The growth on the Internet of Things (IoT) has been astonishing. From Home to Cities, Wearables to Driverless Cars, Agriculture to Factories , more and more things are being connected and embedded with technology and generating big-data. - When Data became really ‘Big-Data’? - What are the characteristics/complexities of Big-Data in IoT?-- - What is the role of Analytics and How is it different in IoT context? - What are the use-cases of IoT with Analytics in Action? - When AI meets IoT: Frameworks, Tools, Technologies

1. BIG-DATA ANALYTICS FOR IOT: MAKING SENSE OF DATA FROM SENSORS
Muralidhar Somisetty : CTO/Co-Founder, Innohabit Technologies
© 2017: Innohabit Technologies and/or it’s affiliates. All rights reserved.

2. 2
http://cdn2.hubspot.net/hubfs/338908/images/Blog_Pictures/Humor_in_IoT.jpg
Imagine Future: when things start to think ☺

3. Is Artificial Intelligence an
Angel or Demon?
Man To Machine: Welcome to the Era of Intelligent Systems

4. Agenda: Let’s step back to understand this evolution better…
When Data became really ‘Big-Data’?
What are the characteristics/complexities of Big-Data in IoT?
AI in IoT : Frameworks, Tools, Technologies
What are the use-cases of IoT with Analytics in Action?
What is the role of Analytics and How is it different in IoT context?
Conclusion

5. “Welcome to the Internet
of Customers. Behind every
app, every device, and
every connection, is a
customer. Billions of
them. And each and every
one is speeding towards
the future.”
- Salesforce.com
A new wave of Internet generation: SMAC, IoT
Source: Salesforce.com

6. Images: SmartInsight
Data Tsunami hit the shores faster than ever…

7. Big data is defined
as the collection
of data sets so
large and complex
that it becomes
difficult to process
using on-hand
database
management
tools or traditional
data processing
methods.

8. IoT is adding more fuel to Big-Data Explosion
The growth on the Internet of
Things (IoT) has been
astonishing.
From Home to Cities,
Wearables to Driverless Cars,
Agriculture to Factories ,
more and more things are being
connected and embedded with
technology and generating big-
data.

9. Source: IoT World Forum (IBM, Cisco)
IoT Reference Architecture

10. Big-Data Complexity and Characteristics in IoT

11. IoT/Analytics System Architecture

12. Data Analytics: Brain behind the IoT Systems.
Image Source:: Dr. Mazlan Abbas (IoT Evangelist / Speaker)

13. ESSENSE OF DATA ANALYTICS
It is the process to uncover hidden patterns, unknown correlations,
trends, and any other useful business information
“No one ever made a decision because of a number.
They need a story. The value of data lies in the narrative
of the story as to why and what to do next.”
- James Richardson, Gartner Research Director
Data Analytics is the science (and art!) of applying statistical techniques to large
data sets to obtain actionable insights for making smart decisions.

14. Business Intelligence to Data Analytics

15. What makes IoT Analytics different?
Source: Gartner BI Summit, 2016

16. Why Analytics is so critical for IoT?
REAL-TIME/ FAST
DATA IN MOTION
PERISHABLE DATA
AND ITS SHELF LIFE
JUST-IN-TIME RESPONSE
WITH ACTIONABLE INSIGHTS
Data at the Edge of IoT networks is lightning fast and time-sensitive
Making sense from endless sea of data from sensors is practically impossible without data analytics.

17. Typical IoT Scenario: Abundance of Data , Scarcity of Insights
McKinsey reported on IoT analytics use cases and found that an oil rig with 30,000
sensors used only 1% of its data — that’s right, one percent.

18. The Wheel of IoT : Sensing to Making Sense
Image Source:: i-scoop IOT guide

19. IoT Use-Cases:
Analytics in Action
DIVERSE APPLICATIONS

20. Smart Wearables (Eg., Fitbit) Smart Assistants (Eg., Home, Echo)
Consumer IoT/Analytics Use-Case Scenarios
Smart Cam (Eg., Dropcam, GoPro)
• Lifestyle (Habit) Tracker
• Emergency Care (advanced)
• Behavioural Analytics
• Predictive Analytics
• Interactive Chatbots
• Smart Home + Skills App Platform
• Voice Analytics
• Deep/Reinforcement Learning
• Connected Cameras
• Live Streaming + Social Media
• Imaging/ Video Analytics
• Optimization Algorithms

21. IOT/Analytics @ Smart Home
Key Use-Cases:
▪ Home Automation
▪ Energy Optimization
▪ Safety & Security
▪ Baby/Elderly Remote Care
Analytics in Action:
▪ Embedded Intelligence in Sensors/Gateways
▪ Real-Time Streaming Analytics
▪ Time Series Forecasting: Patterns, Anomaly
▪ Decision Sciences at the Edge.

