Glassbeam Drives Analytics Innovation

machine data analytics

converges with the internet of things

Glassbeam Drives Analytics Innovation

technologyinnovator

perspectiveHarborResearch

Harbor Research recently completed a review of a new cloud-based platform that takes a refreshingly new approach to machine data analytics. Glassbeam jumps ahead of the current market’s noise and confusion about Big Data by viewing critical machine data analytics from a business and operational perspective that can be addressed by a single, scalable solution. In so doing, Glassbeam is re-de!ning how value is created from machine data.

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Glassbeaminnovator pro!le

The Internet of Things is upon us. Billions of devices, are currently being connected to the Internet. The types of devices being connected today extend far beyond the laptops and cell phones we

have become so accustomed to. Today, virtually all products that use electricity—from toys and co!ee makers to cars and medical diagnostic machines—possess inherent data processing capability and have the potential to be networked. Even though we have been steadily designing devices and products with more and more intelligence, this information has gone largely unleveraged and unharvested. This is surprising, because this information can o!er extraordinary business advantage to the companies that manufacture, deliver and service those products, especially in terms of customer relationships.

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Machine communications and the Internet of Things are combining to create new modes of asset awareness, intelligence, support and decision making.

In its simplest form, the Internet of Things is a concept in which inputs—from machines, sensors, people, video streams, maps and more—is digitized and placed onto networks. These in-puts are integrated into Smart Systems that connect people, processes, and knowledge to enable collective aware-ness, e"ciencies and better decision making.

We prefer “Smart Systems” over other terms in common use—nota-bly “M2M,” which usually stands for “machine-to-machine”—because it captures the profound enormity of the phenomenon - something much greater in scope than just machine connectivity.

Whatever we chose to call it -- “Smart Systems” or “Pervasive Computing” or “The Internet of Things” — we are referring to digital microprocessors and sensors embedded in everyday objects.

We have now entered the age when everyday objects will communicate with, and control, other objects over networks—24/7/365. The objects are everything from consumer appliances to IT infrastructure to the elevator you’ve been waiting for. It’s not “the future,” it’s now and thus vitally impor-tant that business leaders understand this phenomenon, its e#ects on their business, and what they should do right now to position themselves for opportunities that are literally just around the corner:

» Manufacturing equipment, eleva-tors and escalators, appliances and vehicles that know exactly when and why they will fail, and then alert you or your service organiza-tion before the failure occurs.

» Buildings with “digital nervous systems” that ensure occupant comfort and safety, and even enhance productivity.

» IT and network equipment ven-dors raising the bar on customer support e"ciencies and achiev-ing extraordinarily attractive ROI through root cause analytics from connected machine data.

» Retailers and distributors who know exactly where every piece of

The Internet of Things Is Here

IT’S VITALLY IMPORTANT THAT BUSINESS LEADERS UNDERSTAND THE INTERNET OF THINGS PHENOMENON

ABOUT GLASSBEAMGlassbeam is the machine data company. Bringing structure and meaning to data from any connected device, Glassbeam provides actionable intelligence to the Internet of Things. Glassbeam’s next generation cloud-based analytics platform is designed to organize and analyze multi-structured data, delivering powerful product and customer intelligence for companies including IBM, HDS, Aruba Networks and Meru Networks.

For more information visit www.glassbeam .com

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inventory is at any moment, and under what conditions it arrived.

» Industrial customers who save a fortune on energy by being able to see, in real time, exactly how they’re using it.

» Healthcare facilities where ac-curate, up-to-the-minute patient information is always available be-cause every piece of equipment, from digital thermometers to life-support machines, is networked and associated with a patient ID.

» OEMs that are not “disintermedi-ated” at the point of sale, but stay connected to end-customers via a steady stream of status, usage and performance data.

And on, and on, and on. Science !c-tion? Not anymore. The Internet of Things is really here.

This phenomena is not just about peo-ple communicating with people or ma-chines communicating with machines; it also includes people communicating with machines, and machines commu-nicating with people. Smart connected

New Value Driven By Big Data and Analytics

machines are a global and economic phenomenon of unprecedented scale - potentially billions if not trillions of nodes producing valuable data.

