Showing posts with label Vertica. Show all posts
Showing posts with label Vertica. Show all posts

Thursday, November 12, 2015

Powerful Reporting From YP's Enterprise Data Warehouse Helps SMBs Conjure New Business

Transcript of a BriefingsDirect discussion on how Yellow Pages helps small businesses attract, reach out to, and retain customers using big data.

 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 Enterprise (HPE) 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 innovation case study highlights how Yellow Pages (YP) has experimented with and built out a full enterprise data warehouse with powerful reporting capabilities.

We’ll learn how YP pulls massive data and information from across new and legacy resources to report precise metrics to its advertisers, making them more aware about their campaigns -- and how small businesses are fairing.

To learn more, welcome Bill Theisinger, Vice President of Engineering for Platform Data Services at YP in Glendale, California. Welcome, Bill.

Bill Theisinger: Thank you, Dana.

Gardner: Tell us about YP, the digital arm of what people would have known as Yellow Pages a number of years ago. You're all about helping small businesses become better acquainted with their customers, and vice versa.
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Theisinger: YP is a leading local marketing solutions provider in the U.S., dedicated to helping local businesses and communities grow. We help connect local businesses with consumers wherever they are and whatever device they are on, desktop and mobile.

Theisinger
Gardner: As we know, the world has changed dramatically around marketing and advertising and connecting buyers and sellers. So in the digital age, being precise, being aware, being visible is everything, and that means data. Tell us about your data requirements in this new world.

Theisinger: We need to be able to capture how consumers interact with our customers, and that includes where they interact -- whether it’s a mobile device or web device -- and also within our network of partners. We reach about 100 million consumers across the U.S and we do that through both our YP network and our partner network.

Gardner: Tell us too about the evolution. Obviously, you don’t build out data capabilities and infrastructure overnight. Some things are in place, and you move on, you learn, adapt, and you have new requirements. Tell us your data warehouse journey.

Needed to evolve

Theisinger: Yellow Pages saw the shift of their print business moving heavily online and becoming heavily digital. We needed to evolve with that, of course. In doing so, we needed to build infrastructure around the systems that we were using to support the businesses we were helping to grow.

And in doing that, we started to take a look at what the systems requirements were for us to be able to report and message value to our advertisers. That included understanding where consumers were looking, what we were impressing to them, what businesses we were showing them when they searched, what they were clicking on, and, ultimately what businesses they called. We track all of those different metrics.

When we started this adventure, we didn't have the technology and the capabilities to be able to do those things. So we had to reinvent our infrastructure. That’s what we did

Gardner: And as we know, getting more information to your advertisers to help them in their selection and spending expertise is key. It differentiates companies. So this is a core proposition for you. This is at the heart of your business.

Given the mission criticality, what are the requirements? What did you need to do to get that reporting, that warehouse capability?

Theisinger: We need to be able to scale to the size of our network and the size of our partner network, which means no click left behind, if you will, no impression untold, no search unrecognized. That's billions of events we process every day. We needed to look at something that would help us scale. If we added a new partner, if we expanded the YP network, if we added hundreds, thousands, tens of thousands of new advertisers, we needed the infrastructure to able to help us do that.
We need to be able to scale to the size of our network and the size of our partner network, which means no click left behind, if you will, no impression untold, no search unrecognized.

Gardner: I understand that you've been using Hadoop. You might be looking at other technologies as they emerge. Tell us about your Hadoop experience and how that relates to your reporting capabilities.

Theisinger: When I joined YP, Hadoop was a heavy buzz product in the industry. It was a proven product for helping businesses process large amounts of unstructured data. However, it still poses a problem. That unstructured data needs to be structured at some point, and it’s that structure that you report to advertisers and report internally.

That's how we decided that we needed to marry two different technologies -- one that will allow us to scale a large unstructured processing environment like Hadoop and one that will allow us to scale a large structured environment like Hewlett Packard Enterprise (HPE) Vertica.

Business impact

Gardner: How has this impacted your business, now that you've been able to do this and it's been in the works for quite a while? Any metrics of success or anecdotes that can relate back to how the people in your organization are consuming those metrics and then extending that as service and product back into your market? What has been the result?

