Hadoop Summit Talk on IoT and Data Science: The Internet of Things (IoT) will forever change the way businesses interact with consumers and other businesses. Pivotal will present a series of use cases illustrating how such devices and the data from these devices drives real impact across industries. From smart sensors to connected hospitals, each example will highlight the fundamental concepts to success. · Starting with the basics: How data science drives action and outcomes · Avoiding the obstacles: How to avoid the pitfalls that prevent models from driving real action · Building your toolbox: What tools are available

1. 1© Copyright 2015 Pivotal. All rights reserved. 1© Copyright 2013 Pivotal. All rights reserved.
Internet of Things
How Data Science-Driven Software is Eating the
Connected World
Sarah Aerni, Principal Data Scientist
Pivotal
@itweetsarah
Hadoop Summit, San Jose, CA
June 10th

2. 2© Copyright 2015 Pivotal. All rights reserved.
Our everyday devices are smart and talk to us

3. 3© Copyright 2015 Pivotal. All rights reserved.
These devices are now talking to each other

4. 4© Copyright 2015 Pivotal. All rights reserved.
Connected devices take action
to make daily life easier.
But what else?

5. 5© Copyright 2015 Pivotal. All rights reserved.
How can IoT help prevent
accidents like the Macondo
Disaster ?

6. 6© Copyright 2015 Pivotal. All rights reserved.
Gene Sequencing
Smart Grids
COST TO SEQUENCE
ONE GENOME
HAS FALLEN FROM
$100M IN 2001
TO $10K IN 2011
TO $1K IN 2014
READING SMART METERS
EVERY 15 MINUTES IS
3000X MORE
DATA INTENSIVE
Stock Market
Social Media
FACEBOOK UPLOADS
250 MILLION
PHOTOS EACH DAY
In all industries billions of data points represent
opportunities for the Internet of Things
Oil Exploration
Video Surveillance
OIL RIGS GENERATE
25000
DATA POINTS
PER SECOND
Medical Imaging
Mobile Sensors

7. 7© Copyright 2015 Pivotal. All rights reserved.
Smart Systems = Sensors + Digital Brain + Actuators
Problem
Formulation
Modeling
Step
Data Step
Application
Step
Data Science for
Building Models
Sensors &
Actuators
Data Lake

8. 8© Copyright 2015 Pivotal. All rights reserved.
How can data drive true, automated action?
How does this…

9. 9© Copyright 2015 Pivotal. All rights reserved.
How can data drive true, automated action?
How does this…
…become this?

10. 10© Copyright 2015 Pivotal. All rights reserved.
How can data drive true, automated action?
How does this…
…become this?

11. 11© Copyright 2015 Pivotal. All rights reserved.
How can data drive true, automated action?
Ÿ How is data collected?
Ÿ Where is it stored and processed?
Ÿ Is there real signal or just noise?
Ÿ How can we build a predictive model?
Ÿ When is the right time to take action?

12. 12© Copyright 2015 Pivotal. All rights reserved.
Critical considerations for successful modeling
How to build a
predictive model at
scale
Data-driven paradigms, data cleansing and feature engineering
Use Cases
Oil Drilling

13. 13© Copyright 2015 Pivotal. All rights reserved.
Critical considerations for successful modeling
How to build a
predictive model at
scale
Tradeoffs between
model accuracy
and timeliness
Data-driven paradigms, data cleansing and feature engineering
Use Cases
Oil Drilling Vaccine Manufacturing

14. 14© Copyright 2015 Pivotal. All rights reserved.
Critical considerations for successful modeling
How to build a
predictive model at
scale
Data-driven paradigms, data cleansing and feature engineering
Use Cases
Oil Drilling Vaccine Manufacturing
Derive insight from
models to change
processes
Tradeoffs between
model accuracy
and timeliness
Treating Patients