The Internet’s most profound potential lies in the integration of people, infor-mation systems AND smart machines.

In a truly connected world of smart sys-tems, not only people but all electronic and electro-mechanical products and machines will produce mountains of valuable information, all the time.

Consider the following:

» Today the number of connected devices on the planet has surpassed the number of people - 7+ billion - depending on your de!nition of a sensor, there are already many more sensors on earth than people.

» A single large oil re!nery produces more data in a day than all of the New York Stock Exchange and AMEX combined.

» In a 200 turbine wind farm, each turbine has 50 sensors with over 1$$ data points collected every 40 milliseconds, producing over 6,000 data points every second.

» Estimates of data produced by Smart Grid applications could reach

THE INTERNET OF THINGS IS REALLY HERE NOW

What Is Machine Data?Machine data includes all data generated by equipment, devices and sensors, including:

» Computer, network, and other equipment logs;

» Satellite and telemetry data;

» Location data such as RFID readings, GPS system output, etc.;

» Temperature, pressure and other sensor readings from pipelines, factories and the environment;

» Medical device readings for human health parameters.

It covers everything from data centers, telecommunications networks, factories, hospitals, buildings and related machine-to-machine and Internet of Things devices.

Unlike human-generated data, whose growth is constrained by factors such as population, machine-generated data will continue to grow as fast as technology evolution allows, where before long, most data by volume will be machine-generated.

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between 35 and 1000 petabytes per year.

» There are over 500,000 data centers in the world , su#ering an average of 2.5 outages per year with an average duration 134 min-utes. Globally that translates to 2.84 million hours of annual data center downtime, at an estimated cost of $300,000 per hour of downtime, resulting in $426B a year in losses.

The ability to detect patterns from large scale sensor and machine data aggre-gation is the holy grail of smart systems. Machine data analytics, often thought of as part of the evolving “big data” story, allows not only data patterns but a much higher order of intelligence to emerge from large collections of ordi-nary machine and device data.

While machine analytics applications are arising in many sectors of the economy, IT equipment is providing a focused application opportunity for how machine data analytics will get organized and accomplished. IT professionals report that the volume of data from IT assets has more than quadrupled in the last decade, while collection of metrics from IT equipment has increased over 300%.

Because IT equipment produces a variety of “machine logs” in a relatively predictable manner, it is an ideal “stag-ing” area for designing, building and deploying analytics tools.

The implications of mining and analyz-ing machine data are immense . We believe this is where the real core value creation opportunity lies within the Internet of Things.

But very few people are thinking about machine data on that level. Current IT and telecom technologists are oper-ating with outdated models of data, analytics and information management that were conceived in an era when computing power and storage were the limiting factors for operations and busi-ness intelligence.

Today, signi!cantly better tools are avail-able to organize and integrate signi!-cant amounts of big data for analysis, but the tools available for professionals working with machine data still fall far short of real world needs.

“Smart Systems” should automatically be understood as “real-time networked information and machine intelligence,” but it isn’t. The nature and behavior of truly distributed information systems and intelligence tools are concerns that

GLASSBEAM IS PROVIDING A TRUE END-TO-END PLATFORM SOLUTION

. DATA INTENSITY **

IT Services

Industrial

Resources & Energy

Healthcare

Transportation

Ranking Of DataVolume Intensity ** TB per $ Million Annual Revenue

.08

.15

.29

.65

.72

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have yet to really take center stage—not only in business communities, but in most technology communities, too.

This paper is about an important new machine data analytics platform and application o#ering from people who are thinking about the scope and on the scale that machine data deserves—Glassbeam.

The Glassbeam team of innovators un-derstand that the tools we are work-ing with today to discover and analyze machine data were not designed to re-ally address operational and business challenges. The Glassbeam platform provides business insight for sales, support and engineering organiza-tions by mining log data generated from machines and products.

Glassbeam’s machine data analytics application platform is not an incre-mental improvement or new %avor of the existing IT-centric big data tools. Their development represents a true shift in thinking about how device and machine data will be utilized for business intelligence. The Glassbeam approach is about looking forward to a single, uni!ed platform for search,

discovery, analysis and prediction uti-lizing diverse machine data types.

