Tuesday, March 07, 2017

Converged IoT Systems: Bringing the Data Center to the Edge of Everything

Transcript of a discussion on the rapidly evolving architectural shift of moving advanced information technology (IT) capabilities to the edge to support Internet of Things (IoT) requirements for operational integrity benefits.

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

Dana Gardner: Hello, and welcome to the next edition of the 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 thought leadership panel discussion explores the rapidly evolving architectural shift of moving advanced IT capabilities to the edge to support IoT requirements.

The demands of data processing, real-time analytics, and platform efficiency at the intercept of IoT and business benefits have forced new technology approaches. We'll now hear how converged systems and high-performance data analysis platforms are bringing -- in essence -- the data center to the operational technology (OT) edge.

To hear more about the latest capabilities in gaining unprecedented measurements and operational insights where they’re needed most, please join me in welcoming Phil McRell, General Manager of the IoT Consortia at PTC. Welcome, Phil.

McRell
Phil McRell: Great to be here. Thanks, Dana.

Gardner: We're also here with Gavin Hill, IoT Marketing Engineer for Northern Europe at National Instruments (NI) in London. Welcome, Gavin.

Gavin Hill: Hi, Dana. Thanks very much for inviting us.

Gardner: Good to have you with us. Olivier Frank also joins us. He is Senior Director of Worldwide Business Development and Sales for Edgeline IoT Systems at Hewlett Packard Enterprise (HPE). Welcome, Olivier.

Olivier Frank: Thank you, Dana. Great to be here with you.

Gardner: Gentlemen, let's start at a high level. What's driving this need for a different approach to computing when we think about IoT and we think about the “edge” of organizations? Why is this becoming such a hot issue?

McRell: There are several drivers, but the most interesting one is economics. In the past, the costs that would have been required to take an operational site -- a mine, a refinery, or a factory -- and do serious predictive analysis, meant you would have to spend more money than you would get back.

For very high-value assets -- assets that are millions or tens of millions of dollars -- you probably do have some systems in place in these facilities. But once you get a little bit lower in the asset class, there really isn’t a return on investment (ROI) available. What we're seeing now is that's all changing based on the type of technology available.

Gardner: So, in essence, we have this whole untapped tier of technologies that we haven't been able to get a machine-to-machine (M2M) benefit from for gathering information -- or the next stage, which is analyzing that information. How big an opportunity is this? Is this a step change, or is this a minor incremental change? Why is this economically a big deal, Olivier?
Frank

Frank: We're talking about Industry 4.0, the fourth generation of change -- after steam, after the Internet, after the cloud, and now this application of IoT to the industrial world. It’s changing at multiple levels. It’s what's happening within the factories and within this ecosystem of suppliers to the manufacturers, and the interaction with consumers of those suppliers and customers. There's connectivity to those different parties that we can then put together.

While our customers have been doing process automation for 40 years, what we're doing together is unleashing the IT standardization, taking technologies that were in the data centers and applying them to the world of process automation, or opening up.

The analogy is what happened when mainframes were challenged by mini computers and then by PCs. It's now open architecture in a world that has been closed.

Gardner: Phil mentioned ROI, Gavin. What is it about the technology price points and capabilities that have come down to the point where it makes sense now to go down to this lower tier of devices and start gathering information?


Hill
Hill: There are two pieces to that. The first one is that we're seeing that understanding more about the IoT world is more valuable than we thought. McKinsey Global Institute did a study that said that by about 2025 we're going to be in a situation where IoT in the factory space is going to be worth somewhere between $1.2 trillion and $3.7 trillion. That says a lot.

The second piece is that we're at a stage where we can make technology at a much lower price point. We can put that onto the assets that we have in these industrial environments quite cheaply.

Then, you deal with the real big value, the data. All three of us are quite good at getting the value from our own respective areas of expertise.

Look at someone that we've worked with, Jaguar Land Rover. In their production sites, in their power train facilities, they were at a stage where they created an awful lot of data but didn't do anything with it. About 90 percent of their data wasn't being used for anything. It doesn't matter how many sensors you put on something. If you can't do anything with the data, it's completely useless.

They have been using techniques similar to what we've been doing in our collaborative efforts to gain insight from that data. Now, they're at a stage where probably 90 percent of their data is usable, and that's the big change.

Collaboration is key

Gardner: Let's learn more about your organizations and how you're working collaboratively, as you mentioned, before we get back into understanding how to go about architecting properly for IoT benefits. Phil, tell us about PTC. I understand you won an award in Barcelona recently.

McRell: That was a collaboration that our three organizations did with a pump and valve manufacturer, Flowserve. As Gavin was explaining, there was a lot of learning that had to be done upfront about what kind of sensors you need and what kind of signals you need off those sensors to come up with accurate predictions.

When we collaborate, we rely heavily on NI for their scientists and engineers to provide their expertise. We really need to consume digital data. We can't do anything with analog signals and we don't have the expertise to understand what kind of signals we need. When we obtain that, then with HPE, we can economically crunch that data, provide those predictions, and provide that optimization, because of HPE's hardware that now can live happily in those production environments.

