How lastminute.com uses machine learning to improve travel bookings user experience

The next BriefingsDirect Voice of the Customer digital transformation case study highlights how online travel and events pioneer lastminute.com leverages big-data analytics with speed at scale to provide business advantages to online travel services.

We’ll explore how lastminute.com manages massive volumes of data to support cutting-edge machine-learning algorithms to allow for speed and automation in the rapidly evolving global online travel research and bookings business.

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

To learn how a culture of IT innovation helps make highly dynamic customer interactions for online travel a major differentiator, we’re joined by Filippo Onorato, Chief Information Officer at lastminute.com group in Chiasso, Switzerland. The discussion is moderated by BriefingsDirect’s Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Most people these days are trying to do more things more quickly amid higher complexity. What is it that you’re trying to accomplish in terms of moving beyond disruption and being competitive in a highly complex area?

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Onorato: The travel market — and in particular the online travel market — is a very fast-moving market, and the habits and behaviors of the customers are changing so rapidly that we have to move fast.

Disruption is coming every day from different actors … [requiring] a different way of constructing the customer experience. In order to do that, you have to rely on very big amounts of data — just to style the evolution of the customer and their behaviors.

Gardner: And customers are more savvy; they really know how to use data and look for deals. They’re expecting real-time advantages. How is the sophistication of the end user impacting how you work at the core, in your data center, and in your data analysis, to improve your competitive position?

Onorato

Onorato: Once again, customers are normally looking for information, and providing the right information at the right time is a key of our success. The brand we came from was called Bravofly and Volagratis in Italy; that means “free flight.” The competitive advantage we have is to provide a comparison among all the different airline tickets, where the market is changing rapidly from the standard airline behavior to the low-cost ones. Customers are eager to find the best deal, the best price for their travel requirements.

So, the ability to construct their customer experience in order to find the right information at the right time, comparing hundreds of different airlines, was the competitive advantage we made our fortune on.

Gardner: Let’s edify our listeners and reader a bit about lastminute.com. You’re global. Tell us about the company and perhaps your size, employees, and the number of customers you deal with each day.

Most famous brand

Onorato: We are 1,200 employees worldwide. Lastminute.com, the most famous brand worldwide, was acquired by the Bravofly Rumbo Group two years ago from Sabre. We own Bravofly; that was the original brand. We own Rumbo; that is very popular in Spanish-speaking markets. We own Volagratis in Italy; that was the original brand. And we own Jetcost; that is very popular in France. That is actually a metasearch, a combination of search and competitive comparison between all the online travel agencies (OTAs) in the market.

We span across 40 countries, we support 17 languages, and we help almost 10 million people fly every year.

Gardner: Let’s dig into the data issues here, because this is a really compelling use-case. There’s so much data changing so quickly, and sifting through it is an immense task, but you want to bring the best information to the right end user at the right time. Tell us a little about your big-data architecture, and then we’ll talk a little bit about bots, algorithms, and artificial intelligence.

Onorato: The architecture of our system is pretty complex. On one side, we have to react almost instantly to the search that the customers are doing. We have a real-time platform that’s grabbing information from all the providers, airlines, other OTAs, hotel provider, bed banks, or whatever.

We concentrate all this information in a huge real-time database, using a lot of caching mechanisms, because the speed of the search, the speed of giving result to the customer is a competitive advantage. That’s the real-time part of our development that constitutes the core business of our industry.

Gardner: And this core of yours, these are your own data centers? How have you constructed them and how do you manage them in terms of on-premises, cloud, or hybrid?

Onorato: It’s all on-premises, and this is our core infrastructure. On the other hand, all that data that is gathered from the interaction with the customer is partially captured. This is the big challenge for the future — having all that data stored in a data warehouse. That data is captured in order to build our internal knowledge. That would be the sales funnel.

So, the behavior of the customer, the percentage of conversion in each and every step that the customer does, from the search to the actual booking. That data is gathered together in a data warehouse that is based on HPE Vertica, and then, analyzed in order to find the best place, in order to optimize the conversion. That’s the main usage of the date warehouse.

On the other hand, what we’re implementing on top of all this enormous amount of data is session-related data. You can imagine how much a data single interaction of a customer can generate. Right now, we’re storing a short history of that data, but the goal is to have two years’ worth of session data. That would be an enormous amount of data.

Gardner: And when we talk about data, often we’re concerned about velocity and volume. You’ve just addressed volume, but velocity must be a real issue, because any change in a weather issue in Europe, for example, or a glitch in a computer system at one airline in North America changes all of these travel data points instantly.

Unpredictable events

Onorato: That’s also pretty typical in the tourism industry. It’s a very delicate business, because we have to react to unpredictable events that are happening all over the world. In order to do a better optimization of margin, of search results, etc, we’re also applying some machine-learning algorithm, because a human can’t react so fast to the ever-changing market or situation.

In those cases, we use optimization algorithms in order to fine tune our search results, in order to better deal with a customer request, and to propose the better deal at the right time. In very simple terms, that’s our core business right now.

Gardner: And Filippo, only your organization can do this, because the people with the data on the back side can’t apply the algorithm; they have only their own data. It’s not something the end user can do on the edge, because they need to receive the results of the analysis and the machine learning. So you’re in a unique, important position. You’re the only one who can really apply the intelligence, the AI, and the bots to make this happen. Tell us a little bit about how you approached that problem and solved it.

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Onorato: I perfectly agree. We are the collector of an enormous amount of product-related information on one side. On the other side, what we’re collecting are the customer behaviors. Matching the two is unique for our industry. It’s definitely a competitive advantage to have that data.

Then, what you do with all those data is something that is pushing us to do continuous innovation and continuous analysis. By the way, I don’t think something can be implemented without a lot of training and a lot of understanding of the data.

Just to give you an example, what we’re implementing, the machine learning algorithm that is called multi-armed bandit, is kind of parallel testing of different configurations of parameters that are presented to the final user. This algorithm is reacting to a specific set of conditions and proposing the best combination of order, visibility, pricing, and whatever to the customer in order to satisfy their research.

What we really do in that case is to grab information, build our experience into the algorithm, and then optimize this algorithm every day, by changing parameters, by also changing the type of data that we’re inputting into the algorithm itself.

So, it’s an ongoing experience; it’s an ongoing study. It’s endless, because the market conditions are changing and the actors in the market are changing as well, coming from the two operators in the past, the airline and now the OTA. We’re also a metasearch, aggregating products from different OTAs. So, there are new players coming in and they’re always coming closer and closer to the customer in order to grab information on customer behavior.

Gardner: It sounds like you have a really intense culture of innovation, and that’s super important these days, of course. As we were hearing at the HPE Big Data Conference 2016, the feedback loop element of big data is now really taking precedence. We have the ability to manage the data, to find the data, to put the data in a useful form, but we’re finding new ways. It seems to me that the more people use our websites, the better that algorithm gets, the better the insight to the end user, therefore the better the result and user experience. And it never ends; it always improves.

How does this extend? Do you take it to now beyond hotels, to events or transportation? It seems to me that this would be highly extensible and the data and insights would be very valuable.

Core business

Onorato: Correct. The core business was initially the flight business. We were born by selling flight tickets. Hotels and pre-packaged holidays was the second step. Then, we provided information about lifestyle. For example, in London we have an extensive offer of theater, events, shows, whatever, that are aggregated.

Also, we have a smaller brand regarding restaurants. We’re offering car rental. We’re giving also value-added services to the customer, because the journey of the customer doesn’t end with the booking. It continues throughout the trip, and we’re providing information regarding the check-in; web check-in is a service that we provide. There are a lot of ancillary businesses that are making the overall travel experience better, and that’s the goal for the future.

Gardner: I can even envision where you play a real-time concierge, where you’re able to follow the person through the trip and be available to them as a bot or a chat. This edge-to-core capability is so important, and that big data feedback, analysis, and algorithms are all coming together very powerfully.

Tell us a bit about metrics of success. How can you measure this? Obviously a lot of it is going to be qualitative. If I’m a traveler and I get what I want, when I want it, at the right price, that’s a success story, but you’re also filling every seat on the aircraft or you’re filling more rooms in the hotels. How do we measure the success of this across your ecosystem?

Onorato: In that sense, we’re probably a little bit farther away from the real product, because we’re an aggregator. We don’t have the risk of running a physical hotel, and that’s where we’re actually very flexible. We can jump from one location to another very easily, and that’s one of the competitive advantages of being an OTA.

But the success overall right now is giving the best information at the right time to the final customer. What we’re measuring right now is definitely the voice of the customer, the voice of the final customer, who is asking for more and more information, more and more flexibility, and the ability to live an experience in the best way possible.

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So, we’re also providing a brand that is associated with wonderful holidays, having fun, etc.

Gardner: The last question, for those who are still working on building out their big data infrastructure, trying to attain this cutting-edge capability and start to take advantage of machine learning, artificial intelligence, and so forth, if you could do it all over again, what would you tell them, what would be your advice to somebody who is merely more in the early stages of their big data journey?

Onorato: It is definitely based on two factors — having the best technology and not always trying to build your own technology, because there are a lot of products in the market that can speed up your development.

And also, it’s having the best people. The best people is one of the competitive advantages of any company that is running this kind of business. You have to rely on fast learners, because market condition are changing, technology is changing, and the people needs to train themselves very fast. So, you have to invest in people and invest in the best technology available.

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WWT took an enterprise Tower of Babel and delivered comprehensive intelligent search

The next BriefingsDirect Voice of the Customer digital transformation case study highlights how World Wide Technology, known as WWT, in St. Louis, found itself with a very serious yet somehow very common problem — users simply couldn’t find relevant company content.

We’ll explore how WWT reached deep into its applications, data, and content to rapidly and efficiently create a powerful Google-like, pan-enterprise search capability. Not only does it search better and empower users, the powerful internal index sets the stage for expanded capabilities using advanced analytics to engender a more productive and proactive digital business culture.

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

Here to describe how WWT took an enterprise Tower of Babel and delivered cross-applications intelligent search are James Nippert, Enterprise Search Project Manager, and Susan Crincoli, Manager of Enterprise Content, both at World Wide Technology in St. Louis. The discussion is moderated by BriefingsDirect’s Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: It seems pretty evident that the better search you have in an organization, the better people are going to find what they need as they need it. What holds companies back from delivering results like people are used to getting on the web?

Nippert

Nippert:  It’s the way things have always been. You just had to drill down from the top level. You go to your Exchange, your email, and start there. Did you save a file here? “No, I think I saved it on my SharePoint site,” and so you try to find it there, or maybe it was in a file directory.

Those are the steps that people have been used to because it’s how they’ve been doing it their entire lives, and it’s the nature of beast as we bring more and more enterprise applications into the fold. You have enterprises with 100 or 200 applications, and each of those has its own unique data silos. So, users have to try to juggle all of these different content sources where stuff could be saved. They’re just used to having to dig through each one of those to try to find whatever they’re looking for.

Gardner: And we’ve all become accustomed to instant gratification. If we want something, we want it right away. So, if you have to tag something, or you have to jump through some hoops, it doesn’t seem to be part of what people want. Susan, are there any other behavioral parts of this?

