Internet of Things (IoT) analytics presents manufacturers with a vast opportunity to drive revenues and enhance operations, from real-time analytics at the edge of technologies (consumer products, instrumented devices, manufacturing machines, etc.) through systems to evaluate and act on the plethora of detailed insights IoT provides. We offer a roadmap for manufacturers to pursue this crucial monetizing analytics opportunity.

1. • Cognizant Reports
cognizant reports | may 2016
How Manufacturers Can Unlock
Business Value via IoT Analytics
Increasing adoption of the Internet of Things presents
manufacturers with tremendous business opportunities.
By understanding the needs of customers both inside
the organization and across the stakeholder ecosystem,
manufacturers can effectively gather and apply insights from
instrumented devices at the edge of their networks to generate
monetization opportunities that inform cost-containment or
revenue-generation initiatives.

2. cognizant reports 2
Executive Summary
With Internet-connected “things” growing at an
exponential rate1
in the manufacturing space,
readily available low-cost computational and
storage capabilities, and the opportunity to
generate real-time consumer insights from data
at the point of origination, a strong case has
emerged for analytics.
But first, it is important to understand the fun-
damental difference between Internet of Things
(IoT) analytics and traditional analytics. Unlike
traditional analytics where data is analyzed after
being centralized, in IoT analytics data is analyzed
at the point of origination (at the device level) as
well as at the centralized level, depending on the
analytics type and application.
Manufacturers now have an opportunity to mon-
etize insights (worth billions) at different levels
internally and externally in the hierarchy of the
manufacturing ecosystem. This comes in the form
of saving costs as well as opportunities to gener-
ate additional revenue. To leverage IoT’s poten-
tial, manufacturers need to understand why they
need to use IoT analytics and what they need to
do from an infrastructure and skills perspective,
identify the information needs and then start con-
verting proliferating data from raw bits and bytes
into meaningful insights and foresights.
IoT Analytics at the Network’s Edge
The IoT is an intelligent network of interconnected
and instrumented objects with unique identi-
ties that have the ability to sense, interact and
communicate with each other about their states
and environment using embedded communica-
tion and processing capabilities/technologies.2
Analyzing data and applying insights generated
by such networks can help manufacturers make
data-driven business decisions, drive innovation,
develop customized products and services, and
identify new areas for business growth.
As noted, the fundamental difference between
IoT analytics and traditional analytics lies in the
former’s ability to generate insights across vari-
ous levels of an Internet Protocol (IP) network,
while the latter analyzes distributed data at a
central location. This presents a unique ability
to generate insights at the point of consumption
and present a truly distributed decision-making
approach.
A Different Approach to Data Analysis
The computational capability of distributed
“things” within the IoT network, their ability to
communicate/collaborate with each other and
their capacity to apply the analytics insights/
outputs at the local level differentiates IoT
analytics from traditional analytics. The need to
bring data to a central location is eliminated due
to the presence of intelligence and computational
capability embedded at the lowest levels of the
network.
IoT analytics utilizes the ability of the sensory
and supporting hardware, actuating devices/con-
trollers and hierarchical intelligence to analyze
local data to draw locally usable insights/predic-
tions and make/implement local decisions (see
sidebar). Only the insights, not necessarily the
raw data, are shared with the higher or central
levels for their use in more meaningful analytics
at those levels.
Edge analytics processes (i.e., at the edge
of the network) kick in to analyze data at its
point of origin and transmit the results to the
relevant level in the IoT network hierarchy. Edge
How IoT Analytics Works
A vehicle fitted with sensors can collect data and build insights on the wear and tear of critical parts
(e.g., tires) and share it with the owner. This analytics activity can be performed at the level of the vehicle
itself. At the next level, data collected and analyzed about tires from a large number of vehicles can be
used to share insights with the owners to reduce the cost of tire production and ownership.
Taking this to the regional level, the analysis of data of critical parts can provide insights about tire
demand to dealers and manufacturers (to help their supply chain be more proactive to this impending
demand), thus providing an opportunity to monetize this information.
Quick Take

3. cognizant reports 3
analytics greatly improves the time-to-value
of the data (fresh data analyzed in real time),
reduces network bottlenecks, reduces service
latency, improves response time and analyzes only
actionable data.3
It also allows manufacturers
to analyze the huge volumes of IoT data in a
scalable, efficient way and is ideal for those who
need to drive automated decisions and do not
require “heavy duty” analytics. For instance, such
analytics can alert a manufacturer to switch off a
valve once a leak is detected.
