How Waste Management Builds a Powerful Services Continuum Across Operations, Infrastructure, Development, and IT Processes

Transcript of a BriefingsDirect podcast on how a large environmental services company uses HP BSM tools to provide always-on, always-available services to customers and internal users.

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

Our next innovation case study interview highlights how Waste Management in Houston, Texas is improving the quality of their services and operations in IT for a variety of their users, both internal and external.
To help us learn more about Waste Management’s experience, we're here with Gautam Roy, Vice President of Infrastructure, Operations and Technical Services at Waste Management. Welcome.

Gautam Roy: Hi. Thank you.

Gardner: You're a very large organization across North America with more than 20 million customers. This size and scale requires an awful lot of IT. Tell us about the scope and size of your operation.

Roy: Water Management is an environmental services company. We have primarily three lines of business. First is waste service. This is our traditional waste pickup, transfer, and disposal. Our second line of business is renewable energy or green energy, and our third is recycling.

Roy

What makes Waste Management different from others in the waste industry is that we also invest quite a lot of effort in next-generation waste technology. We invest in companies like Agilyx, which converts very hard-to-recycle waste, such as plastic, into crude oil. We convert organic food waste into natural gas. We pressurize, scrub, and dry municipal solid waste into solid fuel, which burns cleaner than coal.

And we're quite diverse, a global company. We have operations in the US and Canada, Asia, and Europe. We have our renewable energy plants. There is quite a large array of technology and IT to support these business processes to ensure consistent business-services availability.

Gardner: As with many organizations, gaining greater visibility into operations -- having earlier detection of problems, and therefore earlier remediation -- means better performance. What were some of the drivers for your organization specifically to mature your IT operations?

Business transformation

Roy: I'll give a few business reasons, and a couple of technology reasons. From the business side, we began business transformation a couple of years ago. We wanted to ensure that we unlocked the value for our customers and for us, and to institutionalize the benefits for Waste Management.

Customer care, providing outstanding, world-class customer service is aligned completely with our business strategy. Business services availability is crucial, it's in our DNA. Our IT business service availability scorecard a few years ago wasn't too good. So we had to put the focus on people, process, and technology to ensure that we provide a very consistent service set to our customers.

Gardner: Moving across the spectrum of development, test, and operations can be challenging for many organizations. You have put in place standardized processes to measure, organize, and perform better across the DevOps spectrum. Tell us how you accomplished that. How did you get there?

Roy: That's a very good question. For us, IT business-service availability is really not about having a great monitoring solution. It starts even before the services are in production. It starts with partnership with our business and business requirements. It starts with having a great development methodology and a robust testing program. It starts with architecture processes, standardization, and communication. All those things have to be in place. And you have to have security services and a monitoring solution to wrap it up.

We try to approach it from the front end, instead of chasing it from the back end.

What we are trying to do is to not fight the issue at the back-end. If a service is down, our monitoring software picks it up, our operational team and engineering team jumps on it, we are able to fix the problem ASAP before it impacts the customer. Great. But, boy, wouldn’t it be nice if those services aren't going down in the first place? So we try to approach it from the front-end, instead of just chasing it from the back-end.

Gardner: So it’s Application Lifecycle Management (ALM) and Business Service Management (BSM), not one or the other, but really both -- and simultaneously?

Roy: Exactly, ALM, BSM, testing, and security products. We also want to make sure that the services are not down from intentional disruption. We want to make sure that we produce code with quality and velocity, and code that is consistent with the experience of our customer.

With our operational processes, ITIL and Lean IT, we want to make sure that the change management and incident management are followed to our prescription. We want to make sure that the disaster-recovery (DR) program, the high-availability (HA) program, the security operation center (SOC), the network operation center (NOC), and the command centers are all working together to ensure that the services are up 24/7, 365.

Gardner: And when you do this well, when you have put in place many of the capabilities that we have been describing, do you have any sense of payback? Do you keep score?

Availability scorecard

Roy: A few years ago, when we were not as good at it, we started rebuilding this all from the ground up, and our availability scorecard was pretty bad. Our services were down. At times, we didn’t know that our services were down. Our first indication of a problem was from customers calling us.

Now, fast-forward a few years, with making the appropriate choices and investments in technology -- such as in people and processes --  and our scorecard is very good. We know of the problems rapidly. We proactively detect problems and fix the problems before they impact our customers.

We have 4 9s availability for our critical applications. We're able to provide services to our customers via wm.com, our digital channel, and it has been quite a success story. We still have work to cover, but it has been following the right trajectory.

Gardner: Here at HP Discover, are there any developments that you're monitoring closely? Are there some things that you're particularly interested in that might help you continue to close the gap on quality?

We want to provide optimal solutions at a right price point for our customers and our business.

Roy: Sure. Things like understanding what's happening in the world of big data and HP’s views and position on that. I want to understand and learn about testing, software testing, how to test faster and produce better code, and to ensure, on a continuous basis that we're reducing the cost of running the business. We want to provide optimal solutions at a right price point for our customers and our business.

Gardner: On that topic of big data, are you referring to the data generated within IT, in your systems, to be able to better analyze and react to that? Or perhaps also the data from your marketplace, things that your customers might be saying in social media, for example? Or is it all of the above?

Roy: It’s all of the above. We have internal data that we're harvesting. We want to understand what it’s telling us. And we'd like to predict certain trends of our system, across the use of our applications.

Externally, we have 18 call centers. We get user calls. We also want to know our customer better and serve them the best. So we want to move into a situation where we can take their issues, frame them into solutions, and proactively service them the best in our industry.

Gardner: I'm afraid we will have to leave it there. We've been discussing how Waste Management improves their IT operations across the BSM spectrum, from development through operations, and then embarking on more use of big data to analyze their business requirements as well as their marketplace.