Gardner: Tell us about PTC specifically; what does your organization do?

McRell: For IoT, we have a complete end-to-end platform that allows everything from the data acquisition gateway with NI all the way up to machine learning, augmented reality, dashboards, and mashups, any sort of interface that might be needed for people or other systems to interact.

In an operational setting, there may be one, two, or dozens of different sources of information. You may have information coming from the programmable logic controllers (PLCs) in a factory and you may have things coming from a Manufacturing Execution System (MES) or an Enterprise Resource Planning (ERP) system. There are all kinds of possible sources. We take that, orchestrate the logic, and then we make that available for human decision-making or to feed into another system.

Gardner: So the applications that PTC is developing are relying upon platforms and the extension of the data center down to the edge. Olivier, tell us about Edgeline and how that fits into this?
Explore
HPE's Edgeline

IoT Systems
Frank: We came up with this idea of leveraging the enterprise computing excellence that is our DNA within HPE. As our CEO said, we want to be the IT in the IoT.

According to IDC, 40 percent of the IoT computing will happen at the edge. Just to clarify, it’s not an opposition between the edge and the hybrid IT that we have in HPE; it’s actually a continuum. You need to bring some of the workloads to the edge. It's this notion of time of insight and time of action. The closer you are to what you're measuring, the more real-time you are.

We came up with this idea. What if we could bring the depth of computing we have in the data center in this sub-second environment, where I need to read this intelligent data created by my two partners here, but also, actuate them and do things with them?

Take the example of an electrical short circuit that for some reason caught fire. You don’t want to send the data to the cloud; you want to take immediate action. This is the notion of real-time, immediate action.

We take the deep compute. We integrate the connectivity with NI. We're the first platform that has integrated an industry standard called PXI, which allows NI to integrate the great portfolio of sensors and acquisition and analog-to-digital conversion technologies into our systems.

Finally, we bring enterprise manageability. Since we have proliferation of systems, system management at the edge becomes a problem. So, we bring our award-winning and millions-of-licenses sold our Integrated Lights-Out (iLO) that we sell in all our ProLiant servers, and we bring that technology at the edge as well.

Gardner: We have the computing depth from HPE, we have insightful analytics and applications from PTC, what does NI bring to the table? Describe the company for us, Gavin?

Working smarter

Hill: As a company, NI is about a $1.2 billion company worldwide. We get involved in an awful lot of industries. But in the IoT space, where we see ourselves fitting within this collaboration with PTC and HPE, is our ability to make a lot of machines smarter.

There are already some sensors on assets, machines, pumps, whatever they may be on the factory floor, but for older or potentially even some newer devices, there are not natively all the sensors that you need to be able to make really good decisions based on that data. To be able to feed in to the PTC systems, the HPE systems, you need to have the right type of data to start off with.

We have the data acquisition and control units that allow us to take that data in, but then do something smart with it. Using something like our CompactRIO System, or as you described, using the PXI platform with the Edgeline products, we can add a certain level of understanding and just a smart nature to these potentially dumb devices. It allows us not only to take in signals, but also potentially control the systems as well.

We not only have some great information from PTC that lets us know when something is going to fail, but we could potentially use their data and their information to allow us to, let’s say, decide to run a pump at half load for a little bit longer. That means that we could get a maintenance engineer out to an oil rig in an appropriate time to fix it before it runs to failure. We have the ability to control as well as to read in.

The other piece of that is that sensor data is great. We like to be as open as possible in taking from any sensor vendor, any sensor provider, but you want to be able to find the needle in the haystack there. We do feature extraction to try and make sure that we give the important pieces of digital data back to PTC, so that can be processed by the HPE Edgeline system as well.
Explore
HPE's Edgeline

IoT Systems

Frank: This is fundamental. Capturing the right data is an art and a science and that’s really what NI brings, because you don’t want to capture noise; it’s proliferation of data. That’s a unique expertise that we're very glad to integrate in the partnership.

Gardner: We certainly understand the big benefit of IoT extending what people have done with operational efficiency over the years. We now know that we have the technical capabilities to do this at an acceptable price point. But what are the obstacles, what are the challenges that organizations still have in creating a true data-driven edge, an IoT rich environment, Phil?

Economic expertise

McRell: That’s why we're together in this consortium. The biggest obstacle is that because there are so many different requirements for different types of technology and expertise, people can become overwhelmed. They'll spend months or years trying to figure this out. We come to the table with end-to-end capability from sensors and strategy and everything in between, pre-integrated at an economical price point.

Speed is important. Many of these organizations are seeing the future, where they have to be fast enough to change their business model. For instance, some OEM discrete manufacturers are going to have to move pretty quickly from just offering product to offering service. If somebody is charging $50 million for capital equipment, and their competitor is charging $10 million a year and the service level is actually better because they are much smarter about what those assets are doing, the $50 million guy is going to go out of business.

