Showing posts with label Hadoop. Show all posts
Showing posts with label Hadoop. Show all posts

Monday, October 05, 2015

How Analytics as a Service Changes the Game and Expands the Market for Big Data Value

Transcript of a BriefingsDirect discussion on how cloud models propel big data as a service benefits.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

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

Gardner
Our next big-data thought leadership discussion highlights how big-data analytics as a service expands the market for advanced analytics and insights. We'll see how bringing analytics to a cloud services model allows smaller and less data-architecture-experienced firms to benefit from the latest in big-data capabilities. And we'll learn how Dasher Technologies is helping usher in this democratization of big data.

Here to share how big data as a service has evolved, we're joined by Justin Harrigan, Data Architecture Strategist at Dasher Technologies in Campbell, California. Welcome, Justin.

Justin Harrigan: Hi, Dana. Thanks for having me.

Gardner: We're glad you could join us. We are also here with Chris Saso, Senior Vice President of Technology at Dasher Technologies. Welcome, Chris.
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Chris Saso: Hi, Dana. Looking forward to our talk.

Gardner: Justin, how have big-data practices changed over the past five years to set the stage for multiple models when it comes to leveraging big-data?

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.

Harrigan
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.

Saso
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.
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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.

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.
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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.

Gardner: Great, I'm afraid we will have to leave it there. We've been discussing how optimizing for a big-data environment really requires a look across many different variables. And we have seen how organizations were able to spread the benefits of big data more generally now, not only the type of organization that can take advantage of it, but the people within those organizations.

We've heard how Dasher Technologies uses advanced technology like HP and HP Vertica to help organizations bring the big-data capabilities to more opportunities for business benefits and across more types of companies and vertical industries.

So a big thank you to our guests, Justin Harrigan, Data Architecture Strategist at Dasher Technologies, and Chris Saso, Senior Vice President of Technology at Dasher Technologies.

And I'd like to thank our audience for joining us as well for this big data thought leadership discussion. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HP-sponsored discussions. Thanks again for listening, and come back next time.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: Hewlett Packard Enterprise.

Transcript of a BriefingsDirect discussion on how cloud models propel big data as a service benefits. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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Monday, July 20, 2015

How Big Data Powers GameStop to Gain Retail Advantage and Deep Insights into its Markets

Transcript of a BriefingsDirect discussion on how a gaming retailer uses big data to gather insights into sales trends and customer wants and needs.

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

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

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

Our next innovation case study interview highlights how GameStop, based in Grapevine, Texas uses big data to improve how it conducts its business and serve its customers. To learn more about how they deploy big data and use the resulting analytics, we are joined by John Crossen, Data Warehouse Lead at GameStop. Welcome, John.

John Crossen: Thank you for having me.
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Gardner: Tell us a little bit about GameStop. Most people are probably familiar with the retail outlets that they see, where you can buy, rent, trade games, and learn more about games. Why is big data important to your organization?

Crossen: We wanted to get a better idea of who our customers are, how we can better serve our customers and what types of needs they may have. With prior reporting, we would get good overall views of here’s how the company is doing or here’s how a particular game series is selling, but we weren’t able to tie that to activities of individual customers and possible future activity of future customers, using more of a traditional SQL-based platform that would just deliver flat reports.

Crossen
So, our goal was to get s more 360-degree view of our customer and we realized pretty quickly that, using our existing toolsets and methodologies, that wasn’t going to be possible. That’s where Vertica ended up coming into play to drive us in that direction.

Gardner: Just so we have a sense of this scale here, how many retail outlets does GameStop support and where are you located?

Crossen:  We're international. There are approximately 4,200 stores in the US and another 2,200 international.

Gardner: And in terms of the type of data that you are acquiring, is this all internal data or do you go to external data sources and how do you to bring that together?

Internal data

Crossen: It's primarily internal data. We get data from our website. We have the PowerUp Rewards program that customers can choose to join, and we have data from individual cash registers and all those stores.

Gardner: I know from experience in my own family that gaming is a very fast-moving industry. We’ve quickly gone from different platforms to different game types and different technologies when we're interacting with the games.