So a big thank you to Gautam Roy, Vice President of Infrastructure, Operations and Technical Services at Waste Management in Houston. Thanks so much.

Roy: Thank you, Dana.

Gardner: And thank you, too, to our audience for joining this special HP Discover 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 joining, and come back next time.

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

Transcript of a BriefingsDirect podcast on how a large environmental services company uses HP BSM tools to provide always-on, always-available services to customers and internal users. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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• Advanced cloud service automation eases application delivery for global service provider NNIT
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GSN Games Wins Big Using HP Vertica to Uncover Deep Insights into User Preferences

Transcript of a BriefingsDirect podcast on how big data and instant analysis can provide valuable feedback on entertainment company user preferences.

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, deliver better user experiences, and stronger business results. We’re coming to you directly from the recent HP Discover Conference in Barcelona.

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

Our next innovation case study interview highlights how GSN Games is using big data to uncover more information to produce and deliver improved entertainment for their audience. Please welcome our guest, Portman Wills, Vice President of Data at GSN Games in San Francisco. Welcome, Portman.

Portman Wills: Hi. Nice to be here.

Gardner: Tell us about GSN Games. What do you do, and who plays these games?

Wills: GSN started as a cable network in the U.S. We’re distributed in 80 million households as the Game Show Network, and then we also have a digital wing that produces casual and social games on Facebook, web, tablets, and mobile. That division has 110 million registered game-players. My team takes data from all over those worlds, throws them into a big data warehouse, and starts trying to find trends and insights for both our TV audience and our online game-players.

Wills

In terms of the games, which is really where the growth is, our core demographic is older females, believe it or not, who love playing casual games. We skew more in the 55-plus age range, and we have players from all over the world.

Because we’re here in Spain, a quick tidbit that we uncovered recently is that our main time-frame in every country on Earth, when people play games, is 7 p.m. to 11 p.m., except in Spain where it’s 1 p.m. to 3 p.m. -- siesta time. That’s just one of the examples of how we use big data to use discover insights about our players and our audiences worldwide.

Understanding the audience

Gardner: I have to imagine that the data that led you to that inference in Spain was something other than what we might consider typical structured data. How did the different data brought together allow you to understand your Spanish audience better?

Wills: We use this product from HP called Vertica, which is just a tremendous data warehouse, that lets us throw every single click, touch, or swipe in all of our games into a big table. By big, I mean right now it’s I think 1.3 trillion rows. We keep saying that we should really archive this thing. Then, we say we’ll archive it when it slows down, and then it just never slows down, so we have yet to archive it.

We put all of the click stream data in there. The traditional joins, schemas, and all of that don’t really have to happen because we have one table with all of the interactions. You have the device, the country, the player, all these attributes. It’s a very wide table. So if you want to do things like ask what is the usage in five-minute slices by country, it’s a simple SQL query, and you get your results.

Gardner: The word “games” means a lot of different things to a lot of people. We’re talking about a heritage of network television games back in the ’60s and ’70s that have led us to what is now your organization. But what sort of newer games are we talking about, and what proportion of them are online games, versus more of the passive watching like that on a cable or other media outlets?

Wills: Originally, when our games division started as a branch of GSN, it was companion games to Wheel of Fortune, Minute to Win It, whatever the hot game show was. That's still a part of it, but the growth in the last few years has been in social games on Facebook, where a lot of our games are more casual titles and have nothing to do with the game shows -- tile-matching games or solitaire games, for example.

In the last year or year-and-a-half for us, like everyone else, there’s been this explosion in mobile.

Then, in the last year or year-and-a-half for us, like everyone else, there’s been this explosion in mobile. So it’s iPad, Android, and iPhone games, and there we have the solitaires and the tile matching, too.

Increasingly, a lot of our success and growth has come from virtual casino games. People are playing Bingo, video poker, even slots, virtual slots. We have this title called GSN Casino. That’s an umbrella app with a lot of mini games that are casino-themed, and that one has really just exploded really in the last six months. It's a long way from the Point A of Family Feud reruns to the Point Z of virtual slot machines, but hopefully you can see how we got there.

Gardner: It seems like a long distance, but it’s been also a fairly short amount of time. It wasn't that long ago that the information you might have in your audience came through Nielsen for passive audiences, and you had basically a one- or two-dimension view of that individual, based on the estimate of what time was devoted to a show. But now, with the mobile devices in particular, you have a plethora of data.

Tell us about the types of data that you can get, and what volumes are we talking about.

Mobile experience

Wills: Let’s take mobile, because I think it's easy to grok. Everything about the device is exposed to us. The fact that you’re playing on an iPad Mini Retina versus an iPad 1 tells us a lot about you, whether you know it or not.

Then, a lot of our users sign-in via Facebook, which is another vector for information. If you sign-in via Facebook, Facebook provides us your age range, gender, some granular location information. For every player, we get between 40 and 50 dimensions of data about that player or about that device.

That’s one bucket. But the actual gameplay is another whole bucket. What games do you choose to play in our catalog? How long do you play them? What time of day do you play them? Those start to classify users into various buckets -- from the casual commute player, who plays for 15 minutes every morning and afternoon, to the hard-core player who spends 8 to 10 hours a day, believe it or not, playing our games on their mobile devices.

Mobile doesn’t necessarily mean mobile, like out and about. A lot of our players are on their iPad, sitting on the couch in their home.

At that point, and this is a little bit of a pet peeve of mine, mobile doesn’t necessarily mean mobile, like out and about. A lot of our players are on their iPad, sitting on the couch in their home.

It’s not mobility. They’re not using 3G. They’re not using augmented reality. It’s just a device that happens to be a very convenient device for playing games. So it’s much more of a laptop replacement than any sort of mobile thing. That’s sort of a side track.

