Big data enables top user experiences and extreme personalization for Intuit TurboTax

The next BriefingsDirect big-data innovation case study highlights how Intuit uses deep-data analytics to gain a 360-degree view of its TurboTax application's users’ behavior and preferences. Such visibility allows for rapid applications improvements and enables the TurboTax user experience to be tailored to a highly detailed degree.

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Here to share how analytics paves the way to better understanding of end-user needs and wants, we're joined by Joel Minton, Director of Data Science and Engineering for TurboTax at Intuit in San Diego. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Let’s start at a high-level, Joel, and understand what’s driving the need for greater analytics, greater understanding of your end-users. What is the big deal about big-data capabilities for your TurboTax applications?

Minton: There were several things, Dana. We were looking to see a full end-to-end view of our customers. We wanted to see what our customers were doing across our application and across all the various touch points that they have with us to make sure that we could fully understand where they were and how we can make their lives better.

Minton

We also wanted to be able to take that data and then give more personalized experiences, so we could understand where they were, how they were leveraging our offerings, but then also give them a much more personalized application that would allow them to get through the application even faster than they already could with TurboTax.

And last but not least, there was the explosion of available technologies to ingest, store, and gain insights that was not even possible two or three years ago. All of those things have made leaps and bounds over the last several years. We’ve been able to put all of these technologies together to garner those business benefits that I spoke about earlier.

Gardner: So many of our listeners might be aware of TurboTax, but it’s a very complex tax return preparation application that has a great deal of variability across regions, states, localities. That must be quite a daunting task to be able to make it granular and address all the variables in such a complex application.

Minton: Our goal is to remove all of that complexity for our users and for us to do all of that hard work behind the scenes. Data is absolutely central to our understanding that full end-to-end process, and leveraging our great knowledge of the tax code and other financial situations to make all of those hard things easier for our customers, and to do all of those things for our customers behind the scenes, so our customers do not have to worry about it.

Gardner: In the process of tax preparation, how do you actually get context within the process?

Always looking

Minton: We're always looking at all of those customer touch points, as I mentioned earlier. Those things all feed into where our customer is and what their state of mind might be as they are going through the application.

To give you an example, as a customer goes though our application, they may ask us a question about a certain tax situation.

When they ask that question, we know a lot more later on down the line about whether that specific issue is causing them grief. If we can bring all of those data sets together so that we know that they asked the question three screens back, and then they're spending a more time on a later screen, we can try to make that experience better, especially in the context of those specific questions that they have.

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As I said earlier, it's all about bringing all the data together and making sure that we leverage that when we're making the application as easy as we can.

Gardner: And that's what you mean by a 360-degree view of the user: where they are in time, where they are in a process, where they are in their particular individual tax requirements?

Minton: And all the touch points that they have with not only things on our website, but also things across the Internet and also with our customer-care employees and all the other touch points that we use try to solve our customers’ needs.

During our peak times of the year during tax season, we have billions and billions of transactions.

Gardner: This might be a difficult question, but how much data are we talking about? Obviously you're in sort of a peak-use scenario where many people are in a tax-preparation mode in the weeks and months leading up to April 15 in the United States. How much data and how rapidly is that coming into you?

Minton: We have a tremendous amount of data. I'm not going to go into the specifics of the complete size of our database because it is proprietary, but during our peak times of the year during tax season, we have billions and billions of transactions.

We have all of those touch points being logged in real-time, and we basically have all of that data flowing through to our applications that we then use to get insights and to be able to help our customers even more than we could before. So we're talking about billions of events over a small number of days.

Gardner: So clearly for those of us that define big data by velocity, by volume, and by variety, you certainly meet the criteria and then some.

Unique challenges

Minton: The challenges are unique for TurboTax because we're such a peaky business. We have two peaks that drive a majority of our experiences: the first peak when people get their W-2s and they're looking to get their refunds, and then tax day on April 15th. At both of those times, we're ingesting a tremendous amount of data and trying to get insights as quickly as we can so we can help our customers as quickly as we can.

Gardner: Let’s go back to this concept of user experience improvement process. It's not just something for tax preparation applications but really in retail, healthcare, and many other aspects where the user expectations are getting higher and higher. People expect more. They expect anticipation of their needs and then delivery of that.

This is probably only going to increase over time, Joel. Tell me a little but about how you're solving this issue of getting to know your user and then being able to be responsive to an entire user experience and perception.

Minton: Every customer is unique, Dana. We have millions of customers who have slightly different needs based on their unique situations. What we do is try to give them a unique experience that closely matches their background and preferences, and we try to use all of that information that we have to create a streamlined interaction where they can feel like the experience itself is tailored for them.

So the most important thing is taking all of that data and then providing super-personalized experience based on the experience we see for that user and for other users like them.

It’s very easy to say, “We can’t personalize the product because there are so many touch points and there are so many different variables.” But we can, in fact, make the product much more simplified and easy to use for each one of those customers. Data is a huge part of that.

Specifically, our customers, at times, may be having problems in the product, finding the right place to enter a certain tax situation. They get stuck and don't know what to enter. When they get in those situations, they will frequently ask us for help and they will ask how they do a certain task. We can then build code and algorithms to handle all those situations proactively and be able to solve that for our customers in the future as well.

