The document discusses machine-to-machine (M2M) communication and how it differs from the Internet of Things (IoT). M2M allows data exchange between machines, while IoT refers to the potential interconnection of smart objects. M2M provides the underlying connectivity that enables IoT by allowing smart objects to communicate. While M2M focuses on data exchange within closed systems, IoT provides a more comprehensive approach by connecting various M2M devices to address solutions across industries. The document also outlines some benefits of M2M data science such as disaster avoidance, risk minimization, cost optimization, and increased revenues.

1. 1Part of my Data Science TutorialsDr. Pradeep Mavuluri , Co-Founder, ATIS (www.analyticaltis.com)Dr. Pradeep Mavuluri , Co-Founder, ATIS (www.analyticaltis.com) Part of my Data Science Tutorials
M2M Data Science Course
(Part of my Data Science Tutorials)
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What is M2M and How it is different from IoT?
 Machine to machine (M2M) is a broad label that can be used to describe any technology
that enables networked devices to exchange information and perform actions without the
manual assistance of humans. – whatis.com
 In simple words, M2M allows data exchange (communication) between machines, i.e.
nothing but all about connecting and communicating with a machine (device, sensor, etc.)
e.g. A remote application enabling controls (while monitoring).
 The Internet of Things (IoT) is a scenario in which objects, animals or people are provided
with unique identifiers and the ability to transfer data over a network without requiring
human-to-human or human-to-computer interaction. IoT has evolved from the
convergence of wireless technologies, micro-electromechanical systems (MEMS) and the
Internet. – whatis.com
 Again, in simple words, the potential interconnection of smart objects1 and the way we
interact with all these objects is “IoT”.
 Relationship: M2M → IoT (M2M is what provides the IoT), as smart objects/products are
required to enable the internet of things.
1 Objects, that are generally built with M2M communication capableness's are often referred to as ‘Smart’. These objects/products pose such layer
of artificial intelligence that enable all those possibilities to merge the physical world with digital (data) world.

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What is M2M and How it is different from IoT ? (Graphical)
M2M
Cloud
M2M
Workbench
IoT
As, one can see from above M2M is an integral part of IoT, nevertheless, one should
understand M2M as more of a vertical (involving all stages of a production or distribution)
and as closed point of view. Further, IoT enables and provides a meaningful (or horizontal)
approach wherein M2M devices are pulled together to address to provide solutions,
whether they are for the industry, people or the environment.
Bridging ThisIsolated Here

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 Disaster & Damage Avoidance:
• Assess and exploit data provided by different applications to predict a disaster or damage before it
happens. e.g. preventing hacks, aircraft/vehicle engine failure preventions, machine failure preventions.
 Risk Minimization in Business (more the information better the decisions):
• Deeper data mining allows a better decision-making than relying on pure strategic methods.
 Costs Optimization:
• Better prediction in hand reduces the costs.
 Increased Revenues:
• Retail’s or CPG’s can track and use the customer behavior in order to create dynamic and optimal
layouts, marketing plans which will increase their revenues.
 Better Healthcare, Energy Utilization, Farming (fertilizer & water distribution across the farm in the right
proportions).
M2M Data Sciences – Why it is in demand? (Pros)
LevelofAnalyticsAdvancement
Current Status Against Demand
Relatively Low
Very High Level
(Unachievable due
to rapid changes)
Moderate Level
(Achievable with
right effort)

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 Can provide better customer service, business can charge by specific usage, effective
advertising, and stability for your business as it opens the doors for precision monitoring of
business activities and optimization of systems for real-time needs.
M2M Analytics – How they help business?
Monitoring of Business Activities
1) Receive events as streams
and store to database
Purchases
Travel
Stream 1
Consumpti
on
Stream 2
Stream 3
Stream 4
2) Can schedule pre-written
scripts
Load Save
1. Data Cleaning
2. Data Transformation
3. Modeling
4. Segmentation
3) Generate Results as streams
4) Can take help of visualization
for better depictions
Stream A
Stream B

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 Initial setup for huge quantities of data itself is difficult, and also it is rapidly spreading to new markets.
 Complex process chain w.r.t. data coming from a potentially large number of communicating devices.
 It is more of long tail business needs then short ones and lagging standards as mentioned earlier.
M2M Analytics – How it differs from others?
Highly un-
sizeable
volumes
Decide what
and how it
matters
Complex
Data
Ingestion
Identifying
Patterns

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M2M Analytics – Future Look Like..contd.
 Advent of M2M devices, and physical interaction is leading to an astounding acceleration of data generation.
 This can be characterized by volume, velocity, variety, variability and complexity.
Time
RequirementofNewScripts
“As notion of data driven strategies increasing, managers are emphasizing on “find some statistics to provide it”.
Let’s see how ‘User Behavior” prediction is getting changed due to M2M
Old Scripts
New Scripts
Earlier, when data was not astounding, industry
was ok with existing analytical models, and used
to run scripts that were thought to best (Old
Scripts).
However, with the advent of M2M, constant
checking and new analytics models are required
(New Scripts)