We come to the table with the ability to come and quickly get that factory, get those assets smart and connected, make sure the right people, parts, and processes are brought to bear at exactly the right time. That drives all the things people are looking for -- the up-time, the safety, the yield,  and performance of that facility. It comes down to the challenge, if you don't have all the right parties together with that technology and expertise, you can very easily get stuck on something that takes a very long time to unravel.

Gardner: That’s very interesting when you move from a Capital Expenditure (CAPEX) to an Operational Expenditure (OPEX) mentality. Every little bit of that margin goes to your bottom line and therefore you're highly incentivized to look for whole new categories of ways to improve process efficiency.

Any other hurdles, Olivier, that you're trying to combat effectively with the consortium?

Frank: The biggest hurdle is the level of complexity, and our customers don't know where to start. So, the promise of us working together is really to show the value of this kind of open architecture injected into a 40-year-old process automation infrastructure and demonstrate, as we did yesterday with our robot powered by our HPE Edgeline is this idea that I can show immediate value to the plant manager, to the quality manager, to the operation manager using the data that resides in that factory already, and that 70 percent or more is unused. That’s the value.

So how do you get that quickly and simply? That’s what we're working to solve so that our customers can enjoy the benefit of the technology faster and faster.

Bridge between OT and IT

Gardner: Now, this is a technology implementation, but it’s done in a category of the organization that might not think of IT in the same way as the business side -- back office applications and data processing. Is the challenge for many organizations a cultural one, where the IT organization doesn't necessarily know and understand this operational efficiency equation and vice versa, and how are we bridging that?

Hill: I'm probably going to give you the high-level end from the operational technology (OT) side as well. These guys will definitely have more input from their own domain of expertise. But, that these guys have that piece of information for that part that they know well is exactly why this collaboration works really well.

You have situations with the idea of the IoT, where a lot of people stood up and said, "Yeah, I can provide a solution. I have the answer," but without having a plan -- never mind a solution. But we've done a really good job of understanding that we can do one part of this system, this solution, really well, and if we partner with the people who are really good in the other aspects, we provide real solutions to customers. I don't think anyone can compete with us with at this stage, and that is exactly why we're in this situation.

Frank: Actually, the biggest hurdle is more on the OT side, not really relying on the IT of the company. For many of our customers, the factory's a silo. At HPE, we haven't been selling too much to that environment. That’s also why, when working as a consortium, it’s important to get to the right audience, which is in the factory. We also bring our IT expertise, especially in the areas of security, because at the moment, when you put an IT device in an OT environment, you potentially have problems that you didn’t have before.

We're living in a closed world, and now the value is to open up. Bringing our security expertise, our managed service, our services competencies to that problem is very important.

Speed and safety out in the open

Hill: There was a really interesting piece in the HPE Discover keynote in December, when HPE Aruba started to talk about how they had an issue when they started bringing conferencing and technology out, and then suddenly everything wanted to be wireless. They said, "Oh, there's a bit of a security issue here now, isn’t there? Everything is out there."

We can see what HPE has contributed to helping them from that side. What we're talking about here on the OT side is a similar state from the security aspect, just a little bit further along in the timeline, and we are trying to work on that as well. Again, we have HPE here and they have a lot of experience in similar transformations.

Frank: At HPE, as you know, we have our Data Center and Hybrid Cloud Group and then we have our Aruba Group. When we do OT or our Industrial IoT, we bring the combination of those skills.

For example, in security, we have HPE Aruba ClearPass technology that’s going to secure the industrial equipment back to the network and then bring in wireless, which will enable the augmented-reality use cases that we showed onstage yesterday. It’s a phased approach, but you see the power of bringing ubiquitous connectivity into the factory, which is a challenge in itself, and then securely connecting the IT systems to this OT equipment, and you understand better the kind of the phases and the challenges of bringing the technology to life for our customers.

McRell: It’s important to think about some of these operational environments. Imagine a refinery the size of a small city and having to make sure that you have the right kind of wireless signal that’s going to make it through all that piping and all those fluids, and everything is going to work properly. There's a lot of expertise, a lot of technology, that we rely on from HPE to make that possible. That’s just one slice of that stack where you can really get gummed up if you don’t have all the right capabilities at the table right from the beginning. 

Gardner: We've also put this in the context of IoT not at the edge isolated, but in the context of hybrid computing and taking advantage of what the cloud can offer. It seems to me that there's also a new role here for a constituency to be brought to the table, and that’s the data scientists in the organization, a new trove of data, elevated abstraction of analytics. How is that progressing? Are we seeing the beginnings of taking IoT data and integrating that, joining that, analyzing that, in the context of data from other aspects of the company or even external datasets?

McRell: There are a couple of levels. It’s important to understand that when we talk about the economics, one of the things that has changed quite a bit is that you can actually go in, get assets connected, and do what we call anomaly detection, pretty simplistic machine learning, but nonetheless, it’s a machine-learning capability.