So the most important thing is taking all of that data and then providing super-personalized experience based on the experience we see for that user and for other users like them.

Gardner: In a sense, you're a poster child for a lot of elements of what you're dealing with, but really on a significant scale above the norm, the peaky nature, around tax preparation. You desire to be highly personalized down to the granular level for each user, the vast amount of data and velocity of that data.

What were some of your chief requirements at your architecture level to be able to accommodate some of this? Tell us a little bit, Joel, about the journey you’ve been on to improve that architecture over the past couple of years?

Lot of detail

Minton: There's a lot of detail behind the scenes here, and I'll start by saying it's not an easy journey. It’s a journey that you have to be on for a long time and you really have to understand where you want to place your investment to make sure that you can do this well.

One area where we've invested in heavily is our big-data infrastructure, being able to ingest all of the data in order to be able to track it all. We've also invested a lot in being able to get insights out of the data, using Hewlett Packard Enterprise (HPE) Vertica as our big data platform and being able to query that data in close to real time as possible to actually get those insights. I see those as the meat and potatoes that you have to have in order to be successful in this area.

On top of that, you then need to have an infrastructure that allows you to build personalization on the fly. You need to be able to make decisions in real time for the customers and you need to be able to do that in a very streamlined way where you can continuously improve.

We use a lot of tactics using machine learning and other predictive models to build that personalization on-the-fly as people are going through the application. That is some of our secret sauce and I will not go into in more detail, but that’s what we're doing at a high level.

Gardner: It might be off the track of our discussion a bit, but being able to glean information through analytics and then create a feedback loop into development can be very challenging for a lot of organizations. Is DevOps a cultural parallel path along with your data-science architecture?

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I don’t want to go down the development path too much, but it sounds like you're already there in terms of understanding the importance of applying big-data analytics to the compression of the cycle between development and production.

Minton: There are two different aspects there, Dana. Number one is making sure that we understand the traffic patterns of our customer and making sure that, from an operations perspective, we have the understanding of how our users are traversing our application to make sure that we are able to serve them and that their performance is just amazing every single time they come to our website. That’s number one.

Number two, and I believe more important, is the need to actually put the data in the hands of all of our employees across the board. We need to be able to tell our employees the areas where users are getting stuck in our application. This is high-level information. This isn't anybody's financial information at all, but just a high-level, quick stream of data saying that these people went through this application and got stuck on this specific area of the product.

We want to be able to put that type of information in our developer’s hands so as the developer is actually building a part of the product, she could say that I am seeing that these types of users get stuck at this part of the product. How can I actually improve the experience as I am developing it to take all of that data into account?

We have an analyst team that does great work around doing the analytics, but in addition to that, we want to be able to give that data to the product managers and to the developers as well, so they can improve the application as they are building it. To me, a 360-degree view of the customer is number one. Number two is getting that data out to as broad of an audience as possible to make sure that they can act on it so they can help our customers.

Major areas

Gardner: Joel, I speak with HPE Vertica users quite often and there are two major areas that I hear them talk rather highly of the product. First, has to do with the ability to assimilate, so that dealing with the variety issue would bring data into an environment where it can be used for analytics. Then, there are some performance issues around doing queries, amid great complexity of many parameters and its speed and scale.

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Your applications for TurboTax are across a variety or platforms. There is a shrink-wrap product from the legacy perspective. Then you're more along the mobile lines, as well as web and SaaS. So is Vertica something that you're using to help bring the data from a variety of different application environments together and/or across different networks or environments?

Minton: I don't see different devices that someone might use as a different solution in the customer journey. To me, every device that somebody uses is a touch point into Intuit and into TurboTax. We need to make sure that all of those touch points have the same level of understanding, the same level of tracking, and the same ability to help our customers.

Whether somebody is using TurboTax on their computer or they're using TurboTax on their mobile device, we need to be able to track all of those things as first-class citizens in the ecosystem. We have a fully-functional mobile application that’s just amazing on the phone, if you haven’t used it. It's just a great experience for our customers.

From all those devices, we bring all of that data back to our big data platform. All of that data can then be queried, because you want to understand, many questions, such as when do users flow across different devices and what is the experience that they're getting on each device? When are they able to just snap a picture of their W-2 and be able to import it really quickly on their phone and then jump right back into their computer and finish their taxes with great ease?

You need to be able to have a system that can handle that concurrency and can handle the performance that’s going to be required by that many more people doing queries against the system.

We need to be able to have that level of tracking across all of those devices. The key there, from a technology perspective, is creating APIs that are generic across all of those devices, and then allowing those APIs to feed all of that data back to our massive infrastructure in the back-end so we can get those insights through reporting and other methods as well.

Gardner: We've talked quite a bit about what's working for you: a database column store, the ability to get a volume variety and velocity managed in your massive data environment. But what didn't work? Where were you before and what needed to change in order for you to accommodate your ongoing requirements in your architecture?