Glassbeam is providing a true end-to-end solution for machine data ana-lytics and intelligence that provides a complete picture of the myriad of interactions and states that machines evolve through including status, con-!guration changes and usage.

Before delving into the new thinking that makes this story possible, let’s talk about why it’s necessary at all. Cur-rent IT technologists are operating with outdated and ill suited models of data management and analytics for the Smart Systems and the Internet of Things era. These models were con-ceived in the past and cannot serve the needs of a truly physical and real time connected world.

From an IT perspective, today’s big data and analytics solutions are a direct descendent of the company mainframe, and work on the same “batched computing” model—an archival model, yielding a historian’s perspective. Information about events is collected, stored, queried, analyzed, and reported upon. But all after the

CURRENT PRACTICES WILL NOT SERVE A TRULY CONNECTED WORLD

Big Data Is Only Part of the Story

Enter Glassbeam

“With Glassbeam, we’ve taken the guesswork out of the support process and brought them to the forefront immediately for resolution.”

Carlos Quezada, Director of Worldwide Operations, Meru Networks

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fact. In machine data applications, much of the historic analysis was conducted using only sampling-based analytics.

That’s a very di#erent thing from feed-ing the real-time inputs of billions of tiny “state machines” into systems that continually compare machine-state to sets of rules and then do something on that basis.

With today’s big data tools analysis can now essentially be conducted on “all the data” that can be collected. However, in the machine world, un-like say the consumer retail arena, the analysis has to be real time and state-based. In short, for machine data to mean anything in business, the prevailing corporate IT model of “batched” big data analytics has to change and new tools need to be developed.

The next cycle of technology and systems development in the smart connected systems arena is supposed to be setting the stage for a multi-year wave of growth based on the con-vergence of innovations in software architectures; back-room data center operations; wireless and broadband communications; and new analytics tools. But is it?

When it comes to preparing for the global information economy of the 21st century, most people assume that “the IT and telco technologists are taking care of it.” They take it on faith that the best possible designs for the future of connected things, people, systems and information will emerge from large corporations and centralized authorities. But those are big, unfounded assumptions. In fact, most of today’s entrenched players are showing little appetite for radi-cal departures from current practice. Yet current practice will not serve the needs of a genuinely connected world.

What are the major obstacles that need to be overcome?

Optimizing machines and physical assets: New software technologies and applications need to help orga-nizations address the key challenge of optimizing the value of all assets including machines and physical sys-tems which will allow organizations to move beyond just !nancial assets and liabilities to their physical assets and liabilities (like IT equipment, produc-tion machines and vehicles). The task of optimizing the value of !nancial

Overcoming Obstacles

MACHINE DATA REQUIRES A WHOLE NEW APPROACH TO ANALYTICS

“Platforms like Glassbeam’s will lead us beyond traditional ‘break-"x’ support to new modes of leveraging customer-partner collaboration.”Services Technology Development Manager, Network Equipment Manufacturer

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assets, physical assets and people assets requires new technologies that will integrate diverse asset information in unprecedented ways to solve more complex business problems.

Automated analytics: When tele-phones !rst came into existence, all calls were routed through switch-boards and had to be connected by a live operator. It was long ago forecast that if telephone tra"c continued to grow in this way, soon everybody in the world would have to be a switch-board operator. Of course that has not happened, because automation was built into the systems to handle com-mon tasks like connecting calls. We are quickly approaching analogous circumstances with the rapidly rising amount of data that machines pro-duce routinely.

Historically, to gain meaning from operational data meant building dedi-cated data warehouses and analytics applications – a major undertaking. A typical project might involve several months of e#ort and expensive infra-structure and software licenses.

If every machine requires this much customization just to perform simple analytic tasks such as search and dis-covery, then we surely need new tools to automate various data analytic

tasks and facilitate re-use, or risk con-straining the growth of this market.