In some cases, we can get that going in hours. That’s a ground zero type capability. Over time, as you learn about a line with multiple assets, about how all these function together, you learn how the entire facility functions, and then you compare that across multiple facilities, at some point, you're not going to be at the edge anymore. You're going to be doing a systems type analytics, and that’s different and combined.

At that point, you're talking about looking across weeks, months, years. You're going to go into a lot of your back-end and maybe some of your IT systems to do some of that analysis. There's a spectrum that goes back down to the original idea of simply looking for something to go wrong on a particular asset.

The distinction I'm making here is that, in the past, you would have to get a team of data scientists to figure out almost asset by asset how to create the models and iterate on that. That's a lengthy process in and of itself. Today, at that ground-zero level, that’s essentially automated. You don't need a data scientist to get that set up. At some point, as you go across many different systems and long spaces of time, you're going to pull in additional sources and you will get data scientists involved to do some pretty in-depth stuff, but you actually can get started fairly quickly without that work.

The power of partnership

Frank: To echo what Phil just said, in HPE we're talking about the tri-hybrid architecture -- the edge, so let’s say close to the things; the data center; and then the cloud, which would be a data center that you don’t know where it is. It's kind of these three dimensions.

The great thing partnering with PTC is that the ThingWorx platform, the same platform, can run in any of those three locations. That’s the beauty of our HPE Edgeline architecture. You don't need to modify anything. The same thing works, whether we're in the cloud, in the data center, or on the Edgeline.

To your point about the data scientists, it's time-to-insight. There are things you want to do immediately, and as Phil pointed out, the notion of anomaly detection that we're demonstrating on the show floor is understanding those nominal parameters after a few hours of running your thing, and simply detecting something going off normal. That doesn't require data scientists. That takes us into the ThingWorx platform.
Explore
HPE's Edgeline

IoT Systems

But then, to the industrial processes, we're involving systems integration partners and using our own knowledge to bring to the mix along with our customers, because they own the intelligence of their data. That’s where it creates a very powerful solution.

Gardner: I suppose another benefit that the IT organization can bring to this is process automation and extension. If you're able to understand what's going on in the device, not only would you need to think about how to fix that device at the right time -- not too soon, not too late -- but you might want to look into the inventory of the part, or you might want to extend it to the supply chain if that inventory is missing, or you might want to analyze the correct way to get that part at the lowest price or under the RFP process. Are we starting to also see IT as a systems integrator or in a process integrator role so that the efficiency can extend deeply into the entire business process?

McRell: It's interesting to see how this stuff plays out. Once you start to understand in your facility -- or maybe it’s not your facility, maybe you are servicing someone's facility -- what kind of inventory should you have on hand, what should you have globally in a multi-tier, multi-echelon system, it opens up a lot of possibilities.

Today PTC provides a lot of network visibility, a lot of spare-parts inventory, management, and systems, but there's a limit to what these algorithms can do. They're really the best that’s possible at this point, except when you now have everything connected. That feedback loop allows you to modify all your expectations in real time, get things on the move proactively so the right person and parts, process, kit, all show up at the right time.

Then, you have augmented reality and other tools, so that maybe somebody hasn't done this service procedure before, maybe they've never seen these parts before, but they have a guided walk-through and have everything showing up all nice and neat the day of, without anybody having to actually figure that out. That's a big set of improvements that can really change the economics of how these facilities run.

Connecting the data

Gardner: Any other thoughts on process integration?

Frank: Again, the premise behind industrial IoT is indeed, as you're pointing out, connecting the consumer, the supplier, and the manufacturer. That’s why you have also the emergence of a low-power communication layer, like LoRa or Sigfox, that really can bring these millions of connected devices together and inject them into the systems that we're creating.

Hill: Just from the conversation, I know that we’re all really passionate about this. IoT and the industrial IoT is really just a great topic for us. It's so much bigger than what we're talking about. You've talked a little bit about security, you have asked us about the cloud, you have asked us about the integration of the inventory and to the production side, and it is so much bigger than what we are talking about now.

We probably could have twice this long of a conversation on any one of these topics and still never get halfway to the end of it. It's a really exciting place to be right now. And the really interesting thing that I think all of us are now realizing, the way that we have made advancements as a partnership as well is that you don't know what you don't know. A lot of companies are waking up to that as well, and we're using our collaborations to allow us to know what we don’t know

Frank: Which is why speed is so important. We can theorize and spend a lot of time in R&D, but the reality is, bring those systems to our customers, and we learn new use cases and new ways to make the technology advance.

Hill: The way that technology has gone, no one releases a product anymore -- that’s the finished piece, and that is going to stay there for 20, 30 years. That’s not what happens. Products and services are being provided that get constantly updated. How many times a week does your phone update with different pieces of firmware, the app is being updated. You have to be able to change and take the data that you get to adjust everything that’s going on. Otherwise you will not stay ahead of the market.

