1. Kirk Borne @KirkDBorne
Find Your Opportunities in the Internet of Things 
(with Big Data and Advanced Analytics)
Principal Data Scientist
Booz-Allen Hamilton
Strategic Innovations Group
Kirk Borne
@KirkDBorne

2.
Internet of Things

3. Everything Interconnected
Everything Interconnected

5. Internet of Things : know your different flavors
Internet of Things (IoT)
Internet of Everything (IoE)
Industrial Internet of Things (IIoT) (e.g., connected vehicles, M2M)

6. Internet of Things (IoT) = so many challenges!
The 3 E’s of an IoT World
Everywhere a sensor
Everything quantified and tracked (temporally & spatially)
Erosion of Data Privacy
The 3 V’s of IoT Data
(Volume) Deep Data = Ubiquitous Sensors
(Variety) Wide Data = Diverse and Complex Data Types
(Velocity) Fast Data = Streaming time series
The 3 D2D’s of IoT ROI (Return On Innovation)
Data2Discovery – Data2Decisions – Data2Dollars
(or Data2Dividends)

7. IoT / IoE / IIoT Use Case examples
Retail (Dynamic Pricing, Smart Supply Chain, Precision Demand Forecasting)
Marketing (Personalized Real-time Ad Campaigns for Next Best Offer)
Smart Highways (monitoring vehicles, weather, road conditions, closures)
Precision Traffic (Self-driving & Self-parking Connected Cars)
Smart Cities (Growth, Dynamic Street-lighting, Smart Energy Usage)
Law Enforcement (Predictive, Prescriptive personnel & resource placements)
Healthcare (Wearables, Personalized Medicine, Patient/Provider Monitoring)
Online Education (Personalized Learning, Real-time interventions)
Forests, Farms, Vineyards,… (Precision Planning, Nurturing, Harvesting)
Financial / Banking / Insurance (Real-time Risk Mitigation, Fraud detection)
Organizations (Smart Ergonomics, Improved Employee Workflow, Process Mining for Efficiencies)
Invisibles (under-the-skin smart sensors – not only measure, but also learn, react, and proactively respond)
Machines (Early Warning, Prescriptive Maintenance, Smart Obsolescence, M2M, IIoT)

8. In a nutshell … the XYZ of IoT: Intelligence at the edge of the network (at the point of data collection)
Smart X
Precision Y
Personalized Z

9. IoT for Societal Good

10. So many sensors, so much data! The Information Overload Problem

11. So many sensors, so much data
So many sensors, so much data! Two categories of solutions: Standards and Analytics

12. Data Science = 4 Types of Discovery Advanced Analytics for the IoT
Correlation (Predictive Power!) Discovery
Finding patterns and dependencies, which reveal new governing principles or behavioral patterns (the “DNA”)
Novelty (Surprise!) Discovery
Finding new, rare, one-in-a-[million / billion / trillion] objects and events
Class Discovery
Finding new classes of objects and behaviors
Learning the rules that constrain class boundaries
Association Discovery
Finding unusual (improbable) co-occurring associations

13. Goal of Data Science: Take Data to Information to Knowledge to Insights (and Action!)
From Sensors (Measurement & Data Collection)…
… to Sentinels (Monitoring & Alerts) …
… to Sense-making (Data Science) …
… to Cents-making (ROI)
… Productizing / Monetizing your Big Data

14. Creating Value from Big Data in the IoT : The 3 D2D’s
Knowledge Discovery
Data-to-Discovery (D2D)
Data-driven Decision Support
Data-to-Decisions (D2D)
Big ROI (Return On Innovation)
Data-to-Dollars (D2D) or Data-to-Dividends
Innovative Applications of sense-making from IoT sensors and sentinels everywhere