We collect all of this data, and it’s a fair amount. Right now, we’re generating about 900 million events per day across all of our players. That’s all streamed into our HP Vertica data warehouse, and there are a few tables, event time series tables, that we put the stuff into. A small table for us would be a few hundred billion records, and a large table, as I said, is 1.3 trillion records right now.

So the scale is big for us. I know that for other companies that seems like peanuts. It’s funny how big data is so broad. What’s big to one person is tiny to someone else, but this is the world that we’re dealing in right now.

We have 110 million players. Thankfully, not all of them are active at one time. That would be really big data. But we will have about 20 million at any given time in peak time playing concurrently. That’s a little bit about the numbers in our data warehouse.

Gardner: Understanding your audience through this data is something fairly new. Before, you couldn’t get this amount of data. Now that you have it, what is it able to do for you? Are you crafting new games based on your findings? Are you finding information that you can deliver back to a marketer or advertiser that links them to the audience better? There must be many things you can do.

No advertising

Wills: First of all, we don’t do any advertising in our mobile games. So that’s one piece that we’re not doing, although I know others are. But there are two broad buckets in which we use data. The first is that we run a lot of the A/B tests, experiments. All of our games are constantly being multivariate tested with different versions of that same game in the field.

We run 20 to 40 tests per week. As an example, we have a Wheel of Fortune game that we recently released, and there was all this debate about the difficulty of the puzzles. How hard should the puzzles be? Should they be very obscure pieces of Eastern literature, mainstream pop culture, or even easier?

So, we tested different levels of difficulty. Some players got the easy, some players got the medium, and some players got the hard ones. We can measure the return rate, the session duration, and the monetization for people who buy power-ups, and we see which level of difficulty performs the best. In the first test of easy, medium, hard, easy overwhelmingly did the best.

So we generated a whole bunch of new puzzles that were even easier than were the previous easy ones and tested that against what was now the control level. The easier puzzles won again. So we generated a whole new set of puzzles that were absurdly easy. We were trying to prove the point that if we gave Wheel of Fortune puzzles that are four-letter words like “bird” and “cups,” nobody would enjoy playing something that simplistic.

Well it turns that they do -- surprise, surprise -- and so that’s how we evolved into a version of Wheel of Fortune that, compared to the game show, looks very different, but it’s actually what customers want. It’s what players want. They want to relax and solve simple puzzles like “door.”

Hopefully faster than overnight. Overnight is a little too slow these days.

Gardner: So Vertica analysis determined that everyone is a winner on GSN, but you’re able to do real-time focus-group types of activities. The data -- because it's so fast, because there is so much information available and you can deal with it so quickly -- means that you’re able to tune your games to the audience virtually overnight.

Wills: Hopefully faster than overnight. Overnight is a little too slow these days. We push twice a day both to our platform code and updates to all of our games in the morning around 11 a.m and in the afternoon around 3:30. Each one of those releases is based on the data that came from the prior release.

So we're constantly evolving these games. I want to go back to your previous question, because I only got to talk about one bucket, which is this experimentation. The other bucket is using the usage patterns that customers have to evolve our product in ways that aren’t necessarily structured around an A/B test.

We thought when we launched our iPhone app that there would be a lot of commuting usage. We had in our head this hypothetical bus player, who plays on the bus in the morning. And so we thought we would build all the stuff around daily patterns. We built this daily return bonus that you can do in the morning and then again in the evening.

The data showed us that that really was only a tiny fraction of our players. There were, in fact, very few players who had this bimodal, morning and evening usage pattern. Most people didn't play at all until after dinner and then they would play a lot, sometimes even binge from 7 p.m. until 2 a.m. on games.

False assumptions

That was an area where we didn't even set up an experiment. We just had false assumptions about our player base. And that happens a surprising amount of the time. We all -- especially the game-design team and people who spent their careers designing video games -- have assumptions about their audience that half the time are just wrong. One of the things we use data for is to challenge all of our assumptions about our own products and our own businesses.

It's really gotten to a point where it's almost religious in our company. The moment two people start debating what should or shouldn't happen, they say, “Well let's just let the data decide.” That's been a core change not just for us, but for the game industry as a whole.

Gardner: I expect that to be a change, too, across many more industries. What you’re describing is very much desired by a lot of types of businesses through understanding a massive amount of data from their audience, to be able to react quickly to that, and then to stop guessing about products and pricing and distribution and logistics and supply chain and be driven purely by the data. You’re a really interesting harbinger of things to come.

One of the things we use data for is to challenge all of our assumptions about our own products and our own businesses.

Portman, tell me little bit about the process by which you were able to do this. Did you have an older data warehouse? What did you use before, and how did you make a transition to HP Vertica?

Wills: When we started the social mobile business three years ago, we were on MySQL, which we are still on for our transactional load. We have three data centers around the world. When people are playing our games, it’s recording, reading, and writing 125,000 transactions per second, and that MySQL, sharded out, works great for that.

When you want to look at your entire player base and do a cross-shard query, we found that MySQL really fell down. Our original Vertica proof of concept (POC) was just to replace these A/B test queries, which have to look across the entire population.

So in comes Vertica. We set up a single node, a Vertica data warehouse. We pull in a year's worth of data, and the same query to synthesize these sessions ran in 800 milliseconds.

So the thing that took 24 hours, which is 86,400 seconds, ran in less than one second. By the way, that 24-hour query was running across dozens of machines, and this Vertica query was running on a single server of commodity hardware.

That's when we really became believers in the power of the column store and column-oriented data warehouses. From the small beginning of just one simple query, it’s now expanded -- and pretty much our whole business runs on top of HP Vertica on the data warehouse side.

Lessons learned

Gardner: As I said, I think GSN Games is a really harbinger of what a lot of other companies in many different vertical industries will be seeking. Do you have any thoughts in terms of lessons learned, as you progressed over the past three years to this size of a data set, to this level of inference, that you can deliver to virtually everyone in your company?