Theisinger: We have roughly 10,000 jobs that we run every day, both to process data and also for analytics. That data represents about five to six petabytes of data that we've been able to capture about consumers, their behaviors, and activities. So we process that data within our Hadoop environment. We then pass that along into HPE Vertica, structure it in a way that we can have analysts, product owners, and other systems retrieve it, pull and look at those metrics, and be able to report on them to the advertisers.
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Gardner: Is there an automation to this as you look to present a more and better analytics on top of the Vertica? What are you doing to make that customizable to people based on their needs, but at the same time, controlled and managed so that it doesn't become unwieldy?

Theisinger: There is a lot of interaction between customers, both internal and external, when we decide how and what we’re going to present in terms of data, and there are a lot of ways we do that. We present data externally through an advertiser portal. So we want to make sure we work very closely with human factors and ergonomics (HFE) and the use experience (UX) designers as well as our advertisers, through focus groups, workshops, and understanding what they want to understand about the data that we present them.

Then, internally, we decide what would make sense and how we feel comfortable being able to present it to them, because we have a universe of a lot more data than what we probably want to show people.

We also do the same thing internally. We've been able to provide various teams internally whether its sales, marketing, or finance, insights into who's clicking on various business listings, who's viewing various businesses, who’s calling businesses, what their segmentation is, and what their demographics look like and it allows us a lot of analytical insight. We do most of that work through the analytics platforms, which is, in this case, HPE Vertica.
Small businesses need to be able to just pick up their mobile device and look at the effectiveness of their campaigns with YP.

Gardner: Now, that user experience is becoming more and more important. It wasn't that long ago when these reports were going to people who were data scientists or equivalent, but now we're taking the amount to those 600,000 small businesses. Can you tell us a little bit about lessons learned when it comes to delivering an end analytics product, versus building out the warehouse? They seem to be interdependent but we're seeing more and more emphasis on that user experience these days.

Theisinger: You need to bridge the gap between analytics and just data storage and processing. So you have to present them in-state. This is what happens. It’s very descriptive of what's going on, and we try to be a little bit more predictive when it comes to the way we want to do analysis at YP. We're looking to go beyond just descriptive analytics.

What has also changed is the platform by which you present the data. It's going highly mobile. Small businesses need to be able to just pick up their mobile device and look at the effectiveness of their campaigns with YP. They're able to do that through a mobile platform we’ve built called YP for Merchants.

They can log in and see their metrics that are core to their business and how those campaigns are performing. They can even see some details, like if they missed a phone call and they want to be able to reach back out to a consumer and see if they need to help, solve a problem, or provide a service.

Developer perspective

Gardner: And given that your developers had to go through the steps of creating that great user experience and taking it to the mobile tier, was there anything about HPE Vertica, your warehouse, or your approach to analytics that made that development process easier? Is there an approach to delivering this from a developer perspective that you think others might learn from?

Theisinger: There is, and it takes a lot more people than just the analytics team in my group or the engineers in my team. It’s a lot of other teams within YP that build this. But first and foremost, people want to see the data as real time and as near real time as they can.

When a small business relies on contact from customers, we track those calls. When a potential customer calls a small business and that small business isn’t able to actually get to the call or respond to that customer because maybe they are on a job, it's important to know that that call happened recently. It's important for that small business to reach back out to the consumer, because that consumer could go somewhere else and get that service from a competitor.

To be able to do that as quickly as possible is a hard-and-fast requirement. So processing the data as quickly as you can and presenting that, whether it be on a mobile device, in this case, as quickly as you can is definitely paramount to making that a success.
Having the right infrastructure puts you in the position to be able to do that. That’s where businesses are going to end up growing, whether it's ours or small businesses.

Gardner: I've spoken to a number of people over the years and one of the takeaways I get is that infrastructure is destiny. It really seems to be the case in your business that having that core infrastructure decision process done correctly has now given you the opportunity to scale up, be innovative, and react to the market. I think it’s also telling that, in this data-driven decade that we’ve been in for a few years now, the whole small business sector of the economy is a huge part of our overall productivity and growth as an economy.

Any thoughts, generally about making infrastructure decisions for the long run, decisions you won't regret, decisions that that can scale over time and are future proof?

Theisinger: Yeah, for speaking about what I've seen through the job that we’ve had it here at YP, we reach over half a million paying advertisers. The shift is happening between just telling the advertisers what's happened to helping them actually drive new business.