15. The Mars Rover Metaphor

16. Mars Rover: intelligent data-gatherer, mobile data mining agent, and autonomous decision-support system
Rove around the surface of Mars and take samples of rocks (experimental data type: mass spectroscopy = data histogram = feature vector)
Intelligent Data Operations in Action:
Classification (assign rocks to known classes)
Supervised Learning (search for rocks with known compositions)
Unsupervised Learning (discover what types of rocks are present, without preconceived biases)
Clustering (find the set of unique classes of rocks)
Association Mining (find unusual associations)
Deviation/Outlier Detection (one-of-kind; interesting?)
On-board Intelligent Data Understanding & Decision Support Systems (Fuzzy Logic & Decision Trees & Cased-Based Reasoning ) =
= Science Goal Monitoring :
“stay here and do more” ; or else “follow trend to most interesting location”
“send results to Earth immediately” ; or “send results later”

17. From Sensors to Sentinels to Sense
Smart Sensors & Sentinels for Data-Driven Sense-Making and Decision Support
From Sensors to Sentinels to Sense
New knowledge and insights are acquired by mining actionable data from all digital inputs (Sensors!)
Sensors acts autonomously, without human intervention, in Deep Space environment, applying machine learning rules for targeted object recognition and classification. (Sentinels!)
“Smart Sensors” (powered by Machine Learning-enabled sentinels) deliver actionable intelligence (Sense!)

18. The BIG Big Data Challenge in the IoT
General example of streaming data analytics:
Real-Time Event Mining for Actionable Intelligence:
Identifying, characterizing, & responding to millions of events in real-time streaming data
Deciding which events (out of millions) need investigation and/or response (triage!)
Web Analytics example:
Web Behavior Modeling and Automated System Response (from online interactions & web browse patterns, behavioral analytics, user segmentation, data-driven discovery,…)
Many other examples:
Health alerts (from EHRs and national health systems)
Tsunami alerts (from geo sensors everywhere)
Cybersecurity alerts (from network logs)
Social event alerts or early warnings (from social media)
Preventive Fraud alerts (from financial applications)
Predictive Maintenance alerts (from machine / engine sensors)
Infrastructure Monitoring alerts (from ubiquitous sensors)
Risk Mitigation

19. The MIPS Architecture Design for Dynamic Data-Driven Application Systems (DDDAS)
MIPS =
Measurement – Inference – Prediction – Steering
This applies to any Network of Sensors:
Web user interactions & actions (web analytics data), Cyber network usage logs, Social network sentiment, Machine logs (of any kind), Manufacturing sensors, Health & Epidemic monitoring systems, Financial transactions, National Security, Utilities and Energy, Remote Sensing, Tsunami warnings, Weather/Climate events, Astronomical sky events, …
Machine Learning enables the “IP” part of MIPS:
Autonomous (or semi-autonomous) Classification
Intelligent Data Understanding
Rule-based
Model-based
Neural Networks
Markov Models
Bayes Inference Engines
Alert & Response systems:
LSST 10million events
“Mars Rover” anywhere
Automation of any data-driven operational system

20. From Sensors to Sentinels to Sense
The MIPS Architecture Design for Dynamic Data-Driven Application Systems (DDDAS)
MIPS =
Measurement – Inference – Prediction – Steering
This applies to any Network of Sensors:
Web user interactions & actions (web analytics data), Cyber network usage logs, Social network sentiment, Machine logs (of any kind), Manufacturing sensors, Health & Epidemic monitoring systems, Financial transactions, National Security, Utilities and Energy, Remote Sensing, Tsunami warnings, Weather/Climate events, Astronomical sky events, …
Machine Learning enables the “IP” part of MIPS:
Autonomous (or semi-autonomous) Classification
Intelligent Data Understanding
Rule-based
Model-based
Neural Networks
Markov Models
Bayes Inference Engines
From Sensors to Sentinels to Sense
Alert & Response systems:
LSST 10million events
“Mars Rover” anywhere
Automation of any data-driven operational system

21. Advanced Analytics for IoT ROI
Learning from Data (Data Science):
Outlier / Anomaly / Novelty / Surprise detection
Clustering (= New Class discovery, Segmentation)
Correlation & Association discovery
Classification, Diagnosis, Prediction
… for D2D in the IoT:
Data-to-Discoveries
Data-to-Decisions
Data-to-Dividends
(big ROI = Return on Innovation)