Find the world

Crincoli: We, as consumers, are getting used to the Google-like searching. We want to go to one place and find the world. In the information age, we want to go to one place and be able to find whatever it is we’re looking for. That easily transfers into business problems. As we store data in myriad different places, the business user also wants the same kind of an interface.

Crincoli

Gardner: Certain tools that can only look at a certain format or can only deal with certain tags or taxonomy are strong, but we want to be comprehensive. We don’t want to leave any potentially powerful crumbs out there not brought to bear on a problem. What’s been the challenge when it comes to getting at all the data, structured, unstructured, in various formats?

Nippert: Traditional search tools are built off of document metadata. It’s those tags that go along with records, whether it’s the user who uploaded it, the title, or the date it was uploaded. Companies have tried for a long time to get users to tag with additional metadata that will make documents easier to search for. Maybe it’s by department, so you can look for everything in the HR Department.

At the same time, users don’t want to spend half an hour tagging a document; they just want to load it and move on with their day. Take pictures, for example. Most enterprises have hundreds of thousands of pictures that are stored, but they’re all named whatever number the camera gave, and they will name it DC0001. If you have 1,000 pictures named that you can’t have a successful search, because no search engine will be able to tell just by that title — and nothing else — what they want to find.

Gardner: So, we have a situation where the need is large and the paybacks could be large, but the task and the challenge are daunting. Tell us about your journey. What did you do in order to find a solution?

Nippert: We originally recognized a problem with our on-premises Microsoft SharePoint environment. We were using an older version of SharePoint that was running mostly on metadata, and our users weren’t uploading any metadata along with their internet content.

We originally set out to solve that issue, but then, as we began interviewing business users, we understood very quickly that this is an enterprise-scale problem. Scaling out even further, we found out it’s been reported that as much as 10 percent of staffing costs can be lost directly to employees not being able to find what they’re looking for. Your average employee can spend over an entire work week per year searching for information or documentation that they need to get their job done.

So it’s a very real problem. WWT noticed it over the last couple of years, but as there is the velocity in volume of data increase, it’s only going to become more apparent. With that in mind, we set out to start an RFI process for all the enterprise search leaders. We used the Gartner Magic Quadrants and started talks with all of the Magic Quadrant leaders. Then, through a down-selection process, we eventually landed on HPE.

We have a wonderful strategic partnership with them. It wound up being that we went with the HPE IDOL tool, which has been one of the leaders in enterprise search, as well as big data analytics, for well over a decade now, because it has very extensible platform, something that you can really scale out and customize and build on top of. It doesn’t just do one thing.

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Gardner: And it’s one solution to let people find what they’re looking for, but when you’re comprehensive and you can get all kinds of data in all sorts of apps, silos and nooks and crannies, you can deliver results that the searching party didn’t even know was there. The results can be perhaps more powerful than they were originally expecting.

Susan, any thoughts about a culture, a digital transformation benefit, when you can provide that democratization of search capability, but maybe extended into almost analytics or some larger big-data type of benefit?

Multiple departments

Crincoli: We’re working across multiple departments and we have a lot of different internal customers that we need to serve. We have a sales team, business development practices, and professional services. We have all these different departments that are searching for different things to help them satisfy our customers’ needs.

With HPE being a partner, where their customers are our customers, we have this great relationship with them. It helps us to see the value across all the different things that we can bring to bear to get all this data, and then, as we move forward, what we help people build more relevant results.

If something is searched for one time, versus 100 times, then that’s going to bubble up to the top. That means that we’re getting the best information to the right people in the right amount of time. I’m looking forward to extending this platform and to looking at analytics and into other platforms.

Gardner: That’s why they call it “intelligent search.” It learns as you go.

Nippert: The concept behind intelligent search is really two-fold. It first focuses on business empowerment, which is letting your users find whatever it is specifically that they’re looking for, but then, when you talk about business enablement, it’s also giving users the intelligent conceptual search experience to find information that they didn’t even know they should be looking for.

If I’m a sales representative and I’m searching for company “X,” I need to find any of the Salesforce data on that, but maybe I also need to find the account manager, maybe I need to find professional services’ engineers who have worked on that, or maybe I’m looking for documentation on a past project. As Susan said, that Google-like experience is bringing that all under one roof for someone, so they don’t have to go around to all these different places; it’s presented right to them.

Gardner: Tell us about World Wide Technology, so we understand why having this capability is going to be beneficial to your large, complex organization?

Humanizes Machine Learning

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Crincoli: We’re a $7-billion organization and we have strategic partnerships with Cisco, HPE, EMC, and NetApp, etc. We have a lot of solutions that we bring to market. We’re a solution integrator and we’re also a reseller. So, when you’re an account manager and you’re looking across all of the various solutions that we can provide to solve the customer’s problems, you need to be able to find all of the relevant information.

You probably need to find people as well. Not only do I need to find how we can solve this customer’s problem, but also who has helped us to solve this customer’s problem before. So, let me find the right person, the right pre-sales engineer or the right post-sales engineer. Or maybe there’s somebody in professional services. Maybe I want the person who implemented it the last time. All these different people, as well as solutions that we can bring in help give that sales team the information they need right at their fingertips.

It’s very powerful for us to think about the struggles that a sales manager might have, because we have so many different ways that we can help our customer solve those problems. We’re giving them that data at their fingertips, whether that’s from Salesforce, all the way through to SharePoint or something in an email that they can’t find from last year. They know they have talked to somebody about this before, or they want to know who helped me. Pulling all of that information together is so powerful.

We don’t want them to waste their time when they’re sitting in front of a customer trying to remember what it was that they wanted to talk about.

Gardner: It really amounts to customer service benefits in a big way, but I’m also thinking this is a great example of how, when you architect and deploy and integrate properly on the core, on the back end, that you can get great benefits delivered to the edge. What is the interface that people tend to use? Is there anything we can discuss about ease of use in terms of that front-end query?

Simple and intelligent

Nippert: As far as ease of use goes, it’s simplicity. If you’re a sales rep or an engineer in the field, you need to be able to pull something up quickly. You don’t want to have to go through layers and layers of filtering and drilling down to find what you’re looking for. It needs to be intelligent enough that, even if you can’t remember the name of a document or the title of a document, you ought to be able to search for a string of text inside the document and it still comes back to the top. That’s part of the intelligent search; that’s one of the features of HPE IDOL.

Whenever you’re talking about front-end, it should be something light and something fast. Again, it’s synonymous with what users are used to on the consumer edge, which is Google. There are very few search platforms out there that can do it better. Look at the  Google home page. It’s a search bar and two buttons; that’s all it is. When users are used to that at home and they come to work, they don’t want a cluttered, clumsy, heavy interface. They just need to be able to find what they’re looking for as quickly and simply as possible.

Gardner: Do you have any examples where you can qualify or quantify the benefit of this technology and this approach that will illustrate why it’s important?

Nippert: We actually did a couple surveys, pre- and post-implementation. As I had mentioned earlier, it was very well known that our search demands weren’t being met. The feedback that we heard over and over again was “search sucks.” People would say that all the time. So, we tried to get a little more quantification around that with some surveys before and after the implementation of IDOL search for the enterprise. We got a couple of really great numbers out of it. We saw that people’s satisfaction with search went up by about 30 percent with overall satisfaction. Before, it was right in the middle, half of them were happy, half of them weren’t.

Now, we’re well over 80 percent that have overall satisfaction with search. It’s gotten better at finding everything from documents to records to web pages across the board; it’s improving on all of those. As far as the specifics go, the thing we really cared about going into this was, “Can I find it on the first page?” How often do you ever go to the second page of search results.

With our pre-surveys, we found that under five percent of people were finding it on the first page. They had to go to second or third page or four through 10. Most of the users just gave up if it wasn’t on the first page. Now, over 50 percent of users are able to find what they’re looking for on the very first page, and if not, then definitely the second or third page.

We’ve gone from a completely unsuccessful search experience to a valid successful search experience that we can continue to enhance on.

Crincoli: I agree with James. When I came to the company, I felt that way, too — search sucks. I couldn’t find what I was looking for. What’s really cool with what we’ve been able to do is also review what people are searching for. Then, as we go back and look at those analytics, we can make those the best bets.

If we see hundreds of people are searching for the same thing or through different contexts, then we can make those the best bets. They’re at the top and you can separate those things out. These are things like the handbook or PTO request forms that people are always searching for.

Gardner: I’m going to just imagine that if I were in the healthcare, pharma, or financial sectors, I’d want to give my employees this capability, but I’d also be concerned about proprietary information and protection of data assets. Maybe you’re not doing this, but wonder what you know about allowing for the best of search, but also with protection, warnings, and some sort of governance and oversight.

Governance suite

Nippert: There is a full governance suite built in and it comes through a couple of different features. One of the main ones is induction, where as IDOL scans through every single line of a document or a PowerPoint slide of a spreadsheet whatever it is, it can recognize credit card numbers, Social Security numbers anything that’s personally identifiable information (PII) and either pull that out, delete it, send alerts, whatever.

You have that full governance suite built in to anything that you’ve indexed. It also has a mapped security engine built in called Omni Group, so it can map the security of any content source. For example, in SharePoint, if you have access to a file and I don’t and if we each ran a search, you would see a comeback in the results and I wouldn’t. So, it can honor any content’s security.

Gardner: Your policies and your rules are what’s implemented, and that’s how it goes?

Nippert: Exactly. It is up to as the search team or working with your compliance or governance team to make sure that that does happen.

Gardner: As we think about the future and the availability for other datasets to be perhaps brought in, that search is a great tool for access to more than just corporate data, enterprise data and content, but maybe also the front-end for some advanced querying analytics, business intelligence (BI), has there been any talk about how to take what you are doing in enterprise search and munge that, for lack of a better word, with analytics BI and some of the other big data capabilities.

Nippert: Absolutely. So HPE has just recently released BI for Human Intelligence (BIFHI), which is their new front end for IDOL and that has a ton of analytics capabilities built into it that really excited to start looking at a lot of rich text, rich media analytics that can pull the words right off the transcript of an MP4 raw video and transcribe it at the same time. But more than that, it is going to be something that we can continue to build on top of, as well and come up with our own unique analytic solutions.

Gardner: So talk about empowering your employees. Everybody can become a data scientist eventually, right, Susan?

Crincoli: That’s right. If you think about all of the various contexts, we started out with just a few sources, but we also have some excitement because we built custom applications, both for our customers and for our internal work. We’re taking that to the next level with building an API and pulling that data into the enterprise search that just makes it even more extensible to our enterprise.

Gardner: I suppose the next step might be the natural language audio request where you would talk to your PC, your handheld device, and say, “World Wide Technology feed me this,” and it will come back, right?

Nippert: Absolutely. You won’t even have to lift a finger anymore.

Cool things

Crincoli: It would be interesting to loop in what they are doing with Cortana at Microsoft and some of the machine learning and some of the different analytics behind Cortana. I’d love to see how we could loop that together. But those are all really cool things that we would love to explore.

Gardner: But you can’t get there until you solve the initial blocking and tackling around content and unstructured data synthesized into a usable format and capability.

