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Automating and Democratizing Cutting Edge Analytics
INTRODUCTIONPTC is an enterprise software company that has very quickly become a leading supplier of
Internet of Things (IoT) software and services. They achieved this position through acquisitions
of IoT application enablement platform leaders ThingWorx and Axeda in December, 2013 and
September, 2014, respectively. Recently they again demonstrated their ability to spot leading IoT
firms by purchasing big data and analytics firm, ColdLight. In an effort to simplify their overall go
to market strategy and technology portfolio, ColdLight has become ThingWorx Analytics residing
within the ThingWorx IoT platform.
ThingWorx Analytics automates the creation and operationalization of advanced, predictive,
and prescriptive analytics. For application and solution developers, it provides them with an
easy way to use advanced and predictive analytics without having to be an expert in data science,
complex mathematics or machine learning. For businesses the immediate benefit is better use
of data scientist resources – analytics automation means data scientists will spend less time
on the repetitive tasks of building, testing and refining data models. The larger benefit is that
analytics automation effectively democratizes these technologies making them available to the
entire enterprise.
Analytics automation effectively democratizes machine learning and artificial intelligence tech-nologies making them available to the entire enterprise
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This white paper dives deeper into the challenges faced by enterprises with respect to their
analytics requirements and investments. It contrasts these challenges with the features of an
analytics automation platform and what this means for enterprises. Finally, the paper discusses
combining application enablement and analytics automation platforms for its benefits to the
broader IoT ecosystem and for solving the issue of IoT supplier diversity and offer complexity.
IoT ANALYTICS – BIG OPPORTUNITY BUT ALSO BIG CHALLENGESAnalytics is the hottest IoT technology area today, as businesses consider greenfield IoT
investments and explore new opportunities in applying IoT to their brownfield investments.
Analytics applied to machine and business data helps understand the key factors explaining an
outcome (descriptive analytics) and predicting future outcomes (predictive analytics), as well as
identify suggested responses to predictive outcomes (prescriptive analytics). In fact, by 2020,
businesses will spend nearly 26% of the entire IoT solution cost on technologies and services that
store, integrate, visualize and analyze IoT data, nearly twice of what is spent today. Considering only
analytics costs, the higher level and arguably more valuable predictive and prescriptive analytics
products and services will grow from approximately 20% enterprise spend today to 56% in 2020.
By 2020, businesses will spend nearly 26% of the entire IoT so-lution cost on technologies and services that store, integrate, visualize and analyze IoT data
IOT REVENUES
26% | Data/Analytics Service Revenues
17% | Module and Connection Revenues
57% | Other Value-Added Services Revenues
0%
10%
20%
30%
40%
50%
60%
2014 2020
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The benefits of analytics to business costs and revenues are clear. For instance, optimizing jet
engine performance using analytics can save airlines millions of gallons of fuel per year. Analytics
applied to railroad wheel failures are estimated to save over $1 billion annually through reductions in
maintenance costs, improvements in operational efficiency, and accident avoidance.
However, generating insights from machine data and analytics technologies is not without challenges.
• Data scientist costs and performance limits – In an analysis by ABI Research, over the
next five years professional services will be the greatest expense associated with analytics
implementation. Recognizing the demand, the market has responded with more training
programs for analytics occupations. The reality is that data modeling is a laborious process.
Not only are there limits to human performance in data modeling endeavors, but adding
more data scientists may not address the problem nor is it cost effective. In addition, more
data scientists does not mean analytics services will be available to more business units.
• Non-optimized use of all algorithmic methods for predictive discovery – There are various
methodologies available to the data scientist to build predictive models. Ideally, firms have
access to all tools and their latest advances. In reality, however, algorithmic methods are not
always optimized, either because of data scientist preference, lack of skills, or due to costs.
• Data type and volume challenges – The best insights are generated when analytics are
applied to various types of data including machine sensors, social media, point-of-sale
systems, web click-through rates, and other data stored in enterprise data marts. Subtle,
yet critical, predictive factors can also be revealed by analyzing large volumes of data.
Unfortunately, current analytics programming tools do not allow efficiently iterating on the
potentially hundreds of variables generated from data variety and volume.
• Static vs Dynamic Modeling – The typical IoT data modeling approach applies algorithmic
tools to a static set of data. While this approach may be sufficient for a single set of data at
a single point in time, it does not account for the dynamic nature of machines or business
process in two areas: First, machines will age and business processes will evolve. Second,
new types of data will become available from additional sensors, business operations
systems or third party data sources. In both cases, data models need to adapt dynamically
to these changes without requiring a complete overhaul of the algorithmic model. But these
needs are clearly understood by most data scientists! The problem is they do not have
access to the tools that can ease addition of new data types and automatically fine tune
their models.