Looking back, if you had to do it again, what might you have done differently or what suggestions might you have for others who would like to be able to do what you are doing?

Wills: I definitely wish that we had switched to a column store sooner. I think the reason that we've been so successful at this is because of our game design team, which was so open to using data.

I definitely wish that we had switched to a column store sooner.

I’ve heard hard stories from other companies where they want to use a data-driven approach, and there's just a lot of cultural inertia and push back against doing that. It's hard to be consistently proven wrong in your job, which is always what happens when you rely on data.

The real thing that's helped us get to the point we are in is a culture and a company where everybody is open to being wrong -- and open to being proven wrong by the data, which I am very thankful for.

Gardner: Well, it's good to be data-driven, and I think you should feel good being responsible for making 110 million people feel good about themselves every day.

I'm afraid we will have to leave it there. We've been talking about how GSN Games is using HP Vertica to gather amazing insights and go beyond instinct and intuition into more of a science for their audiences' benefit -- and for their business’s benefit.

I would like to thank our guest, Portman Wills, Vice President of Data at GSN Games in San Francisco. Thank you, sir.

Wills: Thank you.

Gardner: And thank you to our audience as well for joining us for this special new style of IT discussion, coming to you directly from the recent HP Discover 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 BriefingsDirect podcast on how big data and instant analysis can provide valuable feedback on entertainment company user preferences. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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• HP network management heightens performance while reducing total costs for Nordic telco TDC
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Hybrid Cloud Models Demand More Infrastructure Standardization, Says Global Service Provider Steria

Transcript of a sponsored BriefingsDirect podcast on planning and preparing for a journey to cloud.

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 data, mobile, and cloud, along with converged infrastructure are all supporting their goals.

Our next innovation case study interview highlights how European IT services provider Steria is exploring cloud standards and the use of cloud across hybrid models. We welcome on this subject Eric Fradet, Industrialization Director at Steria in Paris. Welcome, Eric.

Eric Fradet: Thank you, I’m glad to be here.

Gardner: For those of our audience who may not be overly aware of Steria, tell us a little bit about what you do, where you do it, and how your business is going?

Fradet: Steria is a 40-year-old service provider company, mainly based in Europe, with a huge location in India and also Singapore. We provide all types of services related to IT, starting from infrastructure management to application management. We help to develop and deploy new IT services for all our customers.

Gardner: There’s a lot of interest these days in trying to decide to what degree you should have a cloud infrastructure implementation on-premises, with some sort of a hosting provider, or perhaps going fully to a service-delivery model vis-à-vis a software-as-a-service (SaaS) or cloud providers. How are your activities at Steria helping you better deliver this choice to your customers?

Fradet: That change may be quicker than expected. So, we must be in a position to manage the services wherever they’re from. The old model of saying that we’re an outsourcer or on-premises service provider is dead. Today, we’re in a hybrid world and we must manage that type of world. That must be done in collaboration with partners, and we share the same target, the same ambition, and the same vision.

Gardner: We’re also seeing quite a bit of discussion about which platforms, which standards, and which type of cloud infrastructure model to follow. For your customers or prospects, how do you go to them now, when we’re still in a period of indecision? What are your recommendations? What do you think should happen in terms of the standardization of a cloud model?

Benefit, not a pain

Fradet: Roughly, I assume at first that the cloud must not be seen as disruptive by our customers. Cloud is here to accompany your transformation. It must be a benefit for them, and not a pain.

Fradet

A private solution should be the best as a starting point for some customers. The full public solution should be a target. We’re here to manage their journey and to define with the customer what is the best solution for the best need.

Gardner: And in order for that transition from private to public or multiple public or sourced-infrastructure support, a degree of standardization is required. Otherwise, it's not possible. Do you have a preferred approach to standardization? Are you working closely with HP? How do you think you will allow for a smooth transition across a hybrid spectrum?

Fradet: The choice of HP as a partner was based on two main criteria. First of all, the quality of the solution, obviously, but there are multiple good solutions on the market. The second one is the capacity with HP to have a smooth transition, and that means getting to the industrialization benefits and the economic benefits while also being open and interconnected with existing IT systems.

That's why the future model is quite simple. Our work is to know we have on-premises and physical remaining infrastructure. We will have some private-cloud solutions and multiple public clouds, as you mentioned. The challenge is to have the right level of governance, and to be in a position to move the workload and adjust the workloads with the needs.

We continue to invest deeply in ITSM because ITSM is service management.

Gardner: Of course, once you've been able to implement across a spectrum of hosting possibilities, then there is the task of managing that over time, not just putting it there, but being able to govern and have control. Is there anything about the HP portfolio, or what you’re doing in particular, that you think is important, as we try to move beyond strictly implementation, but into going operations?

Fradet: With HP, we have a layer approach which is quite simple. First of all, if you want to manage, you must control, as you mentioned. We continue to invest deeply in IT Service Management (ITSM) because ITSM is service governance. In addition, we have some more innovative solutions based on the last version of  Cloud Services Automation (CSA). Control, automate, and report remain as key whatever the cloud or non-cloud infrastructure.

Gardner: Of course, another big topic these days is big data. I would think that a part of the management capability would be the ability to track all the data from all the systems, regardless of where they’re physically hosted. Do you have a preference or have you embarked on a big-data platform that would allow you to manage and monitor IT systems regardless of the volume, and the location?

Fradet: Yes, we have some very interesting initiatives with HP around HAVEn, which is obviously one of the most mature big-data platforms. The challenge for us is to transform a technologically wonderful solution into a business solution. We’re working with our business units to define use-cases that are totally tailored and adjusted for the business, but big data is one of our big challenges.