It's a very dynamic changeable landscape for the users, as well as, of course, the providers of games. You are sort of in the middle. You're right between the users and the vendors. You must be very important to the whole ecosystem.

Crossen: Most definitely, and there aren’t really many game retailers left anymore. GameStop is certainly the preeminent one. So a lot of customers come not just to purchase a game, but get information from store associates. We have Game Informer Magazine that people like to read and we have content on the website as well.

Gardner: Now that you know where to get the data and you have the data, how big is it? How difficult is it to manage? Are you looking for real-time or batch? How do you then move forward from that data to some business outcome?

Crossen: It’s primarily batch at this point. The registers close at night, and we get data from registers and loads that into HP Vertica. When we started approximately two years ago, we didn't have a single byte in Vertica. Now, we have pretty close to 24 terabytes of data. It's primarily customer data on individual customers, as well Weblogs or mobile application data.
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Gardner: I should think that when you analyze which games are being bought, which ones are being traded, which ones are price-sensitive and move at a certain price or not, you're really at the vanguard of knowing the trends in the gaming industry -- even perhaps before anyone else. How has that worked for you, and what are you finding?

Crossen: A lot of it is just based on determining who is likely to buy which series of games. So you won't market the next Call of Duty 3 or something like that to somebody who's buying your children's games. We are not going to ask people buy Call of Duty 3, rather than My Little Pony 6.

The interesting thing, at least with games and video game systems, is that when we sell them new, there's no price movement. Every game is the same price in any store. So we have to rely on other things like customer service and getting information to the customer to drive game sales. Used games are a bit of a different story.

Gardner: Now back to Vertica. Given that you've been using this for a few years and you have such a substantial data lake, what is it about Vertica that works for you? What are learning here at the conference that intrigues you about the future?

Quick reports

Crossen: The initial push with HP Vertica was just to get reports fast. We had processes that literally took a day to run to accumulate data. Now, in Vertica, we can pull that same data out in five minutes. I think that if we spend a little bit more time, we could probably get it faster than half of that.

The first big push was just speed. The second wave after that was bringing in data sources that were unattainable before, like web-click data, a tremendous amount of data, loading that into SQL, and then being able to query it out of SQL. This wasn't doable before, and it’s made it do that. At first, it was faster data, then acquiring new data and finding different ways to tie different data elements together that we haven’t done before.

Gardner: How about visualization of these reports? How do you serve up those reports and do you make your inference and analytics outputs available to all your employees? How do you distribute it? Is there sort of an innovation curve that you're following in terms of what they do with that data?
We had processes that literally took a day to run to accumulate data. Now, in Vertica, we can pull that same data out in five minutes.

Crossen: As far as a platform, we use Tableau as our visualization tool. We’ve used a kind of an ad-hoc environment to write direct SQL queries to pull data out, but Tableau serves the primary tool.

Gardner: In that data input area, what integration technologies are you interested in? What would you like to see HP do differently? Are you happy with the way SQL, Vertica, Hadoop, and other technologies are coming together? Where would you like to see that go?

Crossen: A lot of our source systems are either SQL-server based or just flat files. For flat files, we use the Copy Command to bring data, and that’s very fast. With Vertica 7, they released the Microsoft SQL Connector.

So we're able to use our existing SQL Server Integration Services (SSIS) data flows and change the output from another SQL table to direct me into Vertica. It uses the Copy Command under the covers and that’s been a major improvement. Before that, we had to stage the data somewhere else and then use the Copy Command to bring it in or try to use Open Database Connectivity (ODBC) to bring it in, which wasn’t very efficient.

20/20 hindsight

Gardner: How about words of wisdom from your 20/20 hindsight? Others are also thinking about moving from a standard relational database environment towards big data stores for analytics and speed and velocity of their reports. Any advice you might offer organizations as they're making that transition, now that you’ve done it?

Crossen: Just to better understand how a column-store database works, and how that's different from a traditional row-based database. It's a different mindset, everything from how you are going to lay out data modeling.
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For example, in a row database you would tend to freak out if you had a 700-column table. In the column stores, that doesn’t really matter. So just to get in the right mindset of here’s how a column-store database works, and not try to duplicate row-based system in the column-store system.