Minton: Previously we were using a different data platform, and it was good for getting insights for a small number of users. We had an analyst team of 8 to 10 people, and they were able to do reports and get insights as a small group.

But when you talk about moving to what we just discussed, a huge view of the customer end-to-end, hundreds of users accessing the data, you need to be able to have a system that can handle that concurrency and can handle the performance that’s going to be required by that many more people doing queries against the system.

Concurrency problems

So we moved away from our previous vendor that had some concurrency problems and we moved to HPE Vertica, because it does handle concurrency much better, handles workload management much better, and it allows us to pull all this data.

The other thing that we've done is that we have expanded our use of Tableau, which is a great platform for pulling data out of Vertica and then being able to use those extracts in multiple front-end reports that can serve our business needs as well.

So in terms of using technology to be able to get data into the hands of hundreds of users, we use a multi-pronged approach that allows us to disseminate that information to all of these employees as quickly as possible and to do it at scale, which we were not able to do before.

There's always going to be more data that you want to track than you have hardware or software licenses to support.

Gardner: Of course, getting all your performance requirements met is super important, but also in any business environment, we need to be concerned about costs.

Is there anything about the way that you were able to deploy Vertica, perhaps using commodity hardware, perhaps a different approach to storage, that allowed you to both accomplish your requirements, goals in performance, and capabilities, but also at a price point that may have been even better than your previous approach?

Minton: From a price perspective, we've been able to really make the numbers work and get great insights for the level of investment that we've made.

How do we handle just the massive cost of the data? That's a huge challenge that every company is going to have in this space, because there's always going to be more data that you want to track than you have hardware or software licenses to support.

So we've been very aggressive in looking at each and every piece of data that we want to ingest. We want to make sure that we ingest it at the right granularity.

Vertica is a high-performance system, but you don't need absolutely every detail that you’ve ever had from a logging mechanism for every customer in that platform. We do a lot of detail information in Vertica, but we're also really smart about what we move into there from a storage perspective and what we keep outside in our Hadoop cluster.

Hadoop cluster

We have a Hadoop cluster that stores all of our data and we consider that our data lake that basically takes all of our customer interactions top to bottom at the granular detail level.

We then take data out of there and move things over to Vertica, in both an aggregate as well as a detail form, where it makes sense. We've been able to spend the right amount of money for each of our solutions to be able to get the insights we need, but to not overwhelm both the licensing cost and the hardware cost on our Vertica cluster.

The combination of those things has really allowed us to be successful to match the business benefit with the investment level for both Hadoop and with Vertica.

Gardner: Measuring success, as you have been talking about quantitatively at the platform level, is important, but there's also a qualitative benefit that needs to be examined and even measured when you're talking about things like process improvements, eliminating bottlenecks in user experience, or eliminating anomalies for certain types of individual personalized activities, a bit more quantitative than qualitative.

We're actually performing much better and we're able to delight our internal customers to make sure that they're getting the answers they need as quickly as possible.

Do you have any insight, either anecdotal or examples, where being able to apply this data analytics architecture and capability has delivered some positive benefits, some value to your business?

Minton: We basically use data to try to measure ourselves as much as possible. So we do have qualitative, but we also have quantitative.

Just to give you a few examples, our total aggregate number of insights that we've been able to garner from the new system versus the old system is a 271 percent increase. We're able to run a lot more queries and get a lot more insights out of the platform now than we ever could on the old system. We have also had a 41 percent decrease in query time. So employees who were previously pulling data and waiting twice as long had a really frustrating experience.

Now, we're actually performing much better and we're able to delight our internal customers to make sure that they're getting the answers they need as quickly as possible.

We've also increased the size of our data mart in general by 400 percent. We've massively grown the platform while decreasing performance. So all of those quantitative numbers are just a great story about the success that we have had.

From a qualitative perspective, I've talked to a lot of our analysts and I've talked to a lot of our employees, and they've all said that the solution that we have now is head and shoulders over what we had previously. Mostly that’s because during those peak times, when we're running a lot of traffic through our systems, it’s very easy for all the users to hit the platform at the same time, instead of nobody getting any work done because of the concurrency issues.

Better tracking

Because we have much better tracking of that now with Vertica and our new platform, we're actually able to handle that concurrency and get the highest priority workloads out quickly, allow them to happen, and then be able to follow along with the lower-priority workloads and be able to run them all in parallel.

The key is being able to run, especially at those peak loads, and be able to get a lot more insights than we were ever able to get last year.

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Gardner: And that peak load issue is so prominent for you. Another quick aside, are you using cloud or hybrid cloud to support any of these workloads, given the peak nature of this, rather than keep all that infrastructure running 365, 24×7? Is that something that you've been doing, or is that something you're considering?

Minton: Sure. On a lot of our data warehousing solutions, we do use cloud in points for our systems. A lot of our large-scale serving activities, as well as our large scale ingestion, does leverage cloud technologies.

We don't have it for our core data warehouse. We want to make that we have all of that data in-house in our own data centers, but we do ingest a lot of the data just as pass-throughs in the cloud, just to allow us to have more of that peak scalability that we wouldn’t have otherwise.

The faster than we can get data into our systems, the faster we're going to be able to report on that data and be able to get insights that are going to be able to help our customers.

