might be able to determine something more about why they drop-off or why they are spending more, or time doing certain things.

So is there an opportunity, and do you have any examples of where you've been able to go after more datasets and then be able to scale to that?

Rollins: This is something that's come up a lot recently. In the past year, we have our own products that we're launching in this space, but the idea of integrating different data types is really big right now.

You have all these different silos -- mobile, web, and even your internal server infrastructure. If you're a retail company that has a mobile app, you might even have physical stores. So you're trying to get all this data in some collective view of your customer.

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You want to know that Sally came to your store and purchased a particular kind of item. Then, you want to be able to know that in your mobile app. Maybe you have a loyalty card that you can tie across the media and then use that to engage with her meaningfully about stuff that might interest her in the mobile app as well.

"We noticed that you bought this a month ago. Maybe you need another one. Here is a coupon for it."

Other datasets

That's a big thing, and we're looking at a lot of different ways of doing that by bringing in other datasets that might not be from just a mobile app itself.

We're not even focused on mobile apps any more. We're really just an app analytics company, and that means the web and desktop. We ship in Windows, for example. We deal with a lot of Microsoft applications. Tying together all of that stuff is kind of the future. [Register for the upcoming HP Big Data Conference in Boston on Aug. 10-13.]

Gardner: For those organizations that are embarking on more of a data-driven business model, that are looking for analytics and platforms and requirements, is there anything that you could offer in hindsight having traveled this path and worked with HP Vertica. What should they keep in mind when they're looking to move into a capability, maybe it's on-prem, maybe it's cloud. What advice could you offer them?

At scale, you have to know what each technology is good at, and how you bring together multiple technologies to accomplish what you want.

Rollins: The journey that we went through was with various platforms. At the end of day, be aware of what the vendor of the big-data platform is pitching, versus the reality of it.

A lot of times, prototyping is very easy, but actually going to large scale is fairly difficult. At scale, you have to know what each technology is good at, and how you bring together multiple technologies to accomplish what you want.

That means a lot of prototyping, a lot of stress testing and benchmarking. You really don’t know until you try it with a lot of these things. There are a lot of promises, but the reality might be different.

Gardner: Any thoughts about Vertica’s track record, given your length of experience?

Rollins: They're really good. I'm both impressed with the speed of it as compared to other things we have looked at, as well as the features that they release. Vertica 7 has a bunch of great stuff in it. Vertica 6, when it came out, had a bunch of great stuff in it. I'm pretty happy with it.

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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Tags:  Andrew Rollins  big data  BriefingsDirect  cloud computing  Converged infrastructure  Dana Gardner  HP  HP Vertica  HPDiscover  Interarbor Solutions  Localytics  Mobile apps 

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HP hyper-converged appliance delivers speedy VDI and apps deployment and a direct onramp to hybrid cloud

Posted By Dana L Gardner, Tuesday, August 04, 2015
Updated: Thursday, August 06, 2015

HP today announced the new HP ConvergedSystem 250-HC StoreVirtual (CS 250), a hyper-converged infrastructure appliance (HCIA) based on HP's new ProLiant Apollo 2000 server and HP StoreVirtual software-defined storage (SDS) technology.

Built on up-to-date HP, Intel, and VMware technologies, the CS 250 combines a virtual server and storage infrastructure that HP says is configurable in minutes for nearly half the price of competitive systems. It is designed for virtual desktops and remote office productivity, as well as  to provide a flexible path to hybrid cloud. [Disclosure: HP is a sponsor of BriefingsDirect.]

Designed to attract customers on a tight budget, the HP CS 250 includes a new three-node configuration that is up to 49 percent more cost effective than comparable configurations from Nutanix, SimpliVity and other competitors, says HP. Because HP's StoreVirtual runs in VMware, Microsoft Hyper-V and KVM virtual environments, the appliance may soon come to support all those hypervisors.

HP recently discontinued the EVO:RAIL version of its HCIA, which was based on the EVO:RAIL software from OEM partner VMware.

Increasingly, even small IT shops want to modernize and simplify how they support existing applications. They want virtualization benefits to extend to storage, backup and recovery, and be ready to implement and consume some cloud services. They want the benefits of software-defined data centers (SDDC), but they don’t want to invest huge amounts of time, money, and risk in a horizontal, pan-IT modernization approach.

That's why, according to IDC, businesses are looking for flexible infrastructure solutions that will allow them to quickly deploy and run new applications. This trend has resulted in a 116 percent year-over-year increase in hyper-converged systems sales and 60 percent compound annual growth rate (CAGR) anticipated through 2019.
The growth in the building blocks approach to IT infrastructure is building rapidly. IDC estimates that in 2015, $10.2 billion will be spent on converged systems, representing 11.4 percent of total IT infrastructure spending. This number will grow to $14.3 billion by 2018, representing 14.9 percent of total IT infrastructure spending, says IDC. Similarly, Technology Business Research, Inc. in Hampton, NH, estimates a $10.6 billion U.S. addressable market over the next 12 months, through mid-2016.

With HCIAs specifically, enterprises can begin making what amounts to mini-clouds based on their required workloads and use cases.  IT can quickly deliver the benefits of modern IT architectures without biting off the whole cloud model. Virtual desktops is a great place to begin, especially as Windows 10 is emerging on the scene.
Indeed, VDI deployments that support as many as 250 desktops on a single appliance at a remote office or agency, for example, allow for ease in administration and deployment on a small footprint while keeping costs clear and predictable. And, if the enterprise wants to scale up and out to hybrid cloud, they can do so with ease and low risk.

Multi-site continuity

The inclusion of three 4TB StoreVirtual Virtual Storage Appliance (VSA) licenses also allows the new HP CS 250 system to replicate data to any other HP StoreVirtual-based solution. This means that customers can leverage their existing infrastructure as a replication target at no additional cost, says HP. The CS 250 also allows customers to tailor the system with a choice of up to 96 processing cores, a mix of SSD and SAS disk drives, and up to 2TB of memory per 4-node appliance -- double that of previous generations.

The CS 250 arrives pre-configured for VMware's vSphere 5.5 or 6.0 and HP OneView InstantOn to enable customers to be production-ready with only 5 minutes of keyboard time and a total of 15 minutes deployment time, with daily management from VMware vCenter via the HP OneView for VMware vCenter plug-in, says HP.

HP sees the CS 250 as a oath to bigger things. For midsize and enterprise customers seeking an efficient and cost-effective cloud entry point, for example, the new HP Helion CloudSystem 9.0 built on the CS 250 provides a direct path to the hybrid cloud. This hyper-converged cloud solution leverages the clustered compute and storage resources of the CS 250 for on-premise workloads but adds self-service portal provisioning and public cloud bursting features for those moving beyond server virtualization.

HP announced that it is enhancing its “Nitro” partner program and opening it up to distributors worldwide, starting with Arrow Electronics in the US.

HP is also introducing new Software-Defined Storage Design and Integration services to help customers deploy highly scalable, elastic cloud storage services, the company announced today. The integration service provides customers with detailed configuration and implementation guidance tailored to their specific needs to accelerate time to value, said HP.

The 4-node CS 250-HC StoreVirtual is available on August 17, while 3-node configurations are available on September 28.  A sample solution price inclusive of the 3-node CS250 with Foundation Carepack and VMware vSphere Enterprise starts at a list price of $121,483, said HP.

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Tags:  BriefingsDirect  ConvergedSystem  CS 250  Dana Gardner  HP  Interarbor Solutions  Software-defined storage 

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