Gardner: Great. I am afraid we’ll have to leave it there. I’d like to thank our guest, John Crossen, the Data Warehouse Lead at GameStop in Grapevine, Texas. I appreciate your input.

Crossen: Thank you.

Gardner: And also thank to our audience for joining us for this special new style of IT discussion. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HP-sponsored discussions. Thanks again for listening, and come back next time.

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

Transcript of a BriefingsDirect discussion on how a gaming retailer uses big data to gather insights into sales trends and customer wants and needs. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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Monday, December 01, 2014

Hortonworks Accelerates the Big Data Mashup between Hadoop and HP Haven

Transcript of a BriefingsDirect podcast on how companies are beginning to capture large volumes of data for past, present and future analysis capabilities.

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

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

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

This time, we're coming to you directly from the recent HP Big Data 2014 Conference in Boston to learn directly from IT and business leaders alike how big data changes everything … for IT, for businesses and governments, as well as for you and me.

Our next innovation interview highlights how Hortonworks is now working with HP on the management of very large datasets. We'll hear how these two will integrate into more of the HP Haven family, but also perhaps into the cloud, and to make it easier for developers to access business intelligence (BI) as a service.
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To learn more about these ongoing big data trends, we are joined by Mitch Ferguson, Vice President of Business Development at Hortonworks. Welcome, Mitch.

Mitch Ferguson: Thank you, Dana. Pleasure to be here.

Gardner: We’ve heard the news earlier this year about HP taking a $50-million stake in Hortonworks, and Hortonworks' IPO plans. Please fill us in little bit about why Hortonworks and HP are coming together.

Ferguson: There are two core parts to that answer. One is that the majority of Hadoop came out of Yahoo. Hortonworks was formed by the major Hadoop engineers at Yahoo moving to Hortonworks. This was all in complete corporation with Yahoo to help evolve the technology faster. We believe the ecosystem around Hadoop is critical to the success of Hadoop and critical to the success of how enterprises will take advantage of big data.

Ferguson
If you look at HP, a major provider of technology to enterprises, both at the compute and storage level but the data management level, the analytics level, the systems management level, and the complimentary nature of Hadoop as part of the modern data architecture with the HP hardware and software assets provides a very strong foundation for enterprises to create the next generation modern data architecture.

Gardner: I'm hearing a lot about the challenges of getting big data into a single set or managing the large datasets.

Users are also trying to figure out how to migrate from SQL or other data stores into Hadoop and into HP Vertica. It’s a challenge for them to understand a roadmap. How do you see these datasets as they grow larger, and we know they will, in terms of movement and integration? How is that path likely to unfold?

Machine data

Ferguson: Look at the enterprises that have been adapting Hadoop. Very early adopters like eBay, LinkedIn, Facebook, and Twitter are generating significant amounts of machine data. Then we started seeing large enterprises, aggressive users of technology adopt it.

One of the core things is that the majority of data being created everyday in an enterprise is not coming from traditional enterprise resource planning (ERP) or customer relationship management (CRM) financial management systems. It's coming from websites like Clickstream, data, log data, or sensor, data. The reason there is so much interest in Hadoop is that it allows companies to cost effectively capture very large amounts of data.

Then, you begin to understand patterns across semi-structured, structured, and unstructured data to begin to glean value from that data. Then, they leverage that data in other technologies like Vertica, analytics technologies, or even applications or move the data back into the enterprise data warehouse.

As a major player in this Hadoop market, one of the core tenets of the company was that the ecosystem is critical to the success of Hadoop. So, from day one, we’ve worked very closely with vendors like Microsoft, HP, and others to optimize how their technologies work with Hadoop.

SQL has been around for a long time. Many people and enterprises understand SQL. That's a critical access mechanism to get data out of Hadoop. We’ve worked with both HP and Microsoft. Who knows SQL better than anyone? Microsoft. We're trying to optimize how SQL access to Hadoop can be leveraged by existing tools that enterprises know about, analytics tools, data management tools, whatever.

That's just one way that we're looking at leveraging existing integration points or access mechanisms that enterprises are used to, to help them more quickly adopt Hadoop.
The technology like Hadoop is optimized to allow an enterprise to capture very, very large amounts of that data.