Gardner: We're coming up toward the end of our discussion time. Let’s look at what comes next, Joel, in terms of where you can take this. You mentioned some really impressive qualitative and quantitative returns and improvements. We can always expect more data, more need for feedback loops, and a higher level of user expectation and experience. Where would you like to go next? How do you go to an extreme focus even more on this issue of personalization?

Minton: There are a few things that we're doing. We built the infrastructure that we need to really be able to knock it out of the park over the next couple of years. Some of the things that are just the next level of innovation for us are going to be, number one, increasing our use of personalization and making it much easier for our customers to get what they need when they need it.

So doubling down on that and increasing the number of use cases where our data scientists are actually building models that serve our customers throughout the entire experience is going to be one huge area of focus.

Another big area of focus is getting the data even more real time. As I discussed earlier, Dana, we're a very peaky business and the faster than we can get data into our systems, the faster we're going to be able to report on that data and be able to get insights that are going to be able to help our customers.

Our goal is to have even more real-time streams of that data and be able to get that data in so we can get insights from it and act on it as quickly as possible.

The other side is just continuing to invest in our multi-platform approach to allow the customer to do their taxes and to manage their finances on whatever platform they are on, so that it continues to be mobile, web, TVs, or whatever device they might use. We need to make sure that we can serve those data needs and give the users the ability to get great personalized experiences no matter what platform they are on. Those are some of the big areas where we're going to be focused over the coming years.

Recommendations

Gardner: Now you've had some 20/20 hindsight into moving from one data environment to another, which I suppose is equivalent of keeping the airplane flying and changing the wings at the same time. Do you have any words of wisdom for those who might be having concurrency issues or scale, velocity, variety type issues with their big data, when it comes to moving from one architecture platform to another? Any recommendations you can make to help them perhaps in ways that you didn't necessarily get the benefit of?

Minton: To start, focus on the real business needs and competitive advantage that your business is trying to build and invest in data to enable those things. It’s very easy to say you're going to replace your entire data platform and build everything soup to nuts all in one year, but I have seen those types of projects be tried and fail over and over again. I find that you put the platform in place at a high-level and you look for a few key business-use cases where you can actually leverage that platform to gain real business benefit.

When you're able to do that two, three, or four times on a smaller scale, then it makes it a lot easier to make that bigger investment to revamp the whole platform top to bottom. My number one suggestion is start small and focus on the business capabilities.

Number two, be really smart about where your biggest pain points are. Don’t try to solve world hunger when it comes to data. If you're having a concurrency issue, look at the platform you're using. Is there a way in my current platform to solve these without going big?

Frequently, what I find in data is that it’s not always the platform's fault that things are not performing. It could be the way that things are implemented and so it could be a software problem as opposed to a hardware or a platform problem.

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So again, I would have folks focus on the real problem and the different methods that you could use to actually solve those problems. It’s kind of making sure that you're solving the right problem with the right technology and not just assuming that your platform is the problem. That’s on the hardware front.

As I mentioned earlier, looking at the business use cases and making sure that you're solving those first is the other big area of focus I would have.

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Spirent leverages big data to keep user experience quality a winning factor for telcos

The next BriefingsDirect big-data case study discussion explores the ways that Spirent Communications advances the use of big data to provide improved user experiences for telecommunications operators.

We'll learn how advanced analytics that draws on multiple data sources provide Spirent’s telco customers’ rapid insights into their networks and operations. That insight, combined with analysis of user actions and behaviors, provides a "total picture" approach to telco services and uses that both improves the actual services proactively -- and also boosts the ability to better support help desks.

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Spirent’s insights thereby help operators in highly competitive markets reduce the spend on support, reduce user churn, and better adhere to service-level agreements (SLAs), while providing significant productivity gains.

To hear how Spirent uses big data to make major positive impacts on telco operations, we're joined by Tom Russo, Director of Product Management and Marketing at Spirent Communications in Matawan, New Jersey. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: User experience quality enhancement is essential, especially when we're talking about consumers that can easily change carriers. Controlling that experience is more challenging for an organization like a telco. They have so many variables across networks. So at a high-level, tell me how Spirent masters complexity using big data to help telcos maintain the best user experience.

Russo: Believe it or not, historically, operators haven't actually managed their customers as much as they've managed their networks. Even within the networks, they've done this in a fairly siloed fashion.

Russo

There would be radio performance teams that would look at whether the different cell towers were operating properly, giving good coverage and signal strength to the subscribers. As you might imagine, they wouldn't talk to the core network people, who would make sure that people can get IP addresses and properly transmit packets back and forth. They had their own tools and systems, which were separate, yet again, from the services people, who would look at the different applications. You can see where it’s going.

There were also customer-care people, who had their own tools and systems that didn’t leverage any of that network data. It was very inefficient, and not wrapped around the customer or the customer experience.

New demands

They sort of got by with those systems when the networks weren't running too hot. When competition wasn't too fierce, they could get away with that. But these days, with their peers offering better quality of service, over-the-top threats, increasing complexity on the network in terms of devices, and application services, it really doesn't work any more.