Adding more data scientists may not address the analytics problem nor is it cost effective
Data scientists do not have ac-cess to the tools that can ease addition of new data types and automatically fine tune their models
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Effectively, analytics is far less automated than it should be, and as a result investments in analytics
resources–both human and otherwise–do not produce the best value nor do they allow extension of
analytics to the broadest set of enterprise problems.
THINGWORX ANALYTICS: MAXIMIZING RESOURCES; AUTOMATING INSIGHT GENERATIONThingWorx Analytics is one of the first platforms to truly tackle these analytic challenges by
automating many of the tasks that a data scientist is forced to take on. ThingWorx Analytics
incorporates the best of machine-learning techniques and automated intelligence to explain, predict,
and prescribe outcomes in a way that everyday business users can understand. ThingWorx Analytics
is different in the following key technology areas:
• Modeling Tools – Building models to predict outcomes regardless of the data type
requires using all algorithmic tools and approaches available. ThingWorx Analytics
leverages all the current modeling tools to build predictive models including the most
advanced methods such as Monte Carlo, Support Vector Machines, and Neural Networks.
ThingWorx Analytics is constantly assessing new techniques and data modeling code to offer
the market the best and most current predictive tools.
• Modeling Automation – This is where ThingWorx Analytics differentiates itself and it is the
hardest part to replicate, as it takes years of development. Its IP resides in the machine
learning technologies that can quickly take error-detection rates on each iteration of the
model and use them to automatically adjust variables, remove old models and methods,
or insert new modeling codes. The result is a highly automated means to build accurate IoT
data models, removing the labor-intensive and circuitous activities of model selection, coding,
and validation.
• Scale: Data Type and Volume – The more variables a model can consume and assess, the
greater the model accuracy and simulation capabilities. Not only can ThingWorx Analytics
Analytics is far less automated than it should be, and as a result investments in analytics resources do not produce the best value
ThingWorx Analytics is one of the first platforms to automate many of the tasks that a data scientist is forced to take on
Static Modeling Approach
Fewer Insights
$
STAT
IC M
OD
ELIN
G
S
TATI
C M
OD
ELIN
G
ST
ATIC
MODEL
ING
STATIC MODELING STATIC MODELING STATIC MODELING STATIC M
OD
ELING
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OD
ELING
STATIC MODELING STATIC MODELING STATIC MODELING S
TATIC MODELING
DATASCIENTIST
ALGORITHMICTOOL
Limited by Human Performance
Less Accessibility Corporate-wide
Model
Too
l Bre
adth
Model Automation Data Volum
e & Variety
rescriptive Toolsets
Prescriptive Toolsets
Improved Utilizationof Data Scientists
AnalyticsDemocratization
Faster Timeto Market
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ingest any data type, it is also not limited by the total variables to iterate on. In fact, it has built
predictive models on data from over 1,400 sensor types. In addition, with experience from
financial industry clients, ThingWorx Analytics can provide real-time predictions from data
streams in high-volume transactional environments.
• Prescriptive Intelligence Tools – Predictive analytics are used not only to detect future events
but also to assess outcomes when hypothetical adjustments are made to current operations
and business processes. The latter use case is called prescriptive analytics and involves
running simulations by changing model variables linked to the operations and business
process drivers. In the absence of automation, running simulations would be quite
laborious and may miss identifying the best prescriptions or recommendations.
ThingWorx Analytics has automated these tasks to provide a more holistic set of
optimization recommendations.
THE BENEFITS OF AUTOMATED ANALYTICS, AS OFFERED BY THINGWORX ANALYTICS, TO ENTERPRISES ARE:
• Democratization of analytics – Automating construction of predictive models will shorten
the time to examine data sets, effectively allowing more internal groups to leverage IoT
analytics services.
• Improve data scientist resource utilization – Data scientists can spend more time on bespoke
and forward-looking analytics challenges. In addition, data scientists can use automation
engines to accelerate their own construction of predictive analytics models and prescriptive
simulations.
• Decreased time to market – Not only are tools available to a broader base of users, but
business processes can be optimized more quickly even with prescriptive analytics.