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Nippert: Absolutely. The flip side of controlling your data sources, as we’re learning, is that there are a lot of important data sources out there that aren’t good candidates for enterprise search whatsoever. When you look at a couple of terabytes or petabytes of MongoDB data that’s completely unstructured and it’s just binaries, that’s enterprise data, but it’s not something that anyone is looking for.

So even though our original knee-jerk is to index everything, get everything to search, you want to able to search across everything. But you also have to take it with a grain of salt. A new content source could be hundreds or thousands of results that could potentially clutter the accuracy of results. Sometimes, it’s actually knowing when not to search something.

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HPE takes aim at customer needs for speed and agility in age of IoT, hybrid everything

A leaner, more streamlined Hewlett Packard Enterprise (HPE) advanced across several fronts at HPE Discover 2016 in London, making inroads into hybrid IT, Internet of Things (IoT), and on to the latest advances in memory-based computer architecture. All the innovations are designed to help customers address the age of digital disruption with speed, agility, and efficiency.

Addressing a Discover audience for the first time since HPE announced spinning off many software lines to Micro Focus, Meg Whitman, HPE President and CEO, said that company is not only committed to those assets, becoming a major owner of Micro Focus in the deal, but building its software investments.

“HPE is not getting out of software but doubling-down on the software that powers the apps and data workloads of hybrid IT,” she said Tuesday at London’s ExCel exhibit center.

“Massive compute resources need to be brought to the edge, powering the Internet of Things (IoT). … We are in a world now where everything computes, and that changes everything,” said Whitman, who has now been at the helm of HPE and HP for five years.

HPE’s new vision: To be the leading provider of hybrid IT, to run today’s data centers, and then bridge the move to multi-cloud and empower the intelligent edge, said Whitman. “Our goal is to make hybrid IT simple and to harness the intelligent edge for real-time decisions” to allow enterprises of all kinds to win in the marketplace, she said.

Hyper-converged systems

To that aim, the company this week announced an extension of HPE Synergy’s fully programmable infrastructure to HPE’s multi-cloud platform and hyper-converged systems, enabling IT operators to deliver software-defined infrastructure as quickly as customers’ businesses demand. The new solutions include:

  • HPE Synergy with HPE Helion CloudSystem 10 — This brings full composability across compute, storage and fabric to HPE’s OpenStack technology-based hybrid cloud platform to enable customers to run bare metal, virtualized, containerized and cloud-native applications on a single infrastructure and dynamically compose and recompose resources for unmatched agility and efficiency.
  • HPE Hyper Converged Operating Environment — The software update leverages composable technologies to deliver new capabilities to the HPE Hyper Converged 380, including new workspace controls that allow IT managers to compose and recompose virtualized resources for different lines of business, making it easier and more efficient for IT to act as an internal service provider to their organization.

This move delivers a full-purpose composable infrastructure platform, treating infrastructure as code, enabling developers to accelerate application delivery, says HPE. HPE Synergy has nearly 100 early access customers across a variety of industries, and is now broadly available. [Disclosure: HPE is a sponsor of BriefingsDirect podcasts.]

This year’s HPE Discover was strong on showcasing the ecosystem approach to creating and maintaining hybrid IT. Heavy hitters from Microsoft Azure, Arista, and Docker joined Whitman on stage to show their allegiance to HPE’s offerings — along with their own — as essential ingredients to Platform 3.0 efficiency.

See more on my HPE Discover analysis on The Cube.

HPE also announced plans to expand Cloud28+, an open community of commercial and public sector organizations with the common goal of removing barriers to cloud adoption. Supported by HPE’s channel program, Cloud28+ unites service providers, solution providers, ISVs, system integrators, and government entities to share knowledge, resources and services aimed at helping customers build and consume the right mix of cloud solutions for their needs.

Internet of Things

Discover 2016 also saw new innovations designed to help organizations rapidly, securely, and cost-effectively deploy IoT devices in wide area, enterprise and industrial deployments. These solutions include:

“Cost-prohibitive economics and the lack of a holistic solution are key barriers for mass adoption of IoT,” said Keerti Melkote, Senior Vice President and General Manager, HPE. “By approaching IoT with innovations to expand our comprehensive framework built on edge infrastructure solutions, software platforms, and technology ecosystem partners, HPE is addressing the cost, complexity and security concerns of organizations looking to enable a new class of services that will transform workplace and operational experiences.”

As organizations integrate IoT into mainstream operations, the onboarding and management of IoT devices remains costly and inefficient particularly at large scale. Concurrently, the diverse variations of IoT connectivity, protocols and security, prevent organizations from easily aggregating data across a heterogeneous fabric of connected things.

To improve the economies of scale for massive IoT deployments over wide area networks, HPE announced the new HPE Mobile Virtual Network Enabler (MVNE) and enhancements to the HPE Universal IoT (UIoT) Platform.

As the amount of data generated from smart “things” grows and the frequency at which it is collected increases, so will the need for systems that can acquire and analyze the data in real-time. Real-time analysis is enabled through edge computing and the close convergence of data capture and control systems in the same box.

HPE Edgeline Converged Edge Systems converge real-time analog data acquisition with data center-level computing and manageability, all within the same rugged open standards chassis. Benefits include higher performance, lower energy, reduced space, and faster deployment times.

“The intelligent edge is the new frontier of the hybrid computing world,” said Whitman. “The edge of the network is becoming a very crowded place, but these devices need to be made more useful.”

This means that the equivalent of a big data crunching data center needs to be brought to the edge affordably.

Biggest of big data

“IoT is the biggest of big data,” said Tom Bradicich, HPE Vice President and General Manager, Servers and IoT Systems. “HPE EdgeLine and [partner company] PTC help bridge the digital and physical worlds for IoT and augmented reality (AR) for fully automated assembly lines.”

IoT and data analysis at the edge helps companies finally predict the future, head off failures and maintenance needs in advance. And the ROI on edge computing will be easy to prove when factory downtime can be greatly eliminated using IoT, data analysis and AR at the edge everywhere.

Along these lines, Citrix, together with HPE, has developed a new architecture around HPE Edgeline EL4000 with XenApp, XenDesktop and XenServer to allow graphically rich, high-performance applications to be deployed right at the edge.  They’re now working together on next-generation IoT solutions that bring together the HPE Edge IT and Citrix Workspace IoT strategies.

In related news, SUSE has entered into an agreement with HPE to acquire technology and talent that will expand SUSE’s OpenStack infrastructure-as-a-service (IaaS) solution and accelerate SUSE’s entry into the growing Cloud Foundry platform-as-a-service (PaaS) market.

The acquired OpenStack assets will be integrated into SUSE OpenStack Cloud, and the acquired Cloud Foundry and PaaS assets will enable SUSE to bring to market a certified, enterprise-ready SUSE Cloud Foundry PaaS solution for all customers and partners in the SUSE ecosystem.

As part of the transaction, HPE has named SUSE as its preferred open source partner for Linux, OpenStack IaaS, and Cloud Foundry PaaS.

#HPE also put force behind its drive to make high performance computing (HPC) a growing part of enterprise data centers and private clouds. Hot on the heels of buying SGI, HPE has recognized that public clouds leave little room for those workloads that do not perform best in virtual machines.

Indeed, if all companies buy their IT from public clouds, they have little performance advantage over one another. But many companies want to gain the best systems with the best performance for the workloads that give them advantage, and which run the most complex — and perhaps value-creating — applications. I predict that HPC will be a big driver for HPE, both in private cloud implementations and in supporting technical differentiation for HPE customers and partners.

Memory-driven computing

Computer architecture took a giant leap forward with the announcement that HPE has successfully demonstrated memory-driven computing, a concept that puts memory, not processing, at the center of the computing platform to realize performance and efficiency gains not possible today.

Developed as part of The Machine research program, HPE’s proof-of-concept prototype represents a major milestone in the company’s efforts to transform the fundamental architecture on which all computers have been built for the past 60 years.

Gartner predicts that by 2020, the number of connected devices will reach 20.8 billion and generate an unprecedented volume of data, which is growing at a faster rate than the ability to process, store, manage, and secure it with existing computing architectures.

“We have achieved a major milestone with The Machine research project — one of the largest and most complex research projects in our company’s history,” said Antonio Neri, Executive Vice President and General Manager of the Enterprise Group at HPE. “With this prototype, we have demonstrated the potential of memory-driven computing and also opened the door to immediate innovation. Our customers and the industry as a whole can expect to benefit from these advancements as we continue our pursuit of game-changing technologies.”

The proof-of-concept prototype, which was brought online in October, shows the fundamental building blocks of the new architecture working together, just as they had been designed by researchers at HPE and its research arm, Hewlett Packard Labs. HPE has demonstrated:

  • Compute nodes accessing a shared pool of fabric-attached memory
  • An optimized Linux-based operating system (OS) running on a customized system on a chip (SOC)
  • Photonics/Optical communication links, including the new X1 photonics module, are online and operational
  • New software programming tools designed to take advantage of abundant persistent memory.

During the design phase of the prototype, simulations predicted the speed of this architecture would improve current computing by multiple orders of magnitude. The company has run new software programming tools on existing products, illustrating improved execution speeds of up to 8,000 times on a variety of workloads. HPE expects to achieve similar results as it expands the capacity of the prototype with more nodes and memory.

In addition to bringing added capacity online, The Machine research project will increase focus on exascale computing. Exascale is a developing area of HPC that aims to create computers several orders of magnitude more powerful than any system online today. HPE’s memory-driven computing architecture is incredibly scalable, from tiny IoT devices to the exascale, making it an ideal foundation for a wide range of emerging high-performance compute and data intensive workloads, including big data analytics.

Commercialization

HPE says it is committed to rapidly commercializing the technologies developed under The Machine research project into new and existing products. These technologies currently fall into four categories: Non-volatile memory, fabric (including photonics), ecosystem enablement and security.

Martin Banks, writing in Diginomica, questions whether these new technologies and new architectures represent a new beginning or a last hurrah for HPE. He poses the question to David Chalmers, HPE’s Chief Technologist in EMEA, and Chalmers explains HPE’s roadmap.

The conclusion? Banks feels that the in-memory architecture has the potential to be the next big step that IT takes. If all the pieces fall into place, Banks says, “There could soon be available a wide range of machines at price points that make fast, high-throughput systems the next obvious choice. . . . this could be the foundation for a whole range of new software innovations.”

Storage initiative

HPE lastly announced a new initiative to address demand for flexible storage consumption models, accelerate all-flash data center adoption, assure the right level of resiliency, and help customers transform to a hybrid IT infrastructure.

Over the past several years, the industry has seen flash storage rapidly evolve from niche application performance accelerator to the default media for critical workloads. During this time, HPE’s 3PAR StoreServ Storage platform has emerged as a leader in all-flash array market share growth, performance, and economics. The new HPE 3PAR Flash Now initiative gives customers a way to acquire this leading all-flash technology on-premises starting at $0.03 per usable Gigabyte per month, a fraction of the cost of public cloud solutions.

“Capitalizing on digital disruption requires that customers be able to flexibly consume new technologies,” said Bill Philbin, vice president and general manager, Storage, Hewlett Packard Enterprise. “Helping customers benefit from both technology and consumption flexibility is at the heart of HPE’s innovation agenda.”