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It takes too long to troubleshoot real customer problems. They spend too much time chasing down blind alleys in terms of solving problems that don't really affect the customer experience, etc. They need to take a more customer-centric approach. As you’d imagine that’s where we come in. We integrate data across those different silos in the context of subscribers.

We collect data across those different silos -- the radio performance, the core network performance, the provisioning, the billing etc. -- and fuse it together in the context of subscribers. Then, we help the operator identify proactively where that customer experience is suffering, what we call hotspots, so that they can act before the customers call and complain, which is expensive from a customer-care perspective and before they churn, which is very expensive in terms of customer replacement. It's a more customer-centric approach to managing the network.

Gardner: So your customer experience management does what your customers had a difficult time doing internally. But one aspect of this is pulling together disparate data from different sources, so that you can get the proactive inference and insights. What did you do better around data acquisition?

We integrate data across those different silos in the context of subscribers.

Russo: The first key step is being able to integrate with a variety of these different systems. Each of the groups had their different tools, different data formats, different vendors.

Our solution has a very strong what we call extract, transform, load (ETL), or data mediation capability, to pull all these different data sources together, map them into a uniform model of the telecom network and the subscriber experience.

This allows us to see the connections between the subscriber experience, the underlying network performance and even things like outcomes -- whether people churn, whether they provide negative survey responses, whether they've called and complained to  customer care, etc.

Then, with that holistic model, we can build high-level metrics like quality of experience scores, predictive models, etc. to look across those different silos, help the operators see where the hot spots of customer dissatisfaction is, where people are going to eventually churn, or where other costs are going to be incurred.

Gardner: Before we go more deeply into this data issue, tell me a bit more about Spirent. Is the customer experience division the only part? Tell me about the larger company, just so we have a sense of the breadth and depths of what you offer.

World leader

Russo: Most people, at least in telecom, know Spirent as a lab vendor. Spirent is one of the world leaders in the markets for simulating, emulating, and testing devices, network elements, applications, and services, as they go from the development phase to the launch phase in their lifecycle. Most of their products focus on that, the lab testing or the launch testing, making sure that devices are, as we call it, "fit for launch."

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Spirent has historically had less of a presence in the live network domain. In the last year or two, they’ve made a number of strategic acquisitions in that space. They’ve made a number of internal investments to leverage the capabilities and knowledge base that they have from the lab side into the live network.

One of those investments, for example, was an acquisition back in early 2014 of DAX Technologies, a leading customer experience management vendor. That acquisition, plus some additional internal investments has led to the growth of our Customer Experience Management (CEM) Business Unit.

Gardner: Tom, tell me some typical use cases where your customers are using Spirent in the field. Who are those that are interacting with the software? What is it that they're doing with it? What are some of the typical ways in which it’s bringing value there?

Russo: Basically, we have two user bases that leverage our analytics. One is the customer-care groups. What they're trying to do is obtain, very quickly, a 360-degree view of the experience that a subscriber is seeing -- who is calling in and complaining about their service and the root causes of problems that they might be having with their services.

If you think about the historic operation, this was a very time-intensive, costly operation, because they would have to swivel chair, as we call it, between a variety of different systems and tools trying to figure out whether I had a network-related issue, a provisioning issue, a billing issue, or something else. These all could potentially take hours, even hundreds of hours, to resolve.

With our system, the customer-care groups have one single pane of glass, one screen, to see all aspects of the customer experience to very quickly identify the root causes of issues that they are having and resolve them. So it keeps customers happier and reduces the cost of the customer-care operation.

The second group that we serve is on the engineering side. We're trying to help them identify hotspots of customer dissatisfaction on the network, whether that be in terms of devices, applications, services, or network elements so that they can prioritize their resources around those hotspots, as opposed to noisy, traditional engineering alarms. The idea here is that this allows them to have maximal impact on the customer experience with minimal costs and minimal resources.

Gardner: You recently rolled out some new and interesting services and solutions. Tell us a little but about that.

Russo: We’ve rolled out the latest iteration of our InTouch solution, our flagship product. It’s called InTouch Customer and Network Analytics (CNA) and it really addresses feedback that we've received from customers in terms of what they want in an analytic solution.

We're hearing that they want to be more proactive and predictive. Don’t just tell me what's going on right now, what’s gone on historically, how things have trended, but help me understand what’s going to happen moving forward, where our customer is going to complain. Where is the network going to experience performance problems in the future. That's an increasing area of focus for us and something that we've embedded to a great degree in the InTouch CNA product.

More flexibility

Another thing that they've told us is that they want to have more flexibility and control on the visualization and reporting side. Don't just give me a stock set of dashboards and reports and have me rely on you to modify those over time. I have my own data scientists, my own engineers, who want to explore the data themselves.

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We've embedded Tableau business intelligence (BI) technology into our product to give them maximum flexibility in terms of report authorship and publication. We really like the combination of Tableau and Hewlett Packard Enterprise (HPE) Vertica because it allows them to be able to do those ad-hoc reports and then also get good performance through the Vertica database.

