Sumo Logic CEO on how modern apps benefit from 'continuous intelligence' and DevOps insights

The next BriefingsDirect applications health monitoring interview explores how a new breed of continuous intelligence emerges by gaining data from systems infrastructure logs -- either on-premises or in the cloud -- and then cross-referencing that with intrinsic business metrics information.

We’ll now explore how these new levels of insight and intelligence into what really goes on underneath the covers of modern applications help ensure that apps are built, deployed, and operated properly.

Today, more than ever, how a company's applications perform equates with how the company itself performs and is perceived. From airlines to retail, from finding cabs to gaming, how the applications work deeply impacts how the business processes and business outcomes work, too.

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

We’re joined by an executive from Sumo Logic to learn why modern applications are different, what's needed to make them robust and agile, and how the right mix of data, metrics and machine learning provides the means to make and keep apps operating better than ever.

To describe how to build and maintain the best applications, welcome Ramin Sayar, President and CEO of Sumo Logic. The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: There’s no doubt that the apps make the company, but what is it about modern applications that makes them so difficult to really know? How is that different from the applications we were using 10 years ago?

Sayar: You hit it on the head a little bit earlier. This notion of always-on, always-available, always-accessible types of applications, either delivered through rich web mobile interfaces or through traditional mechanisms that are served up through laptops or other access points and point-of-sale systems are driving a next wave of technology architecture supporting these apps.

These modern apps are around a modern stack, and so they’re using new platform services that are created by public-cloud providers, they’re using new development processes such as agile or continuous delivery, and they’re expected to constantly be learning and iterating so they can improve not only the user experience -- but the business outcomes.

Gardner: Of course, developers and business leaders are under pressure, more than ever before, to put new apps out more quickly, and to then update and refine them on a continuous basis. So this is a never-ending process.

User experience

Sayar: You’re spot on. The obvious benefits around always on is centered on the rich user interaction and user experience. So, while a lot of the conversation around modern apps tends to focus on the technology and the components, there are actually fundamental challenges in the process of how these new apps are also built and managed on an ongoing basis, and what implications that has for security. A lot of times, those two aspects are left out when people are discussing modern apps.

Sayar

Gardner: That's right. We’re now talking so much about DevOps these days, but in the same breath, we’re taking about SecOps -- security and operations. They’re really joined at the hip.

Sayar: Yes, they’re starting to blend. You’re seeing the technology decisions around public cloud, around Docker and containers, and microservices and APIs, and not only led by developers or DevOps teams. They’re heavily influenced and partnering with the SecOps and security teams and CISOs, because the data is distributed. Now there needs to be better visibility instrumentation, not just for the access logs, but for the business process and holistic view of the service and service-level agreements (SLAs).

Gardner: What’s different from say 10 years ago? Distributed used to mean that I had, under my own data-center roof, an application that would be drawing from a database, using an application server, perhaps a couple of services, but mostly all under my control. Now, it’s much more complex, with many more moving parts.

Sayar: We like to look at the evolution of these modern apps. For example, a lot of our customers have traditional monolithic apps that follow the more traditional waterfall approach for iterating and release. Often, those are run on bare-metal physical servers, or possibly virtual machines (VMs). They are simple, three-tier web apps.

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We see one of two things happening. The first is that there is a need for either replacing the front end of those apps, and we refer to those as brownfield. They start to change from waterfall to agile and they start to have more of an N-tier feel. It's really more around the front end. Maybe your web properties are a good example of that. And they start to componentize pieces of their apps, either on VMs or in private clouds, and that's often good for existing types of workloads.

Now there needs to be better visibility instrumentation, not just for the access logs, but for the business process and holistic view of the service and service-level agreements.

The other big trend is this new way of building apps, what we call greenfield workloads, versus the brownfield workloads, and those take a fundamentally different approach.

Often it's centered on new technology, a stack entirely using microservices, API-first development methodology, and using new modern containers like Docker, Mesosphere, CoreOS, and using public-cloud infrastructure and services from Amazon Web Services (AWS), or Microsoft Azure. As a result, what you’re seeing is the technology decisions that are made there require different skill sets and teams to come together to be able to deliver on the DevOps and SecOps processes that we just mentioned.

Gardner: Ramin, it’s important to point out that we’re not just talking about public-facing business-to-consumer (B2C) apps, not that those aren't important, but we’re also talking about all those very important business-to-business (B2B) and business-to-employee (B2E) apps. I can't tell you how frustrating it is when you get on the phone with somebody and they say, “Well, I’ll help you, but my app is down,” or the data isn’t available. So this is not just for the public facing apps, it's all apps, right?

It's a data problem

Sayar: Absolutely. Regardless of whether it's enterprise or consumer, if it's mid-market small and medium business (SMB) or enterprise that you are building these apps for, what we see from our customers is that they all have a similar challenge, and they’re really trying to deal with the volume, the velocity, and the variety of the data around these new architectures and how they grapple and get their hands around it. At the end of day, it becomes a data problem, not just a process or technology problem.

Gardner: Let's talk about the challenges then. If we have many moving parts, if we need to do things faster, if we need to consider the development lifecycle and processes as well as ongoing security, if we’re dealing with outside third-party cloud providers, where do we go to find the common thread of insight, even though we have more complexity across more organizational boundaries?

Sayar: From a Sumo Logic perspective, we’re trying to provide full-stack visibility, not only from code and your repositories like GitHub or Jenkins, but all the way through the components of your code, to API calls, to what your deployment tools are used for in terms of provisioning and performance.

We spend a lot of effort to integrate to the various DevOps tool chain vendors, as well as provide the holistic view of what users are doing in terms of access to those applications and services. We know who has checked in which code or which branch and which build created potential issues for the performance, latency, or outage. So we give you that 360-view by providing that full stack set of capabilities.

Unlike others that are out there and available for you, Sumo Logic's architecture is truly cloud native and multitenant, but it's centered on the principle of near real-time data streaming.

Gardner: So, the more information the better, no matter where in the process, no matter where in the lifecycle. But then, that adds its own level of complexity. I wonder is this a fire-hose approach or boiling-the-ocean approach? How do you make that manageable and then actionable?

Sayar: We’ve invested quite a bit of our intellectual property (IP) on not only providing integration with these various sources of data, but also a lot in the machine learning  and algorithms, so that we can take advantage of the architecture of being a true cloud native multitenant fast and simple solution.

So, unlike others that are out there and available for you, Sumo Logic's architecture is truly cloud native and multitenant, but it's centered on the principle of near real-time data streaming.