The basic principle of edge analytics is to analyze
locally, consume locally and pass on only the rele-
vant information/insight to other levels of the IoT
network hierarchy as per the need to consume
the information or insight at that location.
Edge analytics will be driven by the business
need of manufacturers. Ideal use case scenarios
include applications that require a great deal of
bandwidth such as smart lighting, parking spaces
and offshore oil rigs.4
As edge analytics transmits
only the results, manufacturers need to exam-
ine and understand what data they can afford to
discard and what they need to store for further
analysis.
One word of caution: edge analytics can create
unintended security vulnerabilities and informa-
tion siloes since a layer of technology is deployed
at the network’s very edge in isolation from
other elements of the enterprise information
architecture. Manufacturers must ensure that
edge analytics is part of their holistic data
strategy to ensure that data is accessible and
integrated across the network and end-to-end
security, from the device level, is deployed to
cover the whole ecosystem.5
Monetization Potential
The monetization potential of IoT analytics
extends beyond traditional end customers to
the larger ecosystem of partners and stakehold-
ers. The ability to achieve localized analytics and
consumption of insights leads to a monetization
potential that goes beyond the traditional arch of
the organization.
Achieving IoT Information Monetization
Consider the example of the vehicle described
earlier: The insights on the performance of tires
under different conditions can be used to provide
an advisory service on tire life enhancement to
vehicle owners of the tires, and to tire manu-
facturers as a service that provides information
Sample Automotive Stakeholder Ecosystem
Figure 1
IT
Auto
Insurance
Government
Travel and
Hospitality
Oil & Gas Auto Finance
Retail
ECU OBD
Telecom
Hosting
Infra
GPS/GIS H/W
Stakeholders
T
elematics Infrastructu
re
IT
Content
Providers
Call Center
OEMs
Customer
Auto Component
Manufacturers
Dealers/
Service
Centers
Mobile
Operators
H/W
Suppliers

4. cognizant reports 4
useful towards improving tire designs. Extending
this analogy to multiple aspects of the vehicle
and other related products increases the moneti-
zation potential.
Combining IoT data with analytics provides
manufacturers with a 360-degree monetization
opportunity. This can be best illustrated by con-
sidering the automotive stakeholder ecosystem,
illustrated in Figure 1 (see previous page), which
comprises two spheres:
• Inner sphere: This consists of players directly
connected with the key components of vehi-
cles such as telecom players, hardware play-
ers, OEMs and end customers.
• Extended sphere: Players not directly
connected with the vehicle but who provide
support to the vehicle ecosystem in direct
or indirect fashion, such as auto insurance
providers, finance providers and retailers.
Insights at different levels in the hierarchy can
be used to serve the needs of players across
the manufacturing ecosystem, saving money for
some and providing opportunities to generate
additional revenue for others.
There are two potential sources of monetization
from IoT analytics:
• The opportunity for cost savings and revenue
improvement from better product sales (i.e.,
more indirect and internal product/service
monetization).
• The opportunity for revenue generation
through the sale of data, insights and/or
advice to benefit the ecosystem stakeholders.
Savings Potential
A combination of business and operational
analytics can help manufacturers fine-tune
their performance metrics and drive financial
improvements. For instance, predictive analytics
can significantly optimize costs for manufactur-
ers.6
Cost savings of between 2% and 4% from
a 50% penetration of IoT in manufacturing can
deliver $500 billion (assuming a global cost base
of manufacturing at $25 trillion). Rio Tinto, a
mining major, saves over $300 million by deploy-
ing “autonomous mining.”7
Similarly, customers
of contract manufacturer Flextronics can quickly
react to irregularities in supply chain components
by performing real-time data correlation, enabled
by analytics and enhanced data visibility.8
Market Potential and Opportunities
The IoT analytics market is expected to grow at a
CAGR of 27.5% and touch $16.35 billion by 2020,
up from $4.85 billion in 2015.9
According to IDC,
55% of discrete manufacturers have undertaken
IoT initiatives (research, pilots and adoption).10
Manufacturing is expected to account for more
than one-fourth of the total IoT market. Oil and
gas and energy equipment manufacturing would
be leading this wave of adoption.11
Manufacturers are being inundated with informa-
tion due to the rising levels of instrumentation and
automation (see Figure 2). Microsoft estimates
Data Produced by IoT Devices
Figure 2
Source: Simafore, RTInsights, Cisco
25 GB/hour
A modern,
fully instrumented car.