Whitman’s HPE, given all of the news at HPE Discover, has assembled the right business path to place HPE and its ecoystems of partners and alliances squarely the very center of the major IT trends of the next five years.

Indeed, I’ve been at HPE Discover conferences for more than 10 years now, and this keynote address and the news makes more sense as pertains to current and future IT market than I’ve ever seen.

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Meet George Jetson – your new AI-empowered chief procurement officer

The next BriefingsDirect technology innovation thought leadership discussion explores how rapid advances in artificial intelligence (AI) and machine learning are poised to reshape procurement — like a fast-forwarding to a once-fanciful vision of the future.

Whereas George Jetson of the 1960s cartoon portrayed a world of household robots, flying cars, and push-button corporate jobs — the 2017 procurement landscape has its own impressive retinue of decision bots, automated processes, and data-driven insights.

We won’t need to wait long for this vision of futuristic business to arrive. As we enter 2017, applied intelligence derived from entirely new data analysis benefits has redefined productivity and provided business leaders with unprecedented tools for managing procurement, supply chains, and continuity risks.

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

To learn more about the future of predictive — and even proactive — procurement technologies, please welcome Chris Haydon, Chief Strategy Officer at SAP Ariba. The discussion is moderated by BriefingsDirect’s Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: It seems like only yesterday that we were content to gain a common view of the customer or develop an end-to-end bead on a single business process. These were our goals in refining business in general, but today we’ve leapfrogged to a future where we’re using words like “predictive” and “proactive” to define what business function should do and be about. Chris, what’s altered our reality to account for this rapid advancement from visibility into predictive — and on to proactive?
Haydon: There are a couple of things. The acceleration of the smarts, the intelligence, or the artificial intelligence, whatever the terminology that you identify with, has really exploded. It’s a lot more real, and you see these use-cases on television all the time. The business world is just looking to go in and adopt that.

And then there’s this notion of the Lego block of being able to string multiple processes together via an API is really exciting — that coupled with the ability to have insight. The last piece, the ability to make sense of big data, either from a visualization perspective or from a machine-learning perspective, has accelerated things.

These trends are starting to come together in the business-to-business (B2B) world, and today, we’re seeing them manifest themselves in procurement.

Gardner: What is it about procurement as a function that’s especially ripe for taking advantage of these technologies?

Transaction intense

Haydon: Procurement is obviously very transaction-intense. Historically, what transaction intensity means is people, processing, exceptions. When we talk about these trends now, the ability to componentize services, the ability to look at big data or machine learning, and the input on top of this contextualizes intelligence. It’s cognitive and predictive by its very nature, a bigger data set, and [improves] historically inefficient human-based processes. That’s why procurement is starting to be at the forefront.

Haydon

Gardner: Procurement itself has changed from the days of when we were highly vertically integrated as corporations. We had long lead times on product cycles and fulfillment. Nowadays, it’s all about agility and compressing the time across the board. So, procurement has elevated its position. Anything more to add?

Haydon: Everyone needs to be closer to the customer, and you need live business. So, procurement is live now. This change in dynamic — speed and responsiveness — is closer to your point. It’s also these other dimensions of the consumer experience that now has to be the business-to-business experience. All that means same-day shipping, real-time visibility, and changing dynamically. That’s what we have to deliver.

Gardner: If we go back to our George Jetson reference, what is it about this coming year, 2017? Do you think it’s an important inception point when it comes to factoring things like the rising role of procurement, the rising role of analytics, and the fact that the Internet of Things (IoT) is going to bring more relevant data to bear? Why now?

Haydon: There are a couple of things. The procurement function is becoming more mature. Procurement leaders have extracted those first and second levels of savings from sourcing and the like. And they have control of their processes.

With cloud-based technologies and more of control of their processes, they’re looking now to how they’re going to serve their internal customers by being value-generators and risk-reducers.

How do you forward the business, how do you de-risk, how do you get supply continuity, how do you protect your brand? You do that by having better insight, real-time insight into your supply base, and that’s what’s driving this investment.

Gardner: We’ve been talking about Ariba being a 20-year-old company. Congratulations on your anniversary of 20 years.

Haydon: Thank you.

AI and bots

Gardner: You’re also, of course, part of SAP. Not only have you been focused on procurement for 20 years, but you’ve got a large global player with lots of other technologies and platform of benefits to avail yourselves of. So, that brings me to the point of AI and bots.

It seems to me that right at the time when procurement needs help, when procurement is more important than ever, that we’re also in a position technically to start doing some innovative things that get us into those words “predictive” and more “intelligent.”

Set the stage for how these things come together.

Haydon: You allude to being part of SAP, and that’s really a great strength and advantage for a domain-focused procurement expertise company.

The machine-learning capabilities that are part of a native SAP HANA platform, which we naturally adopt and get access to, put us on the forefront of not having to invest in that part of the platform, but to focus on how we take that platform and put it into the context of procurement.

There are a couple of pretty obvious areas. There’s no doubt that when you’ve got the largest B2B network, billions in spend, and hundreds and millions of transactions on invoicing, you apply some machine learning on that. We can start doing a lot smarter matching an exception management on that, pretty straightforward. That’s at one end of the chain.

On the other end of the chain, we have bots. Some people get a little bit wired about the word “bot,” “robotics,” or whatever, maybe it’s a digital assistant or it’s a smart app. But, it’s this notion of helping with decisions, helping with real-time decisions, whether it’s identifying a new source of supply because there’s a problem, and the problem is identified because you’ve got a live network. It’s saying that you have a risk or you have a continuity problem, and not just that it’s happening, but here’s an alternative, here are other sources of a qualified supply.

Gardner: So, it strikes me that 2017 is such a pivotal year in business. This is the year where we’re going to start to really define what machines do well, and what people do well, and not to confuse them. What is it about an end-to-end process in procurement that the machine can do better that we can then elevate the value in the decision-making process of the people?

Haydon: Machines can do better in just identifying patterns — clusters, if you want to use a more technical word. They transform category management and enables procurement to be at the front of their internal customer set by looking not just at their traditional total cost of ownership (TCO), but total value and use. That’s a part of that real dynamic change.

What we call end-to-end, or even what SAP Ariba defined in a very loose way when we talked about upstream, it was about outsourcing and contracting, and downstream was about procurement, purchasing, and invoicing. That’s gone, Dana. It’s not about upstream and downstream, it’s about end-to-end process, and re-imagining and reinventing that.

The role of people

Gardner: When we give more power to a procurement professional by having highly elevated and intelligent tools, their role within the organization advances and the amount of improvement they can make financially advances. But I wonder where there’s risk if we automate too much and whether companies might be thinking that they still want people in charge of these decisions. Where do we begin experimenting with how much automation to bring, now that we know how capable these machines have been, or is this going to be a period of exploration for the next few years?

Haydon: It will be a period of exploration, just because businesses have different risk tolerances and there are actually different parts of their life cycle. If you’re in a hyper growth mode and you’re pretty profitable, that’s a little bit different than if you’re under a very big margin pressure.

For example, maybe if you’re in high tech in the Silicon Valley, and some big names that we could all talk about are, you’re prepared to be able to go at it, and let it all come.

If you’re in a natural-resource environment, every dollar is even more precious than it was a year ago.

That’s also the beauty, though, with technology. If you want to do it for this category, this supplier, this business unit, or this division you can do that a lot easier than ever before and so you go on a journey.

Gardner: That’s an important point that people might not appreciate, that there’s a tolerance for your appetite for automation, intelligence, using machine learning, and AI. They might even change, given the context of the certain procurement activity you’re doing within the same company. Maybe you could help people who are a little bit leery of this, thinking that they’re losing control. It sounds to me like they’re actually gaining more control.

Haydon: They gain more control, because they can do more and see more. To me, it’s layered. Does the first bot automatically requisition something — yes or no? So, you put tolerances on it. I’m okay to do it if it is less than $50,000, $5,000, or whatever the limit is, and it’s very simple. If the event is less than $5,000 and it’s within one percent of the last time I did it, go and do it. But tell me that you are going to do it or let’s have a cooling-off period.

If you don’t tell me or if you don’t stop me, I’m going to do it, and that’s the little bit of this predictive as well. So you still control the gate, you just don’t have to be involved in all the sub-processes and all that stuff to get to the gate. That’s interesting.

Gardner: What’s interesting to me as well, Chris, is because the data is such a core element of how successful this is, it means that companies in a procurement intelligence drive will want more data, so they can make better decisions. Suppliers who want to be competitive in that environment will naturally be incentivized to provide more data, more quickly, with more openness. Tell us some of the implications for intelligence brought to procurement on the supplier? What we should expect suppliers to do differently as a result?

Notion of content

Haydon: There’s no doubt that, at a couple of levels, suppliers will need to let the buyers know even more about themselves than they have ever known before.

That goes to the notion of content. It’s like there is unique content to be discovered, which is whom am I, what do I do well and demonstrate that I do well. That’s being discovered. Then, there is the notion of being able to transact. What do I need to be able to do to transact with you efficiently whether that’s a payment, a bank account, or just the way in which I can consume this?

Then, there is also this last notion of the content. What content do I need to be able to provide to my customer, aka the end user, for them to be able to initiate the business with them?

These three dimensions of being discovered, how to be dynamically transacted with, and then actually providing the content of what you do even as a material of service to the end user via the channel. You have to have all of these dimensions right.

That’s why we fundamentally believe that a network-based approach, when it’s end to end, meaning a supplier can do it once to all of the customers across the [Ariba] Discovery channel, across the transactional channel, across the content channel is really value adding. In a digital economy, that’s the only way to do it.

Gardner: So this idea of the business network, which is a virtual repository for all of this information isn’t just quantity, but it’s really about the quality of the relationship. We hear about different business networks vying for attention. It seems to me that understanding that quality aspect is something you shouldn’t lose track of.

Haydon: It’s the quality. It’s also the context of the business process. If you don’t have the context of the business process between a buyer and a seller and what they are trying to affect through the network, how does it add value? The leading-practice networks, and we’re a leading-practice network, are thinking about Discovery. We’re thinking about content; we’re thinking about transactions.

Gardner: Again, going back to the George Jetson view of the future, for organizations that want to see the return on their energy and devotion to these concepts around AI, bots, and intelligence. What sort of low-hanging fruit do we look for, for assuring them that they are on the right path? I’m going to answer my own question, but I want you to illustrate it a bit better, and that’s risk and compliance and being able to adjust to unforeseen circumstances seems to me an immediate payoff for doing this.

Severance of pleadings

Haydon: The United Kingdom is enacting a law before the end of the year for severance of pleadings. It’s the law, and you have to comply. The real question is how you comply.

You eye your brand, you eye your supply chain, and having the supply-chain profile information at hand right now is top of mind. If you’re a Chief Procurement Officer (CPO) and you walk into the CEO’s office, the CEO could ask, “Can you tell me that I don’t have any forced labor, I don’t have any denied parties, and I’m Office of Foreign Assets Control (OFAC) compliant? Can you tell me that now?”

You might be able to do it for your top 50 suppliers or top 100 suppliers, and that’s great, but unfortunately, a small, $2,000 supplier who uses some forced labor in any part of the world is potentially a problem in this extended supply chain. We’ve seen brands boycotted very quickly. These things roll.