Gardner: But isn’t it clear that what happens in many cases is that they run out of gas with a certain type of database and that they seek alternatives? Is that not what's driving the market for Hadoop?

Ferguson: It's not that they're running out of gas with an enterprise data warehouse (EDW) or relational database. As I said earlier, it's the sheer amount of data. By far, the majority of data is not coming from those traditional ERP,  CRM, or transactional systems. As a result, the technology like Hadoop is optimized to allow an enterprise to capture very, very large amounts of that data.

Some of that data may be relevant today. Some of that data may be relevant three months or six months from now, but if I don't start capturing it, I won't know. That's why companies are looking at leveraging Hadoop.

Many of the earlier adopters are looking at leveraging Hadoop to drive a competitive advantage, whether they're providing a high level of customer service, doing things more cost-effectively than their competitors, or selling more to their existing customers.

The reason they're able to do that is because they're now being able to leverage more data that their businesses are creating on a daily basis, understanding that data, and then using it for their business value.

More than size

Gardner: So this is an alternative for an entirely new class of data problem for them in many cases, but there's more than just the size. We also heard that there's interest in moving from a batch approach to a streaming approach, something that HP Vertica is very popular around.

What's the path that you see for Hortonworks and for Hadoop in terms of allowing it to be used in more than a batch sense, perhaps more toward this streaming and real-time analytics approach?

Ferguson: That movement is under way. Hadoop 1.0 was very batch-oriented. We're now in 2.0 and it's not only batch, but interactive and also real-time, and there's a common layer within Hadoop.  Hortonworks is very influential in evolving this technology. It's called YARN. Think of it as a data operating system that is part of Hadoop, and it sits on top of the file system.

Via YARN, applications or integration points, whether they're for batch oriented applications, interactive integration, or real-time like streaming or Spark, are access mechanisms. Then, those payloads or applications, when they leverage Hadoop, will go through these various batch interactive, real-time integration points.

They don't need to worry about where the data resides within Hadoop. They'll get the data via their batch real-time interactive access point, based on what they need. YARN will take advantage of moving that data in and out of those applications. Streaming is just one way of moving data into Hadoop. That's very common for sensor data. It’s also a way to move it out. SQL is a way, among others, to move data.
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Gardner: So this is giving us choice about how to manage larger scales of data. We're seeing choice about the way in which we access that data. There's also choice around the type of the underlying infrastructure to reduce costs and increase performance. I am thinking about in-memory or columnar.

What is there about the Hadoop community and Hortonworks, in particular, that allows you to throw the right horsepower at the problem?

Ferguson: It was very important, from Hortonworks perspective from day one, to evolve the Hadoop technology as fast as possible. We decided to do everything in open source to move the technology very quickly and leverage the community effective open-source, meaning lots of different individuals helping to evolve this technology fast.

The ability for the ecosystem to easily and optimally integrate with Hadoop is important. So there are very common integration points. For example, for systems management, there is the Ambari Hadoop services integration point.

Whether it's an HP OpenView or System Center in the Microsoft world, that allows it to leverage, manage, or monitor Hadoop along with other IT assets that those management technologies integrate with.

Access points

Then there's SQL's access via Hive, an access point to allow any technology that integrates or understands SQL to access Hadoop.

Storm and Spark are other access points. So, common open integration points well understood by the ecosystem are really designed to help optimize how various technologies at the virtualization layer, at the operating system layer, data movement, data management, access layer can optimally leverage Hadoop.

Gardner: One of the things that I hear a lot from folks who don't understand yet how things will unfold, is where data and analytics applications align with the creation of other applications or services, perhaps in a cloud setting like a platform as a service (PaaS).

It seems to me that, at some point, more and more application development will be done through PaaS with an associated or integrated cloud. We're also seeing a parallel trajectory here with the data, along the same lines of moving from traditional systems of record into relational, and now into big data and analytics in a cloud setting. It makes a lot of sense.
What a number of people are doing with this concept is called the data lake. They're provisioning large Hadoop clusters on prem, moving large amounts of data into this data lake.

I talked to lot of people about that. So the question, Mitch, is how do we see a commingling and even an intersection between the paths of PaaS in general application development and PaaS in BI services, or BI as a service, somehow relating?