Traditional approach

Gardner: Have you been using a more traditional data-warehouse approach, or are you not yet architecting the capability? Are you still in a proof-of-concept stage?

Fradet: Unfortunately, we have hundreds of data-warehouse solutions, which are customer-dedicated, starting from very old-fashioned level to operational key performance indicators (KPI) to advanced business intelligence (BI).

The challenge now is really to design for what will be top requirements for the data warehouse, and you know that there is a mix of needs in terms of data warehouses. Some are pure operational KPIs, some are analytics, and some are really big data needs. To design the right solution for the customer remains a challenge. But, we’re very confident that with HAVEn, sometime in 2014, we will have the right solution for those issues.

Gardner: Lastly, Eric, the movement toward cloud models for a lot of organizations is still in the planning stages. They are mindful of the vision, but they have also IT  housecleaning to do internally. Do you have any suggestions as to how to properly modernize, or move toward a certain architecture that would then give them a better approach to cloud and set them up for less risk and less disruption? What are some observations that you have had for how to prepare for moving toward a cloud model?

Cloud can offer many combinations or many benefits, but you have to define as a first step your preferred benefits.

Fradet: As with any transformation program, the cloud’s eligibility program remains key. That means we have to define the policy with the customer. What is their expectation -- time to market, cost saving, to be more efficient in terms of management?

Cloud can offer many combinations or many benefits, but you have to define as a first step your preferred benefits. Then, when the methodology is clearly defined, the journey to the cloud is not very different than from any other program. It must not be seen as disruptive, keeping in mind that you do it for benefits and not only for technical reasons or whatever.

So don't jump to the cloud without having strong resources below the cloud.

Gardner: Please join me in thanking our guest. We've been discussing transition to cloud with Eric Fradet, Industrialization Director at Steria in Paris. Steria is a large and leading European IT services provider. Thank you.

Fradet: Thank you.

Gardner: And also 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 joining, and come back next time.

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

Transcript of a sponsored BriefingsDirect on planning and preparing for a journey to the cloud. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.

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Service Providers Gain New Levels of Actionable Customer Intelligence from Big Data Analytics

Transcript of a BriefingsDirect podcast on how service providers are harnessing the power of data analytics to improve customer service and customer relations.

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 Big Data Podcast Series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your moderator for this ongoing sponsored discussion on how data is analyzed and used to enrich the way you live and work. 

Gardner

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

Our next Big Data innovation discussion highlights how the telecommunication service-provider industry is gaining new business analytic value and strategic return through the better use and refinement of their Big Data assets. To learn more about how Big Data analytics has become a business imperative for communication service providers (CSPs), please join me in welcoming Oded Ringer, Worldwide Solution Enablement Lead for HP Communication and Media Solutions. Welcome, Oded.

Oded Ringer: Hi, Dana, thanks for bringing me in. It's great being here.

Gardner: What are some of the major trends and even competitive pressures that are leading CSPs to now view themselves as being more data-driven organizations?

Ringer: It’s not a secret that CSPs are under a lot of pressure. On one hand, this industry has never been more central. Everybody is connected, spending so much more time online than ever before, and carrying with them small devices through which they connect to the network. So CSPs are central to our work and personal lives – as a result, they’re under lot of pressure.

Ringer

They’re under a lot of pressure, because they’re required to make massive investments in the networks, but they also need to deal with shrinking margins and revenues to subsidize these investments. So, at the end of the day, they’re squeezed between these two motions. 

One approach many CSPs have adopted in the last year was to reduce cost and to cut operations. But this is pretty much a trip to nowhere. Going into most basic services and commodity services is no way for these kinds of things to survive. 

In the last two to three years, more and more traditional operators understand that they must go beyond what they did before. They need to offer more compelling services to reduce churn and acquire new customers. They need to leverage their position as a central place between consumers and what they are looking for to become some kind of brokers of information. 

The key asset they have in their hand to become such brokers is the huge amount of information that they maintain. It’s exactly where analytics comes into play.

Talking about mobile

Gardner: When we say CSP and telecommunication companies these days, we’re more and more talking about mobile, right? How big a shift has mobile been in terms of the need to analyze use patterns and get to know what's really happening out in the mobile network?

Ringer: Mobile services are certainly the leading tool in most operator’s arsenals. Operators that have the subscriber “connected” with them wherever they go, around the clock, have an advantage over those that are more dependent upon or only provide tethered services. 

But we need to keep in mind that there’s also a whole space for analytics solutions that are related to fixed-line services, like cable, satellite, broadband, and other, landline services. CSPs are investing a lot in becoming more predictive, finding out what the subscriber really wants, what the quality of those services are at any given time, and how we can reduce churn in their customer base. 

Another kind of analytics practices that operators take is trying to be predictive in their investments in the network, understanding which network segments are used by more high-worth individuals, those that they do want to improve service to, beefing up those networks and not the other networks.

Again, it’s these mobile operators who are on the front lines of doing more with subscriber data and information in general, but it is also true for cable operators and pay-TV operators, and landline CSPs.

CSPs, unlike most enterprises, need to handle not only the structured data that’s coming from databases and so on, but also unstructured data.

Gardner: Oded, what are some of the data challenges that are specific to CSPs? We know of course that Big Data is an issue associated with rapidly increasing velocity, volume, and variety of data for just about any organization, but is there something specific about the Big Data challenge in order to get these all important analytics specific to CSP?

Ringer: In the CSP industry, Big Data is bigger than in any other industry. Bigger, first of all, in volume. There is no other industry that runs this amount of data – if you take into consideration they’re carrying everybody’s data, consumer and enterprise. But that’s one aspect and is not even the most complicated one. 

The more complicated thing is the fact that CSPs, unlike most enterprises, need to handle not only the structured data that’s coming from databases and so on, but also unstructured data, such as web communication, voice communication, and video content. They want to analyze all those things, and this requires analyzing unstructured data. 