Data scientists can use auto-mation engines to accelerate their own construction of predictive analytics models and prescriptive simulations
Improved Utilizationof Data Scientists
AnalyticsDemocratization
Faster Timeto Market
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ONE-STOP SHOP IOT SERVICES – SOLVING THE SUPPLIER DIVERSITY AND OFFER COMPLEXITY PROBLEMDesigning and building an IoT solution including advanced analytics on collected data is no easy task.
Complicating this process is assessing the breadth of suppliers that offer various hardware, software
and service components. In fact, from a recent survey conducted by ABI Research of industrial firms,
once an enterprise decides to invest in IoT, a top reason for slowing or stopping their IoT solution
deployment is supplier diversity and offer complexity.
Combining IoT application enablement and analytics automation services takes this
challenge head on with a perfect example being the integration of the PTC’s ThingWorx Analytics
(ThingWorx Analytics) and ThingWorx Application Enablement Platforms. The ThingWorx
SIs AND VARs ONE-STOPSHOP
APPLICATIONDEVELOPERS
OEMS
ENTERPRISEINVESTMENTS
One-stop shop suppliers that combine application enable-ment services with analytics services can clearly benefit enterprises and the broader ecosystem of IoT suppliers
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Application Enablement Platform offers a full suite of software and services to collect, act and store
data from connected assets. More importantly, ThingWorx Application Enablement Platform is a
rapid application development environment for visualizing and acting on connected asset data. By
adding ThingWorx Analytics to its IoT portfolio, PTC has one of the most complete sets of tools to not
only build a connected product but to also maximize value of generated IoT data.
“One-stop shop suppliers” that combine application enablement services with analytics services
can clearly benefit enterprises. But the broader ecosystem of IoT suppliers that partner with the
one-stop shop suppliers can also benefit.
• SIs and VARs – These players bring to the table a deep understanding of a business’s
application and enterprise systems requirements. One-stop shop suppliers provide SIs
and VARs with a single source of tools to help enterprises continually reinvent themselves.
Application-enablement tools allow customizing IoT solutions to the unique needs of the
business; analytics generate insights that can then be used to enhance the newly created IoT
applications or create new services.
• Application Developers – Application enablement services are especially valuable to
developers because they remove the messy aspects of development involved in linking
devices and extracting data. This can be achieved through APIs or through a complete
software stack including device agents and messaging protocol programming tools.
However, even the most accomplished programmers will not have the experience in building
IoT applications. Regardless of programming skill and experience, one-stop shop suppliers
that offer drag-and-drop development environments allow developers the chance to extend
their services into IoT.
• OEMs – Device and hardware vendors can add services on top of their offerings using the
services of a one-stop shop supplier. This is particularly relevant to brownfield markets that
have existing connected assets in the field.
• Enterprise Investments – One-stop shop suppliers can complement existing enterprise
investments. IoT application development services can add IoT data to existing server-side
applications. Analytics tools and services can enhance existing business intelligence and data
visualization tools with predictive and prescriptive analytics.
The acquisition of ColdLight is yet another example of PTC’s prowess in recognizing leading companies that advance the state of the IoT market
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SUMMARYPTC recognized in its acquisition of the ThingWorx Application Enablement Platform that businesses
needed a platform that simplified building IoT applications. This platform not only helps enterprises
quickly connect assets, but it is also a rapid application development environment for visualizing and
acting on connected asset data.
The acquisition of ColdLight is yet another example of PTC’s prowess in recognizing leading
companies that advance the state of the IoT market. But like the ThingWorx Application Enablement
Platform, ThingWorx Analytics is more than an IoT analytics platform. ThingWorx Analytics automates
modeling on any type and volume of data so that the manual and repetitive tasks of building, testing
and refining IoT data models are effectively eliminated. ThingWorx Analytics is also a dynamic
data modeling engine continuously learning from new and real-time data streams to provide
the most accurate and complete set of business insights. Finally, ThingWorx Analytics offers
prescriptive tools such as simulation modeling to proactively improve machine operational
parameters and business processes.
The benefits of ThingWorx Analytics for data scientists are clear. But automating analytics provides
a network effect that extends to a much broader market. Internally, more analytics problems can
be addressed, effectively democratizing the use of analytics to more business units and functional
groups. Externally, the broader ecosystem of suppliers and partners, including SI/VARs, developers,
and OEMs, can efficiently build analytics into their products and services.
Analytics automation is the future of IoT analytics services, and ThingWorx Analytics represents
the first of these types of solutions. ThingWorx Analytics is also ushering in the next evolution in
intelligent business — enhancing human intelligence with machine learning and artificial
intelligence technologies.
Published December 2015©2015 ABI Research
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