Ferguson: I'll answer that question in two ways. One is about the companies that are using Hadoop today, and using it very aggressively. Their goal is to provide Hadoop as a service, irrespective of whether it's on premises or in the cloud.

Then we'll talk about what we see with HP, for example, with their whole cloud strategy, and how that will evolve into a very interesting hybrid opportunity and maybe pure cloud play.

When you think about PaaS in the cloud, the majority of enterprise data today is on premises. So there's a physics issue of trying to run all of my big data in the cloud. As a result, what a number of people are doing with this concept is called the data lake. They're provisioning large Hadoop clusters on premises, moving large amounts of data into this data lake.

That's providing data as a service to those business units that need data in Hadoop -- structured, semi-structured, unstructured for new applications, for existing analytics processes, for new analytics processes -- but they're providing effectively data as a service, capturing it all in this data lake that continues to evolve.

Think about how companies may want to leverage then a PaaS. It's the same thing on premises. If my data is on premises, because that's where the physics requires that, I can leverage various development tools or application frameworks on top of that data to create new business apps. About 60 percent of our initial sales at Hortonworks are new business applications by an enterprise. It’s business and IT being involved.

Leveraging datasets

Within the first five months, 20 percent of those customers begin to migrate to the data-lake concept, where now they are capturing more data and allowing other business entities within the company to leverage these datasets for additional applications or additional analytics processes. We're seeing Hadoop as a service on premises already. When we move to the cloud, we'll begin to see more of a hybrid model.

We are already starting to see this with one of Hortonworks large partners, where you put archive data from on premises to store in the cloud at low-cost storage. I think HP will have that same opportunity with Hadoop and their cloud strategy.

Already, through an initiative at HP, they're providing Hadoop as a service in the cloud for those entities that would like to run Hadoop in a managed service environment.
We're seeing Hadoop as a service on prem already. When we move to the cloud, we'll begin to see more of a hybrid model.

That’s the first step of HP beginning to provide Hadoop in a managed service environment off premises. I believe you'll begin to see that migrate to on-prem/off-prem integration in a hybrid opportunity in the some companies as their data moves off prem. They just want to run all of their big-data services or have Hadoop as a service running completely in HP cloud, for example.

Gardner: So, we're entering in an era now where we're going to be rationalizing how we take our applications as workloads, and continue to use them either on premises, in the cloud, or hybrid. At the same time, over on the side, we're thinking along the same lines architecturally with our data, but they're interdependent.

You can’t necessarily do a lot with the data without applications, and the applications aren’t as valuable without access to the analytics and the data. So how do these start to come together? Do you have a vision on that yet? Does HP have a vision? How do you see it?

Ferguson: The Hadoop market is very young. The vision today is that companies are implementing Hadoop to capture data that they're just letting fall on the floor. Now, they're capturing it. The majority of that data is on premises. They're capturing that data and they're beginning to use it in new a business applications or existing analytics processes.
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As they begin to capture that data, as they begin to develop new applications, and as vendors like HP working in combination with Hortonworks provide the ability to effectively move data from on premises to off premises and provide the ability to govern where that data resides in a secure and organized fashion, you'll begin to see much tighter integration of new business or big-data applications being developed on prem, off prem, or an integration of the two. It won't matter.

Gardner: Great. We've been learning quite a bit about how Hortonworks and Hadoop are changing the game for organizations as they seek to use all of their data and very massive datasets. We’ve heard that that aligns with HP Vertica and HP Haven's strategy around enabling more business applications for more types of data.

With that, I'd like to thank our guest, Mitch Ferguson, Vice President of Business Development at Hortonworks. Thank you, Mitch.

Ferguson: Thank you very much, Dana.

Gardner: This is Dana Gardner. I'd like our audience for joining us for a new style of IT discussion coming to you from the recent HP Big Data 2014 Conference in Boston. Thanks to HP for sponsoring our discussion, and don't forget to come back next time.

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

Transcript of a BriefingsDirect podcast on how companies are beginning to capture large volumes of data for past, present and future analysis capabilities. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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Thursday, June 26, 2014

How Capgemini's UK Financial Services Unit Helps Clients Manage Risk Using Big Data Analysis

Transcript of a sponsored BriefingsDirect podcast on how HP tools are helping companies harness big data to provide better risk assessment.