So that’s a significant change in that type of process flow. They are also facing the need to look at new sets of structured data, data from IT management and security log files, from sensors and end-point mobile device telematics, cable set-top boxes, etc.

And two, in the CSP industry, because everything is coming from the wire, there’s no such thing as off-line analytics or batch analytics. Everything needs to be real-time analytics. Of course, this doesn’t mean that there will not be off-line or batch analytics, but even these are becoming more complex and span many more data sets across multiple enterprise silos.

More real time

If you analyze subscriber behavior right now and you want to make an offer to improve the experience that he’s having in real time, you need to capture the degradation of service right now and correlate it with what you know about the subscriber right now. So it's so much more real time than in any other industry. 

In short, CSP Big Data analytics is Big Data analytics on steroids.

Gardner: Of course, all these different types of media, information, and data need to be associated in order to get those bigger analytic payoffs. That is to say, having separate pools of analysis isn’t as valuable as analyzing them all together. How do the CSPs pull together data and assets that up until now they really didn’t try to join or analyze in conjunction or in association with one another?

Ringer: There are different data sources or information sources, and it makes no sense to consolidate everything in one database, because it's endless, and most of the existing databases are limited in purpose and scale to their existing databases – not to mention the exponential increase in governance problems associated with wholesale transfer from existing siloed data stewards to a single consolidated repository. 

The idea is that we need to have good tools of mediation and collection of data from different places, collecting them in a staging database for trend analysis over time and connecting them via event triggering for real-time analytics. So the sources of information remain separate and many times, isolated. 

The market is still young. So it's very hard to say which one will be more dominant.

We’re not talking here about projects of data consolidation. It may be necessary in some cases, but that’s not really the practice that we’re talking about here. We’re talking about federating, referring to external information, analyzing in the context of the logic that we want to apply, and making real-time decisions.

Gardner: We've outlined some of the issues and challenges that are specific to CSPs. We recognize that this is extremely important to how they conduct their business, how they make their investments and how they satisfy and engage their customers. 

What does a long-term solution look like, rather than cherry picking against some of these analytics requirements? Is there a more strategic overview approach that would pay off longer term and put these organizations in a better position as they know more and more requirements will be coming their way?

Ringer: Actually we see two kinds of behaviors. The market is still young. So it's very hard to say which one will be more dominant. We see some CSPs that are coming to us with a very clear idea on what business process they want to implement and how they believe a data-driven approach can be applied to it. 

They have clear model, a clear return on investment (ROI) and they want to go for it and implement it. Of course, they need the technology, the processes, and the business projects, but their focus is pretty much on a single use case or a variety of use cases that are interrelated. That’s one trend.

There’s another trend in which operators say they need to start looking at their data as an asset, as an area that they want to centralize. They want to control it in a productive manner, both for security, for privacy, and for the ability to leverage it to different purposes.

Central asset

Those will typically come with a roadmap of different implementations that they would like to do via this Big Data facility that they have in mind and want to implement. But what’s more important for them is not the quickest time to launch specific processes, but to start treating the data as a central asset and to start building a business plan around it. 

I guess both trends will continue for quite some while, but we see them both in the market sometimes even in the same company in different organizations.

Gardner: Let's look at what harnessing Big Data can bring to an organization, whether they do it tactically or strategically. It seems to me that the business case for this is simply getting more and more pronounced and more powerful. 

Let’s look at some examples. There’s a new retail model called smart shopping that takes the advantage of geolocation in the mobile tier. There’s electronic fraud detection and prevention, where they can help people protect themselves and do more commerce to gain trust on their networks. 

Operators realize that they need to use the data to differentiate themselves, be more relevant to the subscribers, and to be more proactive in their behavior.

There are marketing benefits that can be brought back to providers of services, sellers that want to engage. And of course, it's important to track all the use patterns along the way for all of these and be able to make that data available to as many parties as possible. Tell me more about why this is essential and not something that’s likely to go away or even diminish any time soon.

Ringer: First of all, it’s essential because operators realize that they need to use the data to differentiate themselves, be more relevant to the subscribers, and to be more proactive in their behavior. They can’t continue to be a dumb pipe. They realize that. That’s clear to everybody. 

It's interesting that you mentioned those areas. Some are very similar to the way we also define this space that we’re active in. You mentioned the implementation of smart shopper, which is something that we actually did with a large North American operator in collaboration with a chain of malls in North America. 

Gardner: When we think about these really important business imperatives and how a CSP can really change their identity from being a pipe, a conduit, to being more of a rich services provider on top of communications, I can see why they’re really putting a lot of emphasis on this. 

What is it that HP is bringing to the table? What is it about HP HAVEn, in particular, that is well suited to where the telecommunications industry is going and what the requirements are?

Ringer: HP has made huge investments in the space of Big Data in general and analytics in particular, both in-house developments, multiple products, as well as acquisitions of external assets. 

Complete platform

HAVEn is now the complete platform that includes multiple best-in-class product elements based multiple, cutting edge yet proven technologies, for exploiting Big Data and analytics. Our solution for the space is pretty much based on HAVEn and expanded with specific solutions for CSP needs, with a wide gallery of connectors for external data sources that exist within the CSP space. 

In short, we’re taking HAVEn and using it for the CSP industry with lots of knowledge about what traditional CSP operators need to become next-generation CSPs. Why? 

Because we have a very large group within HP of telecom experts who interact with and leverage what we’re doing in other industries and with many of the new age service providers like the Amazons, Googles, Facebook and Twitters of the world. We go a long way back in expertise in telecom -- but combine this with forward thinking customers and our internal visionaries in HP Labs and across our business units. 