Real time predictive intelligence: In today’s rapidly changing business en-vironment, organizational agility not only depends on monitoring how the business is performing but also on the prediction of future outcomes which is critical for a sustainable competi-tive position. Traditionally, business intelligence systems have provided a retrospective view of the business by querying data warehouses contain-ing historical data. Contrary to this, we need systems to analyze real-time complex event streams to perform operational monitoring and to sup-port decision-agility. Machine data analytics systems will need to become much more business and operations-focused -- analytics that can be em-bedded into machines and business processes.

IT professionals rarely talk these days about the need for ever-evolving information and analytics services that can be made available and have high applied value in business and operations. Instead, they talk about cloud services and such. Leverag-ing machine and physical assets will require tools that business and operat-

THE TOOLS WE ARE WORKING WITH TO ANALYZE “SMART” MACHINES WERE NOT DESIGNED FOR TODAY’S DATA VOLUME AND COMPLEXITY

“Aruba Networks leverages machine data from thousands of systems in the "eld, and the Glassbeam platform has enabled us to quickly extract valuable business insights from these logs. We use the Glassbeam apps to obtain product and customer intelligence that allows us to proactively understand, support and manage our installed base and adopt a data-driven approach to business decision making.”

Ash Chowdappa, VP of Product Management, Aruba Networks

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perpetual attempts to get itself o# the ground. But some things that should be kept simple are allowed to get unnecessarily complex, and that’s the other part of the story. The drive to de-velop technology can inspire grandi-ose visions that make simple thinking seem somehow embarrassing or not worthwhile. That’s not a good thing when de!ning and deploying real-world technology to deliver innova-tion. This is where the new values of Glassbeam’s platform really come into focus.

The fact that a rapidly expand-ing range of machines and devices have the capability to automatically transmit information about status, performance and usage and can interact with people and other sys-tems anywhere in real time, points to the increasing complexity of manag-ing machines and systems. This only compounds when we consider the billions or more of networked devices that many observers are forecasting will be deployed.

ing people can easily use and don’t require an “IT specialist ” to apply.

Leveraging collective intelligence: For all its sophistication, many of today’s M2M systems are a direct descendent of the traditional remote services or cellular telephony mod-els where each device acts in a “hub and spoke” mode. The inability of today’s popular enterprise systems to interoperate and perform well with distributed heterogeneous device environments is a signi!cant obstacle. The many “nodes” of a network may not be very “smart” in themselves, but if they are networked in a way that allows them to connect e#ortlessly and interoperate seamlessly, they begin to give rise to complex, system-wide behavior. This allows an entirely new order of intelligence to emerge from the system as a whole—an intelligence that could not have been predicted by looking at any of the nodes individually. What’s required is to shift the focus from simple device monitoring to a model where device data is aggregated into new analytics applications to achieve true systems intelligence.

Some things that look easy turn out to be hard. That’s part of the strange saga of the Internet of Things and its

The Coming Machine Data Glut

CUSTOMERS EXPECT EVOLVING SOFTWARE TOOLS TO BE FUNCTIONAL, UBIQUITOUS, AND EASY-TO-USE

Fusing Machine Data WIth Re-lated Business and Infrastructure

Data Will Create Many New Values

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The tools we are working with to-day to monitor and analyze “smart” machines on networks were not designed to handle the scope of data generated, the diversity of device data types and the massive volume of data-points and data sets generated from machine interactions.

These challenges are diluting the ability of organizations to e"ciently and e#ectively manage machines and systems. The fragmented nature of analytics software o#erings available today make it extremely di"cult, if not impossible, to manage smart systems.

What is needed is a common means of deploying machine data analytics applications that can leverage tools across families of interrelated devices and diverse domains. What would this entail?

» Analytics tools and applications to address a broad range of machine data types that move beyond just searching and indexing functions. Increasingly, customers will need a single, uni!ed end-to-end frame-work to search, explore, analyze and predict machine and systems behaviors that can operate across diverse data environments and under widely di#ering usage scenarios;

» Analytic tools and applications that allow business and opera-tions personnel - not IT specialists - to quickly build their own ma-chine data analytics capabilities and applications with business and operations value. Users need to be able to quickly develop new applications for analytics that are easy to develop, use and collabo-rate with.