So yes, I think that’s just right at the forefront. Then, it’s applying intelligence to that to give that risk threshold and to think about where those challenges are. It’s being smart and saying, “Here is a high risk category. Look at this category first and all the suppliers in the category. We’re not saying that the suppliers are bad, but you better have a double or triple look at that, because you’re at high risk just because of the nature of the category.”

Gardner: Technically, what should organizations be thinking about in terms of what they have in place in order for their systems and processes to take advantage of these business network intelligence values? If I’m intrigued by this concept, if I see the benefits in reducing risk and additional efficiency, what might I be thinking about in terms of my own architecture, my own technologies in order to be in the best position to take advantage of this?

Haydon: You have to question how much of that you think you can build yourself. If you think you’re asking different questions than most of your competitors, you’re probably not. I’m sure there are specific categories and specific areas on tight supplier relationships and co-innovation development, but when it comes to the core risk questions, more often, they’re about an industry, a geography, or the intersection of both.

Our recommendation to corporations is never try and build it yourself. You might need to have some degree of privacy, but look to have it as more industry-based. Think larger than yourself in trying to solve that problem differently. Those cloud deployment models really help you.

Gardner: So it really is less of a technical preparatory thought process than process being a digital organization, availing yourself of cloud models, being ready to think about acting intelligently and finding that right demarcation between what the machines do best and what the people do best.

More visible

Haydon: By making things digital they are actually more visible. You have to be able to balance the pure nature of visibility to get at the product; that’s the first step. That’s why people are on a digital journey.

Gardner: Machines can’t help you with a paper-based process, right?

Haydon: Not as much. You have to scan it and throw it in. Then, you are then digitizing it.

Gardner: We heard about Guided Buying last year from SAP Ariba. It sounds like we’re going to be getting a sort of “Guided Buying-Plus” next year and we should keep an eye on that.

Haydon: We’re very excited. We announced that earlier this year. We’re trying to solve two problems quickly through Guided Buying.

One is the nature of the ad-hoc user. We’re all ad-hoc users in the business today. I need to buy things, but I don’t want to read the policy, I don’t want to open the PDF on some corporate portal on some threshold limit that, quite honestly, I really need to know about once or twice a year.

So our Guided Buying has a beautiful consumer-based look and feel, but with embedded compliance. We hide the complexity. We just show the user what they need to know at the time, and the flow is very powerful.

Listen to the podcast. Find it on iTunes. Get the mobile app. Download the transcript. Sponsor: SAP Ariba.

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Strategic view across more data delivers digital business boost for AmeriPride

The next BriefingsDirect Voice of the Customer digital transformation case study explores how linen services industry leader AmeriPride Services uses big data to gain a competitive and comprehensive overview of its operations, finances and culture.

We’ll explore how improved data analytics allows for disparate company divisions and organizations to come under a single umbrella — to become more aligned — and to act as a whole greater than the sum of the parts. This is truly the path to a digital business.

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

Here to describe how digital transformation has been supported by innovations at the big data core, we’re joined by Steven John, CIO, and Tony Ordner, Information Team Manager, both at at AmeriPride Services in Minnetonka, Minnesota. The discussion is moderated by BriefingsDirect’s Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Let’s discuss your path to being a more digitally transformed organization. What were the requirements that led you to become more data-driven, more comprehensive, and more inclusive in managing your large, complex organization?

John

John: One of the key business drivers for us was that we’re a company in transition — from a very diverse organization to a very centralized organization. Before, it wasn’t necessarily important for us to speak the same data language, but now it’s critical. We’re developing the lexicon, the Rosetta Stone, that we can all rely on and use to make sure that we’re aligned and heading in the same direction.

Gardner: And Tony, when we say “data,” are we talking about just databases and data within applications? Or are we being even more comprehensive — across as many information types as we can?

Ordner: It’s across all of the different information types. When we embarked on this journey, we discovered that data itself is great to have, but you also have to have processes that are defined in a similar fashion. You really have to drive business change in order to be able to effectively utilize that data, analyze where you’re going, and then use that to drive the business. We’re trying to institute into this organization an iterative process of learning.

Gardner: For those who are not familiar with AmeriPride Services, tell us about the company. It’s been around for quite a while. What do you do, and how big of an umbrella organization are we talking about?

Long-term investments

John: The company is over 125 years old. It’s family-owned, which is nice, because we’re not driven by the quarter. We can make longer-term investments through the family. We can have more of a future view and have ambition to drive change in different ways than a quarter-by-quarter corporation does.

We’re in the laundry business. We’re in the textiles and linen business. What that means is that for food and beverage, we handle tablecloths, napkins, chef coats, aprons, and those types of things. In oil and gas, we provide the safety garments that are required. We also provide the mats you cross as you walk in the door of various restaurants or retail stores. We’re in healthcare facilities and meet the various needs of providing and cleansing the garments and linens coming out of those institutions. We’re very diverse. We’re the largest company of our kind in Canada, probably about fourth in the US, and growing.

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Gardner: And this is a function that many companies don’t view as core and they’re very happy to outsource it. However, you need to remain competitive in a dynamic world. There’s a lot of innovation going on. We’ve seen disruption in the taxicab industry and the hospitality industry. Many companies are saying, “We don’t want to be a deer in the headlights; we need to get out in front of this.”

Tony, how do you continue to get in front of this, not just at the data level, but also at the cultural level?

Ordner: Part of what we’re doing is defining those standards across the company. And we’re coming up with new programs and new ways to get in front and to partner with the customers.

Ordner

As part of our initiative, we’re installing a lot of different technology pieces that we can use to be right there with the customers, to make changes with them as partners, and maybe better understand their business and the products that they aren’t buying from us today that we can provide. We’re really trying to build that partnership with customers, provide them more ways to access our products, and devise other ways they might not have thought of for using our products and services.

With all of those data points, it allows us to do a much better job.

Gardner: And we have heard from Hewlett Packard Enterprise (HPE) the concept that it’s the “analytics that are at the core of the organization,” that then drive innovation and drive better operations. Is that something you subscribe to, and is that part of your thinking?

John: For me, you have to extend it a little bit further. In the past, our company was driven by the experience and judgment of the leadership. But what we discovered is that we really wanted to be more data-driven in our decision-making.

Data creates a context for conversation. In the context of their judgment and experience, our leaders can leverage that data to make better decisions. The data, in and of itself, doesn’t drive the decisions — it’s that experience and judgment of the leadership that’s that final filter.

We often forget the human element at the end of that and think that everything is being driven by analytics, when analytics is a tool and will remain a tool that helps leaders lead great companies.
Gardner: Steven, tell us about your background. You were at a startup, a very successful one, on the leading edge of how to do things different when it comes to apps, data, and cloud delivery.

New ways to innovate

John: Yes, you’re referring to Workday. I was actually Workday’s 33rd customer, the first to go global with their product. Then, I joined Workday in two roles: as their Strategic CIO, working very closely with the sales force, helping CIOs understand the cloud and how to manage software as a service (SaaS); and also as their VP of Mid-Market Services, where we were developing new ways to innovate, to implement in different ways and much more rapidly.

And it was a great experience. I’ve done two things in my life, startups and turnarounds, and I thought that I was kind of stepping back and taking a relaxing job with AmeriPride. But in many ways, it’s both; AmeriPride’s both a turnaround and a startup, and I’m really enjoying the experience.

Gardner: Let’s hear about how you translate technology advancement into business advancement. And the reason I ask it in that fashion is that it seems as a bit of a chicken and the egg, that they need to be done in parallel — strategy, ops, culture, as well as technology. How are you balancing that difficult equation?
John: Let me give you an example. Again, it goes back to that idea of, if you just have the human element, they may not know what to ask, but when you add the analytics, then you suddenly create a set of questions that drive to a truth.

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We’re a route-based business. We have over a 1,000 trucks out there delivering our products every day. When we started looking at margin we discovered that our greatest margin was from those customers that were within a mile of another customer.

So factoring that in changes how we sell, that changes how we don’t sell, or how we might actually let some customers go — and it helps drive up our margin. You have that piece of data, and suddenly we as leaders knew some different questions to ask and different ways to orchestrate programs to drive higher margin.

Gardner: Another trend we’ve seen is that putting data and analytics, very powerful tools, in the hands of more people can have unintended, often very positive, consequences. A knowledge worker isn’t just in a cube and in front of a computer screen. They’re often in the trenches doing the real physical work, and so can have real process insights. Has that kicked in yet at AmeriPride, and are you democratizing analytics?

Ordner: That’s a really great question. We’ve been trying to build a power-user base and bring some of these capabilities into the business segments to allow them to explore the data.

You always have to keep an eye on knowledge workers, because sometimes they can come to the wrong conclusions, as well as the right ones. So it’s trying to make sure that we maintain that business layer, that final check. It’s like, the data is telling me this, is that really where it is?

I liken it to having a flashlight in a dark room. That’s what we are really doing with visualizing this data and allowing them to eliminate certain things, and that’s how they can raise the questions, what’s in this room? Well, let me look over here, let me look over there. That’s how I see that.

Too much information

John: One of the things I worry about is that if you give people too much information or unstructured information, then they really get caught up in the academics of the information — and it doesn’t necessarily drive a business process or drive a business result. It can cause people to get lost in the weeds of all that data.

You still have to orchestrate it, you still have to manage it, and you have to guide it. But you have to let people go off and play and innovate using the data. We actually have a competition among our power-users where they go out and create something, and there are judges and prizes. So we do try to encourage the innovation, but we also want to hold the reins in just a little bit.

Gardner: And that gets to the point of having a tight association between what goes on in the core and what goes on at the edge. Is that something that you’re dabbling in as well?

John: It gets back to that idea of a common lexicon. If you think about evolution, you don’t want a Madagascar or a Tasmania, where groups get cut off and then they develop their own truth, or a different truth, or they interpret data in a different way — where they create their own definition of revenue, or they create their own definition of customer.

If you think about it as orbits, you have to have a balance. Maybe you only need to touch certain people in the outer orbit once a month, but you have to touch them once a month to make sure they’re connected. The thing about orbits and keeping people in the proper orbits is that if you don’t, then one of two things happens, based on gravity. They either spin out of orbit or they come crashing in. The idea is to figure out what’s the right balance for the right groups to keep them aligned with where we are going, what the data means, and how we’re using it, and how often.

Gardner: Let’s get back to the ability to pull together the data from disparate environments. I imagine, like many organizations, that you have SaaS apps. Maybe it’s for human capital management or maybe it’s for sales management. How does that data then get brought to bear with internal apps, some of them may even be on a mainframe still, or virtualized apps from older code basis and so forth? What’s the hurdle and what words of wisdom might you impart to others who are earlier in this journey of how to make all that data common and usable?

Ordner: That tends to be a hurdle. As to the data acquisition piece, as you set these things up in the cloud, a lot of the times the business units themselves are doing these things or making the agreements. They don’t put into place the data access that we’ve always needed. That’s been our biggest hurdle. They’ll sign the contracts, not getting us involved until they say, “Oh my gosh, now we need the data.” We look at it and we say, “Well, it’s not in our contracts and now it’s going to cost more to access the data.” That’s been our biggest hurdle for the cloud services that we’ve done.