51,200 GB/hour
A fully instrumented
jet engine.
500 GB/day
A single turbine
compressor blade.
150,000 data points/
second
A typical wind farm.
500 million
data readings/day
A smart meter project.
40% of all data by 2020
Produced by sensors.
Source: Simafore, RTInsights, Cisco
Figure 2

5. cognizant reports 5
that $371 billion in additional revenue can be
generated by taking advantage of this data (see
Figure 3).12
Another estimate by McKinsey pegs
the value created by IoT applications at $1.2 to $3.7
trillion in 2025, mostly through improvements in
inventory optimization, operations management
and predictive maintenance.13
Importantly, a para-
digm shift to preventive and predictive mainte-
nance from traditional corrective maintenance in
the next five years is expected to provide addi-
tional revenue generation opportunities.14
Crucial IoT Analytics Considerations
While IoT analytics is the path for manufactur-
ers to pursue, there are numerous dimensions
that need attention in order for the approach to
achieve the desired outcomes.
Customers’ demand for high levels of service,
experience and personalized products is
pressuring manufacturers to quickly overhaul
their business and production processes. For
instance, Harley-Davidson reconfigured and
equipped its facility in York, PA, with sensors and
location awareness to reduce the time it takes to
produce customized motorbikes from a 21-day
cycle to six hours.15
A key distinction is that “customers” extends to
two types: first, the traditional end customers,
and second, ecosystem players that serve and
support these end customers. It is this second
category that is making IoT analytics essential
and relevant to manufacturers.
Production activity is complex; every small activ-
ity/task has a significant impact on the final yield.
To improve yields, manufacturers must take a
granular approach to diagnosing and correcting
issues in the production process. At a high level,
manufacturers can also do an in-depth analysis of
a richer and greater volume of data to find the
truth contained therein. Analyzing the rapidly
rising manufacturing data in a centralized fashion
is neither necessary nor feasible. Instead, manu-
facturers can utilize the ability of their networks
to generate distributed intelligence and consume
it as necessary.
Manufacturers should also consider three fun-
damental use cases that reflect manufacturing’s
unique challenges:
• Connected products:
These are products that
are embedded with three
core elements: physical
components, “smart”
components and con-
nectivity components.16
The embedded sensor(s)
utilize(s) the connectiv-
ity to communicate and
exchange data about the
state of the product with
other products and sys-
tems in its environment.17
This enables improved
functionality, reliability
and utilization of these
products.
• Connectedsupplychain:
A production line, when connected to suppliers,
enables all stakeholders to understand inter-
dependencies, material flows, information and
process cycle times. IoT systems enable remote
tracking, monitoring and reporting of material
consumption as it moves through the supply
chain. Manufacturers can then identify and
resolve issues before they happen by access-
ing predictive analytics using real-time data
generated by the supply chain.18
Additional Revenue from IoT Data
Figure 3
Source: : Microsoft
$162 billion
Employee Productivity.
$117 billion
Operational Improvement.
$55 billion
Product Innovation.
$38 billion
Customer Facing Activities.
Analyzing the
rapidly rising
manufacturing data
in a centralized
fashion is neither
necessary nor
feasible. Instead,
manufacturers can
utilize the ability of
their networks to
generate distributed
intelligence and
consume it as
necessary.
Source: Microsoft
Figure 3

6. cognizant reports 6
• Informed manufacturing: An intended state
of operations where all relevant insights are
made available at the right time, in the right
form, to the relevant stakeholders (people,
process, products and infrastructure) across
the manufacturing supply chains.19
Prioritizing Requirements
First and foremost, manufacturers must iden-
tify and understand their customers’ needs, and
the impact information has on them. Customers
come in different shapes and sizes – internal or
external, traditional or nontraditional, physical
or virtual. Second, by understanding the specific
needs of different types of customers, manufac-
turers will have a better idea of the type and gran-
ularity of information/data required to meet them
through the use of IoT analytics. This will help
manufacturers better understand the ecosystem
of their stakeholders who consume the insights
generated by IoT data. Further, manufacturers
must also identify the essential insights/informa-
tion/data elements that form an integral part of
the processes that cover the ecosystem’s stake-
holders and their business processes.