As the data is coming in, our data-streaming engine is allowing developers, IT ops administrators, sys admins, and security professionals to be able to have their own view, coarse-grained or granular-grained, from our back controls that we have in the system to be able to leverage the same data for different purposes, versus having to wait for someone to create a dashboard, create a view, or be able to get access to a system when something breaks.

Gardner: That’s interesting. Having been in the industry long enough, I remember when logs basically meant batch. You'd get a log dump, and then you would do something with it. That would generate a report, many times with manual steps involved. So what's the big step to going to streaming? Why is that an essential part of making this so actionable?

Sayar: It’s driven based on the architectures and the applications. No longer is it acceptable to look at samples of data that span 5 or 15 minutes. You need the real-time data, sub-second, millisecond latency to be able to understand causality, and be able to understand when you’re having a potential threat, risk, or security concern, versus code-quality issues that are causing potential performance outages and therefore business impact.

The old way was hope and pray, when I deployed code, that I would find something when a user complains is no longer acceptable. You lose business and credibility, and at the end of the day, there’s no real way to hold developers, operations folks, or security folks accountable because of the legacy tools and process approach.

Center of the business

Those expectations have changed, because of the consumerization of IT and the fact that apps are the center of the business, as we’ve talked about. What we really do is provide a simple way for us to analyze the metadata coming in and provide very simple access through APIs or through our user interfaces based on your role to be able to address issues proactively.

Conceptually, there’s this notion of wartime and peacetime as we’re building and delivering our service. We look at the problems that users -- customers of Sumo Logic and internally here at Sumo Logic -- are used to and then we break that down into this lifecycle -- centered on this concept of peacetime and wartime.

Peacetime is when nothing is wrong, but you want to stay ahead of issues and you want to be able to proactively assess the health of your service, your application, your operational level agreements, your SLAs, and be notified when something is trending the wrong way.

Then, there's this notion of wartime, and wartime is all hands on deck. Instead of being alerted 15 minutes or an hour after an outage has happened or security risk and threat implication has been discovered, the real-time data-streaming engine is notifying people instantly, and you're getting PagerDuty alerts, you're getting Slack notifications. It's no longer the traditional helpdesk notification process when people are getting on bridge lines.

No longer do you need to do “swivel-chair” correlation, because we're looking at multiple UIs and tools and products.

Because the teams are often distributed and it’s shared responsibility and ownership for identifying an issue in wartime, we're enabling collaboration and new ways of collaboration by leveraging the integrations to things like Slack, PagerDuty notification systems through the real-time platform we've built.

So, the always-on application expectations that customers and consumers have, have now been transformed to always-on available development and security resources to be able to address problems proactively.

Gardner: It sounds like we're able to not only take the data and information in real time from the applications to understand what’s going on with the applications, but we can take that same information and start applying it to other business metrics, other business environmental impacts that then give us an even greater insight into how to manage the business and the processes. Am I overstating that or is that where we are heading here?

Sayar: That’s exactly right. The essence of what we provide in terms of the service is a platform that leverages the machine logs and time-series data from a single platform or service that eliminates a lot of the complexity that exists in traditional processes and tools. No longer do you need to do “swivel-chair” correlation, because we're looking at multiple UIs and tools and products. No longer do you have to wait for the helpdesk person to notify you. We're trying to provide that instant knowledge and collaboration through the real-time data-streaming platform we've built to bring teams together versus divided.

Gardner: That sounds terrific if I'm the IT guy or gal, but why should this be of interest to somebody higher up in the organization, at a business process, even at a C-table level? What is it about continuous intelligence that cannot only help apps run on time and well, but help my business run on time and well?

Need for agility

Sayar: We talked a little bit about the whole need for agility. From a business point of view, the line-of-business folks who are associated with any of these greenfield projects or apps want to be able to increase the cycle times of the application delivery. They want to have measurable results in terms of application changes or web changes, so that their web properties have either increased or potentially decreased in terms of user satisfaction or, at the end of the day, business revenue.

So, we're able to help the developers, the DevOps teams, and ultimately, line of business deliver on the speed and agility needs for these new modes. We do that through a single comprehensive platform, as I mentioned.

At the same time, what’s interesting here is that no longer is security an afterthought. No longer is security in the back room trying to figure out when a threat or an attack has happened. Security has a seat at the table in a lot of boardrooms, and more importantly, in a lot of strategic initiatives for enterprise companies today.

At the same time we're helping with agility, we're also helping with prevention. And so a lot of our customers often start with the security teams that are looking for a new way to be able to inspect this volume of data that’s coming in -- not at the infrastructure level or only the end-user level -- but at the application and code level. What we're really able to do, as I mentioned earlier, is provide a unifying approach to bring these disparate teams together.

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Gardner: And yet individuals can extract the intelligence view that best suits what their needs are in that moment.

Sayar: Yes. And ultimately what we're able to do is improve customer experience, increase revenue-generating services, increase efficiencies and agility of actually delivering code that’s quality and therefore the applications, and lastly, improve collaboration and communication.

Gardner: I’d really like to hear some real world examples of how this works, but before we go there, I’m still interested in the how. As to this idea of machine learning, we're hearing an awful lot today about bots, artificial intelligence (AI), and machine learning. Parse this out a bit for me. What is it that you're using machine learning  for when it comes to this volume and variety in understanding apps and making that useable in the context of a business metric of some kind?

Sayar: This is an interesting topic, because of a lot of noise in the market around big data or machine learning and advanced analytics. Since Sumo Logic was started six years ago, we built this platform to ensure that not only we have the best in class security and encryption capabilities, but it was centered on the fundamental purpose around democratizing analytics, making it simpler to be able to allow more than just a subset of folks get access to information for their roles and responsibilities, whether you're security, ops, or development teams.

To answer your question a little bit more succinctly, our platform is predicated on multiple levels of machine learning and analytics capabilities. Starting at the lowest level, something that we refer to as LogReduce is meant to separate the signal-to-noise ratio. Ultimately, it helps a lot of our users and customers reduce mean time to identification by upwards of 90 percent, because they're not searching the irrelevant data. They're searching the relevant and oftentimes occurring data that's not frequent or not really known, versus what’s constantly occurring in their environment.

In doing so, it’s not just about mean time to identification, but it’s also how quickly we're able to respond and repair. We've seen customers using LogReduce reduce the mean time to resolution by upwards of 50 percent.

Predictive capabilities

Our core analytics, at the lowest level, is helping solve operational metrics and value. Then, we start to become less reactive. When you've had an outage or a security threat, you start to leverage some of our other predictive capabilities in our stack.