And that’s exactly what Phil described earlier when he was talking about whether you sell a product or a service that goes alongside a set of products. For me, one of the biggest things is that constant innovation -- where we are going. And we've changed. We were in kind of a linear motion of progression. In the last little while, we've seen a huge amount of exponential growth in these areas.

We had a video at the end of the London HPE Discover keynote, where it was one of HPE’s pieces of what the future could be. We looked at it and thought it was quite funny. There was an automated suitcase that would follow you after you left the airport. I started to laugh at that, but then I took a second and I realized that maybe that’s not as ridiculous as it sounds, because we as humans think linearly. That’s incumbent upon us. But if the technology is changing in an exponential way, that means that we physically cannot ignore some of the most ridiculous ideas that are out there, because that’s what’s going to change the industry.

And even by having that video there and by seeing what PTC is doing with the development that they have and what we ourselves are doing in trying out different industries and different applications, we see three companies that are constantly looking through what might happen next and are ready to pounce on that to take advantage of it, each with their own expertise.

Gardner: We're just about out of time, but I'd like to hear a couple of ridiculous examples -- pushing the envelope of what we can do with these sorts of technologies now. We don’t have much time, so less than a minute each, if you can each come up perhaps with one example, named or unnamed, that might have seemed ridiculous at the time, but in hindsight has proven to be quite beneficial and been productive. Phil?

McRell: You can do this as engineering with us, you can do this in service, but we've been talking a lot about manufacturing. In a manufacturing journey, the opportunity, as Gavin and Olivier are describing here, is at the level of what happened between pre- and post-electricity. How fast things will run, the quality at which they will produce products, and then therefore the business model that now you can have because of that capability. These are profound changes. You will see up-times in some of the largest factories in the world go up double digits. You will see lines run multiple times faster over time.

These are things that, if you just walked in today and walked in in a couple of years to some of the people who run the hardest, it would be really hard to believe what your eyes are seeing at that point, just like somebody who was around before factories had electricity would be astounded by what they see today.

Back to the Future

Gardner: One of the biggest issues at the most macro level in economics is the fact that productivity has plateaued for the past 10 or 15 years. People want to get back to what productivity was -- 3 or 4 percent a year. This sounds like it might be a big part of getting there. Olivier, an example?

Frank: Well, an example would be more like an impact on mankind and wealth for humanity. Think about that with those technologies combined with 3D printing, you can have new class of manufacturers anywhere in the world -- in Africa, for example. With real-time engineering, some of the concepts that we are demonstrating today, you have designing.

Another part of PTC is Computer-Aided Design (CAD) systems and Product Lifecycle Management (PLM), and we're showing real-time engineering on the floor again. You design those products and you do quick prototyping with your 3D printing. That could be anywhere in the world. And you have your users testing the real thing, understanding whether your engineering choices were relevant, if there are some differences between the digital model and the physical model, this digital twin ID.

Then, you're back to the drawing board. So, a new class of manufacturers that we don’t even know, serving customers across the world and creating wealth in areas that are (not) up to date, not industrialized.

Gardner: It's interesting that if you have a 3D printer you might not need to worry about inventory or supply chain.

Hill: Just to add on that one point, the bit that really, really excites me about where we are with technology, as a whole, not even just within the collaboration, you have 3D printing, you have the availability of open software. We all provide very software-centric products, stuff that you can adjust yourself, and that is the way of the future.

That means that among the changes that we see in the manufacturing industry, the next great idea could come from someone who has been in the production plant for 20 years, or it could come from Phil who works in the bank down the road, because at a really good price point, he has the access to that technology, and that is one of the coolest things that I can think about right now.

Where we've seen this sort of development and this use of these sort of technologies and implementations and seen a massive difference, look at someone like Duke Energy in the US. We worked with them before we realized where our capabilities were, never mind how we could implement a great solution with PTC and with HPE. Even there, based on our own technology, those guys in the para-production side of things in some legacy equipment decided to try and do this sort of application, to have predictive maintenance to be able to see what’s going on in their assets, which are across the continent.

They began this at the start of 2013 and they have seen savings of an estimated $50 billion up to this point. That’s a number.

Gardner: That is a big number, yes. I'm afraid we'll have to leave it there. We've been exploring the rapidly evolving architectural shift of moving advanced IT capabilities to the edge to support Internet of Things requirements for operational integrity benefits.

And we've learned how converged systems and high-performance data analysis platforms are bringing in essence the data center to the operational technology edge, and we've heard about some great results from that evolution.

Please join me in thanking our panelists, Phil McRell, General Manager of IoT Consortium at PTC; Gavin Hill, IoT Marketing Engineer for Northern Europe at NI in London, and Olivier Frank, Senior Director of Worldwide Business Development and Sales for Edgeline IoT Systems at HPE.

And thanks 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 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 the rapidly evolving architectural shift of moving advanced IT capabilities to the edge to support Internet of Things requirements for operational integrity benefits. Copyright Interarbor Solutions, LLC, 2005-2017. All rights reserved.