Manufacturers must have a strong data strategy;
the best approach here is to start with the busi-
ness problem. Also, manufacturers that focus
on improving their data capabilities and process
agility will realize more value than those that do
not. Utilizing IoT data will require organizational
changes to be carefully thought through. Taking
action in three important areas can help:
• Develop capabilities that can mine data to
identify opportunities and solve problems.
• Develop and implement appropriate analytics
that are relevant to your business.
• Develop an in-house capability and a culture
of insights/information/data-driven execution
and analysis of the processes, making this an
absolutely essential part of your day-to-day
operations.
Getting the Data/Information Right
A key component of IoT analytics is to find
the right kind of information or data. The data/
information needs are driven by the kind of
insights required by individual stakeholders.
The data/information needs change at different
levels of the manufacturer (mostly at a functional
and regional level) and the
larger stakeholder ecosys-
tem.
IoT analytics strategy is
typically defined by a com-
bination of design of the
IoT network for companies’
products/organization, an
understanding of the exist-
ing IoT that plays into this
ecosystem and the analytics
needs. All of this needs to be
achieved as an integrated
process and not separately
or sequentially since they
are tightly connected and
drive one another. An essen-
tial part of this is to perform
a clear mapping of all data
elements and the poten-
tial insights utilized by key
stakeholders.
To achieve long-term IoT success, manufacturers
need to focus on solving critical business prob-
lems, contextual data, security and intelligent
assets. Contextual data, especially, provides clar-
ity and meaning to the opportunity or problem
that manufacturers are trying to address.20
Man-
ufacturers need to hone their skills to find data
that is of high-value, easy to access, available in
real time, provides information about a significant
portion of their business processes and custom-
ers and, importantly, provides inputs which, when
properly analyzed, can help effect meaningful
change.
The next step is to bring the data into shape.
This starts with automating the collection of
data and compressing it before it is transmitted
to a storage device. Manufacturers must use a
common data model that can combine unstruc-
tured data with structured data to make it easier
for them to run big data analytics. Similarly, stan-
dardized and interoperable interfaces across the
organization will help promote integration and
scalability.
Advancing the IoT Analytics Value
Proposition
To increase the odds that IoT analytics will
succeed within a given manufacturer’s ecosys-
tem, we suggest the following:
Manufacturers need
to hone their skills
to find data that is
of high-value, easy
to access, available
in real time, provides
information about a
significant portion
of their business
processes and
customers and,
importantly, provides
inputs which, when
properly analyzed,
can help effect
meaningful change.

7. cognizant reports 7
• Detail business-critical insights and their appli-
cation at multiple points in the ecosystem.
• Identify key stakeholders and their needs to
orient analytics for the purpose of addressing
business challenges. Identify the processing
and decision-making required at every level
and stage of the IoT network (this is primarily
focused on reporting and advanced analytics).
• Lay down an approach for making IoT analyt-
ics distributed both in terms of information
analysis and usage of insights, as needed. This
includes detailing the strategy, including the
use of edge analytics.
• Make data/information/insights an integral
part of systems and processes within the orga-
nization and extended ecosystem.
• Close the loop on insights by establishing
decision-making logic based on insights drawn
from advanced analytics.
Manufacturers must consider utilizing edge ana-
lytics where appropriate, based on their busi-
ness needs. According to IDC, by 2018, 40% of
data created by IoT devices will be stored, pro-
cessed, analyzed and acted on at the device
level.21
Manufacturers must focus on harness-
ing the low-cost computational power and
capabilities of smart devices to run analytics.22
Extending cloud capabilities to the edge of
networks can help to move analytics to the
source of data production and enable real-time
processing of the data.23
By taking a distributed and hierarchical approach,
the results and insights from the analysis of data
can be moved to the next level in the hierarchy.
This can be used to drive automated decision (by
devices) or manual decision-making depending
on the requirements of the process. Manufactur-
ers can choose from descriptive, predictive or
prescriptive analytics, or a
combination of all of these,
based on what best suits
their business processes.