And another thing that we are doing more and more is what we call Closed Loop Analytics. It's not just identifying an issue or a customer problem on the network, but it's also being able to trigger an action. We have an integration and partnership with another business unit in Spirent called Mobilethink that can change device settings for example.

If we see a device is mis-provisioned, we can send alert to Mobilethink, and they can re-provision the device to correct something like a mis-provisioned access point name (APN) and resolve the problem. Then, we can use our system to confirm indeed that the fix was made and that the experience has improved.

We're trying to tie it all together, everything from the subscriber transactions and experience to the underlying network performance, again to the outcome type information.

Gardner: It’s clear to me, Tom, how we can get great benefits from doing this properly and how the value escalates the more data and the more information you get, and the better you can serve those customers. Let's drill down a bit into how you can make this happen. As far as data goes, are we talking about 10 different data types, 50? Given the stream and the amount of data that comes off of a network, what size data we are talking about and how do you get a handle on that?

Russo: In our largest deployment, we're talking about a couple of dozen different data sources and a total volume of data on the order of 50 to 100 billion transactions a day. So, it’s large volume, especially on the transactional side, and high variety. In terms of what we're talking about, it’s a lot of machine data. As I mentioned before, there is the radio performance, core network performance, and service performance type of information.

We also look at things like whether you're provisioning correctly for the services that you're trying to interact with. We look at your trouble ticket history to try and correlate things like network performance and customer care activity. We will look at survey data, net promoter score (NPS) type information, billing churn, and related information.

We're trying to tie it all together, everything from the subscriber transactions and experience to the underlying network performance, again to the outcome type information -- what was the impact of the experience on your behavior?

Gardner: What specifically is your history with HPE Vertica? Has this been something that's been in place for some time? Did you switch to it from something else? How did that work out?

Finishing migration

Russo: Right now, we're finishing the migration to HP Vertica technology, and it will be embedded in our InTouch CNA solution. There are a couple of things that we like about Vertica. One is the price-performance aspects. The columnar lookups, the projections, give us very strong query response performance, but it's also able to run on commodity hardware, which gives us price advantage that's also bolstered by the columnar compression.

So price performance-wise and maturity-wise we like it. It’s a field-proven, tested solution. There are some other features in terms of strong Hadoop integration that we like. A lot of carriers will have their own Hadoop clusters, data oceans, etc. that they want us to integrate with. Vertica makes that fairly straightforward, and we like a lot of the embedded analytics as well, the Distributed R capability for predictive analytics and things along those lines.

Gardner: It occurs to me that the effort that you put into this at Spirent and being able to take vast amounts of data across a complex network and then come out with these analytic benefits could be extended to any number of environments. Is there a parallel between what you are doing with mobile and telco carriers that could extend to maybe networks that are managing the Internet of Things (IoT) types of devices?

We definitely see our solution helping operators who are trying to be IoT platform providers to ensure the performances of those IoT services and the SLAs that they have for them.

Russo: Absolutely. We're working with carriers on IoT already. The requirements that these things have in terms of the performance that they need to operate properly are different than that of human beings, but nevertheless, the underlying transactions that have to take place, the ability to get a radio connection and set up an IP address and communicate data back and forth to one another and do it in a robust reliable way, is still critical.

We definitely see our solution helping operators who are trying to be IoT platform providers to ensure the performances of those IoT services and the SLAs that they have for them. We also see a potential use for our technology going a step further into the vertical IoT applications themselves in doing, for example, predictive analytics on sensor data itself. That could be a future direction for us.

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Gardner: Any words of wisdom for folks that are starting to do with large data volumes across wide variety of sources and are looking also for that more real-time analytics benefit? Any lessons learned that you could share from where Spirent has been and gone for others that are going to be facing some of these same big data issues?

Russo: It's important to focus on the end-user value and the use cases as opposed to the technology. So, we never really focus on getting data for the sake of getting data. We focus more on what problem a customer is trying to accomplish and how we can most simply and elegantly solve it. That steered us clear from jumping on the latest and greatest technology bandwagons, instead going with the proven technologies and leveraging our subject-matter expertise.

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

You may also be interested in:

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Powerful reporting from YP's data warehouse helps SMBs deliver the best ad campaigns

The next BriefingsDirect big-data innovation case study highlights how Yellow Pages (YP) has developed a massive enterprise data warehouse with near real-time reporting capabilities that pulls oceans of data and information from across new and legacy sources.

We explore how YP then continuously delivers precise metrics to over half a million paying advertisers -- many of them SMBs and increasingly through mobile interfaces -- to best analyze and optimize their marketing and ad campaigns.

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

To learn more, BriefingsDirect recently sat down with Bill Theisinger, Vice President of Engineering for Platform Data Services at YP in Glendale, California. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

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?

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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.

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

You may also be interested in:

• IoT brings on development demands that DevOps manages best, say experts
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• How fast analytics changes the game and expands the market for big data value
• How HTC centralizes storage management to gain visibility and IT disaster avoidance
• Big data, risk, and predictive analysis drive use of cloud-based ITSM, says panel
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• Redcentric orchestrates networks-intensive merger using advanced configuration management database
• HP pursues big data opportunity with updated products, services, developer program

Big data generates new insights into what’s happening in the world's tropical ecosystems

The next BriefingsDirect big-data innovation case study interview explores how large-scale monitoring of rainforest biodiversity and climate has been enabled and accelerated by cutting-edge big-data capture, retrieval, and analysis.