You may also be interested in:

Thursday, February 23, 2017

IDOL-Powered Appliance Delivers Better Decisions Via Comprehensive Business Information Searches

Transcript of a discussion on how HPE's platform and data solutions have been combined by SEC 1.01 for an appliance approach to index and deliver comprehensive business information results.

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 a Swiss engineering firm created an appliance that quickly deploys to index and deliver comprehensive business information. It performs a simulation across thousands of formats and hundreds of languages and then provides via a simple search interface unprecedented access to trends, leads, and the makings of highly informed business decisions.

We will now explore how SEC 1.01 AG delivers a truly intelligent services solution -- one that returns new information to ongoing queries and combines internal and external information on all sorts of resources to produce a 360-degree view of end users’ areas of intense interest.

Join us as we learn how finding and using the best available information can be done in about half the usual time. We're here with our guest David Meyer, Chief Technology Officer at SEC 1.01 AG in Switzerland.
 
Welcome, David.

David Meyer: Thank you.

Meyer
Gardner: What are some of the trends that are driving the need for what you've developed. It's called the i5 appliance?

Meyer: The most important thing is that we can provide instant access to company-relevant information. This is one of today’s biggest challenges that we address with our i5 appliance.

Decisions are only as good as the information bases they are made on. The i5 provides the ability to access more complete information bases to make substantiated decisions. Also, you don’t want to search all the time; you want to be proactively informed. We do that with our agents and our automated programs that are searching for new information that you're interested in.

Gardner: As an organization, you've been around for quite a while and involved with  large applications, packaged applications -- SAP, for example and R/3 -- but over time, more data sources and ability to gather information came on board, and you saw the need in the market for this appliance. Tell us a little bit about what led you to create it?

Accelerating the journey

Meyer: We started to dive into big data about the time that HPE acquired Autonomy, December 2011, and we saw that it’s very hard for companies to start to become a data-driven organization. With the i5 appliance, we would like to help companies accelerate their journey to become such a company.

Gardner: Tell us what you mean by a 360-degree view? What does that really mean in terms of getting the right information to the right people at the right time?

Meyer: In a company's information scope, you don’t just talk about internal information, but you also have external information like news feeds, social media feeds, or even governmental or legal information that you need and don’t have to time to search for every day.

So, you need to have a search appliance that can proactively inform you about things that happen outside. For example, if there's a legal issue with your customer or if you're in a contract discussion and your partner loses his signature authority to sign that contract, how would you get this information if you don't have support from your search engine?
Mission Critical
Server Choices

Have Never Been Better
Gardner: And search has become such a popular paradigm for acquiring information, asking a question, and getting great results. Those results are only as good as the data and content they can access. Tell us a little bit about your company SEC 1.01 AG, your size and your scope or your market. Give us a little bit of background about your company.

Meyer: We've been an HPE partner for 26 years, and we build business-critical platforms based on HPE hardware and also the HPE operating system, HP-UX. Since the merger of Autonomy and HPE in 2011, we started to build solutions based on HPE's big-data software, particularly IDOL and Vertica.

Gardner: What was it about the environment that prevented people from doing this on their own? Why wouldn't you go and just do this yourself in your own IT shop?

Meyer: The HPE IDOL software ecosystem, is really an ecosystem of different software, and these parts need to be packed together to something that can be installed very quickly and that can provide very quick results. That’s what we did with the i5 appliance.

We put all this good software from HPE IDOL together into one simple appliance, which is simple to install. We want to accelerate the time that is needed to start with big data to get results from it and to get started with the analytical part of using your data and gain money out of it.

Multiple formats

Gardner: As we mentioned earlier, getting the best access to the best data is essential. There are a lot of APIs and a lot of tools that come with the IDOL ecosystem as you described it, but you were able to dive into a thousand or more file formats, support a 150 languages, and 400 data sources. That's very impressive. Tell us how that came about.

Meyer: When you start to work with unstructured data, you need some important functionality. For example, you need to have support for lot of languages. Imagine all these social media feeds in different languages. How do you track that if you don't support sentiment analysis on these messages?

On the other hand, you also need to understand any unstructured format. For example, if you have video broadcasts or radio broadcasts and you want to search for the content inside these broadcasts, you need to have a tool to translate the speech to text. HPE IDOL brings all the functionality that is needed to work with unstructured data, and we packed that together in our i5 appliance.

Gardner: That includes digging into PDFs and using OCR. It's quite impressive how deep and comprehensive you can be in terms of all the types of content within your organization.
Access the Free
HPE Vertica

Community Edition
How do you physically do this? If it's an appliance, you're installing it on-premises, you're able to access data sources from outside your organization, if you choose to do that, but how do you actually implement this and then get at those data sources internally? How would an IT person think about deploying this?

Meyer: We've prepared installable packages. Mainly, you need to have connectors to connect to repositories, to data ports. For example, if you have a Microsoft Exchange Server, you have a connector that understands very well how the Exchange server can communicate to that connector. So, you have the ability to connect to that data source and get any content including the metadata.