For example, I mentioned this concept of peacetime and wartime. In the notion of peacetime, you're looking at changes over time when you've deployed code and/or applications to various geographies and locations. A lot of times, developers and ops folks that use Sumo want to use log compare or outlier predictor operators that are in their machine learning capabilities to show and compare differences of branches of code and quality of their code to relevancy around performance and availability of the service and app.

We allow them, with a click of a button, to compare this window for these events and these metrics for the last hour, last day, last week, last month, and compare them to other time slices of data and show how much better or worse it is. This is before deploying to production. When they look at production, we're able to allow them to use predictive analytics to look at anomalies and abnormal behavior to get more proactive.

So, reactive, to proactive, all the way to predictive is the philosophy that we've been trying to build in terms of our analytics stack and capabilities.

Sumo Logic is very relevant for all these customers that are spanning the data-center infrastructure consolidation to new workload projects that they may be building in private-cloud or public-cloud endpoints.

Gardner: How are some actual customers using this and what are they getting back for their investment?

Sayar: We have customers that span retail and e-commerce, high-tech, media, entertainment, travel, and insurance. We're well north of 1,200 unique paying customers, and they span anyone from Airbnb, Anheuser-Busch, Adobe, Metadata, Marriott, Twitter, Telstra, Xora -- modern companies as well as traditional companies.

What do they all have in common? Often, what we see is a digital transformation project or initiative. They either have to build greenfield or brownfield apps and they need a new approach and a new service, and that's where they start leveraging Sumo Logic.

Second, what we see is that's it’s not always a digital transformation; it's often a cost reduction and/or a consolidation project. Consolidation could be tools or infrastructure and data center, or it could be migration to co-los or public-cloud infrastructures.

The nice thing about Sumo Logic is that we can connect anything from your top of rack switch, to your discrete storage arrays, to network devices, to operating system, and middleware, through to your content-delivery network (CDN) providers and your public-cloud infrastructures.

As it’s a migration or consolidation project, we’re able to help them compare performance and availability, SLAs that they have associated with those, as well as differences in terms of delivery of infrastructure services to the developers or users.

So whether it's agility-driven or cost-driven, Sumo Logic is very relevant for all these customers that are spanning the data-center infrastructure consolidation to new workload projects that they may be building in private-cloud or public-cloud endpoints.

Gardner: Ramin, how about a couple of concrete examples of what you were just referring to.

Cloud migration

Sayar: One good example is in the media space or media and entertainment space, for example, Hearst Media. They, like a lot of our other customers, were undergoing a digital-transformation project and a cloud-migration project. They were moving about 36 apps to AWS and they needed a single platform that provided machine-learning analytics to be able to recognize and quickly identify performance issues prior to making the migration and updates to any of the apps rolling over to AWS. They were able to really improve cycle times, as well as efficiency, with respect to identifying and resolving issues fast.

Another example would be JetBlue. We do a lot in the travel space. JetBlue is also another AWS and cloud customer. They provide a lot of in-flight entertainment to their customers. They wanted to be able to look at the service quality for the revenue model for the in-flight entertainment system and be able to ascertain what movies are being watched, what’s the quality of service, whether that’s being degraded or having to charge customers more than once for any type of service outages. That’s how they're using Sumo Logic to better assess and manage customer experience. It's not too dissimilar from Alaska Airlines or others that are also providing in-flight notification and wireless type of services.

The last one is someone that we're all pretty familiar with and that’s Airbnb. We're seeing a fundamental disruption in the travel space and how we reserve hotels or apartments or homes, and Airbnb has led the charge, like Uber in the transportation space. In their case, they're taking a lot of credit-card and payment-processing information. They're using Sumo Logic for payment-card industry (PCI) audit and security, as well as operational visibility in terms of their websites and presence.

They were able to really improve cycle times, as well as efficiency, with respect to identifying and resolving issues fast.

Gardner: It’s interesting. Not only are you giving them benefits along insight lines, but it sounds to me like you're giving them a green light to go ahead and experiment and then learn very quickly whether that experiment worked or not, so that they can find refine. That’s so important in our digital business and agility drive these days.

Sayar: Absolutely. And if I were to think of another interesting example, Anheuser-Busch is another one of our customers. In this case, the CISO wanted to have a new approach to security and not one that was centered on guarding the data and access to the data, but providing a single platform for all constituents within Anheuser-Busch, whether security teams, operations teams, developers, or support teams.

We did a pilot for them, and as they're modernizing a lot of their apps, as they start to look at the next generation of security analytics, the adoption of Sumo started to become instant inside AB InBev. Now, they're looking at not just their existing real estate of infrastructure and apps for all these teams, but they're going to connect it to future projects such as the Connected Path, so they can understand what the yield is from each pour in a particular keg in a location and figure out whether that’s optimized or when they can replace the keg.

So, you're going from a reactive approach for security and processes around deployment and operations to next-gen connected Internet of Things (IoT) and devices to understand business performance and yield. That's a great example of an innovative company doing something unique and different with Sumo Logic.

Gardner: So, what happens as these companies modernize and they start to avail themselves of more public-cloud infrastructure services, ultimately more-and-more of their apps are going to be of, by, and for somebody else’s public cloud? Where do you fit in that scenario?

Data source and location

Sayar: Whether you’re running on-prem, whether you're running co-los, whether you're running through CDN providers like Akamai, whether you're running on AWS or Azure, Heroku, whether you're running SaaS platforms and renting a single platform that can manage and ingest all that data for you. Interestingly enough, about half our customers’ workloads run on-premises and half of them run in the cloud.

We’re agnostic to where the data is or where their applications or workloads reside. The benefit we provide is the single ubiquitous platform for managing the data streams that are coming in from devices, from applications, from infrastructure, from mobile to you, in a simple, real-time way through a multitenant cloud service.

Gardner: This reminds me of what I heard, 10 or 15 years ago about business intelligence (BI), drawing data, analyzing it, making it close to being proactive in its ability to help the organization. How is continuous intelligence different, or even better, and something that would replace what we refer to as BI?

The expectation is that it’s sub-millisecond latency to understand what's going on, from a security, operational, or user-experience point of view.

Sayar: The issue that we faced with the first generation of BI was it was very rear-view and mirror-centric, meaning that it was looking at data and things in the past. Where we're at today with this need for speed and the necessity to be always on, always available, the expectation is that it’s sub-millisecond latency to understand what's going on, from a security, operational, or user-experience point of view.

I'd say that we're on V2 or next generation of what was traditionally called BI, and we refer to that as continuous intelligence, because you're continuously adapting and learning. It's not only based on what humans know and what rules and correlation that they try to presuppose and create alarms and filters and things around that. It’s what machines and machine intelligence needs to supplement that with to provide the best-in-class type of capability, which is what we refer to as continuous intelligence.