15. 15© Copyright 2015 Pivotal. All rights reserved.
Critical considerations for successful modeling
Data-driven paradigms, data cleansing and feature engineering
Use Cases
Oil Drilling Vaccine Manufacturing Treating Patients
How to build a
predictive model at
scale
Derive insight from
models to change
processes
Tradeoffs between
model accuracy
and timeliness

16. 16© Copyright 2015 Pivotal. All rights reserved.
Data: The New Oil
Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGS
Ÿ Oil & gas exploration and production activities generate large
amounts of data from sensors
Ÿ What opportunities exist for data-driven approaches to improve
operations?
*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry

17. 17© Copyright 2015 Pivotal. All rights reserved.
Data: The New Oil
Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGS
Ÿ Oil & gas exploration and production activities generate large
amounts of data from sensors
Ÿ What opportunities exist for data-driven approaches to improve
operations?
Drilling operations Predictive maintenance
*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry

18. 18© Copyright 2015 Pivotal. All rights reserved.
Data: The New Oil
Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGS
Ÿ Oil & gas exploration and production activities generate large
amounts of data from sensors
Ÿ What opportunities exist for data-driven approaches to improve
operations?
Drilling operations
• Predicting drill rate-of-penetration (ROP)
• Motivation: Shorter time to oil production
lowers costs and increases production
• Goals
– Identify optimal parameters for rapid
drilling
– Create an initial approach for drilling
– Potential reduction in damage to
equipment
*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry
Predictive maintenance

19. 19© Copyright 2015 Pivotal. All rights reserved.
Data: The New Oil
Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGS
Ÿ Oil & gas exploration and production activities generate large
amounts of data from sensors
Ÿ What opportunities exist for data-driven approaches to improve
operations?
Drilling operations
• Predicting drill rate-of-penetration (ROP)
• Motivation: Shorter time to oil production
lowers costs and increases production
• Goals
– Identify optimal parameters for rapid
drilling
– Create an initial approach for drilling
– Potential reduction in damage to
equipment
Predictive maintenance
• Predict equipment function and failure
• Motivation: Failure costs estimated at
$150,000/incident (billions annually)*
• Goals
– Early warning system
– Insights into prominent features impacting
operation and failure
– Reduction of non-productive drill time
– Reduced incidents
*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry

20. 20© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
Feature
Building
Modeling

21. 21© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
Feature
Building
Modeling
Integrated Data
Primary data sources
Operator Data
( thousands of records )
• Failure details
• Component details
• Drill Bit details
Drill Rig Sensor Data
( billions of records )
• Rate of Penetration (ROP)
• RPM
• Weight on Bit (WOB)

22. 22© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
ROP
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
6080100120140 df$ts_utc
df$rop
Primary data sources
Operator Data
( thousands of records )
• Failure details
• Component details
• Drill Bit details
Drill Rig Sensor Data
( billions of records )
• Rate of Penetration (ROP)
• RPM
• Weight on Bit (WOB)

23. 23© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
ROP
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
6080100120140 df$ts_utc
df$rop
Primary data sources
Operator Data
( thousands of records )
• Failure details
• Component details
• Drill Bit details
Drill Rig Sensor Data
( billions of records )
• Rate of Penetration (ROP)
• RPM
• Weight on Bit (WOB)
Drill bit changes

24. 24© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
WOB
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
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Primary data sources
Operator Data
( thousands of records )
• Failure details
• Component details
• Drill Bit details
Drill Rig Sensor Data
( billions of records )
• Rate of Penetration (ROP)
• RPM
• Weight on Bit (WOB)

25. 25© Copyright 2015 Pivotal. All rights reserved.
How are models built using sensor data?
Integrating
& Cleansing
WOB
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520 df$ts_utc
df$wob
Primary data sources
Operator Data
( thousands of records )
• Failure details
• Component details
• Drill Bit details
Drill Rig Sensor Data
( billions of records )
• Rate of Penetration (ROP)
• RPM
• Weight on Bit (WOB)