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

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

Gardner
Once again, we’re focusing on how companies are adapting to the new style of IT to improve IT performance and deliver better user experiences, and business results. This time, we’re coming to you directly from the recent HP Discover 2013 Conference in Barcelona.

We’re here to learn directly from IT and business leaders alike how big datamobile, and cloud, along with converged infrastructure are all supporting their goals.

Our next innovation case study interview highlights how Capgemini's Financial Services Global Business Unit in the United Kingdom is using big data and analysis to help its organization clients better manage risk.

To tell us more about how they do that, we're joined by Ernie Martinez, Business Information Management Head at the Capgemini Financial Services Global Business Unit in London. Welcome Ernie.

Ernie Martinez: Thank you. Glad to be here.

Gardner: Ernie, risk has always been with us. I suppose it will always remain with us in some fashion or another. Is there anything new, pressing, or different about the types of risks that your clients are trying to reduce and understand in this climate and market?

Martinez
Martinez: As you said, risk has always been with us. I don't think it's as much about what's new within the risk world, as much as it's about the time it takes to provision the data so companies can make the right decisions faster, therefore limiting the amount of risk they may take on in issuing policies or taking on policies with new clients.

Gardner: In addition to the risk issue, of course, there is competition. The speed of business is picking up, and we’re still seeing difficult economic climates in many markets. How do you step into this environment and find a technology that can improve things? What have you found?

Martinez: There is the technology aspect of delivering the right information to business faster. There is also the business-driven way of delivering that information faster to business.

Bottom up

Why Capgemini and our business information management (BIM) practices jumped in with a partnership with HP and Vertica in the HAVEn platform is really about the ability to deliver the right information to business faster from the bottom up. That means the infrastructure and the middleware by which we serve that data to business. From the top down, we work with business in a more iterative fashion in delivering value quickly out of the data that they are trying to harvest.

Gardner: Capgemini is a large global organization. Perhaps you could tell us a bit about what your unit does and the types of clients you have.

Martinez: The BIM practice is a global practice. We’re ranked in the top upper right-hand quadrant in Gartner as one of the best BIM practices out there with about 7,000 BIM resources worldwide.

Our focus is on driving better value to the customer. So we have principal-level and senior-level consultants that work with group-level CEOs in the financial services, insurance, and capital markets arenas. Their main focus is to drive a strategy and roadmap, consulting work, enterprise information architecture, and enterprise information strategy with a lot of those, the COO- and CFO-level customers.

We then drive more business into the technical design and architectural way of delivering information in business intelligence (BI) and analytics. Once we define what the road to good looks like for an organization, when you talk about integrating information across the enterprise, it's about what is that path to good looks like and what are the key initiatives that an organization must do to be able to get there.

This is where our technical design, business analysis, and data analysis consultants fit in. They’re actually going in to work with business to define what do they need to see out of their information to help them make better decisions.

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Gardner: Of course, the very basis of this is to identify the information, find the information, and put the information in a format that can be analyzed. Then, do the analysis, speed this all up, and manage it at scale and at the lowest possible cost. It’s a piece of cake, right? Tell us about the process you go through and how you decide what solutions to use and where the best bang for the buck comes from?

Martinez: Our approach is to take that senior-level expertise in big data and analytics, bring that into our practice, put that together with our business needs across financial services, insurance, and capital markets, and begin to define valid use cases that solve real business problems out there.

We’re a consulting organization, and I expect our teams to be able to be subject matter experts on what's happening in the space and also have a good handle on what the business problems are that our customers are facing. If that’s true, then we should be able to outline some valid use cases that are going to solve some specific problems for business customers out there.

In doing so, we’ll define that use case. We’ll do the research to validate that indeed it is a business problem that's real. Then we’ll build the business case that outlines that if we do build this piece of intellectual property (IP), we believe we can go out and proactively affect the marketplace and help customers out there. This is exactly what we did with HP and the HAVEn platform.