We are reaching a critical juncture in market development where organi-zations will soon be crying out for a completely new approach - one that moves beyond “!rst-level” search and indexing tools built for systems administrators to a new generation of tools that put the power of machine data analytics into the hands of opera-tions and business users where the e#ort invested to develop new ma-chine data applications can be quickly and easily be utilized again and again across an ever broader spectrum of machines and domains.

Customers expect evolving software tools to be functional, ubiquitous, and easy-to-use. Within this construct, however, the !rst two expectations run counter to the third. In order to achieve all three, a new approach is re-quired -- but what kind of approach?

GLASSBEAM ENABLES MACHINE DATA ANALYTICS PROCESSES TO BE RAPIDLY BUILT AND DEPLOYED

Internet of ThingsData Management, Analytics

and Visualization Market Potential

2013 2018

$13.6bn

$54.7bn

IT EquipAnayltics

$2.4bn

IT EquipAnayltics

$7.6bn

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In today’s world, information is not free (and that’s free as in “freedom,” not free as in “free of charge”). In fact, thanks to present information archi-tectures, it’s not free to easily merge with other information and enable any kind of search-based intelligence.

What would truly liberated informa-tion be like? It might help to think of the atoms and molecules of the physi-cal world. They have distinct identities, of course, but they are also capable of bonding with other atoms and molecules to create entirely di#erent kinds of matter. Often this bonding

requires special circumstances, such as extreme heat or pressure.

In the world of information, such bonding is not all that easy. Today’s software platforms focus on execution processes that generate one of three types of data - unstructured, transac-tional or time series.

For each of these data types, a speci!c set of intelligence tools have evolved to provide “insight” but, in most cases, these tools limit the questions that can be answered to those known in advance. So for a user attempting to do something as simple as asking a certain multi-dimensional ques-tion, creating new information from

GLASSBEAM ENABLES A DEEPER EXAMINATION OF MACHINE DATA

Data Is Not Free

Glassbeam tools allow users to view systems, applications and their performance characteris-tics over time. By mining huge volumes of machine data pro-duced by intelligent connected devices, Glassbeam provides product managers, engineers and other business unit profes-sionals with an unprecedented level of insight into how prod-ucts are being used, based on actual machine-generated data from the installed base.

Glassbeam Explorer

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multiple data types that is an easily perceivable, manipulable, or map-pable “model” of the answer to that question is a signi!cant challenge.

Real time intelligence fundamentally changes this paradigm, treating data from things, people, systems and the physical world as augmented repre-sentations. In other words, treating di-verse data types equally. This enables processes connecting diverse data in any combination to be rapidly built and deployed.

The traditional approaches to data discovery and systems intelligence

have three failings: they can’t provide a holistic view of these diverse data types or, the types of intelligence tools available to users are, at best, arcane

and typically limited in use to “special-ists,” or the tools allow for only super!-cial analysis of machine data sets.

The Glassbeam platform fundamen-tally changes this paradigm, treating diverse data types from machine logs equally. This enables processes con-necting diverse data in any combina-tion to be rapidly built and deployed.

A critical requirement in prod-uct management and engi-neering is understanding how customers are using what they have today. What features are and aren’t being used? Where are problems occurring most often? What limitations on per-formance, capacity or other key metrics are being reached?

Installed Base Analytics

TRADITIONAL APPROACHES TO DATA DISCOVERY AND SYSTEMS INTELLIGENCE HAVE MANY FAILINGS

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The bit, the byte, and later the packet made possible the entire enterprise of digital computing and global networking possible. Until the world agreed upon basic concepts like using SQL with databases, it was not pos-sible to move forward. The next great step in machine data technology—completely %uid multi-dimensional machine data analytics—requires an equally simple, %exible, and universal tool that will make diverse machine data types easily accessed, integrated and interpreted for business and op-erations sta#.

