Automating and Democratizing Cutting Edge Analytics · Automating and Democratizing Cutting Edge...

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

www.abiresearch.comAUTOMATING AND DEMOCRATIZING CUTTING EDGE ANALYTICS WHITE PAPER

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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

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Improved Utilizationof Data Scientists

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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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