Wide applicability

Gardner: So we’re talking about a situation where you want to have wide applicability of the technology across many aspects of what you are doing, that make sense economically, but of course it also has to be the right tool for the job, that's to go deep and wide. You’re in a proof-of-concept (POC) stage. How did you come to that? What were some of the chief requirements you had for doing this at that right balance of deep and wide?

Martinez: We, as an organization, believe that our goal as BI and analytics professionals is to deliver the right information faster to business. In doing so, you look at the technologies that are out there that are positioned to do that. You look at the business partners that have that mentality to actually execute in that manner. And then you look at the organization, like ours, whose sole purpose is to mobilize quickly and deliver value to customer.

I think it was a natural fit. When you look at HP Vertica in the HAVEn platform, the ability to integrate social media data through Autonomy and then of course through Vertica and Hadoop -- the integration of the entire architecture -- gives us the ability to do many things.

But number one, it's the ability to bring in structured and unstructured data, and be able to slice and dice that data in a rapid fashion; not only deploy it, but also execute rapidly for organizations out there.
Being here at HP Discover this week has certainly solidified in my mind that we’re betting on the right horse.

Over the course of the last six months of 2013, that conversation began to blossom into a relationship. We all work together as a team and we think we can mobilize not just the application or the solution that we’re thinking about, but the entire infrastructure derivatives to our customers quickly. That's where we’re at.

What that means is that once we partnered and got the go ahead with HP Vertica to move forward with the POC, we mobilized a solution in less than 45 days, which I think shows the value of the relationship from the HP side as well as from Capgemini.

Gardner: Down the road, after some period of implementation, there are general concerns about scale when you’re dealing with big data. Because you’re near the beginning of this, how do you feel about the ability for the platform to work to whatever degree you may need?

Martinez: Absolutely no concern at all. Being here at HP Discover has certainly solidified in my mind that we’re betting on the right horse with their ability to scale. If you heard some of the announcements coming out, they’re talking about the ability to take on big data. They’re using Vertica and the HAVEn network.

There’s absolutely zero question in my mind that organizations out there can leverage this platform and grow with it over time. Also, it gives us the ability to be able to do some things that we couldn’t do a few years back.

Business value

Gardner: Ernie, let's get back to the business value here. Perhaps you can identify some of the types of companies that you think would be in the best position to use this. How will this hit the road? What are the sweet spots in the market, the applications you think would be the most urgently that make a right fit for this?

Martinez: When you talk about the largest insurers around the world, whether from Zurich to Farmers in the US to Liberty Mutual, you name it, these are some of our friendly customers that we are talking to that are providing feedback to us on this solution.

We’ll incorporate that feedback. We’ll then take that to some targeted customers in North America, UK, and across Europe, that are primed and in need of a solution that will give them the ability to not only assess risk more effectively, but reduce the time to be able to make these type of decisions.

Reducing the time to provision data reduces costs by integrating data across multiple sources, whether it be customer sentiment from the Internet, from Twitter and other areas, to what they are doing around their current policies. It allows them to identify customers that they might want to go after. It will increase their market share and reduce their costs. It gives them the ability to do many more things than they were able to do in the past.
It allows them to identify customers that they might want to go after. It will increase their market share and reduce their costs.

Gardner: And Capgemini is in the position of mastering this platform and being able to extend the value of that platform across multiple clients and business units. Therefore, that reduces the total cost of that technology, but at the same time, you’re going to have access to data across industries, and perhaps across boundaries that individual organizations might not be able to attain.

So there's a value-add here in terms of your penetration into the industry and then being able to come up with the inferences. Tell me a little bit about how the access-to-data benefit works for you?

Martinez: If you take a look at the POC or the use case that he POC was built on, it was built on a commercial insurance risk assessment. If you take a look at the underlying architecture around commercial insurance risk, our goal was to be able to build an architecture that will serve the uses case that HP bought into, but at the same time, flatten out that data model and that architecture to also bring in better customer analytics for commercial insurance risk.

So we’ve flattened out that model and we’ve built the architecture so we could go after additional business, instead of more clients, across not just commercial insurance, but also general insurance. Then, you start building in the customer analytics capability within that underlying architecture and it gives us the ability to go from the insurance market over to the financial services market, as well as into the capital markets area.

