Friday, January 06, 2017

How Lastminute.com Uses Machine Learning to Improve Real-Time Travel Bookings

Transcript of a discussion on how lastminute.com manages massive volumes of data to support a cutting-edge machine-learning algorithmic approach to matching the best experience in travel with end user requirements.

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

Dana Gardner: Hello, and welcome to the next edition to the Hewlett Packard Enterprise (HPE) Voice of the Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on digital transformation. Stay with us now to learn how agile businesses are fending off disruption -- in favor of innovation.

Gardner
Our next case study highlights how online travel and events pioneer lastminute.com leverages big-data analytics with speed at scale to provide business advantages to travel bookings. We'll explore how lastminute.com manages massive volumes of data to support cutting-edge machine-learning algorithms to allow for speed and automation while buying such services online.

So join us as we learn how a culture of IT innovation helps make highly dynamic customer interactions for online travel a major differentiator for lastminute.com. To describe how machine learning is improving travel services fulfillment, we're joined by Filippo Onorato, Chief Information Officer at lastminute.com group in Chiasso, Switzerland. Welcome, Filippo.

Filippo Onorato: Thank you very much.

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.
Right now, we're storing a short history of that data, but the goal is to have two years worth of session data.

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.
It's endless, because the market conditions are changing and the actors in the market are changing as well.

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?
We can jump from one location to another very easily, and that's one of the competitive advantages of being an OTA.

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.

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.

Gardner: I'm afraid we will have to leave it there. We've been exploring how online travel and events pioneer lastminute.com group leverages big-data analytics with incredible speed and at huge scale to provide business advantages in travel and other bookings.

And we've learned how the OTA manages these massive volumes of data to support a cutting-edge machine-learning algorithmic approach to matching the best experience in travel with end user requirements.

So, please join me in thanking our guest, Filippo Onorato, Chief Information Officer at lastminute.com group in Chiasso, Switzerland. Thank you, sir.
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Onorato: Thank you very much.

Gardner: And thanks to our audience as well for joining us for this Hewlett Packard Enterprise Voice of the Customer digital transformation discussion. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host for this ongoing series of HPE-sponsored interviews. Thanks again for listening, and please do come back next time.

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

Transcript of a discussion on how lastminute.com manages massive volumes of data to support a cutting-edge machine-learning algorithmic approach to matching the best experience in travel with end user requirements. Copyright Interarbor Solutions, LLC, 2005-2017. All rights reserved.

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Monday, December 19, 2016

Veikkaus Digitally Transforms as it Emerges as New Combined Finnish National Gaming Company

Transcript of a discussion on how a culture of IT innovation is helping to establish a single wholly nationally owned company to operate gaming in Finland.

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

Dana Gardner: Welcome to the next edition to the Hewlett Packard Enterprise (HPE) Voice of the Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on digital transformation. Stay with us now to learn how agile businesses are fending off disruption in favor of innovation.

Gardner
Our next case study highlights how combined Finnish national gaming company, Veikkaus, manages a complex merger process while bringing a digital advantage to both its operations and business model. We'll explore how Veikkaus uses a powerful big-data analytics platform to respond rapidly to the challenges of digitization.

Learn how a culture of IT innovation has established a single nationally owned company to operate gaming and gambling in Finland. To describe how Veikkaus is transforming itself for better customer experience from the computing core to the edge, we're joined by Harri Räsänen, Information and Communications Technology Architect at Veikkaus in Helsinki.

Welcome, Harri.

Harri Räsänen: Thank you. It’s nice to be here.
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Gardner: Why has Veikkaus reinvented its data infrastructure technology?

Räsänen: Our data warehouse solution was a traditional data warehouse, and had been around for 10 years. Different things had gone wrong. One of the key issues we faced was that our data wasn’t real-time. It was far from real time -- it was data that was one or two days old.

We decided that we need to get data quicker and in more detail because we now had aggregate data.

Gardner: What were some of your top requirements technically in order to accomplish that?

Real-time data

Räsänen: As I said, we had quite a old-fashioned data warehouse. Initially, we needed our game-service provider to feed us data more in real-time. They needed to build up a mechanism to complete data, and we needed to build out capabilities to gather it. We needed to rethink the information structure -- totally from scratch.

Räsänen
Gardner: When we think about gambling, gaming, or lotteries, in many cases, this is an awful lot of data, a very big undertaking. Give us a sense of the size of the data and the disparity of the three organizations that came together including the Finnish national football gaming reorganization.

Räsänen: I'll talk about our current-situation records, for the new combined company we are starting up in 2017.

We have a big company from a customer point of view. We have 1.8 million consumers. Finland has a population of 5.5 million. So, we have a quite a lot of Finnish consumers. When it comes to transactions, we get one to three million transactions per day. So it’s quite large, if you think about the transactional data.