Manufacturers must also
focus on making the appli-
cation of analytical insights
an integral part of their sys-
tems and processes. To do
this, it is essential to include
an intelligence layer that
connects the decision-mak-
ers with the shop floor to
deliver real-time analytics
and insights.24
To take action on the insights that
are driven by data, manufacturers must define
new processes in an easily understandable for-
mat for managers and frontline workers. This
way, decision-making at all levels will be quicker
and based on insights generated by data.
Moving Forward: Realizing the Value of
Insights
An essential part of benefiting from IoT insights
is to take them to their highest value form. The
monetization value of information, or an insight
derived from the information, grows with its
ability to be forward-looking. “What would
Manufacturers
can choose from
descriptive,
predictive or
prescriptive
analytics, or a
combination of all
of these, based on
what best suits their
business processes.
Analytics Value Graph
Figure 4
Source: Vitria
Fast Data
Ingestion
Real-Time
Streaming
Analytics
Historical
Analytics
Predictive
Analytics
Prescriptive
Analytics
Intelligent
Actions
Value
Source: Vitria
Figure 4

8. cognizant reports 8
happen rather than what happened” or “What
to do rather than what would happen” is the
direction of increasing value of analytics. The
value hierarchy is informative  reactive 
predictive  prescriptive in the direction of
increasing value.
To derive maximum value, manufacturers must
embed appropriate analytics across each stage
of the value chain (see Figure 4, previous page).
Ideally, manufacturers need to ensure data pro-
duced by devices and sensors is absorbed quickly
and analyzed in real time. Adding historical and
contextual data can provide a base for running
advanced analytics. At this stage, predictive ana-
lytics can be used to predict anomalies, patterns
and failures by taking into account data such as
Footnotes
1
Gartner, Inc. forecasts that 6.4 billion connected things will be in use worldwide in 2016,
up 30 percent from 2015, and will reach 20.8 billion by 2020. In 2016, 5.5 million new things will
get connected every day. “Gartner Says 6.4 Billion Connected "Things" Will Be in Use in 2016,
Up 30 Percent From 2015,” Gartner, November 2015,
http://www.gartner.com/newsroom/id/3165317.
While there were 1000 Internet connected devices in 1984, 1 million in 1992 and 10 billion in 2008,
they are expected to grow to 50 billion by 2020. “The Internet of Everything is the New Economy,”
Cisco, September 2015,
http://www.cisco.com/c/en/us/solutions/collateral/enterprise/cisco-on-cisco/Cisco_IT_Trends_IoE_Is_
the_New_Economy.html.
2
“Reaping the Benefits of the Internet of Things,” Cognizant Technology Solutions, May 2014,
https://www.cognizant.com/InsightsWhitepapers/Reaping-the-Benefits-of-the-Internet-of-Things.pdf.
3
“IoT Architectures for Edge Analytics,” RT Insights, November 2015,
http://www.rtinsights.com/iot-architectures-for-edge-analytics/.
4
“Why IoT Edge Computing Is Crucial,” RT Insights, November 2015,
http://www.rtinsights.com/why-edge-computing-and-analytics-is-crucial-for-the-iot/.
5
“Edge Analytics An Antidote To IoT Data Deluge,” Information Week, January 2016,
http://www.informationweek.com/iot/edge-analytics-an-antidote-to-iot-data-deluge-/d/d-id/1323942.
6
“Manufacturing Services Evolve into an Integrated Ecosystem in Line with IoIT-based Concepts,”
Frost & Sullivan, August 2015,
http://images.discover.frost.com/Web/FrostSullivan/EU_PR_JNikishkina_MB1C-10_11Aug15.pdf.
7
“The ‘Internet of Things’ Is Now: Connecting the Real Economy,” Morgan Stanley, April 2014,
http://www.technologyinvestor.com/wp-content/uploads/2014/09/internet-of-Things-2.pdf.
equipment condition and present plant utiliza-
tion.25
Finally, manufacturers can use prescrip-
tive analytics, the step that creates the greatest
value, to suggest the best action they can take to
improve their key operational metrics.
Manufacturers must focus on building a multitude
of services (internal or external) that are based
on the insights and their delivery through various
channels (mobile, Web and internal systems) for
different stakeholders. This will help them create
services ripe for monetization (cost-saving type/
revenue-earning type). Another essential element
for realizing the value is to focus on the medium
of delivery of insights and the right economic
models around it.