Gardner: We’re almost out of time, but I wanted to look to the future a little bit. Obviously, there's a lot of investing going on now around big data and analytics as it pertains to many different elements of many different businesses, depending on their verticals. Then, we're talking about some of the logic benefit and continuous intelligence as it applies to applications and their lifecycle.

Where do we start to see crossover between those? How do I leverage what I’m doing in big data generally in my organization and more specifically, what I can do with continuous intelligence from my systems, from my applications?

Business Insights

Sayar: We touched a little bit on that in terms of the types of data that we integrate and ingest. At the end of the day, when we talk about full-stack visibility, it's from everything with respect to providing business insights to operational insights, to security insights.

We have some customers that are in credit-card payment processing, and they actually use us to understand activations for credit cards, so they're extracting value from the data coming into Sumo Logic to understand and predict business impact and relevant revenue associated with these services that they're managing; in this case, a set of apps that run on a CDN.

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At the same time, the fraud and risk team are using us for threat and prevention. The operations team is using us for understanding identification of issues proactively to be able to address any application or infrastructure issues, and that’s what we refer to as full stack.

Full stack isn’t just the technology; it's providing business visibility insights to line the business users or users that are looking at metrics around user experience and service quality, to operational-level impacts that help you become more proactive, or in some cases, reactive to wartime issues, as we've talked about. And lastly, the security team helps you take a different security posture around reactive and proactive, around threat, detection, and risk.

In a nutshell, where we see these things starting to converge is what we refer to as full stack visibility around our strategy for continuous intelligence, and that is technology to business to users.

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

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OCSL sets its sights on the Nirvana of hybrid IT—attaining the right mix of hybrid cloud for its clients

The next BriefingsDirect digital transformation case study explores how UK IT consultancy OCSL has set its sights on the holy grail of hybrid IT -- helping its clients to find and attain the right mix of hybrid cloud.

We'll now explore how each enterprise -- and perhaps even units within each enterprise -- determines the path to a proper mix of public and private cloud. Closer to home, they're looking at the proper fit of converged infrastructure, hyper-converged infrastructure (HCI), and software-defined data center (SDDC) platforms.

Implementing such a services-attuned architecture may be the most viable means to dynamically apportion applications and data support among and between cloud and on-premises deployments.

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

To describe how to rationalize the right mix of hybrid cloud and hybrid IT services along with infrastructure choices on-premises, we are joined by Mark Skelton, Head of Consultancy at OCSL in London. The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: People increasingly want to have some IT on premises, and they want public cloud -- with some available continuum between them. But deciding the right mix is difficult and probably something that’s going to change over time. What drivers are you seeing now as organizations make this determination?

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Skelton: It’s a blend of lot of things. We've been working with enterprises for a long time on their hybrid and cloud messaging. Our clients have been struggling just to understand what hybrid really means, but also how we make hybrid a reality, and how to get started, because it really is a minefield. You look at what Microsoft is doing, what AWS is doing, and what HPE is doing in their technologies. There's so much out there. How do they get started?

We've been struggling in the last 18 months to get customers on that journey and get started. But now, because technology is advancing, we're seeing customers starting to embrace it and starting to evolve and transform into those things. And, we've matured our models and frameworks as well to help customer adoption.

Gardner: Do you see the rationale for hybrid IT shaking down to an economic equation? Is it to try to take advantage of technologies that are available? Is it about compliance and security? You're probably temped to say all of the above, but I'm looking for what's driving the top-of-mind decision-making now.

Start with the economics

Skelton: The initial decision-making process begins with the economics. I think everyone has bought into the marketing messages from the public cloud providers saying, "We can reduce your costs, we can reduce your overhead -- and not just from a culture perspective, but from management, from personal perspective, and from a technology solutions perspective."

Skelton

CIOs, and even financial officers, are seeing economics as the tipping point they need to go into a hybrid cloud, or even all into a public cloud. But it’s not always cheap to put everything into a public cloud. When we look at business cases with clients, it’s the long-term investment we look at. Over time, it’s not always cheap to put things into public cloud. That’s where hybrid started to come back into the front of people’s minds.

We can use public cloud for the right workloads and where they want to be flexible and burst and be a bit more agile or even give global reach to long global businesses, but then keep the crown jewels back inside secured data centers where they're known and trusted and closer to some of the key, critical systems.

So, it starts with the finance side of the things, but quickly evolves beyond that, and financial decisions aren't the only reasons why people are going to public or hybrid cloud.

Gardner: In a more perfect world, we'd be able to move things back and forth with ease and simplicity, where we could take the A/B testing-type of approach to a public and private cloud decision. We're not quite there yet, but do you see a day where that choice about public and private will be dynamic -- and perhaps among multiple clouds or multi-cloud hybrid environment?

Skelton: Absolutely. I think multi-cloud is the Nirvana for every organization, just because there isn't one-size-fits-all for every type of work. We've been talking about it for quite a long time. The technology hasn't really been there to underpin multi-cloud and truly make it easy to move on-premises to public or vice versa. But I think now we're getting there with technology.

Are we there yet? No, there are still a few big releases coming, things that we're waiting to be released to market, which will help simplify that multi-cloud and the ability to migrate up and back, but we're just not there yet, in my opinion.

There are still a few big releases coming, things that we're waiting to be released to market, which will help simplify that multi-cloud and the ability to migrate up and back, but we're just not there yet.

Gardner: We might be tempted to break this out between applications and data. Application workloads might be a bit more flexible across a continuum of hybrid cloud, but other considerations are brought to the data. That can be security, regulation, control, compliance, data sovereignty, GDPR, and so forth. Are you seeing your customers looking at this divide between applications and data, and how they are able to rationalize one versus the other?

Skelton: Applications, as you have just mentioned, are the simpler things to move into a cloud model, but the data is really the crown jewels of the business, and people are nervous about putting that into public cloud. So what we're seeing lot of is putting applications into the public cloud for the agility, elasticity, and global reach and trying to keep data on-premises because they're nervous about those breaches in the service providers’ data centers.

That's what we are seeing, but we are seeing an uprising of things like object storage, so we're working with Scality, for example, and they have a unique solution for blending public and on-premises solutions, so we can pin things to certain platforms in a secure data center and then, where the data is not quite critical, move it into a public cloud environment.

Gardner: It sounds like you've been quite busy. Please tell us about OCSL, an overview of your company and where you're focusing most of your efforts in terms of hybrid computing.