In addition to that, we gather different kinds of information about our web store; it’s one of the biggest retail web stores in Finland.

Gardner: It’s one thing to put in a new platform, but it’s another to then change the culture and the organization -- and transform into a digital business. How is the implementation of your new data environment aiding in your cultural shift?

Räsänen: Luckily, Veikkaus has a background of doing things quite analytically. If you think about a web store, there is a culture that we need to be able to monitor what we're doing if we're running some changes in our web store -- whether it works or not. That’s a good thing.

But, we are redoing our whole data technology. We added the Apache Kafka integration point and then, Cloudera, the Hadoop system. Then, we added a new ETL tool for us, Pentaho, and last but not least, HPE Vertica. It's been really challenging for us, with lots of different things to consider and learn.

Luckily, we've been able to use good external consultants to help us out, but as you said, we can always make the technology work better. In transforming the culture of doing things, we're still definitely in the middle of our journey.

Gardner: I imagine you'll want to better analyze what takes place within your organization so it’s not just serving the data and managing the transactions. There's an opportunity to have a secondary benefit, which is more control of your data. The more insight you have allows you to adapt and improve your customer experience and customer service. Have you been able to start down that path of that secondary analysis of what goes on internally?

New level of data

Räsänen: Some of our key data was even out of our hands in our service-provider environments. We wanted to get all the relevant data with us, and now we've been working on that new level of data access. We have analysts working on that, both IT and business people, browsing the data. They already have some findings on things that previously they could have asked or even thought about. So, we have been getting our information up-to-date.

Gardner: Can you give us more specific examples of how you've been able to benefit from this new digital environment?

Räsänen: Yeah, consumer communication on CRM is one of the key successes, things we needed to have in place. We've been able to constantly improve on that. Before, we had data that was too old, but now, we have near real-time data. We get one-minute-old data, so we can communicate with the consumers better. We know whether they've been playing their lotteries or betting on football matches.

We can say, "It’s time for football today, and you haven’t yet placed a bet." We can communicate, and on the other hand, we can avoid disturbing customers by sending out e-mails or SMS messages about things they've already done.
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Gardner: Yes, less spam, but more help. It’s important, of course, with any organization like this in a government environment, for trust and safety to be involved. I should think that there's some analysis to help keep people from overdoing it and managing the gaming for a positive outcome.

Räsänen: Definitely. That’s one of the key metrics we're measuring with our consumer so that gaming is responsible. We need to see that all things they do can be thought of as good, because as you said, we're a national company, it’s a very regulated market, and that kind of thing.

Gardner: But a great deal of good comes from this. I understand that more than 1 billion euros a year go to the common good of people living in Finland. So, there are a lot of benefits when this is done properly.

Now that you've gone quite a ways into this, and you're going to need to be going to the new form and new organization the first of 2017, what advice would you be able to give to someone who is beginning a big data consolidation and modernization journey? What lessons have you learned that you might share?

Out of the box

Räsänen: If you're experimenting, you need to start to think a little bit out of the box. Integration is one of crucial part, and avoid all direct integration as much as possible.

We're utilizing Apache Kafka as an integration point, and that’s one of the crucial things, because then you can "appify" everything. You're going to provide an application interface for integrating systems and that will help those of us in gaming companies.

Gardner: A lot a services-orientation?

Räsänen: That’s one of the components of our data architecture. We have been using our Cloudera Hadoop system for archiving and we are building our capabilities on top of that. In addition, of course, we have HPE Vertica. It’s one of our most crucial things in our data ecosystem because it’s a traditional enterprise data warehousing in that sense it is a SQL database. Users can take a benefit out of that, and it’s lightning-fast. You need to design all the components and make those work on that role that they are based at.

Gardner: And of course SQL is very commonly understood as the query language. There's no great change there, but it's really putting it into the hands of more people.

Räsänen: I've been writing or talking in SQL since the beginning of the ’90s, and it’s actually a pretty easy language to communicate, even between business and IT, because at least, at some level, it’s self-explanatory. That’s where the communication matters.

Gardner: Just a much better engine under the hood, right?

Räsänen: Yeah, exactly.

Gardner: I am afraid we'll have to leave it there. We've been exploring how combined Finnish state gaming company, Veikkaus, is managing a complex merger process, while also bringing more of a digital advantage to its operations. And we've learned how a culture of IT innovation is helping to establish a state company to operate gaming in Finland.

Please join me in thanking our guest, Harri Räsänen, Information and Communications Technology Architect at Veikkaus in Helsinki. Thank you, Harri.
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Räsänen: Thank you.

Gardner: And a big thank you as well to our audience for joining us for this Hewlett Packard Enterprise Voice of the Customer digital transformation discussion.

