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.

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.

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?
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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.
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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.
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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.
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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, 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.

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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.

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.
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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.

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.
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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.

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.
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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.

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