Once you get past that, web services have been a great thing. Once you get the licensing and the contract in place, it becomes a very simple process, and it becomes a lot more seamless.

Gardner: So, maybe something to keep in mind is always think about the data before, during, and after your involvement with any acquisition, any contract, and any vendor?

Ordner: Absolutely.

You own three things

John: With SaaS, at the end of the day, you own three things: the process design, the data, and the integration points. When we construct a contract, one of the things I always insist upon is what I refer to as the “prenuptial agreement.”

What that simply means is, before the relationship begins, you understand how it can end. The key thing in how it ends is that you can take your data with you, that it has a migration path, and that they haven’t created a stickiness that traps you there and you don’t have the ability to migrate your data to somebody else, whether that’s somebody else in the cloud or on-premise.

Gardner: All right, let’s talk about lessons learned in infrastructure. Clearly, you’ve had an opportunity to look at a variety of different platforms, different requirements that you have had, that you have tested and required for your vendors. What is it about HPE Vertica, for example, that is appealing to you, and how does that factor into some of these digital transformation issues?

Ordner: There are two things that come to mind right away for me. One is there were some performance implications. We were struggling with our old world and certain processes that ran 36 hours. We did a proof of concept with HPE and Vertica and that ran in something like 17 minutes. So, right there, we were sold on performance changes.

As we got into it and negotiated with them, the other big advantage we discovered is that the licensing model with the amount of data, versus the core model that everyone else runs in the CPU core. We’re able to scale this and provide that service at a high speed, so we can maintain that performance without having to take penalties against licensing. Those are a couple of things I see. Anything from your end, Steven?

John: No, I think that was just brilliant.

Gardner: How about on that acquisition and integration of data. Is there an issue with that that you have been able to solve?

Ordner: With acquisition and integration, we’re still early in that process. We’re still learning about how to put data into HPE Vertica in the most effective manner. So, we’re really at our first source of data and we’re looking forward to those additional pieces. We have a number of different telematics pieces that we want to include; wash aisle telematics as well as in-vehicle telematics. We’re looking forward to that.

There’s also scan data that I think will soon be on the horizon. All of our garments and our mats have chips in them. We scan them in and out, so we can see the activity and where they flow through the system. Those are some of our next targets to bring that data in and take a look at that and analyze it, but we’re still a little bit early in that process as far as multiple sources. We’re looking forward to some of the different ways that Vertica will allow us to connect to those data sources.

Gardner: I suppose another important consideration when you are picking and choosing systems and platforms is that extensibility. RFID tags are important now; we’re expecting even more sensors, more data coming from the edge, the information from the Internet of Things (IoT). You need to feel that the systems you’re putting in place now will scale out and up. Any thoughts about the IoT impact on what you’re up to?

Overcoming past sins

John: We have had several conversations just this week with HPE and their teams, and they are coming out to visit with us on that exact topic. Being about a year into our journey, we’ve been doing two things. We’ve been forming the foundation with HPE Vertica and we’ve been getting our own house in order. So, there’s a fair amount of cleanup and overcoming the sins of the past as we go through that process.

But Vertica is a platform; it’s a platform where we have only tapped a small percentage of its capability. And in my personal opinion, even HPE is only aware of a portion of its capability. There are a whole set of things that it can do, and I don’t believe that we have discovered all of them.

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With that said, we’re going to do what you and Tony just described; we’re going to use the telematics coming out of our trucks. We’re going to track safety and seat belts. We’re going to track green initiatives, routes, and the analytics around our routes and fuel consumption. We’re going to make the place safer, we’re going to make it more efficient, and we’re going to get proactive about being able to tell when a machine is going to fail and when to bring in our vendor partners to get it fixed before it disrupts production.

Gardner: It really sounds like there is virtually no part of your business in the laundry services industry that won’t be in some way beneficially impacted by more data, better analytics delivered to more people. Is that fair?

Ordner: I think that’s a very fair statement. As I prepared for this conference, one of the things I learned, and I have been with the company for 17 years, is that we’ve done a lot technology changes, and technology has taken an added significance within our company. When you think of laundry, you certainly don’t think of technology, but we’ve been at the leading edge of implementing technology to get closer to our customers, closer to understanding our products.

[Data technology] has become really ingrained within the industry, at least at our company.

John: It is one of those few projects where everyone is united, everybody believes that success is possible, and everybody is willing to pay the price to make it happen.

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Swift and massive data classification advances score a win for better securing sensitive information

The next BriefingsDirect Voice of the Customer digital transformation case study explores how — in an era when cybersecurity attacks are on the rise and enterprises and governments are increasingly vulnerable — new data intelligence capabilities are being brought to the edge to provide better data loss prevention (DLP).

We’ll learn how Digital Guardian in Waltham, Massachusetts analyzes both structured and unstructured data to predict and prevent loss of data and intellectual property (IP) with increased accuracy.

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

To learn how data recognition technology supports network and endpoint forensic insights for enhanced security and control, we’re joined by Marcus Brown, Vice President of Corporate Business Development for Digital Guardian. The discussion is moderated by BriefingsDirect’s Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: What are some of the major trends making DLP even more important, and even more effective?

Brown: Data protection has very much to come to the forefront in the last couple of years. Unfortunately, we wake up every morning and read in the newspapers, see on television, and hear on the radio a lot about data breaches. It’s pretty much every type of company, every type of organization, government organizations, etc., that’s being hit by this phenomenon at the moment.

Brown

So, awareness is very high, and apart from the frequency, a couple of key points are changing. First of all, you have a lot of very skilled adversaries coming into this, criminals, nation-state actors, hactivists, and many others. All these people are well-trained and very well resourced to come after your data. That means that companies have a pretty big challenge in front of them. The threat has never been bigger.

In terms of data protection, there are a couple of key trends at the cyber-security level. People have been aware of the so-called insider threat for a long time. This could be a disgruntled employee or it could be someone who has been recruited for monetary gain to help some organization get to your data. That’s a difficult one, because the insider has all the privilege and the visibility and knows where the data is. So, that’s not a good thing.

Then, you have employees, well-meaning employees, who just make mistakes. It happens to all of us. We touch something in Outlook, and we have a different email address than the one we were intending, and it goes out. The well-meaning employees, as well, are part of the insider threat.

Outside threats

What’s really escalated over the last couple of years are the advanced external attackers or the outside threat, as we call it. These are well-resourced, well-trained people from nation-states or criminal organizations trying to break in from the outside. They do that with malware or phishing campaigns.

About 70 percent of the attacks stop with the phishing campaign, when someone clicks on something that looked normal. Then, there’s just general hacking, a lot of people getting in without malware at all. They just hack straight in using different techniques that don’t rely on malware.

People have become so good at developing malware and targeting malware at particular organizations, at particular types of data, that a lot of tools like antivirus and intrusion prevention just don’t work very well. The success rate is very low. So, there are new technologies that are better at detecting stuff at the perimeter and on the endpoint, but it’s a tough time.

There are internal and external attackers. A lot of people outside are ultimately after the two main types of data that companies have. One is a customer data, which is credit card numbers, healthcare information, and all that stuff. All of this can be sold on the black market per record for so-and-so many dollars. It’s a billion-dollar business. People are very motivated to do this.

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Most companies don’t want to lose their customers’ data. That’s seen as a pretty bad thing, a bad breach of trust, and people don’t like that. Then, obviously, for any company that has a product where you have IP, you spent lots of money developing that, whether it’s the new model of a car or some piece of electronics. It could be a movie, some new clothing, or whatever. It’s something that you have developed and it’s a secret IP. You don’t want that to get out, as well as all of your other internal information, whether it’s your financials, your plans, or your pricing. There are a lot of people going after both of those things, and that’s really the challenge.

In general, the world has become more mobile and spread out. There is no more perimeter to stop people from getting in. Everyone is everywhere, private life and work life is mixed, and you can access anything from anywhere. It’s a pretty big challenge.

Gardner: Even though there are so many different types of threats, internal, external, and so forth, one of the common things that we can do nowadays is get data to learn more about what we have as part of our inventory of important assets.

While we might not be able to seal off that perimeter, maybe we can limit the damage that takes place by early detection of problems. The earlier that an organization can detect that something is going on that shouldn’t be, the quicker they can come to the rescue. How does the instant analysis of data play a role in limiting negative outcomes?

Can’t protect everything

Brown: If you want to protect something, you have to know it’s sensitive and that you want to protect it. You can’t protect everything. You’re going to find which data is sensitive, and we’re able to do that on-the-fly to recognize sensitive data and nonsensitive data. That’s a key part of the DLP puzzle, the data protection puzzle.

We work for some pretty large organizations, some of the largest companies and government organizations in the world, as well as lot of medium- and smaller-sized customers. Whatever it is we’re trying to protect, personal information or indeed the IP, we need to be in the right place to see what people are doing with that data.

Our solution consists of two main types of agents. Some agents are on endpoint computers, which could be desktops or servers, Windows, Linux, and Macintosh. It’s a good place to be on the endpoint computer, because that’s where people, particularly the insider, come into play and start doing something with data. That’s where people work. That’s how they come into the network and it’s how they handle a business process.

So the challenge in DLP is to support the business process. Let people do with data what they need to do, but don’t let that data get out. The way to do that is to be in the right place. I already mentioned the endpoint agent, but we also have network agents, sensors, and appliances in the network that can look at data moving around.

The endpoint is really in the middle of the business process. Someone is working, they’re working with different applications, getting data out of those applications, and they’re doing whatever they need to do in their daily work. That’s where we sit, right in the middle of that, and we can see who the user is and what application they’re working with it. It could be an engineer working with the computer-aided design (CAD) or the product lifecycle management (PLM) system developing some new automobile or whatever, and that’s a great place to be.

We rely very heavily on the HPE IDOL technology for helping us classify data. We use it particularly for structured data, anything like a credit card number, or alphanumeric data. It could be also free text about healthcare, patient information, and all this sort of stuff.

We use IDOL to help us scan documents. We can recognize regular expressions, that’s a credit card number type of thing, or Social Security. We can also recognize terminology. We rely on the fact that IDOL supports hundreds of languages and many different subject areas. So, using IDOL, we’re able to recognize a whole lot of anything that’s written in textual language.

Our endpoint agent also has some of its own intelligence built in that we put on top of what we call contextual recognition or contextual classification. As I said, we see the customer list coming out of Salesforce.com or we see the jet fighter design coming out of the PLM system and we then tag that as well. We’re using IDOL, we’re using some of our technology, and we’re using our vantage point on the endpoint being in the business process to figure out what the data is.

We call that data-in-use monitoring and, once we see something is sensitive, we put a tag on it, and that tag travels with the data no matter where it goes.

An interesting thing is that if you have someone making a mistake, an unintentional, good-willed employee, accidentally attaching the wrong doc to something that it goes out, obviously it will warn the user of that.

We can stop that

If you have someone who is very, very malicious and is trying to obfuscate what they’re doing, we can see that as well. For example, taking a screenshot of some top-secret diagram, embedding that in a PowerPoint and then encrypting the PowerPoint, we’re tagging those docs. Anything that results from IP or top-secret information, we keep tagging that. When the guy then goes to put it on a thumb drive, put it on Dropbox, or whatever, we see that and stop that.