Rebrand and refresh

Skelton: OCSL had been around for 26 years as a business. Recently, we've been through a re-brand and a refresh of what we are focusing on, and we're moving more to a services organization, leading with our people and our consultants.

We're focusing on transforming customers and clients into the cloud environment, whether that's applications or, if it's data center, cloud, or hybrid cloud. We're trying to get customers on that journey of transformation and engaging with business-level people and business requirements and working out how we make cloud a reality, rather than just saying there's a product and you go and do whatever you want with it. We're finding out what those businesses want, what are the key requirements, and then finding the right cloud models that to fit that.

Gardner: So many organizations are facing not just a retrofit or a rethinking around IT, but truly a digital transformation for the entire organization. There are many cases of sloughing off business lines, and other cases of acquiring. It's an interesting time in terms of a mass reconfiguration of businesses and how they identify themselves.

Skelton: What's changed for me is, when I go and speak to a customer, I'm no longer just speaking to the IT guys, I'm actually engaging with the finance officers, the marketing officers, the digital officers -- that's he common one that is creeping up now. And it's a very different conversation.

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We're looking at business outcomes now, rather than focusing on, "I need this disk, this product." It's more: "I need to deliver this service back to the business." That's how we're changing as a business. It's doing that business consultancy, engaging with that, and then finding the right solutions to fit requirements and truly transform the business.

Gardner: Of course, HPE has been going through transformations itself for the past several years, and that doesn't seem to be slowing up much. Tell us about the alliance between OCSL and HPE. How do you come together as a whole greater than the sum of the parts?

Skelton: HPE is transforming and becoming a more agile organization, with some of the spinoffs that we've had recently aiding that agility. OCSL has worked in partnership with HPE for many years, and it's all about going to market together and working together to engage with the customers at right level and find the right solutions. We've had great success with that over many years.

Gardner: Now, let’s go to the "show rather than tell" part of our discussion. Are there some examples that you can look to, clients that you work with, that have progressed through a transition to hybrid computing, hybrid cloud, and enjoyed certain benefits or found unintended consequences that we can learn from?

Skelton: We've had a lot of successes in the last 12 months as I'm taking clients on the journey to hybrid cloud. One of the key ones that resonates with me is a legal firm that we've been working with. They were in a bit of a state. They had an infrastructure that was aging, was unstable, and wasn't delivering quality service back to the lawyers that were trying to embrace technology -- so mobile devices, dictation software, those kind of things.

We came in with a first prospectus on how we would actually address some of those problems. We challenged them, and said that we need to go through a stabilization phase. Public cloud is not going to be the immediate answer. They're being courted by the big vendors, as everyone is, about public cloud and they were saying it was the Nirvana for them.

We challenged that and we got them to a stable platform first, built on HPE hardware. We got instant stability for them. So, the business saw immediate returns and delivery of service. It’s all about getting that impactful thing back to the business, first and foremost.

Building cloud model

Now, we're working through each of their service lines, looking at how we can break them up and transform them into a cloud model. That involves breaking down those apps, deconstructing the apps, and thinking about how we can use pockets of public cloud in line with the hybrid on-premise in our data-center infrastructure.

They've now started to see real innovative solutions taking that business forward, but they got instant stability.

Gardner: Were there any situations where organizations were very high-minded and fanciful about what they were going to get from cloud that may have led to some disappointment -- so unintended consequences. Maybe others might benefit from hindsight. What do you look out for, now that you have been doing this for a while in terms of hybrid cloud adoption?

Skelton: One of the things I've seen a lot of with cloud is that people have bought into the messaging from the big public cloud vendors about how they can just turn on services and keep consuming, consuming, consuming. A lot of people have gotten themselves into a state where bills have been rising and rising, and the economics are looking ridiculous. The finance officers are now coming back and saying they need to rein that back in. How do they put some control around that?

People have bought into the messaging from the big public-cloud vendors about how they can just turn on services and keep consuming, consuming, consuming.

That’s where hybrid is helping, because if you start to hook up some workloads back in an isolated data center, you start to move some of those workloads back. But the key for me is that it comes down to putting some thought process into what you're putting into cloud. Just think through to how can you transform and use the services properly. Don't just turn everything on, because it’s there and it’s click of a button away, but actually think about put some design and planning into adopting cloud.

Gardner: It also sounds like the IT people might need to go out and have a pint with the procurement people and learn a few basics about good contract writing, terms and conditions, and putting in clauses that allow you to back out, if needed. Is that something that we should be mindful of -- IT being in the procurement mode as well as specifying technology mode?

Skelton: Procurement definitely needs to be involved in the initial set-up with the cloud  whenever they're committing to a consumption number, but then once that’s done, it’s IT’s responsibility in terms of how they are consuming that. Procurement needs to be involved all the way through in keeping constant track of what’s going on; and that’s not happening.

The IT guys don’t really care about the cost; they care about the widgets and turning things on and playing around that. I don’t think they really realized how much this is going to cost-back. So yeah, there is a bit of disjoint in lots of organizations in terms of procurement in the upfront piece, and then it goes away, and then IT comes in and spends all of the money.

Gardner: In the complex service delivery environment, that procurement function probably should be constant and vigilant.

Big change in procurement

Skelton: Procurement departments are going to change. We're starting to see that in some of the bigger organizations. They're closer to the IT departments. They need to understand that technology and what’s being used, but that’s quite rare at the moment. I think that probably over the next 12 months, that’s going to be a big change in the larger organizations.

Gardner: Before we close, let's take a look to the future. A year or two from now, if we sit down again, I imagine that more micro services will be involved and containerization will have an effect, where the complexity of services and what we even think of as an application could be quite different, more of an API-driven environment perhaps.

So the complexity about managing your cloud and hybrid cloud to find the right mix, and pricing that, and being vigilant about whether you're getting your money’s worth or not, seems to be something where we should start thinking about applying artificial intelligence (AI), machine learning, what I like to call BotOps, something that is going to be there for you automatically without human intervention.

Hopefully, in 12 months, we can have those platforms and we can then start to embrace some of this great new technology and really rethink our applications.

Does that sound on track to you, and do you think that we need to start looking to advanced automation and even AI-driven automation to manage this complex divide between organizations and cloud providers?

Skelton: You hit a lot of key points there in terms of where the future is going. I think we are still in this phase if we start trying to build the right platforms to be ready for the future. So we see the recent releases of HPE Synergy for example, being able to support these modern platforms, and that’s really allowing us to then embrace things like micro services. Docker and Mesosphere are two types of platforms that will disrupt organizations and the way we do things, but you need to find the right platform first.

Hopefully, in 12 months, we can have those platforms and we can then start to embrace some of this great new technology and really rethink our applications. And it’s a challenge to the ISPs. They've got to work out how they can take advantage of some of these technologies.