So it's around the fact that I know who my customers are now, how do I find more of them, or how do I reach out to them, how do I market to them? That's where the real shift is. You have to have a really strong scalable and extensible platform to be able to answer that question. Having the right infrastructure puts you in the position to be able to do that. That’s where businesses are going to end up growing, whether it's ours or small businesses.

And our success is hinged to whether or not we can get these small businesses to grow. So we are definitely 100 percent focused on trying to make that happen.

Gardner: It’s also telling that you’ve been able to adjust so rapidly. Obviously, your business has been around for a long time. People are very familiar with the Yellow Pages, the actual physical product, but you've gone to make software so core to your value and your differentiation. I'm impressed and I commend you on being able to make that transitions fairly rapidly.

Core talent

Theisinger: Yeah, well thank you. We’ve invested a lot in the people within the technology team we have there in Glendale. We've built our own internal search capabilities, our own internal products. We’ve pulled a lot of good core talent from other companies.

I used to work at Yahoo with other folks, and YP is definitely focused on trying to make this transition a successful one, but we have our eye on our heritage. Over a hundred years of being very successful in the print business is not something you want to turn your back on. You want to be able to embrace that, and we’ve learned a lot from it, too.

So we're right there with small businesses. We have a very large sales force, which is also very powerful and helpful in making this transition a success. We've leaned on all of that and we become one big kind of happy family, if you will. We all worked very closely together to make this transition successful.
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Gardner: I am afraid we will have to leave it there. We've been learning about how Yellow Pages, or YP, has experimented with and built out a full enterprise data warehouse capability, built with also powerful near real-time reporting capabilities. We've heard why pulling massive data and information from across new and legacy sources is essential to be able to report precise metrics to YP’s advertisers, and how that's differentiating the company in the new world of online marketing and advertising.

So join me in extending a big thank you to Bill Theisinger, Vice President of Engineering for Platform Data Services at YP. Thank you.

Theisinger: Thank you, Dana. I appreciate the time.

Gardner: And a big thank you also to our audience for joining us for this Big Data innovation case study discussion. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HPE-sponsored discussions. Thanks again for listening, and do 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 Yellow Pages help small businesses attract, reach out to, and retain customers using big data. Copyright Interarbor Solutions, LLC, 2005-2015. All rights reserved.

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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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Tuesday, July 28, 2015

How Big Data Technologies Hadoop and Vertica Drive Business Results at Snagajob

Transcript of a BriefingsDirect discussion on how an employment search company uses data analysis to bring better matches for job seekers and employers.

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

Gardner
Our next innovation case study interview highlights how Snagajob in Richmond, Virginia -- one of the largest hourly employment networks for job seekers and employers – uses big data to improve their performance and to better understand how their systems provide rapid services to their users.

Snagajob recently delivered nearly 500,000 new jobs in a single month through their systems. To learn how they're managing such impressive scale, we welcome Robert Fehrmann, Data Architect at Snagajob in Richmond, Virginia.

Robert Fehrmann: Thank you for the introduction.

Gardner: First, tell us about your organization. You’ve been doing this successfully since 2000. How are hourly workers different from regular employment? What type of employment are we talking about? Let's understand the role you play in the employment market.

Fehrmann: Snagajob, as you mentioned, is America's largest hourly network for employees and employers. The hourly market means we have, relatively speaking, high turnover.
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Another aspect, in comparison to some of our competitors, is that we provide an inexpensive service. So our subscriptions are on the low end, compared to our competitors.

Gardner: Tell us how you use big data to improve your operations. I believe that among the first ways that you’ve done that is to try to better analyze your performance metrics. What were you facing as a problem when it came to performance? [Register for the upcoming HP Big Data Conference in Boston on Aug. 10-13.]

Signs of stress

Fehrmann: A couple of years ago, we started looking at our environment, and it became obvious that our traditional technology was showing some signs of stress. As you mentioned, we really have data at scale here. We have 20,000 to 25,000 postings per day, and we have about 700,000 unique visitors on a daily basis. So data is coming in very, very quickly.

Fehrmann
We also realized that we're sitting on a gold mine and we were able to ingest data pretty well. But we had problem getting information and innovation out of our big data lake.