Glassbeam’s unique approach to ma-chine data analytics is based on a new class of tools enabled by its break-through Semiotic Parsing Language (SPL) language that is speci!cally de-signed to let users extract value from multidimensional machine data types.Glassbeam’s unique SPL-driven tools and iterative development environ-ment allows users to explore how a product or system is con!gured, how it is used and how well it is performing. Data can be aggregated to perform a variety of functions, including:

» The organization of details on devices, con!gurations, locations, status and related usage;

» Gathering and analyzing perfor-mance data across various prod-ucts and segments;

» Aggregating and analyzing multiple, parallel levels of data to allow interpretation by product development engineers, support technicians, and other functions. like sales and marketing.

Businesses can bene!t from a deeper examination of machine data in many ways including deeper diagnostics, proactive problem identi!cation and intelligence on product usage and behaviors. Unlocking these values can only be achieved by using e#ective tools that not only search and index but extract critical insights about ma-chine performance and behaviors.

Glassbeam’s back-end system tech-nology enables high velocity and high volume streaming data to be processed in real time. Extracting new insights into equipment health, sup-port and usage require they be acted on real time.

Machine Data Analytics Needs To Drive User Innovation

GLASSBEAM’S UNIQUE APPROACH TO MACHINE DATA ANALYTICS IS BASED ON A NEW CLASS OF TOOLS

“With what we know about our customer base because of machine data, we’re able to recommend more and more solutions and products as their equipment ages and replacement becomes necessary. “

Managed Service Marketing Manager, IT Equipment and Solutions Provider

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A networked machine generates infor-mation value over its entire lifespan. Product manufacturers can know where the device is located, when it was installed, critical speci!cations, diagnostics, availability of spare parts, usage patterns, support status and so on.

Traditional customer relationship and product support programs yield only intermittent, uneven and incomplete

windows into how customers interact with a product. Once a product is shipped to a customer, the manufac-turer loses sight of who buys it, how it is con!gured, what its use is and what the customer experiences with it. When products become networked and support is automated, the envi-ronment in which they are utilized be-comes more “aware” and responsive.

Eventually, this environment helps customers optimize their processes, save money, and become signi!cantly more e"cient.

Machine Data Analytics Enables Re-Imagination of Business Functions

Glassbeam’s platform solution that delivers a continuous pulse on the “health” of products:

» Proactive capacity planning – At a quick glance, customers can tell what percentage of a product is being used, and whether it is time to add or reallocate resources well in advance of capacity overload.

» Event analysis – Problems, errors or other potential concerns can be viewed in summary fashion, alerting administrators to situations that require attention.

» License summary – Software versions are tracked auto-matically, ensuring that all products are up-to-date.

Machine Health Analytics

WHEN PRODUCTS BECOME NETWORKED AND SUPPORT IS AUTOMATED, THE ENVIRONMENT IN WHICH THEY ARE UTILIZED BECOMES MORE “AWARE” AND RESPONSIVE.

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Up till now, most of the discussions concerning machine data analytics and customer support automation focus almost exclusively on simple monitored values such as alarms and alerts. However, basic monitoring alone steals the limelight and poten-tially eclipses the real revolution. By utilizing connectivity and analytic tools to more tightly integrate cus-tomers and their equipment partners, equipment players can drive a “closed loop” relationship of intimacy with their customers. This is where the real value lies. The advent of machine data analytics makes the state of (i.e., the information about) a business’s assets vastly more visible.

As customers increasingly narrow their focus on their true core skills, they want to assume less responsibility for managing and maintaining the physi-cal assets they utilize in their busi-ness. The responsibilities are shifting towards those who manufacture, sell and support these assets. Now, the objective for equipment suppliers is to use a new generation of machine data analytics services as a game changer that will:

» Allow the equipment vendor to deliver detailed and proactive information and services that are

tailored to the unique needs of individual customers;

» Create a closed, real-time loop, between the equipment vendor’s support resources, product devel-opment organization and related business units allowing products to be speci!cally designed for, and implementations tuned to customer requirements and usage patterns;

» Improve the value and pro!tabil-ity of partners service o#erings and allow the equipment vendor to establish closer, more proactive relations with their customers.

This will allow equipment manufactur-ers to look beyond simply providing the minimum service required to attain customer satisfaction and utilize analytics as an intelligence tool which will, in turn, allow manufacturers to create lasting and binding relations with customers.