We'll learn how quantitative analysis and modeling are generating new insights into what’s happening in tropical ecosystems worldwide, and we'll hear how such insights are leading to better ways to attain and verify sustainable development and preservation methods and techniques.

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

To learn more about data science -- and how hosting that data science in the cloud -- helps the study of biodiversity, we're pleased to welcome Eric Fegraus, Senior Director of Technology of the TEAM Network at Conservation International and Jorge Ahumada, Executive Director of the TEAM Network, also at Conservation International in Arlington, Virginia. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Knowing what’s going on in environments in the tropics helps us understand what to do and what not to do to preserve them. How has that changed? We spoke about a year ago, Eric. Are there any trends or driving influences that have made this data gathering more important than ever.

Fegraus: Over this last year, we’ve been able to roll out our analytic systems across the TEAM Network. We're having more-and-more uptake with our protected-area managers using the system and we have some good examples where the results are being used.

Fegraus

For example, in Uganda, we noticed that a particular cat species was trending downward. The folks there were really curious why this was happening. At first, they were excited that there was this cat species, which was previously not known to be there.

This particular forest is a gorilla reserve, and one of the main economic drivers around the reserve is ecotourism, people paying to go see the gorillas. Once they saw that these cats are going down, they started asking what could be impacting this. Our system told them that the way they were bringing in the eco-tourists to see the gorillas had shifted and that was potentially having an impact of where the cats were. It allowed them to readjust and think about their practices to bring in the tourists to the gorillas.

Information at work

Gardner: Information at work.

Fegraus: Information at work at the protected-area level.

Gardner: Just to be clear for our audience, the TEAM Network stands for the Tropical Ecology Assessment and Monitoring. Jorge, tell us a little bit about how that came about, the TEAM Network and what it encompasses worldwide?

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Ahumada: The TEAM Network was a program that started about 12 years ago and it was started to fill a void in the information we have from tropical forests. Tropical forests cover a little bit less than 10 percent of the terrestrial area in the world, but they have more than 50 percent of the biodiversity.

Ahumda

So they're the critical places to be conserved from that point of view, despite the fact we didn’t have any information about what's happening in these places. That’s how the TEAM Network was born, and the model was to use data collection methods that were standardized, that were replicated across a number of sites, and have systems that would store and analyze that data and make it useful. That was the main motivation.

Gardner: Of course, it’s super-important to be able to collect and retrieve and put that data into a place where it can be analyzed. It’s also, of course, important then to be able to share that analysis. Eric, tell us what's been happening lately that has led to the ability for all of those parts of a data lifecycle to really come to fruition?

Fegraus: Earlier this year, we completed our end-to-end system. We're able to take the data from the field, from the camera traps, from the climate stations, and bring it into our central repository. We then push the data into Vertica, which is used for the analytics. Then, we developed a really nice front-end dashboard that shows the results of species populations in all the protected areas where we work.

The analytical process also starts to identify what could be impacting the trends that we're seeing at a per-species level. This dashboard also lets the user look at the data in a lot of different ways. They can aggregate it and they can slice and dice it in different ways to look at different trends.

Gardner: Jorge, what sort of technologies are they using for that slicing and dicing? Are you seeing certain tools like Distributed R or visualization software and business-intelligence (BI) packages? What's the common thread or is it varied greatly?

Ahumada: It depends on the analysis, but we're really at the forefront of analytics in terms of big data. As Michael Stonebraker and other big data thinkers have said, the big-data analytics infrastructure has concentrated on the storage of big data, but not so much on the analytics. We break that mold because we're doing very, very sophisticated Bayesian analytics with this data.

One of the problems of working with camera-trap data is that you have to separate the detection process from the actual trend that you're seeing because you do have a detection process that has error.

Hierarchical models

We do that with hierarchical models, and it's a fairly complicated model. Just using that kind of model, a normal computer will take days and months. With the power of Vertica and power of processing, we’ve been able to shrink that to a few hours. We can run 500 or 600 species from 13 sites, all over the world in five hours. So it’s a really good way to use the power of processing.

We’d been also more recently working with Distributed R, a new package that was written by HP folks at Vertica, to analyze satellite images, because we're also interested in what’s happening at these sites in terms of forest loss. Satellite images are really complicated, because you have millions of pixels and you don’t really know what each pixel is. Is it forest, agricultural land, or a house? So running that on normal R, it's kind of a problem.

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Distributed R is a package that actually takes some of those functions, like random forest and regression trees, and takes full power of the vertical processing of Vertica. So we’ve seen a 10-fold increase in performance with that, and it allows us to get much more information out of those images.

Gardner: Not only are you on the cutting-edge for the analytics, you've also moved to the bleeding edge on infrastructure and distribution mechanisms. Eric, tell us a little bit about your use of cloud and hybrid cloud?