Gardner: All the data in one place makes a big difference.

Martinez: It makes a huge difference, absolutely.

Future plans

Gardner: Tell us a bit about the future. We’ve talked about a couple of aspects of the HAVEn suite. Autonomy, Vertica, and Hadoop seem to be on everyone's horizon at some point or another due to scale and efficiencies. Have you already been using Hadoop, or how do expect to get there?

Martinez: We haven’t used Hadoop, but certainly, with its capability, we plan to. I’ve done a number of different strategies and roadmaps in engaging with larger organizations, from American Express to the largest retailer in the world. In every case, they have a lot of issues around how they’re processing the massive amounts of data that are coming into their organization.

When you look at the extract, transform, load (ETL) processes by which they are taking data from systems of record, trying to massage that data and move it into their large databases, they are having issues around load and meeting load windows.

The HAVEn platform, in itself, gives us the ability to leverage Hadoop, maybe take out some of that processing pre-ETL, and then, before we go into the Vertica environment, be able to take out some of that load and make the Vertica even more efficient than it is today, which is one of the biggest selling points of Vertica. It certainly is in our plans.
This is a culture that organizations absolutely have to adopt if they are going to be able to manage the amount of data at the speed at which that data is coming to their organizations.

Gardner: Another announcement here at Discover has been around converged infrastructure, where they’re trying to make the hardware-software efficiency and integration factor come to bear on some of these big-data issues. Have you thought about the deployment platform as well as the software platform?

Martinez: You bet. At the beginning of this interview, we talked about the ability to deliver the right information faster to business. This is a culture that organizations absolutely have to adopt if they are going to be able to manage the amount of data at the speed at which that data is coming to their organizations. To be able to have a partner like HP who is talking about the convergence of software and infrastructure all at the same time to help companies manage this better, is one of the biggest reasons why we're here.

We, as a consulting organization, can provide the consulting services and solutions that are going to help deliver the right information, but without that infrastructure, without that ability to be able to integrate faster and then be able to analyze what's happening out there, it’s a moot point. This is where this partnership is blossoming for us.

Gardner: Before we sign off, Ernie, now that you have gone through this understanding and have developed some insights into the available technologies and made some choices, is there any food for thought for others who might just be beginning to examine how to enter big data, how to create a common platform across multiple types of business activities? What did you not think of before that you wish you had known?

Lessons learned

Martinez: If I look back at lessons learned over the last 60 to 90 days for us within this process, it’s one thing to say that you're mobilizing the team right from the bottom up, meaning from the infrastructure and the partnership with HP, and as well as the top-down with your business needs to finding the right business requirements and then actually building to that solution.

In most cases, we’re dealing with individuals. While we might talk about an entrepreneurial way of delivering solutions into the marketplace, we need to challenge ourselves, and all of the resources that we bring into the organization, to actually have that mentality.

What I’ve learned is that while we have some very good tactical individuals, having that entrepreneurial way of thinking and actually delivering that information is a different mindset altogether. It's about mentoring our resources that we currently have, bringing in that talent that has more of an entrepreneurial way of delivering, and trying to build solutions to go to market into our organization.

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I didn’t really think about the impact of our current resources and how it would affect them. We were a little slow as we started the POC. Granted, we did this in 45 days, so that’s the perfectionist coming out in me, but I’d say it did highlight a couple of areas within our own team that we can improve on.

Gardner: So, it’s important to either identify or find a culture of innovation?

Martinez: That's correct.

Gardner: Well, great. I am afraid we’ll have to leave it there. We’ve been talking about how the Capgemini Financial Services Global Business Unit has been entering into a proof-of-concept phase around big data and some of the choices that they have been making. I want to thank our guest, Ernie Martinez, the Business Information Management Head at Capgemini Financial Services Global Business Unit in London. Thank you, Ernie.

Martinez: Thanks, Dana. I appreciate your time.

Gardner: Thank you to our audience as well for joining us for this special new style of IT discussion coming to you directly from the HP Discover 2013 Conference in Barcelona.

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

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

Transcript of a sponsored BriefingsDirect podcast on how HP tools are helping companies harness big data to provide better risk assessment. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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