It will allow manufacturers to see pat-terns and signatures that reveal robust information about the product’s behavior and usage by allowing the manufacturer to aggregate not only information about the product and its

THE ADVENT OF MACHINE DATA ANALYTICS MAKES THE STATE OF A BUSINESS’S ASSETS VASTLY MORE VISIBLE

“We have given our customers a perspective into their own data center and storage equipment that they never had or even imagined was possible.”

Customer Support Marketing Manager, Storage Equipment Manufacturer

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con!guration but also about how it performs.

This expansion of machine data ana-lytics capabilities will foster:

» Higher value, more di#erentiated services and higher service levels;

» Develop and capture new annuity revenue streams;

» Reduce a vendor’s own product support and customer support costs;

» Utilize customer con!guration, usage and problem data to design better, more highly targeted products and systems for their customers;

» Tailor marketing campaigns and sales e#orts around highly cus-tomized value propositions; and,

» Establish themselves as a high value partner and a trusted advi-sor.

The traditional notion of “M2M” ap-plications has largely grown up in a B2B context with equipment manu-facturers developing remote services and support automation tied closely to their equipment service contracts. These models are focused almost exclusively on equipment OEMs providing improved customer support e"ciencies and not focused on new Smart Services values beyond just support automation.

As the use of new machine data analytics tools begins to shift from just equipment OEMs to end customer operations and business users, and the focus of machine data analytics players shifts to adjacent equipment segments and end use verticals, Glassbeam’s platform and applications are naturally positioned to evolve from analyzing IT equipment to addressing diverse machine assets. By analyzing log data in IT equipment, Glassbeam has developed tools that can be used on a wide variety of machines with similar [embedded] computing power – in other words a vast array of machines, such as MRI machines, semiconductor processing equipment and beyond.

GLASSBEAM’S APPLICATIONS ARE NATURALLY POSITIONED TO EVOLVE FROM ANALYZING IT EQUIPMENT TO ADDRESS-ING DIVERSE MACHINE ASSETS

Glassbeam Futures

Next Generation Cloud-Based Machine Data Analytics Platform: Glassbeam SCALARGlassbeam SCALAR is a fast, secure, and scalable next generation platform for machine data analytics -- a hyper scale cloud-based platform designed to organize and analyze unstructured and multi-structured machine data.

Glassbeam SCALAR is powered by a parallel asynchronous engine which leverages the company’s domain-speci!c language to describe the structure, meaning and relationships of unstructured data. Leveraging a stack of open source big data components including Casandra and Solr, SCALAR is built for scale and speed to handle complex log bundles and analyze terabytes of data.

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Big Data is already creating an enor-mous impact, but that is nothing com-pared to its potential impact when the power of machine data analytics is accessible to the layman business person who can easily use it across all machines in an enterprise. IT stops being a back o"ce, cost-driven focus, but rather a new set of tools to deliver real impact and value across the entire enterprise.

The analytical tools themselves in Glassbeam’s platform and applications aren’t the only features that enable it to naturally migrate to adjacent mar-kets and applications, but the inherent accessibility for use by non-IT profes-sionals as well as Glassbeam’s software as a services (SaaS) delivery model. platform. On !rst appearances the end value of log data seems to only apply to the IT department, but the raw data from machines has a wealth of information that is applicable to everything from reactive diagnostics to identifying customer behaviors. The applications are near limitless and limited only by imagination. Glass-beam’s accessibility and intuitiveness means that non-IT professionals can easily use its software tools to solve a variety of related business and opera-tions challenges. ABOUT HARBOR RESEARCH

Founded in 1984, Harbor Research Inc. has more than twenty five years of expe-rience in providing strategic consulting and research services that enable our clients to understand and capitalize on emergent and disruptive opportunities driven by information and communica-tions technology. The firm has estab-lished a unique competence in develop-ing business models and strategy for the convergence of pervasive computing, global networking and smart systems.

“For us, the value of machine data analytics goes beyond operational intelligence. It enables us to build better products and prioritize features intelligently based on “true” product usage stats.”

Senior Director, Leading Enterprise Storage Company

Glassbeam Drives Analytics Innovation

Technology

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