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We're seeing a lot of talk about Cervalis and computing. It's where there is nothing and you need to spin up results as and when you need to. The classic use case for that is Uber; and they have built a whole business on that Cervalis type model. I think that in 12 months time, we're going to see a lot more of that and more of the enterprise type organizations.

I don’t think we have it quite clear in our minds how we're going to embrace that but it’s the ISV community that really needs to start driving that. Beyond that, it's absolutely with AI and bots. We're all going to be talking to computers, and they're going to be responding with very human sorts of reactions. That's the next way.

I am bringing that into enterprise organizations for how we can solve some business challenges. Service test management is one of the use cases where we're seeing, in some of our clients, whether they can get immediate response from bots and things like that to common queries, so they don’t need as many support staff. It’s already starting to happen.

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

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Fast acquisition of diverse unstructured data sources makes IDOL API tools a star at LogitBot

The next BriefingsDirect Voice of the Customer digital transformation case study highlights how high-performing big-data analysis powers an innovative artificial intelligence (AI)-based investment opportunity and evaluation tool. We'll learn how LogitBot in New York identifies, manages, and contextually categorizes truly massive and diverse data sources.

By leveraging entity recognition APIs, LogitBot not only provides investment evaluations from across these data sets, it delivers the analysis as natural-language information directly into spreadsheets as the delivery endpoint. This is a prime example of how complex cloud-to core-to edge processes and benefits can be managed and exploited using the most responsive big-data APIs and services.

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

To describe how a virtual assistant for targeting investment opportunities is being supported by cloud-based big-data services, we're joined by Mutisya Ndunda, Founder and CEO of LogitBot and Michael Bishop, CTO of LogicBot, in New York. The discussion is moderated by BriefingsDirect's Dana Gardner, Principal Analyst at Interarbor Solutions.

Here are some excerpts:

Gardner: Let’s look at some of the trends driving your need to do what you're doing with AI and bots, bringing together data, and then delivering it in the format that people want most. What’s the driver in the market for doing this?

Ndunda: LogitBot is all about trying to eliminate friction between people who have very high-value jobs and some of the more mundane things that could be automated by AI.

Ndunda

Today, in finance, the industry, in general, searches for investment opportunities using techniques that have been around for over 30 years. What tends to happen is that the people who are doing this should be spending more time on strategic thinking, ideation, and managing risk. But without AI tools, they tend to get bogged down in the data and in the day-to-day. So, we've decided to help them tackle that problem.

Gardner: Let the machines do what the machines do best. But how do we decide where the demarcation is between what the machines do well and what the people do well, Michael?

Bishop: We believe in empowering the user and not replacing the user. So, the machine is able to go in-depth and do what a high-performing analyst or researcher would do at scale, and it does that every day, instead of once a quarter, for instance, when research analysts would revisit an equity or a sector. We can do that constantly, react to events as they happen, and replicate what a high-performing analyst is able to do.

Gardner: It’s interesting to me that you're not only taking a vast amount of data and putting it into a useful format and qualitative type, but you're delivering it in a way that’s demanded in the market, that people want and use. Tell me about this core value and then the edge value and how you came to decide on doing it the way you do?

Evolutionary process

Ndunda: It’s an evolutionary process that we've embarked on or are going through. The industry is very used to doing things in a very specific way, and AI isn't something that a lot of people are necessarily familiar within financial services. We decided to wrap it around things that are extremely intuitive to an end user who doesn't have the time to learn technology.

So, we said that we'll try to leverage as many things as possible in the back via APIs and all kinds of other things, but the delivery mechanism in the front needs to be as simple or as friction-less as possible to the end-user. That’s our core principle.

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Bishop: Finance professionals generally don't like black boxes and mystery, and obviously, when you're dealing with money, you don’t want to get an answer out of a machine you can’t understand. Even though we're crunching a lot of information and  making a lot of inferences, at the end of the day, they could unwind it themselves if they wanted to verify the inferences that we have made.

Bishop

We're wrapping up an incredibly complicated amount of information, but it still makes sense at the end of the day. It’s still intuitive to someone. There's not a sense that this is voodoo under the covers.

Gardner: Well, let’s pause there. We'll go back to the data issues and the user-experience issues, but tell us about LogitBot. You're a startup, you're in New York, and you're focused on Wall Street. Tell us how you came to be and what you do, in a more general sense.

Ndunda: Our professional background has always been in financial services. Personally, I've spent over 15 years in financial services, and my career led me to what I'm doing today.

In the 2006-2007 timeframe, I left Merrill Lynch to join a large proprietary market-making business called Susquehanna International Group. They're one of the largest providers of liquidity around the world. Chances are whenever you buy or sell a stock, you're buying from or selling to Susquehanna or one of its competitors.

What had happened in that industry was that people were embracing technology, but it was algorithmic trading, what has become known today as high-frequency trading. At Susquehanna, we resisted that notion, because we said machines don't necessarily make decisions well, and this was before AI had been born.

Internally, we went through this period where we had a lot of discussions around, are we losing out to the competition, should we really go pure bot, more or less? Then, 2008 hit and our intuition of allowing our traders to focus on the risky things and then setting up machines to trade riskless or small orders paid off a lot for the firm; it was the best year the firm ever had, when everyone else was falling apart.

That was the first piece that got me to understand or to start thinking about how you can empower people and financial professionals to do what they really do well and then not get bogged down in the details.

Then, I joined Bloomberg and I spent five years there as the head of strategy and business development. The company has an amazing business, but it's built around the notion of static data. What had happened in that business was that, over a period of time, we began to see the marketplace valuing analytics more and more.

Make a distinction

Part of the role that I was brought in to do was to help them unwind that and decouple the two things -- to make a distinction within the company about static information versus analytical or valuable information. The trend that we saw was that hedge funds, especially the ones that were employing systematic investment strategies, were beginning to do two things, to embrace AI or technology to empower your traders and then also look deeper into analytics versus static data.

That was what brought me to LogitBot. I thought we could do it really well, because the players themselves don't have the time to do it and some of the vendors are very stuck in their traditional business models.

Bishop: We're seeing a kind of renaissance here, or we're at a pivotal moment, where we're moving away from analytics in the sense of business reporting tools or understanding yesterday. We're now able to mine data, get insightful, actionable information out of it, and then move into predictive analytics. And it's not just statistical correlations. I don’t want to offend any quants, but a lot of technology [to further analyze information] has come online recently, and more is coming online every day.

For us, Google had released TensorFlow, and that made a substantial difference in our ability to reason about natural language. Had it not been for that, it would have been very difficult one year ago.