Fegraus: To back up a little bit, we ended up building a system that uses Vertica. It’s an on-premise solution and that's what we're using in the TEAM Network. We've since realized that this solution we built for the TEAM Network can also be readily scalable to other organizations and government agencies, etc., different people that want to manage camera trap data, they want to do the analytics.

So now, we're at a process where we’ve been essentially doing software development and producing software that’s scalable. If an organization wants to replicate what we’re doing, we have a solution that we can spin up in the cloud that has all of the data management, the analytics, the data transformations and processing, the collection, and all the data quality controls, all built into a software instance that could be spun up in the cloud.

In many of these countries, it's very difficult for some of those governments to expand out their old solutions on the ground. Cloud solutions offer a very good, effective way to manage data.

Gardner: And when you say “in the cloud,” are you talking about a specific public cloud, in a specific country or all the above, some of the above?

Fegraus: All of the above. We'll be using Vertica or we're using Vertica OnDemand. We're actually going to transition our existing on-premise solution into Vertica OnDemand. The solution we’re developing uses mostly open-source software and it can be replicated in the Amazon cloud or other clouds that have the right environments where we can get things up and running.

Gardner: Jorge, how important is that to have that global choice for cloud deployment and attract users and also keep your cost limited?

Ahumada: It’s really key, because in many of these countries, it's very difficult for some of those governments to expand out their old solutions on the ground. Cloud solutions offer a very good, effective way to manage data. As Eric was saying, the big limitation here is which cloud solutions are available in each country. Right now, we have something with cloud OnDemand here, but in some of the countries, we might not have the same infrastructure. So we'll have to contract different vendors or whatever.

But it's a way to keep cost down, deliver the information really quick, and store the data in a way that is safe and secure.

What's next?

Gardner: Eric, now that we have this ability to retrieve, gather, analyze, and now distribute, what comes next in terms of having these organizations work together? Do we have any indicators of what the results might be in the field? How can we measure the effectiveness at the endpoint -- that is to say, in these environments based on what you have been able to accomplish technically?

Fegraus: One of the nice things about the software that we built that can run in the various cloud environments, is that it can also be connected. For example, if we start putting these solutions in a particular continent, and there are countries that are doing this next to each other, there are not going to be silos that will be unable to share an aggregated level of data across each other so that we can get a holistic picture of what's happening.

So that was very important when we started going down this process, because one of the big inhibitors for growth within the environmental sciences is that there are these traditional silos of data that people in organizations keep and sit on and essentially don't share. That was a very important driver for us as we were going down this path of building software.

Gardner: Jorge, what comes next in terms of technology. Are the scale issues something you need to hurdle to get across? Are there analytics issues? What's the next requirements phase that you would like to work through technically to make this even more impactful?

Ahumada: As we scale up in size and  start  having more granularity in the countries where we work, the challenge is going to be keeping these systems responsive and information coming. Right now, one of the big limitations is the analytics. We do have analytics running at top speeds, but once we started talking about countries, we're going to have an the order of many more species and many more protected areas to monitor.

This is something that the industry is starting to move forward on in terms of incorporating more of the power of the hardware into the analytics, rather than just the storage and the management of data.

This is something that the industry is starting to move forward on in terms of incorporating more of the power of the hardware into the analytics, rather than just the storage and the management of data. We're looking forward to keep working with our technology partners, and in particular HP, to help them guide this process. As a case study, we're very well-positioned for that, because we already have that challenge.

Gardner: Also it appears to me that you are a harbinger, a bellwether, for the Internet of Things (IoT). Much of your data is coming from monitoring, sensors, devices, and cameras. It's in the form of images and raw data. Any thoughts about what others who are thinking about the impact of the IoT should consider, now that you have been there?

Fegraus: When we talk about big data, we're talking about data collected from phones, cars, and human devices. Humans are delivering the data. But here we have a different problem. We're talking about nature delivering the data and we don't have that infrastructure in places like Uganda, Zimbabwe, or Brazil.

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So we have to start by building that infrastructure and we have the camera traps as an example of that. We need to be able to deploy much more, much larger-scale infrastructure to collect data and diversify the sensors that we currently have, so that we can gather sound data, image data, temperature, and environmental data in a much larger scale.

Satellites can only take us some part of the way, because we're always going to have problems with resolution. So it's really deployment on the ground which is going to be a big limitation, and it's a big field that is developing now.

Gardner: Drones?

Fegraus: Drones, for example, have that capacity, especially small drones that are showing to be intelligent, to be able to collect a lot of information autonomously. This is at the cutting edge right now of technological development, and we're excited about it.

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

You may also be interested in:

• DevOps and security, a match made in heaven
• How Sprint employs orchestration and automation to bring IT into DevOps readiness
• How fast analytics changes the game and expands the market for big data value
• How HTC centralizes storage management to gain visibility and IT disaster avoidance
• Big data, risk, and predictive analysis drive use of cloud-based ITSM, says panel
• Rolta AdvizeX experts on hastening big data analytics in healthcare and retail
• The future of business intelligence as a service with GoodData and HP Vertica 
• Enterprises opting for converged infrastructure as stepping stone to hybrid cloud 
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