Gardner: Just to be clear for our audience, HAVEn translates to Hadoop, Autonomy, Vertica, and Enterprise Security, along with a whole suite of horizontally and vertically integrated set of applications that are vertical industry specific. Is that right?

It’s coming from the business people that understand that they need to do something with the data and monetize it.

Ringer: Exactly.

Gardner: Tell me what you do in terms of how you reach out to communications organizations. Is there something about meeting them at the hardware level and then alerting them to what these other Big Data capabilities are? Is this a cross-discipline type of approach? How do you actually integrate HP services and then take that and engage with these CSPs?

Ringer: Those things exist, like engaging at a hardware level, but those are the less common go-to-market motions that we see. The more popular ones are more top-down, in the sense that we are meeting with business stakeholders who wants to know how to leverage Big Data and analytics to improve their business. 

They don’t care about the data other than how it’s going to be result in actionable intelligence. So, at the CSP level, it can be with marketing officers within the CSP who are looking to create more personalized services or more sticky services to increase the attention of their subscribers. They’re looking to analytics for that. 

It can be with business-development managers within the CSP organization that are looking to create models of collaboration with the Yahoos and Facebooks of the world, with retailers, or with any kind of other participants of their ecosystem where they can bring the ability to provide the pipe, back-end hosting of services and intelligence about how the pipe is providing the services and the sentiment of the customers on the other end of the pipe. 

They want to share information of value to their customers, making them dependent on them in new ways that aren’t just about the pipe thereby gaining new revenue streams. That’s the kind of motivation they have. It can be with IT folks as well, but at the end of the day the discussion about CSP Big Data isn’t coming from the technology. It’s coming from the business people that understand that they need to do something with the data and monetize it.

Then, of course, it becomes pretty quickly a technical discussion that the motion is business to technology, rather than infrastructure to technology. 

Support practice

We also developed the support practice within our organization that does exactly that, business advisory workshops. It’s for stakeholders of different roles to realize what the priorities are in using Big Data. What is the roadmap that they want to implement? 

The purpose of this exercise is to quickly bring everybody to the same room, sit together for a day or two, and come out with an agreement on how to turn themselves from conventional services to more personalized services and diversify the business channels via using information data.

Gardner: Let’s go to some examples to demonstrate what telecommunications and service provider organizations are doing to accomplish that, to become smarter in their services, to get more personalized, and leverage Big Data to do that. 

Are there use cases you can think of or anecdotes of how this is being used? Or perhaps you have some named customers that you could use to show us what they’re doing and what they are getting from their investments.

We can pretty accurately get the quality of experience for every single video streaming session. It’s no big deal.

Ringer: For several years now, we have one large customer, Telefónica a Latin American conglomerate, has been working with us on analytics projects to improve the quality of experience of their subscribers. 

In Latin America, most people are interested in football, and many of them want to watch it on their mobile device. The challenge is that they all want to watch it during the same 90 minutes. That’s a challenge for any mobile operator, and that’s exactly where we started a critical project with Telefónica. 

We’re helping them analyze the quality of experience. Realizing the quality of the experience isn’t a very complicated thing. There are probes in the network to do that. We can pretty accurately get the quality of experience for every single video streaming session. It’s no big deal.

Analytics kicks in when you want to correlate this aggregation of quality with who the subscriber is, how the subscriber is expected to behave, and what he’s interested in. We know that the quality isn’t good enough for many subscribers during the football game, but we need to differentiate and know to which one of them we want to make an offer to upgrade his package. What’s the right offer? When’s the right time to make the offer? How many different offers do we test to zero in on the best set of offers?

We want to know which one of them we don’t want to promote anything to, but just want to make him happy. We want to give him a better quality experience for free, because he is a good customer and we don’t want to lose him. And we want to know which customer we want to come back to later, apologize, and offer him a better deal.

Real-time analytics

Based on real-time triggering of events from the network, degradation of quality with information that is ongoing about the subscriber, who the subscriber is, what marketing segment he belongs to, what package is he subscribed to and so on, we do the analytics in real time, and decide what the right action is and what the right move is, in order for us to give the best experience for the individual subscriber. 

It’s working very nicely for them. I like this example, first of all, because it’s real, but also because it shows the variety of processes we have here with correlation of real-time information with ongoing information for the subscribers. We have contextual action that is taken to monetize and to improve quality and to improve satisfaction. 

This example touches so many needs of an operator and is all done in a pretty straightforward manner. The implementation is rather simple. It’s all based on running the right processes and putting the right business process in place. But this isn’t always straightforward for enterprise customers, particularly those in the small to medium enterprise segment so imagine what CSPs could do for their customers once they’ve gotten a handle on this for their own businesses.

We have contextual action that is taken to monetize and to improve quality and to improve satisfaction. 

Gardner: It seems to me that that helps reduce the risk of a provider or their customers coming out with new services. If they know that they can adjust rapidly and can make good on services, perhaps this gives them more runway to take off with new services, knowing that they can adjust and be more agile. It seems like it really fundamentally changes how well they can do their business.

Ringer: Absolutely. It also reduces quite a lot the risk of investment. If you launch a new service and you find out that you need to beef up your entire network, that is a major hit for your investment strategy. At the same time, if you realize that you can be very granular and very selective in your investment, you can do it much more easily and justify subsequent investments more clearly.

Gardner: Are there any other examples of how this is manifesting itself in the market -- the use of Big Data in the telecommunication’s industry? 

Ringer: Let me give another example in North America. This is an implementation that we did for a large mobile operator in North America, in collaboration with a chain of retail malls. 

What we did there is combine their ongoing information that the mobile operator has about its subscribers -- he knows what the subscriber is interested in, what they’re prior buying pattern and transactions were and so on -- with the location information of where the individual person is at the mall. 