At the moment, technology is really taking off in a lot of areas at once. That enabled us to move from static analysis of what's happened in the past and move to insightful and actionable information.

Relying on a backward-looking mechanism of trying to interpret the future is kind of really dangerous, versus having a more grounded approach.

Ndunda: What Michael kind of touched on there is really important. A lot of traditional ways of looking at financial investment opportunities is to say that historically, this has happened. So, history should repeat itself. We're in markets where nothing that's happening today has really happened in the past. So, relying on a backward-looking mechanism of trying to interpret the future is kind of really dangerous, versus having a more grounded approach that can actually incorporate things that are nontraditional in many different ways.

So, unstructured data, what investors are thinking, what central bankers are saying, all of those are really important inputs, one part of any model 10 or 20 years ago. Without machine learning and some of the things that we are doing today, it’s very difficult to incorporate any of that and make sense of it in a structured way.

Gardner: So, if the goal is to make outlier events your friend and not your enemy, what data do you go to to close the gap between what's happened and what the reaction should be, and how do you best get that data and make it manageable for your AI and machine-learning capabilities to exploit?

Ndunda: Michael can probably add to this as well. We do not discriminate as far as data goes. What we like to do is have no opinion on data ahead of time. We want to get as much information as possible and then let a scientific process lead us to decide what data is actually useful for the task that we want to deploy it on.

As an example, we're very opportunistic about acquiring information about who the most important people at companies are and how they're connected to each other. Does this guy work on a board with this or how do they know each other? It may not have any application at that very moment, but over the course of time, you end up building models that are actually really interesting.

We scan over 70,000 financial news sources. We capture news information across the world. We don't necessarily use all of that information on a day-to-day basis, but at least we have it and we can decide how to use it in the future.

We also monitor anything that companies file and what management teams talk about at investor conferences or on phone conversations with investors.

Bishop: Conference calls, videos, interviews.

Audio to text

Ndunda: HPE has a really interesting technology that they have recently put out. You can transcribe audio to text, and then we can apply our text processing on top of that to understand what management is saying in a structural, machine-based way. Instead of 50 people listening to 50 conference calls you could just have a machine do it for you.

Gardner: Something we can do there that we couldn't have done before is that you can also apply something like sentiment analysis, which you couldn’t have done if it was a document, and that can be very valuable.

Bishop: Yes, even tonal analysis. There are a few theories on that, that may or may not pan out, but there are studies around tone and cadence. We're looking at it and we will see if it actually pans out.

Gardner: And so do you put this all into your own on-premises data-center warehouse or do you take advantage of cloud in a variety of different means by which to corral and then analyze this data? How do you take this fire hose and make it manageable?

Bishop: We do take advantage of the cloud quite aggressively. We're split between SoftLayer and Google. At SoftLayer we have bare-metal hardware machines and some power machines with high-power GPUs.

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On the Google side, we take advantage of Bigtable and BigQuery and some of their infrastructure tools. And we have good, old PostgreSQL in there, as well as DataStax, Cassandra, and their Graph as the graph engine. We make liberal use of HPE Haven APIs as well and TensorFlow, as I mentioned before. So, it’s a smorgasbord of things you need to corral in order to get the job done. We found it very hard to find all of that wrapped in a bow with one provider.

We're big proponents of Kubernetes and Docker as well, and we leverage that to avoid lock-in where we can. Our workload can migrate between Google and the SoftLayer Kubernetes cluster. So, we can migrate between hardware or virtual machines (VMs), depending on the horsepower that’s needed at the moment. That's how we handle it.

Gardner: So, maybe 10 years ago you would have been in a systems-integration capacity, but now you're in a services-integration capacity. You're doing some very powerful things at a clip and probably at a cost that would have been impossible before.

Bishop: I certainly remember placing an order for a server, waiting six months, and then setting up the RAID drives. It's amazing that you can just flick a switch and you get a very high-powered machine that would have taken six months to order previously. In Google, you spin up a VM in seconds. Again, that's of a horsepower that would have taken six months to get.

Gardner: So, unprecedented innovation is now at our fingertips when it comes to the IT side of things, unprecedented machine intelligence, now that the algorithms and APIs are driving the opportunity to take advantage of that data.

Let's go back to thinking about what you're outputting and who uses that. Is the investment result that you're generating something that goes to a retail type of investor? Is this something you're selling to investment houses or a still undetermined market? How do you bring this to market?

Natural language interface

Ndunda: Roboto, which is the natural-language interface into our analytical tools, can be custom tailored to respond, based on the user's level of financial sophistication.

At present, we're trying them out on a semiprofessional investment platform, where people are professional traders, but not part of a major brokerage house. They obviously want to get trade ideas, they want to do analytics, and they're a little bit more sophisticated than people who are looking at investments for their retirement account.  Rob can be tailored for that specific use case.

He can also respond to somebody who is managing a portfolio at a hedge fund. The level of depth that he needs to consider is the only differential between those two things.

In the back, he may do an extra five steps if the person asking the question worked at a hedge fund, versus if the person was just asking about why is Apple up today. If you're a retail investor, you don’t want to do a lot of in-depth analysis.

Bishop: You couldn’t take the app and do anything with it or understand it.

If our initial findings here pan out or continue to pan out, it's going to be a very powerful interface.

Ndunda: Rob is an interface, but the analytics are available via multiple venues. So, you can access the same analytics via an API, a chat interface, the web, or a feed that streams into you. It just depends on how your systems are set up within your organization. But, the data always will be available to you.

Gardner: Going out to that edge equation, that user experience, we've talked about how you deliver this to the endpoints, customary spreadsheets, cells, pivots, whatever. But it also sounds like you are going toward more natural language, so that you could query, rather than a deep SQL environment, like what we get with a Siri or the Amazon Echo. Is that where we're heading?

Bishop: When we started this, trying to parameterize everything that you could ask into enough checkboxes and forums pollutes the screen. The system has access to an enormous amount of data that you can't create a parameterized screen for. We found it was a bit of a breakthrough when we were able to start using natural language.

TensorFlow made a huge difference here in natural language understanding, understanding the intent of the questioner, and being able to parameterize a query from that. If our initial findings here pan out or continue to pan out, it's going to be a very powerful interface.

I can't imagine having to go back to a SQL query if you're able to do it natural language, and it really pans out this time, because we’ve had a few turns of the handle of alleged natural-language querying.

Gardner: And always a moving target. Tell us specifically about SentryWatch and Precog. How do these shake out in terms of your go-to-market strategy?