You talk about metadata for an e-mail, for example, the “From” to “To”, to “Subject,” whatever. You have the ability to put all that content and this metadata into a centralized index, and then you're able to search that information and refine the information. Then, you have a reference to your original document.

When you want to enrich the information that you have in your company with external information, we developed a so-called SECWebConnector that can capture any information from the Internet. For example, you just need to enter an RSS feed or a webpage, and then you can capture the content and the metadata you want it to search for or that is important for your company.

Gardner: So, it’s actually quite easy to tailor this specifically to an industry focus, if you wish, to a geographic focus. It’s quite easy to develop an index that’s specific to your organization, your needs, and your people.

Informational scope

Meyer: Exactly. In our crowded informational system that we have with the Internet and everything, it’s important that companies can choose where they want to have the information that is important for them. Do I need legal information, do I need news information, do I need social media information, and do I need broadcasting information? It’s very important to build your own informational scope that you want to be informed about, news that you want to be able to search for.

Gardner: And because of the way you structured and engineered this appliance, you're not only able to proactively go out and request things, but you can have a programmatic benefit, where you can tell it to deliver to you results when they arise or when they're discovered. Tell us a little bit how that works.

Meyer: We call them agents. You can define which topics you're interested in, and when some new documents are found by that search or by that topic, then you get informed, with an email or with a push notification on the mobile app.

Gardner: Let’s dig into a little bit of this concept of an appliance. You're using IDOL and you're using Vertica, the column-based or high-performance analytics engine, also part of HPE, but soon to be part of Micro Focus. You're also using 3PAR StoreServ and ProLiant DL380 servers. Tell us how that integration happened and why you actually call this an appliance, rather than some other name?
In our crowded informational system that we have with the Internet and everything, it’s important that companies can choose where they want to have the information that is important for them.

Meyer: Appliance means that all the software is patched together. Every component can talk to the others, talks the same language, and can be configured the same way. We preconfigure a lot, we standardize a lot, and that’s the appliance thing.

And it’s not bound on hardware. So, it doesn’t need to be this DL380 or whatever. It also depends on how big your environment will be. It can also be a c7000 Blade Chassis or whatever.

When we install an appliance, we have one or two days until it’s installed, and then it starts the initial indexing program, and this takes a while until you have all the data in the index. So, the initial load is big, but after two or three days, you're able to search for information.

You mentioned the HPE Vertica part. We use Vertica to log every action that goes on, on the appliance. On one hand, this is a security feature. You need to prove if nobody has found the salary list, for example. You need to prove that and so you need to log it.

On the other hand, you can analyze what users are doing. For example, if they don’t find something and it’s always the same thing that people are searching in the company and can't find, perhaps there's some information you need to implement into the appliance.

Gardner: You mentioned security and privileges. How does the IT organization allow the right people to access the right information? Are you going to use some other policy engine? How does that work?

Mapped security

Meyer: It's included. It's called mapped security. The connector takes the security information with the document and indexes that security information within the index. So, you will never be able to find a document that you don't have access to in your environment. It's important that this security is given by default.

Gardner: It sounds to me, David, like were, in a sense, democratizing big data. By gathering and indexing all the unstructured data that you can possibly want to, point at it, and connect to, you're allowing anybody in a company to get access to queries without having to go through a data scientist or a SQL query author. It seems to me that you're really opening up the power of data analysis to many more people on their terms, which are basic search queries. What does that get an organization? Do you have any examples of the ways that people are benefiting by this democratization, this larger pool of people able to use these very powerful tools?

Meyer: Everything is more data-driven. The i5 appliance can give you access to all of that information. The appliance is here to simplify the beginning of becoming a data-driven organization and to find out what power is in the organization's data.
Mission Critical
Server Choices

Have Never Been Better
For example, we enabled a Swiss company called Smartinfo to become a proactive news provider. That means they put lots of public information, newspapers, online newspapers, TV broadcasts, radio broadcasts into that index. The customers can then define the topics they're interested in and they're proactively informed about new articles about their interests.

Gardner: In what other ways do you think this will become popular? I'm guessing that a marketing organization would really benefit from finding relationships within their internal organization, between product and service, go-to market, and research and development. The parts of a large distributed organization don't always know what the other part is doing, the unknown unknowns, if you will. Any other examples of how this is a business benefit?

Meyer: You mentioned the marketing organization. How could a marketing organization listen what customers are saying? For example, on social media they're communicating there, and when you have an engine like i5, you can capture these social media feeds, you can do sentiment analysis on that, and you will see an analyzed view on what's going on about your products, company, or competitors.

You can detect, for example, a shitstorm about your company, a shitstorm about your competitor, or whatever. You need to have an analytic platform to see that, to visualize that, and this is a big benefit.

On the other hand, it's also this proactive information you get from it, where you can see that your competitor has a new campaign and you get that information right now because you have an agent with the customer's name. You can see that there is something happening and you can act on that information.

Gardner: When you think about future capabilities, are there other aspects that you can add on? It seems extensible to me. What would we be talking about a year from now, for example?