9. cognizant reports 9
8
“Attaining IoT Value: How To Move from Connecting Things to Capturing Insights,” Cisco, 2014,
http://www.cisco.com/c/dam/en_us/solutions/trends/iot/docs/iot-data-analytics-white-paper.PDF.
9
“Internet of Things (IoT) Analytics Market by Application Platform (Telematics, Wearable, Building
Automation, Manufacturing, Retail, Healthcare), Solutions (Sensor and Gateway Analytics),
Deployment, Process, Regions - Global Forecast to 2020,” MarketsandMarkets, June 2015,
http://www.marketsandmarkets.com/Market-Reports/iot-analytics-market-52329619.html.
10
“Transforming Manufacturing with the Internet of Things,” IDC and Cognizant Technology Solutions,
May 2015,
https://www.cognizant.com/InsightsWhitepapers/transforming-manufacturing-with-the-Internet-of-
Things.pdf.
11
“IoT market segments – Biggest opportunities in industrial manufacturing,” IOT Analytics,
October 2014,
http://iot-analytics.com/iot-market-segments-analysis/.
12
“The $371 Billion Opportunity for “Data Smart” Manufacturers,” IDC and Microsoft, May 2014,
http://blogs.microsoft.com/transform/2014/05/22/the-371-billion-opportunity-for-data-smart-
manufacturers/.
13
“Unlocking the potential of the Internet of Things,” McKinsey, June 2015,
http://www.mckinsey.com/business-functions/business-technology/our-insights/the-internet-of-
things-the-value-of-digitizing-the-physical-world.
14
“Manufacturing Services Evolve into an Integrated Ecosystem in Line with IoIT-based Concepts,”
Frost & Sullivan, August 2015,
http://images.discover.frost.com/Web/FrostSullivan/EU_PR_JNikishkina_MB1C-10_11Aug15.pdf.
15
“Personalization Is Changing The Game For Consumer Products,” Forbes, March 2016,
http://www.forbes.com/sites/sap/2016/03/16/personalization-is-changing-the-game-for-consumer-
products/#5fbd5ef73533.
16
“How Smart, Connected Products Are Transforming Competition,” Harvard Business Review,
November 2014,
https://hbr.org/2014/11/how-smart-connected-products-are-transforming-competition.
17
“Informed Manufacturing: The Next Industrial Revolution,” Cognizant Technology Solutions,
April 2014,
https://www.cognizant.com/InsightsWhitepapers/Informed-Manufacturing-The-Next-Industrial-
Revolution.pdf.
18
Ibid.
19
Ibid.
20
“Internet of Things: Industrial automation industry exploring and implementing IoT,” ISA, April 2014,
https://www.isa.org/standards-and-publications/isa-publications/intech-magazine/2014/mar-apr/
cover-story-internet-of-things/.
21
“IDC Reveals Worldwide Internet of Things Predictions for 2015,” IDC, December 2014,
https://www.idc.com/getdoc.jsp?containerId=prUS25291514.
22
“The Data of Things: How Edge Analytics and IoT Go Hand In Hand,” Datanami, September 2015,
http://www.datanami.com/2015/09/22/the-data-of-things-how-edge-analytics-and-iot-go-hand-in-
hand/.
23
“Fog Analytics: Turning Data into Real-Time Insight and Action,” Cisco, August 2015,
http://blogs.cisco.com/digital/fog-analytics-turning-data-into-real-time-insight-and-action.

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About Cognizant
Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process
outsourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered
in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep
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Credits
Author and Analyst
Aala Santhosh Reddy, Senior Researcher, Cognizant Research Center
Subject Matter Expert
Dr. Gautam Sardar, Senior Director, Innovation, Cognizant Manufacturing & Logistics
Design
Harleen Bhatia, Design Team Lead
Murali Krishna Adusumalli, Senior Designer
24
“Transforming Manufacturing with the Internet of Things,” IDC and Cognizant Technology Solutions,
May 2015,
https://www.cognizant.com/InsightsWhitepapers/transforming-manufacturing-with-the-Internet-of-
Things.pdf.
25
“Can Advanced Analytics for IoT Drive Significant Business Value for Manufacturers?” Vitria,
September 2015,
http://www.vitria.com/blog/advanced-analytics-for-manufacturing.
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