The mall operator runs a private wi-fi network there, so he has his own system of being able to track where the individual is exactly within the mall. He knows within two meters where a person is in the mall but with the map overlay of the physical mall and all product and service offerings to the same grid.

When we know a person is in the mall, we can correlate it with what the CSP knows about this person already. He knows that the specific person has high probability of looking for a specific running shoe. The mobile operator knows it because he tracks the web behavior of the specific individual. He tracks the profile of the specific individual and he can have pretty good accuracy in telling that this guy, for the right offer, will say yes for running shoes. 

Targeted and timely

So combining these two things, the ongoing analytics of the preferences, together with real-time location information, give us the ability to push out targeted and timely promotions and coupons.

Imagine that you go in the mall and suddenly you pass next to the shoe store. Here, your device pops up a message and that says right now, Nike shoes are 50 percent off for the next 15 minutes. You know that you’re looking for Nike shoes. So the chance that you’ll go into the store is very good, and the results are very good because you create a “buy-now or you’ll miss-out” feeling in the prospect. Many subscribers take the coupons that are pushed to them in this way. 

Of course, it’s all based on opt-in, and of course, it’s very granular in the sense that there are analytics that we do on subscriber information that is opted in at the level of what they allow us to look at. For instance, a specific person may allow us to look at his behavior on retail sites, but not on financial sites. 

Gardner: Again, this shows a fundamental shift that the communications provider is not just a conduit for information, but can also offer value-added services to both the seller and the buyer -- radically changing their position in their markets. 

If I am an organization in the CSP industry and I listen to you and I have some interest in pursuing better Big Data analytics, how do I get started? Where can I go for more information? What is it that you’ve put together that allows me to work on this rather quickly?

Ringer: As I mentioned before, we typically recommend engaging in a two-day workshop with our business consultants. We have a large team of Big Data advisory consultants, and that’s exactly what they do. They understand the priorities and work together with the telecom organizations to come up with some kind of a roadmap -- what they want to do, what they can do, what they are going to do first, and what they are going to do later. 

They all look to become more proactive, they all realize that data is an asset and is something that you need to keep handy, keep private, and keep secured.

That’s our preferred way of approaching this discipline. Overall, there are so many kinds of use cases, and we need to decide where to start. So that’s how we start. To engage, the best place is to go to our website. We have lots of information there. The URL is hp.com/go/telcoBigData, that’s one word, and from there you just click Contact Us, and we’ll get back to you. We’ll take you from there. There are no commitments, but chances are very good.

Gardner: Before we sign off, I just wanted to look into the future. As you pointed out, more and more entertainment and media services are being delivered through communication providers. The mobile aspect of our lives continues to grow rapidly. And, of course, now that cloud computing has become more prominent, we can expect that more data will be available across cloud infrastructures, which can be daunting, but also very powerful. Where do you see the future challenges, and what are some of the opportunities?

Ringer: We can summarize four main trends that we’re seeing increasing and accelerating. One is that CSPs are becoming more active in enabling new business models with partnerships, collaborations, internet players, and so on. This is a major trend. 

The second trend that we see increasing quite intensively is operators becoming like marketing organizations, promoting services for their own or for others.

The third one is more related to the operation of the CSP itself. They need to be more aware of where they invest, what’s their risk and probability of seeing an specific ROI and when will that occur. In short, Big Data and Analytics will make them smarter and more proactive in making the investments. That’s another driver that increases their interest in using the data. 

Overall they all look to become more proactive, they all realize that data is an asset and is something that you need to keep handy, keep private, and keep secured, but be able to use it for variety of use cases and processes to be ready for the next move. 

Gardner: I am afraid we’ll have to leave it there. You’ve been listening to a Big Data innovation discussion that highlights how the telecommunications service provider industry is gaining new business analytics value and strategic returns through better use and refinement of their Big Data assets. And we have seen how Big Data capabilities and advanced business analytics have become essential to CSPs, especially as mobile and e-commerce drives their business’s future.

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

Please join me now in thanking today’s guest, Oded Ringer, Worldwide Solution Enablement Lead for HP Communication and Media Solutions. Thank you so much, Oded.

Ringer: Thank you very much, Dana.

Gardner: To learn more about how businesses anywhere can best capture knowledge, gain deeper analysis, and rapidly and securely make those insights available to more people, visit the HP HAVEn Resource Center at hp.com/HAVEn, and for more CSP-specific Big Data information visit, hp.com/go/telcoBigData. 

I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your moderator for this ongoing sponsored journey into how data is analyzed and used to advance the way we all live and work. Thanks for listening, and come back next time.

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

Transcript of a BriefingsDirect podcast on how service providers are harnessing the power of data analytics to improve customer service and customer relations. Copyright Interarbor Solutions, LLC, 2005-2014. All rights reserved.

You may also be interested in:

• How UK data solutions developer Systems Mechanics uses HP Vertica for BI, streaming and data analysis
• Advanced cloud service automation eases application delivery for global service provider NNIT
• HP network management heightens performance while reducing total costs for Nordic telco TDC
• How Capgemini's UK financial services unit helps clients manage risk using big data analysis
• Perfecto Mobile goes to cloud-based testing so developers can build the best apps faster
• Network virtualization eases developer and operations snafus in the mobile and cloud era
• Big data should eclipse cloud as priority for enterprises
• Big data’s big payoff arrives as customer experience insights drive new business advantages
• How healthcare SaaS provider PointClickCare masters quality and DevOps using cloud ITSM
• Software security pays off: How Heartland Payment Systems gains steep ROI via software assurance tools and methods
• HP ART documentation and readiness tools bring better user experiences to Nordic IT solutions provider EVRY
• NASCAR attains intimacy and affinity with fans worldwide using big data analytics
• HP HAVEn CTO Mundada on new ways for businesses to gain transformation from big data and new wave analysis