Very extensible

Meyer: It's pretty much extensible. I think about all these different verticals. You can expand it for the health sector, for the transportation sector, whatever. It doesn't really matter.

We do network analysis. That means when you prepare yourself to visit a company, you can have a network picture, what relationships this company has, what employees work there, who is a shareholder of that company, which company has contracts with any of other companies?

This is a new way to get a holistic image of a company, a person, or of something that you want to know. It's thinking how to visualize things, how to visualize information, and that's the main part we are focusing on. How can we visualize or bring new visualizations to the customer?

Gardner: In the marketplace, because it's an ecosystem, we're seeing new APIs coming online all the time. Many of them are very low cost and, in many cases, open source or free. We're also seeing the ability to connect more adequately to LinkedIn and Salesforce, if you have your license for that of course. So, this really seems to me a focal point, a single pane of glass to get a single view of a customer, a market, or a competitor, and at the same time, at an affordable price.

Let's focus on that for a moment. When you have an appliance approach, what we're talking about used to be only possible at very high cost, and many people would need to be involved -- labor, resources, customization. Now, we've eliminated a lot of the labor, a lot of the customization, and the component costs have come down.
Access the Free
HPE Vertica

Community Edition
We've talked about all the great qualitative benefits, but can we talk about the cost differential between what used to be possible five years ago with data analysis, unstructured data gathering, and indexing, and what you can do now with the i5?

Meyer: You mentioned the price. We have an OEM contract, and that that's something that makes us competitive in the market. Companies can build their own intelligence service. It's affordable also for small and medium businesses. It doesn't need to be a huge company with own engineering and IT staff. It's affordable, it's automated, it's packed together, and simple to install.

Companies can increase the workplace performance and shorten the processes. Anybody has access to all the information they need in their daily work, and they can focus more on their core business. They don't lose time in searching for information and not finding it and stuff like that.

Gardner: For those folks who have been listening or reading, are intrigued by this, and want to learn more, where would you point them? How can they get more information on the i5 appliance and some of the concepts we have been discussing?

Meyer: That's our company website, sec101.ch. There you can find any information you would like to have.
Anybody has access to all the information they need in their daily work, and they can focus more on their core business. They don't lose time in searching for information and not finding it and stuff like that.

Gardner: And this is available now.

Meyer: This is available now.

Gardner: Well, great, I'm afraid we will have to leave it there. We have been exploring how SEC 1.01 AG delivers a true intelligence services solution, one that returns new information to ongoing queries and combines internal and external information on all sorts of sources to produce a 360 degree view of any user's interests that they choose.

We've learned how HPE's platform and data solutions have also been uniquely combined by SEC 1.01 for an appliance approach that quickly deploys to index and deliver these comprehensive business information results.

Please join me in thanking our guest, David Meyer, Chief Technology Officer at SEC 1.01 AG in Switzerland. Thank you so much, David.

Meyer: Thank you, Dana.

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 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 HPE's platform and data solutions have been combined by SEC 1.01 for an appliance approach to index and deliver comprehensive business information results. Copyright Interarbor Solutions, LLC, 2005-2017. All rights reserved.

You may also be interested in:


Wednesday, February 22, 2017

How Development and Management of Modern Applications Benefits from Data-Driven Continuous Intelligence

Transcript of a discussion on how modern applications are different, and what data and insight are needed to make them more robust, agile and responsive.

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

Dana Gardner: Welcome to the next edition of BriefingsDirect. I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator.

Gardner
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.

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

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.
Access the Webinar
On Gaining Operational Visibility
Into AWS
We’re here with an executive from Sumo Logic to learn how 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.

With that, please join me in welcoming our guest, Ramin Sayar, President and CEO of Sumo Logic. Welcome to BriefingsDirect, Ramin.

Ramin Sayar: Thank you very much, Dana. I appreciate it.

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.

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.
Download the State
Of Modern Applications
In AWS Report
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.

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.
Try Sumo Logic for Free
To Get Critical Data and Insights
Into Apps and Infrastructure Operations
Gardner: I’m afraid we will have to leave it here. You've been listening to a sponsored BriefingsDirect discussion on how modern applications are different and what's needed to make them more robust, agile, and responsive. We've heard how new levels of insight and intelligence of what really goes on underneath the covers of modern apps across your lifecycle can ensure that those apps are built, deployed, and operated properly.

So, please join me in thanking our guest, Ramin Sayar, President and CEO of Sumo Logic. Thank you so much.

Sayar: Thank you very much.

Gardner: I’m Dana Gardner, Principal Analyst at Interarbor Solutions, your host and moderator for this ongoing series of BriefingsDirect discussions. A big thank you to our sponsor today, Sumo Logic, and a big thank you as well to our audience. Please come back for our next edition.

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

Transcript of a discussion on how modern applications are different, and what data and insight are needed to make them more robust, agile and responsive. Copyright Interarbor Solutions, LLC, 2005-2017. All rights reserved.

You may also be interested in: