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-defining how value is created from machine data.

1. machine
data analytics
converges with
the internet of things
Glassbeam
Drives Analytics
Innovation
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-defining how value is created from machine data.
technology
innovator
perspective
Harbor
Research

2. innovator profile
Glassbeam
2
T
he 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 coffee
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 offer
extraordinary business advantage to the companies
that manufacture, deliver and service those products,
especially in terms of customer relationships.

3. innovator profile
Glassbeam
IT’S VITALLY IMPORTANT THAT BUSINESS LEADERS
UNDERSTAND THE INTERNET OF THINGS PHENOMENON
The Internet of Things
Is Here
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 inputs are integrated into Smart Systems
that connect people, processes, and
knowledge to enable collective awareness, efficiencies and better decision
making.
We prefer “Smart Systems” over
other terms in common use—notably “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 important that business leaders understand
this phenomenon, its effects on their
business, and what they should do
right now to position themselves for
opportunities that are literally just
around the corner:
»
Manufacturing equipment, elevators and escalators, appliances and
vehicles that know exactly when
and why they will fail, and then
alert you or your service organization before the failure occurs.
»
Buildings with “digital nervous
systems” that ensure occupant
comfort and safety, and even
enhance productivity.
»
IT and network equipment vendors raising the bar on customer
support efficiencies and achieving extraordinarily attractive ROI
through root cause analytics from
connected machine data.
»
Retailers and distributors who
know exactly where every piece of
ABOUT GLASSBEAM
Glassbeam 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 multistructured data, delivering
powerful product and
customer intelligence for
companies including IBM,
HDS, Aruba Networks and
Meru Networks.
For more information visit
www.glassbeam .com
3

4. innovator profile
Glassbeam
THE INTERNET OF THINGS
IS REALLY HERE NOW
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 accurate, up-to-the-minute patient
information is always available because every piece of equipment,
from digital thermometers to lifesupport machines, is networked
and associated with a patient ID.
OEMs that are not “disintermediated” 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 fiction? Not anymore. The Internet of
Things is really here.
New Value Driven By Big
Data and Analytics
This phenomena is not just about people communicating with people or machines communicating with machines;
it also includes people communicating
with machines, and machines communicating with people. Smart connected
4
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, information systems AND smart machines.
In a truly connected world of smart systems, 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 definition of
a sensor, there are already many
more sensors on earth than people.
»
A single large oil refinery 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
100 data points collected every 40
milliseconds, producing over 6,000
data points every second.
»
Estimates of data produced by
Smart Grid applications could reach
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.

5. innovator profile
Glassbeam
GLASSBEAM IS PROVIDING A TRUE
END-TO-END PLATFORM SOLUTION
between 35 and 1000 petabytes
per year.
»
There are over 500,000 data
centers in the world , suffering an
average of 2.5 outages per year
with an average duration 134 minutes. 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 aggregation 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 ordinary 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%.
5
Because IT equipment produces a
variety of “machine logs” in a relatively
predictable manner, it is an ideal “staging” area for designing, building and
deploying analytics tools.
The implications of mining and analyzing machine data are immense . We
believe this is where the real core value
creation opportunity lies within the
Internet of Things.
DATA INTENSITY **
.
Resources
But very few people are thinking about
machine data on that level. Current IT
and telecom technologists are operating 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 business intelligence.
.08
Today, significantly better tools are available to organize and integrate significant amounts of big data for analysis,
but the tools available for professionals
working with machine data still fall far
short of real world needs.
Ranking Of Data
Volume Intensity
“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
& Energy
.15
Transportation
.29
Industrial
.65
.72
** TB per $ Million
Annual Revenue
Healthcare
IT Services

6. innovator profile
Glassbeam
CURRENT PRACTICES WILL NOT SERVE
A TRULY CONNECTED WORLD
have yet to really take center stage—
not only in business communities, but
in most technology communities, too.
Enter Glassbeam
This paper is about an important
new machine data analytics platform
and application offering from people
who are thinking about the scope
and on the scale that machine data
deserves—Glassbeam.
The Glassbeam team of innovators understand that the tools we are working with today to discover and analyze
machine data were not designed to really address operational and business
challenges. The Glassbeam platform
provides business insight for sales,
support and engineering organizations by mining log data generated
from machines and products.
Glassbeam’s machine data analytics
application platform is not an incremental improvement or new flavor 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, unified platform for search,
6
discovery, analysis and prediction utilizing diverse machine data types.
Glassbeam is providing a true end-toend solution for machine data analytics and intelligence that provides
a complete picture of the myriad of
interactions and states that machines
evolve through including status, configuration changes and usage.
Before delving into the new thinking
that makes this story possible, let’s talk
about why it’s necessary at all. Current 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 conceived in the past and cannot serve
the needs of a truly physical and real
time connected world.
Big Data Is Only Part of
the Story
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
“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

7. innovator profile
Glassbeam
MACHINE DATA REQUIRES A WHOLE
NEW APPROACH TO ANALYTICS
fact. In machine data applications,
much of the historic analysis was
conducted using only sampling-based
analytics.
That’s a very different thing from feeding 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, unlike 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 convergence of innovations in software
architectures; back-room data center
operations; wireless and broadband
communications; and new analytics
tools. But is it?
7
Overcoming Obstacles
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 radical 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 organizations address the key challenge
of optimizing the value of all assets
including machines and physical systems which will allow organizations to
move beyond just financial assets and
liabilities to their physical assets and
liabilities (like IT equipment, production machines and vehicles). The task
of optimizing the value of financial
“Platforms like
Glassbeam’s will lead
us beyond traditional
‘break-fix’ support to
new modes of leveraging
customer-partner
collaboration.”
Services Technology Development
Manager, Network Equipment
Manufacturer

8. innovator profile
Glassbeam
THE TOOLS WE ARE WORKING WITH TO ANALYZE
“SMART” MACHINES WERE NOT DESIGNED FOR
TODAY’S DATA VOLUME AND COMPLEXITY
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 telephones first came into existence, all
calls were routed through switchboards and had to be connected by a
live operator. It was long ago forecast
that if telephone traffic continued to
grow in this way, soon everybody in
the world would have to be a switchboard operator. Of course that has not
happened, because automation was
built into the systems to handle common tasks like connecting calls. We
are quickly approaching analogous
circumstances with the rapidly rising
amount of data that machines produce routinely.
Historically, to gain meaning from
operational data meant building dedicated data warehouses and analytics
applications – a major undertaking. A
typical project might involve several
months of effort and expensive infrastructure and software licenses.
If every machine requires this much
customization just to perform simple
analytic tasks such as search and discovery, then we surely need new tools
to automate various data analytic
8
tasks and facilitate re-use, or risk constraining the growth of this market.
Real time predictive intelligence: In
today’s rapidly changing business environment, 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 competitive position. Traditionally, business
intelligence systems have provided a
retrospective view of the business by
querying data warehouses containing historical data. Contrary to this,
we need systems to analyze real-time
complex event streams to perform
operational monitoring and to support decision-agility. Machine data
analytics systems will need to become
much more business and operationsfocused -- analytics that can be embedded 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. Leveraging machine and physical assets will
require tools that business and operat-
“Aruba Networks
leverages machine
data from thousands of
systems in the field, 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

9. innovator profile
Glassbeam
CUSTOMERS EXPECT EVOLVING SOFTWARE
TOOLS TO BE FUNCTIONAL, UBIQUITOUS,
AND EASY-TO-USE
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 models 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 significant 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 effortlessly
and interoperate seamlessly, they
begin to give rise to complex, systemwide 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
perpetual attempts to get itself off 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 develop technology can inspire grandiose visions that make simple thinking
seem somehow embarrassing or not
worthwhile. That’s not a good thing
when defining and deploying realworld technology to deliver innovation. This is where the new values of
Glassbeam’s platform really come into
focus.
The Coming Machine
Data Glut
The fact that a rapidly expanding range of machines and devices
have the capability to automatically
transmit information about status,
performance and usage and can
interact with people and other systems anywhere in real time, points to
the increasing complexity of managing machines and systems. This only
compounds when we consider the
billions or more of networked devices
that many observers are forecasting
will be deployed.
9
Fusing Machine Data WIth Related Business and Infrastructure
Data Will Create Many New Values

10. innovator profile
Glassbeam
GLASSBEAM ENABLES MACHINE DATA ANALYTICS
PROCESSES TO BE RAPIDLY BUILT AND DEPLOYED
The tools we are working with today 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 datapoints and data sets generated from
machine interactions.
These challenges are diluting the
ability of organizations to efficiently
and effectively manage machines and
systems. The fragmented nature of
analytics software offerings available
today make it extremely difficult, 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, unified end-to-end framework to search, explore, analyze
and predict machine and systems
behaviors that can operate across
diverse data environments and
under widely differing usage
scenarios;
»
Analytic tools and applications
that allow business and operations personnel - not IT specialists
- to quickly build their own machine 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 collaborate with.
We are reaching a critical juncture in
market development where organizations will soon be crying out for a
completely new approach - one that
moves beyond “first-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 operations and business users where the
effort invested to develop new machine 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 first two expectations
run counter to the third. In order to
achieve all three, a new approach is required -- but what kind of approach?
10
Internet of Things
Data Management, Analytics
and Visualization
Market Potential
$54.7bn
IT Equip
Anayltics
$7.6bn
$13.6bn
IT Equip
Anayltics
$2.4bn
2013
2018

11. innovator profile
Glassbeam
GLASSBEAM ENABLES A DEEPER
EXAMINATION OF MACHINE DATA
Data Is Not Free
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 architectures, it’s not free to easily merge
with other information and enable any
kind of search-based intelligence.
11
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, transactional or time series.
Glassbeam Explorer
Glassbeam tools allow users to
view systems, applications and
their performance characteristics over time. By mining huge
volumes of machine data produced by intelligent connected
devices, Glassbeam provides
product managers, engineers
and other business unit professionals with an unprecedented
level of insight into how products are being used, based on
actual machine-generated data
from the installed base.
What would truly liberated information be like? It might help to think of
the atoms and molecules of the physical world. They have distinct identities,
of course, but they are also capable
of bonding with other atoms and
molecules to create entirely different
kinds of matter. Often this bonding
For each of these data types, a specific
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 question, creating new information from

12. innovator profile
Glassbeam
TRADITIONAL APPROACHES TO DATA DISCOVERY
AND SYSTEMS INTELLIGENCE HAVE MANY FAILINGS
multiple data types that is an easily
perceivable, manipulable, or mappable “model” of the answer to that
question is a significant challenge.
12
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
Installed Base Analytics
A critical requirement in product management and engineering 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 performance, capacity or other key
metrics are being reached?
Real time intelligence fundamentally
changes this paradigm, treating data
from things, people, systems and the
physical world as augmented representations. In other words, treating diverse 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
and typically limited in use to “specialists,” or the tools allow for only superficial analysis of machine data sets.
The Glassbeam platform fundamentally changes this paradigm, treating
diverse data types from machine logs
equally. This enables processes connecting diverse data in any combination to be rapidly built and deployed.

13. innovator profile
Glassbeam
GLASSBEAM’S UNIQUE APPROACH TO MACHINE DATA
ANALYTICS IS BASED ON A NEW CLASS OF TOOLS
Machine Data Analytics
Needs To Drive User
Innovation
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 possible to move forward. The next great
step in machine data technology—
completely fluid multi-dimensional
machine data analytics—requires an
equally simple, flexible, and universal
tool that will make diverse machine
data types easily accessed, integrated
and interpreted for business and operations staff.
Glassbeam’s unique approach to machine data analytics is based on a new
class of tools enabled by its breakthrough Semiotic Parsing Language
(SPL) language that is specifically designed to let users extract value from
multidimensional machine data types.
Glassbeam’s unique SPL-driven tools
and iterative development environment allows users to explore how a
product or system is configured, 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, configurations, locations,
status and related usage;
»
Gathering and analyzing performance data across various products 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 benefit from a deeper
examination of machine data in many
ways including deeper diagnostics,
proactive problem identification and
intelligence on product usage and
behaviors. Unlocking these values can
only be achieved by using effective
tools that not only search and index
but extract critical insights about machine performance and behaviors.
Glassbeam’s back-end system technology enables high velocity and
high volume streaming data to be
processed in real time. Extracting new
insights into equipment health, support and usage require they be acted
on real time.
“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
13

14. innovator profile
Glassbeam
WHEN PRODUCTS BECOME NETWORKED AND
SUPPORT IS AUTOMATED, THE ENVIRONMENT
IN WHICH THEY ARE UTILIZED BECOMES MORE
“AWARE” AND RESPONSIVE.
Machine Data Analytics
Enables Re-Imagination
of Business Functions
A networked machine generates information value over its entire lifespan.
Product manufacturers can know
where the device is located, when it
was installed, critical specifications,
diagnostics, availability of spare parts,
usage patterns, support status and so
on.
14
windows into how customers interact
with a product. Once a product is
shipped to a customer, the manufacturer loses sight of who buys it, how
it is configured, what its use is and
what the customer experiences with
it. When products become networked
and support is automated, the environment in which they are utilized becomes more “aware” and responsive.
Machine Health Analytics
Glassbeam’s platform solution
that delivers a continuous pulse
on the “health” of products:
»
»
Eventually, this environment helps
customers optimize their processes,
save money, and become significantly
more efficient.
Event analysis – Problems,
errors or other potential
concerns can be viewed in
summary fashion, alerting
administrators to situations
that require attention.
»
Traditional customer relationship and
product support programs yield only
intermittent, uneven and incomplete
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.
License summary – Software
versions are tracked automatically, ensuring that all
products are up-to-date.

15. innovator profile
Glassbeam
THE ADVENT OF MACHINE DATA ANALYTICS
MAKES THE STATE OF A BUSINESS’S ASSETS
VASTLY MORE VISIBLE
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 potentially eclipses the real revolution. By
utilizing connectivity and analytic
tools to more tightly integrate customers 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 physical assets they utilize in their business. 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
15
tailored to the unique needs of
individual customers;
»
»
Create a closed, real-time loop,
between the equipment vendor’s
support resources, product development organization and related
business units allowing products
to be specifically designed for,
and implementations tuned to
customer requirements and usage
patterns;
Improve the value and profitability of partners service offerings
and allow the equipment vendor
to establish closer, more proactive
relations with their customers.
This will allow equipment manufacturers 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 patterns 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
“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

16. innovator profile
Glassbeam
GLASSBEAM’S APPLICATIONS ARE NATURALLY POSITIONED
TO EVOLVE FROM ANALYZING IT EQUIPMENT TO ADDRESSING DIVERSE MACHINE ASSETS
configuration but also about how it
performs.
This expansion of machine data analytics capabilities will foster:
»
Higher value, more differentiated
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 configuration,
usage and problem data to design
better, more highly targeted
products and systems for their
customers;
»
Tailor marketing campaigns and
sales efforts around highly customized value propositions; and,
»
Establish themselves as a high
value partner and a trusted advisor.
Glassbeam Futures
The traditional notion of “M2M” applications has largely grown up in a
B2B context with equipment manufacturers 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
efficiencies 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.
Next Generation
Cloud-Based Machine
Data Analytics Platform:
Glassbeam SCALAR
Glassbeam 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 domainspecific 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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17. innovator profile
Glassbeam
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The analytical tools themselves in
Glassbeam’s platform and applications
aren’t the only features that enable it
to naturally migrate to adjacent markets and applications, but the inherent
accessibility for use by non-IT professionals as well as Glassbeam’s software
as a services (SaaS) delivery model.
platform. On first 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. Glassbeam’s accessibility and intuitiveness
means that non-IT professionals can
easily use its software tools to solve a
variety of related business and operations challenges.
Big Data is already creating an enormous impact, but that is nothing compared 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 office, cost-driven focus,
but rather a new set of tools to deliver
real impact and value across the entire
enterprise.
“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
ABOUT HARBOR RESEARCH
Founded in 1984, Harbor Research Inc.
has more than twenty five years of experience in providing strategic consulting
and research services that enable our
clients to understand and capitalize on
emergent and disruptive opportunities
driven by information and communications technology. The firm has established a unique competence in developing business models and strategy for the
convergence of pervasive computing,
global networking and smart systems.