So that’s still a part of the problem, but the two points are classify it, that’s what we rely on IDOL a lot for, and then stop it from going out, that’s what our agent is responsible for.

Gardner: Let’s talk a little bit about the results here, when behaviors, people and the organization are brought to bear together with technology, because it’s people, process and technology. When it becomes known in the organization that you can do this, I should think that that must be a fairly important step. How do we measure effectiveness when you start using a technology like Digital Guardian? Where does that become explained and known in the organization and what impact does that have?

Brown: Our whole approach is a risk-based approach and it’s based on visibility. You’ve got to be able to see the problem and then you can take steps and exercise control to stop the problems.

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When you deploy our solution, you immediately gain a lot of visibility. I mentioned the endpoints and I mentioned the network. Basically, you get a snapshot without deploying any rules or configuring in any complex way. You just turn this on and you suddenly get this rich visibility, which is manifested in reports, trends, and all this stuff. What you get, after a very short period of time, is a set of reports that tell you what your risks are, and some of those risks may be that your HR information is being put on Dropbox.

You have engineers putting the source code onto thumb drives. It could all be well-meaning, they want to work on it at home or whatever, or it could be some bad guy.

One the biggest points of risk in any company is when an employee resigns and decides to move on. A lot of our customers use the monitoring and the reporting we have at that time to actually sit down with the employee and say, “We noticed that you downloaded 2,000 files and put them on a thumb drive. We’d like you to sign this saying that you’re going to give us that data back.”

That’s a typical use case, and that’s the visibility you get. You turn it on and you suddenly see all these risks, hopefully, not too many, but a certain number of risks and then you decide what you’re going to do about it. In some areas you might want to be very draconian and say, “I’m not going to allow this. I’m going to completely block this. There is no reason why you should put the jet fighter design up on Dropbox.”

Gardner: That’s where the epoxy in the USB drives comes in.

Warning people

Brown: Pretty much. On the other hand, you don’t want to stop people using USB, because it’s about their productivity, etc. So, you might want to warn people, if you’re putting some financial data on to a thumb drive, we’re going to encrypt that so nothing can happen to it, but do you really want to do this? Is this approach appropriate? People get a feeling that they’re being monitored and that the way they are acting maybe isn’t according to company policy. So, they’ll back out of it.

In a nutshell, you look at the status quo, you put some controls in place, and after those controls are in place, within the space of a week, you suddenly see the risk posture changing, getting better, and the incidence of these dangerous actions dropping dramatically.

Very quickly, you can measure the security return on investment (ROI) in terms of people’s behavior and what’s happening. Our customers use that a lot internally to justify what they’re doing.

Generally, you can get rid of a very large amount of the risk, say 90 percent, with an initial pass, or initial first two passes of rules to say, we don’t want this, we don’t want that. Then, you’re monitoring the status, and suddenly, new things will happen. People discover new ways of doing things, and then you’ve got to put some controls in place, but you’re pretty quickly up into the 90 percent and then you fine-tuning to get those last little bits of risk out.

Gardner: Because organizations are becoming increasingly data-driven, they’re getting information and insight across their systems and their applications. Now, you’re providing them with another data set that they could use. Is there some way that organizations are beginning to assimilate and analyze multiple data sets including what Digital Guardian’s agents are providing them in order to have even better analytics on what’s going on or how to prevent unpleasant activities?

Brown: In this security world, you have the security operations center (SOC), which is kind of the nerve center where everything to do with security comes into play. The main piece of technology in that area is the security information and event management (SIEM) technology. The market leader is HPE’s ArcSight, and that’s really where all of the many tools that security organizations use come together in one console, where all of that information can be looked at in a central place and can also be correlated.

We provide a lot of really interesting information for the SIEM for the SOC. I already mentioned we’re on the endpoint and the network, particularly on the endpoint. That’s a bit of a blind spot for a lot of security organizations. They’re traditionally looking at firewalls, other network devices, and this kind of stuff.

We provide rich information about the user, about the data, what’s going on with the data, and what’s going on with the system on the endpoint. That’s key for detecting malware, etc. We have all this rich visibility on the endpoint and also from the network. We actually pre-correlate that. We have our own correlation rules. On the endpoint computer in real time, we’re correlating stuff. All of that gets populated into ArcSight.

At the recent HPE Protect Show in National Harbor in September we showed the latest generation of our integration, which we’re very excited about. We have a lot of ArcSight content, which helps people in the SOC leverage our data, and we gave a couple of presentations at the show on that.

Gardner: And is there a way to make this even more protected? I believe encryption could be brought to bear and it plays a role in how the SIEM can react and behave.

Seamless experience

Brown: We actually have a new partnership, related to HPE’s acquisition of Voltage, which is a real leader in the e-mail security space. It’s all about applying encryption to messages and managing the keys and making that user experience very seamless and easy to use.

Adding to that, we’re bundling up some of the classification functionality that we have in our network sensors. What we have is a combination between Digital Guardian Network, DOP, and the HPE Data Security Encryption solution, where an enterprise can define a whole bunch of rules based on templates.

We can say, “I need to comply with HIPAA,” “I need to comply with PCI,” or whatever standard it is. Digital Guardian on the network will automatically scan all the e-mail going out and automatically classify according to our rules which e-mails are sensitive and which attachments are sensitive. It then goes on to the HPE Data Security Solution where it gets encrypted automatically and then sent out.

It’s basically allowing corporations to apply standard set of policies, not relying on the user to say they need to encrypt this, not leaving it to the user’s judgment, but actually applying standard policies across the enterprise for all e-mail making sure they get encrypted. We are very excited about it.

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Gardner: That sounds key — using encryption to the best of its potential, being smart about it, not just across the waterfront, and then not depending on a voluntary encryption, but doing it based on need and intelligence.

Brown: Exactly.

Gardner: For those organizations that are increasingly trying to be data-driven, intelligent, taking advantage of the technologies and doing analysis in new interesting ways, what advice might you offer in the realm of security? Clearly, we’ve heard at various conferences and other places that security is, in a sense, the killer application of big-data analytics. If you’re an organization seeking to be more data-driven, how can you best use that to improve your security posture?

Brown: The key, as far as we’re concerned, is that you have to watch your data, you have to understand your data, you need to collect information, and you need visibility of your data.

The other key point is that the security market has been shifting pretty dramatically from more of a network view much more toward the endpoint. I mentioned earlier that antivirus and some of these standard technologies on the endpoint aren’t really cutting it anymore. So, it’s very important that you get visibility down at the endpoint and you need to see what users are doing, you need to understand what your systems are running, and you need to understand where your data is.

So collect that, get that visibility, and then leverage that visibility with analytics and tools so that you can profit from an automated kind of intelligence.

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2016 election campaigners look to big data analysis to gain an edge in intelligently reaching voters

The next BriefingsDirect Voice of the Customer digital transformation case study explores how data-analysis services startup BlueLabs in Washington, DC helps presidential election campaigns better know and engage with potential voters.

We’ll learn how BlueLabs relies on high-performing analytics platforms that allow a democratization of querying, of opening the value of vast data resources to discretely identify more of those in the need to know.

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

Here to describe how big data is being used creatively by contemporary political organizations for two-way voter engagement, we’re joined by Erek Dyskant Co-Founder and Vice President of Impact at BlueLabs Analytics in Washington. The discussion is moderated by BriefingsDirect’s Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Obviously, this is a busy season for the analytics people who are focused on politics and campaigns. What are some of the trends that are different in 2016 from just four years ago. It’s a fast-changing technology set, it’s also a fast-changing methodology. And of course, the trends about how voters think, react, use social, and engage are also dynamic. So what’s different this cycle?

Dyskant: From a voter-engagement perspective, in 2012, we could reach most of our voters online through a relatively small set of social media channels — Facebook, Twitter, and a little bit on the Instagram side. Moving into 2016, we see a fragmentation of the online and offline media consumption landscape and many more folks moving toward purpose-built social media platforms.

If I’m at the HPE Conference and I want my colleagues back in D.C. to see what I’m seeing, then maybe I’ll use Periscope, maybe Facebook Live, but probably Periscope. If I see something that I think one of my friends will think is really funny, I’ll send that to them on Snapchat.

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Where political campaigns have traditionally broadcast messages out through the news-feed style social-media strategies, now we need to consider how it is that one-to-one social media is acting as a force multiplier for our events and for the ideas of our candidates, filtered through our campaign’s champions.

Gardner: So, perhaps a way to look at that is that you’re no longer focused on precincts physically and you’re no longer able to use broadcast through social media. It’s much more of an influence within communities and identifying those communities in a new way through these apps, perhaps more than platforms.

Social media

Dyskant: That’s exactly right. Campaigns have always organized voters at the door and on the phone. Now, we think of one more way. If you want to be a champion for a candidate, you can be a champion by knocking on doors for us, by making phone calls, or by making phone calls through online platforms.

You can also use one-to-one social media channels to let your friends know why the election matters so much to you and why they should turn out and vote, or vote for the issues that really matter to you.

Gardner: So, we’re talking about retail campaigning, but it’s a bit more virtual. What’s interesting though is that you can get a lot more data through the interaction than you might if you were physically knocking on someone’s door.

Dyskant: The data is different. We’re starting to see a shift from demographic targeting. In 2000, we were targeting on precincts. A little bit later, we were targeting on combinations of demographics, on soccer moms, on single women, on single men, on rural, urban, or suburban communities separately.

Dyskant

Moving to 2012, we’ve looked at everything that we knew about a person and built individual-level predictive models, so that we knew each person’s individual set of characteristics made that person more or less likely to be someone that our candidate would have an engaging conversation through a volunteer.

Now, what we’re starting to see is behavioral characteristics trumping demographic or even consumer data. You can put whiskey drinkers in your model, you can put cat owners in your model, but isn’t it a lot more interesting to put in your model that fact that this person has an online profile on our website and this is their clickstream? Isn’t it much more interesting to put into a model that this person is likely to consume media via TV, is likely to be a cord-cutter, is likely to be a social media trendsetter, is likely to view multiple channels, or to use both Facebook and media on TV?

That lets us have a really broad reach or really broad set of interested voters, rather than just creating an echo chamber where we’re talking to the same voters across different platforms.

Gardner: So, over time, the analytics tools have gone from semi-blunt instruments to much more precise, and you’re also able to better target what you think would be the right voter for you to get the right message out to.

One of the things you mentioned that struck me is the word “predictive.” I suppose I think of campaigning as looking to influence people, and that polling then tries to predict what will happen as a result. Is there somewhat less daylight between these two than I am thinking, that being predictive and campaigning are much more closely associated, and how would that work?

Predictive modeling

Dyskant: When I think of predictive modeling, what I think of is predicting something that the campaign doesn’t know. That may be something that will happen in the future or it may be something that already exists today, but that we don’t have an observation for it.

In the case of the role of polling, what I really see about that is understanding what issues matter the most to voters and how it is that we can craft messages that resonate with those issues. When I think of predictive analytics, I think of how is it that we allocate our resources to persuade and activate voters.