How everything relates

Ndunda: One of the things that we have to do to be able to answer a lot of questions that our customers may have is to monitor financial markets and what's impacting them on a continuous basis. SentryWatch is literally a byproduct of that process where, because we're monitoring over 70,000 financial news sources, we're analyzing the sentiment, we're doing deep text analysis on it, we're identifying entities and how they're related to each other, in all of these news events, and we're sticking that into a knowledge graph of how everything relates to everything else.

It ends up being a really valuable tool, not only for us, but for other people, because while we're building models. there are also a lot of hedge funds that have proprietary models or proprietary processes that could benefit from that very same organized relational data store of news. That's what SentryWatch is and that's how it's evolved. It started off with something that we were doing as an import and it's actually now a valuable output or a standalone product.

Precog is a way for us to showcase the ability of a machine to be predictive and not be backward looking. Again, when people are making investment decisions or allocation of capital across different investment opportunities, you really care about your forward return on your investments. If I invested a dollar today, am I likely to make 20 cents in profit tomorrow or 30 cents in profit tomorrow?

We're using pretty sophisticated machine-learning models that can take into account unstructured data sources as part of the modeling process. That will give you these forward expectations about stock returns in a very easy-to-use format, where you don't need to have a PhD in physics or mathematics.

We're using pretty sophisticated machine-learning models that can take into account unstructured data sources as part of the modeling process.

You just ask, "What is the likely return of Apple over the next six months," taking into account what's going on in the economy.  Apple was fined $14 billion. That can be quickly added into a model and reflect a new view in a matter of seconds versus sitting down in a spreadsheet and trying to figure out how it all works out.

Gardner: Even for Apple, that's a chunk of change.

Bishop: It's a lot money, and you can imagine that there were quite a few analysts on Wall Street in Excel, updating their models around this so that they could have an answer by the end of the day, where we already had an answer.

Gardner: How do the HPE Haven OnDemand APIs help the Precog when it comes to deciding those sources, getting them in the right format, so that you can exploit?

Ndunda: The beauty of the platform is that it simplifies a lot of development processes that an organization of our size would have to take on themselves.

The nice thing about it is that a drag-and-drop interface is really intuitive; you don't need to be specialized in Java, Python, or whatever it is. You can set up your intent in a graphical way, and then test it out, build it, and expand it as you go along. The Lego-block structure is really useful, because if you want to try things out, it's drag and drop, connect the dots, and then see what you get on the other end.

For us, that's an innovation that we haven't seen with anybody else in the marketplace and it cuts development time for us significantly.

Gardner: Michael, anything more to add on how this makes your life a little easier?

Lowering cost

Bishop: For us, lowering the cost in time to run an experiment is very important when you're running a lot of experiments, and the Combinations product enables us to run a lot of varied experiments using a variety of the HPE Haven APIs in different combinations very quickly. You're able to get your development time down from a week, two weeks, whatever it is to wire up an API to assist them.

In the same amount of time, you're able to wire the initial connection and then you have access to pretty much everything in Haven. You turn it over to either a business user, a data scientist, or a machine-learning person, and they can drag and drop the connectors themselves. It makes my life easier and it makes the developers’ lives easier because it gets back time for us.

Gardner: So, not only have we been able to democratize the querying, moving from SQL to natural language, for example, but we’re also democratizing the choice on sources and combinations of sources in real time, more or less for different types of analyses, not just the query, but the actual source of the data.

The power of a lot of this stuff is in the unstructured world, because valuable information typically tends to be hidden in documents.

Bishop: Correct.

Ndunda: Again, the power of a lot of this stuff is in the unstructured world, because valuable information typically tends to be hidden in documents. In the past, you'd have to have a team of people to scour through text, extract what they thought was valuable, and summarize it for you. You could miss out on 90 percent of the other valuable stuff that's in the document.

With this ability now to drag and drop and then go through a document in five different iterations by just tweaking, a parameter is really useful.

Gardner: So those will be IDOL-backed APIs that you are referring to.

Ndunda: Exactly.

Bishop: It’s something that would be hard for an investment bank, even a few years ago, to process. Everyone is on the same playing field here or starting from the same base, but dealing with unstructured data has been traditionally a very difficult problem. You have a lot technologies coming online as APIs; at the same time, they're also coming out as traditional on-premises [software and appliance] solutions.

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We're all starting from the same gate here. Some folks are little ahead, but I'd say that Facebook is further ahead than an investment bank in their ability to reason over unstructured data. In our world, I feel like we're starting basically at the same place that Goldman or Morgan would be.

Gardner: It's a very interesting reset that we’re going through. It's also interesting that we talked earlier about the divide between where the machine and the individual knowledge worker begins or ends, and that's going to be a moving target. Do you have any sense of how that changes its characterization of what the right combination is of machine intelligence and the best of human intelligence?

Empowering humans

Ndunda: I don’t foresee machines replacing humans, per se. I see them empowering humans, and to the extent that your role is not completely based on a task, if it's based on something where you actually manage a process that goes from one end to another, those particular positions will be there, and the machines will free our people to focus on that.

But, in the case where you have somebody who is really responsible for something that can be automated, then obviously that will go away. Machines don't eat, they don’t need to take vacation, and if it’s a task where you don't need to reason about it, obviously you can have a computer do it.

What we're seeing now is that if you have a machine sitting side by side with a human, and the machine can pick up on how the human reasons with some of the new technologies, then the machine can do a lot of the grunt work, and I think that’s the future of all of this stuff.

I don’t foresee machines replacing humans, per se. I see them empowering humans.

Bishop: What we're delivering is that we distill a lot of information, so that a knowledge worker or decision-maker can make an informed decision, instead of watching CNBC and being a single-source reader. We can go out and scour the best of all the information, distill it down, and present it, and they can choose to act on it.

Our goal here is not to make the next jump and make the decision. Our job is to present the information to a decision-maker.

Gardner: It certainly seems to me that the organization, big or small, retail or commercial, can make the best use of this technology. Machine learning, in the end, will win.

Ndunda: Absolutely. It is a transformational technology, because for the first time in a really long time, the reasoning piece of it is within grasp of machines. These machines can operate in the gray area, which is where the world lives.

Gardner: And that gray area can almost have unlimited variables applied to it.

Ndunda: Exactly. Correct.

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

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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.

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.

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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.

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

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• Big data enables top user experiences and extreme personalization for Intuit TurboTax
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• Spirent leverages big data to keep user experience quality a winning factor for telcos
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• IoT brings on development demands that DevOps manages best, say experts
• Big data generates new insights into what’s happening in the world's tropical ecosystems
• DevOps and security, a match made in heaven
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