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

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

Transcript of a discussion on how a culture of IT innovation is helping to establish a single wholly owned state company to operate gaming in Finland. Copyright Interarbor Solutions, LLC, 2005-2016. All rights reserved.

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Wednesday, December 14, 2016

How WWT Took an Enterprise Tower of Babel and Delivered Comprehensive Intelligent Search

Transcript of a discussion on how WWT reached deep into its applications data and content to rapidly and efficiently create a powerful Google-like, pan-enterprise search capability.

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

Dana Gardner: Welcome to the next edition to the Hewlett Packard Enterprise (HPE) Voice of the Customer podcast series. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing discussion on digital transformation. Stay with us now to learn how agile companies are fending off disruption in favor of innovation.

Gardner
Our next enterprise 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 power users, it sets the stage for expanded capabilities using advanced analytics to engender a more productive and proactive digital business culture.

Here to describe how WWT took an enterprise Tower of Babel and delivered cross-applications intelligent search, we’re joined by James Nippert, Enterprise Search Project Manager at World Wide Technology. Welcome, James.

James Nippert: Hello, thank you for having me.
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Gardner: We're also here with Susan Crincoli, Manager of Enterprise Content at World Wide Technology. Welcome, Susan.

Susan Crincoli: Good afternoon.

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.
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.

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.

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.
That means that we're getting the best information to the right people in the right amount of time.

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?
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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?
It’s gotten better at finding everything from documents to records to web pages across the board; it’s improving on all of those.

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.
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.

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.

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.
The flip side of controlling your data sources, as we're learing, is that there are a lot of important data sources out there that aren’t good candidates for enterprise search.

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.

Gardner: That would be the "not-too-intelligent" search, right?

Nippert: Exactly.

Gardner: It sounds like this is an essential part of any organization to become a digital company and data-driven, an intelligent and fit-for-purpose approach to gathering that assets wherever they are.

I want to thank our guests. We've been exploring with World Wide Technology how a very serious and somehow difficult problem of users simply finding relevant content can be solved. We've seen how WWT has reached deep into its applications data and content to rapidly and efficiently create a powerful Google-like, pan-enterprise search capability.

So, please join me in thanking our guests, James Nippert, the Enterprise Search Project Manager at World Wide Technology. Thanks, James.

Nippert: Thank you very much for having me.
Humanizes Machine Learning
For Big Data Success
Gardner:  And we've also been joined by Susan Crincoli, Manager of Enterprise Content at World Wide Technology. Thank you, Susan.

Crincoli:  Thanks, Dana, I appreciate it.

Gardner:  And a big thank you as well to our audience for joining us for this Hewlett-Packard Enterprise Voice of the Customer digital transformation discussion.

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

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

Transcript of a discussion on how WWT reached deep into its applications data and content to rapidly and efficiently create a powerful Google-like, pan-enterprise search capability. Copyright Interarbor Solutions, LLC, 2005-2016. All rights reserved.

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Monday, November 21, 2016

Meet George Jetson – Your New Chief Procurement Officer

Transcript of a discussion on how rapid advances in artificial intelligence and machine learning are poised to reshape procurement.

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

Dana Gardner: Hi, this is Dana Gardner, Principal Analyst at Interarbor Solutions, and you're listening to BriefingsDirect.

Gardner

Our next 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.

To learn more about the future of predictive -- and even proactive procurement technologies -- please join me in welcoming back Chris Haydon, Chief Strategy Officer at SAP Ariba. Good to have you with us, Chris.

Chris Haydon: Great to be here again.

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.
It's not about upstream and downstream, it's about end-to-end process, and re-imagining and reinventing that.

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.
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.

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.
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?

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."
Think larger than yourself in trying to solve that problem differently. Those cloud deployment models really help you.

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.
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.

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.

Gardner: Well, it certainly sounds like an area where intelligence would have a very marked improvement, and we'll look for some interesting news there as well.

I'm afraid we'll have to leave it there. You've been listening to a BriefingsDirect thought leadership podcast discussion on how rapid advances in AI and machine learning are poised to reshape procurement.

We've heard how, as we enter 2017, applied intelligence, derived from entirely new data analysis, benefits redefines productivity. Lastly, we've been presented with SAP Ariba’s view on where we can take business intelligence aspects into more types of process and more refinement of the procurement function.

With that, please join me in thanking our guest, Chris Haydon, Chief Strategy Officer at SAP Ariba. Thank you, sir.

Haydon: Thank you.

Gardner: And a big thank you as well to our audience for joining this SAP Ariba-sponsored business innovation thought leadership discussion. I'm Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator. Thanks again for listening, and do come back next time.

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

Transcript of a discussion on how rapid advances in artificial intelligence and machine learning are poised to reshape procurement. Copyright Interarbor Solutions, LLC, 2005-2016. All rights reserved.

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