22. Home Automation: Autonomous Vacuum Cleaners
• Learns Home Layout and Remembers It.
• Adapts to Different Surfaces or New Items
• Improvises on movement pattern for efficiency
• Knows when to recharge and automatically docks itself
• Smart IoT Device controlled via remote Mobile App
• Piezoelectric , Optical Onboard Sensors
• Employs Machine Learning to Adapt and Improvise.
iRobot: Advanced Machine Learning in Action

23. IoT/Analytics for Smart Cities: Increasing Quality of Public Life
Telecom Renewable Energy Water ResourcesRetail
Cloud Signage PrescriptiveAnalytics
Telco OSS/BSS
Geo-Fencing, Enterprise VAS
4G/5G IoT Applications
PersonalizedShoppingExperience
Smart Signages, Seamless Checkout
Energy Efficiency, Operational
Intelligence,PredictiveMaintenance
Leakage Detection,
Utilization Analytics,Predictive
Maintenance
Real-Time Streaming
Big-Data
IoT Stack Grid/Sensor Network
ML/DL Models
Push Notifications

24. Smart Retail: Brick is the IoT AND Mortar is Data Analytics
ImageSource: Cisco IoT RetailWhite Paper
Shopper Exprience
Planogram Compliance
Staff Optimization

25. Data Sciencein Action in SmartRetail
Engagement
• Footfall Demographics
• Dynamic Classification
• Dwell Time Analytics
• Preferred Shopping Path
• Sentiment Analysis
Personalization
• Shopper Profile
• Purchase Patterns
• Brand Affinity Analysis
• Product/Offer Recommender
Optimization
• Audience Impressions
• Campaign Effectiveness
• Product Placement Strategies
• Propensity, inference, trends,
engagement, reach, ranking,
comparisons, etc.

26. Industrial IoT/4.0: Connected Factories
Amazon embraced IoT/ Industry 4.0 streamlining their factory, fulfilment, shipping and storage experiences.
• Smart Storage Systems
• Automated Order Fulfilment Centres
• Supply Chain Optimization
• Robotics, Drones in Action
• Baseline Analytics for Machine Performance
• Automated Order Fulfilment Centres
• Predictive Failure Detection of Various Machines
• Deep Learning Algorithms in Action
Image Source: http://money.cnn.com/2016/10/06/technology/amazon-warehouse-robots/

27. AI to IA: Value of Data Analytics in Industrial IoT
In the industrial space, there is a great deal of interest in using analytics to optimize asset maintenance,
production operations, supply chain, product design, field service, and other areas.

28. Top Analytics Use-Cases in Industrial IoT
Source: https://iot-analytics.com/product/industrial-analytics-report-201617/

29. IoT /Analytics Top Use-Cases By Sector

30. When AI meets IoT
Artificial Intelligence provides us the framework and tools to go beyond trivial
real-time decision and automation use cases for IoT.

31. Branches of Artificial Intelligence

32. IOT  ANALYTICS TECHNOLOGY/VENDOR CHOICES

33. 14/05/2016 STARTUP PRODUCT MANAGEMENT 33
Tools and Frameworks for Machine/Deep Learning

34. ML Algorithms Mind Map:
When to choose what?
Source: http://scikit-learn.org/

35. THANK YOU Thoughts/Questions Welcome
muralidhars@innohabit.com@muralidhar9

36. Webinar Synopsis
Big data analytics is undoubtedly one of the most exciting areas in computing today, and
remains an area of fast evolution. Thanks to the data deluge from millions of sensors from IoT
networks, it is humanly impossible to analyse and make sense of the data from sensors without
analytics tools and processes. In this webinar, we will go over basics of big-data analytics, how
analytics is different from traditional data warehouses or business intelligence systems, different
tiers of data analytics etc., We will also see different use-cases of IoT from Smart Home to
Transporation to Smart City context and how analytics can be applied for various use-cases for
actionable insights. Webinar also briefly touches upon machine learning tools / techniques that
are available as-a-service on cloud today.