Gardner: And of course, near real time is important. You want to catch degradation in any fashion from your systems right away. How do you then go about getting this in real time? How do you do the analysis?

Fehrmann: We started using Hadoop. I'll use a lot of technical terms here. From our website, we're getting events. Events are routed via Flume directly into Hadoop. We're collecting about 600 million key-value pairs on a daily basis. It's a massive amount of data, 25 gigabytes on a daily basis.

The second piece in this journey to big data was analyzing these events, and that’s where we're using HP Vertica. Second, our original use case was to analyze a funnel. A funnel is where people come to our site. They're searching for jobs, maybe by keyword, maybe by zip code. A subset of that is an interest in a job, and they click on a posting. A subset of that is applying for the job via an application. A subset is interest in an employer, and so on. We had never been able to analyze this funnel.

The dataset is about 300 to 400 million rows, and 30 to 40 gigabytes. We wanted to make this data available, not just to our internal users, but all external users. Therefore, we set ourselves a goal of a five-second response time. No query on this dataset should run for more than five seconds -- and Vertica and Hadoop gave us a solution for this.

Gardner: How have you been able to increase your performance reach your key performance indicators (KPIs) and service-level agreements (SLAs)? How has this benefited you?

Fehrmann: Another application that we were able to implement is a recommendation engine. A recommendation engine is that use where our jobseekers who apply for a specific job may not know about all the other jobs that are very similar to this job or that other people have applied to.
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We started analyzing the search results that we were getting and implemented a recommendation engine. Sometimes it’s very difficult to have real comparison between before and after. Here, we were able to see that we got an 11 percent increase in application flow. Application flow is how many applications a customer is getting from us. By implementing this recommendation engine, we saw an immediate 11 percent increase in application flow, one of our key metrics.

Gardner: So you took the success from your big-data implementation and analysis capabilities from this performance task to some other areas. Are there other business areas, search yield, for example, where you can apply this to get other benefits?

Brand-new applications

Fehrmann: When we started, we had the idea that we were looking for a solution for migrating our existing environment, to a better-performing new environment. But what we've seen is that most of the applications we've developed so far are brand-new applications that we hadn't been able to do before.

You mentioned search yield. Search yield is a very interesting aspect. It’s a massive dataset. It's about 2.5 billion rows and about 100 gigabytes of data as of right now and it's continuously increasing. So for all of the applications, as well as all of the search requests that we have collected since we have started this environment, we're able to analyze the search yield.
Most of the applications we've developed so far are brand-new applications that we hadn't been able to do before.

For example, that's how many applications we get for a specific search keyword in real time. By real time, I mean that somebody can run a query against this massive dataset and gets result in a couple of seconds. We can analyze specific jobs in specific areas, specific keywords that are searched in a specific time period or in a specific location of the country.

Gardner: And once again, now that you've been able to do something you couldn't do before, what have been the results? How has that impacted change your business? [Register for the upcoming HP Big Data Conference in Boston on Aug. 10-13.]

Fehrmann: It really allows our salespeople to provide great information during the prospecting phase. If we're prospecting with a new client, we can tell him very specifically that if they're in this industry, in this area, they can expect an application flow, depending on how big the company is, of let’s say in a hundred applications per day.

Gardner: How has this been a benefit to your end users, those people seeking jobs and those people seeking to fill jobs?

Fehrmann: There are certainly some jobs that people are more interested in than others. On the flip side, if a particular job gets a 100 or 500 applications, it's just a fact that only a small number going to get that particular job. Now if you apply for a job that isn't as interesting, you have much, much higher probability of getting the job.
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Gardner: I'm afraid we will have to leave it there. We've been talking with Snagajob about how they use big data on multiple levels to improve their business performance, their system’s performance, and ultimately how they go about understanding their new challenges and opportunities.

With that, I'd like to thank our guest, Robert Fehrmann, Data Architect at Snagajob in Richmond, Virginia. Thank you.

Fehrmann: Thank you, Dana.

Gardner: And I’d like to thank our audience as well for joining us for this special new style of IT discussion. [Register for the upcoming HP Big Data Conference in Boston on Aug. 10-13.]

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

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

Transcript of a BriefingsDirect discussion on how an employment search company uses data analysis to bring better matches for job seekers and employers. 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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