Over the course of elections, what we’ve seen is an exponential trajectory of the amount of data that is considered by predictive models. Even more important than that is an exponential set of the use cases of models. Today, we see every time a predictive model is used, it’s used in a million and one ways, whereas in 2012 it might have been used in 50, 20, or 100 sessions about each voter contract.

Gardner: It’s a fascinating use case to see how analytics and data can be brought to bear on the democratic process and to help you get messages out, probably in a way that’s better received by the voter or the prospective voter, like in a retail or commercial environment. You don’t want to hear things that aren’t relevant to you, and when people do make an effort to provide you with information that’s useful or that helps you make a decision, you benefit and you respect and even admire and enjoy it.

Dyskant: What I really want is for the voter experience to be as transparent and easy as possible, that campaigns reach out to me around the same time that I’m seeking information about who I’m going to vote for in November. I know who I’m voting for in 2016, but in some local actions, I may not have made that decision yet. So, I want a steady stream of information to be reaching voters, as they’re in those key decision points, with messaging that really is relevant to their lives.

I also want to listen to what voters tell me. If a voter has a conversation with a volunteer at the door, that should inform future communications. If somebody has told me that they’re definitely voting for the candidate, then the next conversation should be different from someone who says, “I work in energy. I really want to know more about the Secretary’s energy policies.”

Gardner: Just as if a salesperson is engaging with process, they use customer relationship management (CRM), and that data is captured, analyzed, and shared. That becomes a much better process for both the buyer and the seller. It’s the same thing in a campaign, right? The better information you have, the more likely you’re going to be able to serve that user, that voter.

Dyskant: There definitely are parallels to marketing, and that’s how we at BlueLabs decided to found the company and work across industries. We work with Fortune 100 retail organizations that are interested in how, once someone buys one item, we can bring them back into the store to buy the follow-on item or maybe to buy the follow-on item through that same store’s online portal. How it is that we can provide relevant messaging as users engage in complex processes online? All those things are driven from our lessons in politics.

Politics is fundamentally different from retail, though. It’s a civic decision, rather than an individual-level decision. I always want to be mindful that I have a duty to voters to provide extremely relevant information to them, so that they can be engaged in the civic decision that they need to make.

Gardner: Suffice it to say that good quality comparison shopping is still good quality comparison decision-making.

Dyskant: Yes, I would agree with you.

Relevant and speedy

Gardner: Now that we’ve established how really relevant, important, and powerful this type of analysis can be in the context of the 2016 campaign, I’d like to learn more about how you go about getting that analysis and making it relevant and speedy across large variety of data sets and content sets. But first, let’s hear more about BlueLabs. Tell me about your company, how it started, why you started it, maybe a bit about yourself as well.

Dyskant: Of the four of us who started BlueLabs, some of us met in the 2008 elections and some of us met during the 2010 midterms working at the Democratic National Committee (DNC). Throughout that pre-2012 experience, we had the opportunity as practitioners to try a lot of things, sometimes just once or twice, sometimes things that we operationalized within those cycles.

Jumping forward to 2012 we had the opportunity to scale all that research and development to say that we did this one thing that was a different way of building models, and it worked for in this congressional array. We decided to make this three people’s full-time jobs and scale that up.

Moving past 2012, we got to build potentially one of the fastest-growing startups, one of the most data-driven organizations, and we knew that we built a special team. We wanted to continue working together with ourselves and the folks who we worked with and who made all this possible. We also wanted to apply the same types of techniques to other areas of social impact and other areas of commerce. This individual-level approach to identifying conversations is something that we found unique in the marketplace. We wanted to expand on that.

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Increasingly, what we’re working on is this segmentation-of-media problem. It’s this idea that some people watch only TV, and you can’t ignore a TV. It has lots of eyeballs. Some people watch only digital and some people consume a mix of media. How is it that you can build media plans that are aware of people’s cross-channel media preferences and reach the right audience with their preferred means of communications?

Gardner: That’s fascinating. You start with the rigors of the demands of a political campaign, but then you can apply in so many ways, answering the types of questions anticipating the type of questions that more verticals, more sectors, and charitable organizations would want to be involved with. That’s very cool.

Let’s go back to the data science. You have this vast pool of data. You have a snappy analytics platform to work with. But, one of the things that I am interested in is how you get more people whether it’s in your organization or a campaign, like the Hillary Clinton campaign, or the DNC to then be able to utilize that data to get to these inferences, get to these insights that you want.

What is it that you look for and what is it that you’ve been able to do in that form of getting more people able to query and utilize the data?

Dyskant: Data science happens when individuals have direct access to ask complex questions of a large, gnarly, but well-integrated data set. If I have 30 terabytes of data across online contacts, off-line contacts, and maybe a sample of clickstream data, and I want to ask things like of all the people who went to my online platform and clicked the password reset because they couldn’t remember their password, then never followed up with an e-mail, how many of them showed up at a retail location within the next five days? They tried to engage online, and it didn’t work out for them. I want to know whether we’re losing them or are they showing up in person.

That type of question maybe would make it into a business-intelligence (BI) report a few months from that, but people who are thinking about what we do every day, would say, “I wonder about this, turn it into a query, and say, “I think I found something.” If we give these customers phone calls, maybe we can reset their passwords over the phone and reengage them.

Human intensive

That’s just one tiny, micro example, which is why data science is truly a human-intensive exercise. You get 50-100 people working at an enterprise solving problems like that and what you ultimately get is a positive feedback loop of self-correcting systems. Every time there’s a problem, somebody is thinking about how that problem is represented in the data. How do I quantify that. If it’s significant enough, then how is it that the organization can improve in this one specific area?

All that can be done with business logic is the interesting piece. You need very granular data that’s accessible via query and you need reasonably fast query time, because you can’t ask questions like that when you’re going to get coffee every time you run a query.

Layering predictive modeling allows you to understand the opportunity for impact if you fix that problem. That one hypothesis with those users who cannot reset their passwords is that maybe those users aren’t that engaged in the first place. You fix their password but it doesn’t move the needle.

The other hypothesis is that it’s people who are actively trying to engage with your server and are unsuccessful because of this one very specific barrier. If you have a model of user engagement at an individual level, you can say that these are really high-value users that are having this problem, or maybe they aren’t. So you take data science, align it with really smart individual-level business analysis, and what you get is an organization that continues to improve without having to have at an executive-decision level for each one of those things.

Gardner: So a great deal of inquiry experimentation, iterative improvement, and feedback loops can all come together very powerfully. I’m all for the data scientist full-employment movement, but we need to do more than have people have to go through data scientist to use, access, and develop these feedback insights. What is it about the SQL, natural language, or APIs? What is it that you like to see that allows for more people to be able to directly relate and engage with these powerful data sets?

Dyskant: One of the things is the product management of data schemas. So whenever we build an analytics database for a large-scale organization I think a lot about an analyst who is 22, knows VLOOKUP, took some statistics classes in college, and has some personal stories about the industry that they’re working in. They know, “My grandmother isn’t a native English speaker, and this is how she would use this website.”

So it’s taking that hypothesis that’s driven from personal stories, and being able to, through a relatively simple query, translate that into a database query, and find out if that hypothesis proves true at scale.

Then, potentially take the result of that query, dump them into a statistical-analysis language, or use database analytics to answer that in a more robust way. What that means is that our schemas favor very wide schemas, because I want someone to be able to write a three-line SQL statement, no joins, that enters a business question that I wouldn’t have thought to put in a report. So that’s the first line — is analyst-friendly schemas that are accessed via SQL.

The next line is deep key performance indicators (KPIs). Once we step out of the analytics database, consumers drop into the wider organization that’s consuming data at a different level. I always want reporting to report on opportunity for impact, to report on whether we’re reaching our most valuable customers, not how many customers are we reaching.

“Are we reaching our most valuable customers” is much more easily addressable; you just talk to different people. Whereas, when you ask, “Are we reaching enough customers,” I don’t know how find out. I can go over to the sales team and yell at them to work harder, but ultimately, I want our reporting to facilitate smarter working, which means incorporating model scores and predictive analytics into our KPIs.

Getting to the core

Gardner: Let’s step back from the edge, where we engage the analysts, to the core, where we need to provide the ability for them to do what they want and which gets them those great results.

It seems to me that when you’re dealing in a campaign cycle that is very spiky, you have a short period of time where there’s a need for a tremendous amount of data, but that could quickly go down between cycles of an election, or in a retail environment, be very intensive leading up to a holiday season.

Do you therefore take advantage of the cloud models for your analytics that make a fit-for-purpose approach to data and analytics pay as you go? Tell us a little bit about your strategy for the data and the analytics engine.

Dyskant: All of our customers have a cyclical nature to them. I think that almost every business is cyclical, just some more than others. Horizontal scaling is incredibly important to us. It would be very difficult for us to do what we do without using a cloud model such as Amazon Web Services (AWS).

Also, one of the things that works well for us with HPE Vertica is the licensing model where we can add additional performance with only the cost of hardware or hardware provision through the cloud. That allows us to scale up our cost areas during the busy season. We’ll sometimes even scale them back down during slower periods so that we can have those 150 analysts asking their own questions about the areas of the program that they’re responsible for during busy cycles, and then during less busy cycles, scale down the footprint of the operation.

Gardner: Is there anything else about the HPE Vertica OnDemand platform that benefits your particular need for analysis? I’m thinking about the scale and the rows. You must have so many variables when it comes to a retail situation, a commercial situation, where you’re trying to really understand that consumer?

Dyskant: I do everything I can to avoid aggregation. I want my analysts to be looking at the data at the interaction-by-interaction level. If it’s a website, I want them to be looking at clickstream data. If it’s a retail organization, I want them to be looking at point-of-sale data. In order to do that, we build data sets that are very frequently in the billions of rows. They’re also very frequently incredibly wide, because we don’t just want to know every transaction with this dollar amount. We want to know things like what the variables were, and where that store was located.

Getting back to the idea that we want our queries to be dead-simple, that means that we very frequently append additional columns on to our transaction tables. We’re okay that the table is big, because in a columnar model, we can pick out just the columns that we want for that particular query.

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Then, moving into some of the in-database machine-learning algorithms allows us to perform more higher-order computation within the database and have less data shipping.

Gardner: We’re almost out of time, but I wanted to do some predictive analysis ourselves. Thinking about the next election cycle, midterms, only two years away, what might change between now and then? We hear so much about machine learning, bots, and advanced algorithms. How do you predict, Erek, the way that big data will come to bear on the next election cycle?

Behavioral targeting

Dyskant: I think that a big piece of the next election will be around moving even more away from demographic targeting, toward even more behavioral targeting. How is it that we reach every voter based on what they’re telling us about them and what matters to them, how that matters to them? That will increasingly drive our models.

To do that involves probably another 10X scale in the data, because that type of data is generally at the clickstream level, generally at the interaction-by-interaction level, incorporating things like Twitter feeds, which adds an additional level of complexity and laying in computational necessity to the data.

Gardner: It almost sounds like you’re shooting for sentiment analysis on an issue-by-issue basis, a very complex undertaking, but it could be very powerful.

Dyskant: I think that it’s heading in that direction, yes.

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