Strata Conference talk on how Data Science brings IoT to life through predictive modeling. The Internet of Things (IoT) will forever change the way businesses interact with each other and their customers. In 2020, 25 billion connected “things” will be in use, reports Gartner. IDC predictions are even higher as analysts estimate IoT will grow from 15 billion devices in 2015 to 30 billion devices in 2020. With the adoption of these devices, effective leveraging of the torrent of data will be critical to driving the transformation of industries. Central to the fundamental shift will be the ability to pool data, and build models that drive real and significant actions. From smart sensors to connected hospitals, Sarah will use illustrative use cases to demonstrate the fundamental concepts required to drive true impact from these connected devices. She will cover which models are most appropriate for a variety of actions and outcomes, what considerations around data access and processing are critical, and which tools are available to accomplish your task at hand.

1. 1© Copyright 2015 Pivotal. All rights reserved. 1© Copyright 2013 Pivotal. All rights reserved.
Business impact from IoT?
Just add data science
Sarah Aerni, Principal Data Scientist
Pivotal
@itweetsarah
Strata + Hadoop World, New York
September 30th

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.
How can connected devices in
our home be smart enough to
make daily life easier?

5. 5© Copyright 2015 Pivotal. All rights reserved.
How can we know a tree has
fallen on a power line before
the residents complain?

6. 6© Copyright 2015 Pivotal. All rights reserved.
How can we use data
to help prevent
accidents like the Macondo
Disaster ?

7. 7© Copyright 2015 Pivotal. All rights reserved.
How does this…

8. 8© Copyright 2015 Pivotal. All rights reserved.
How does this…
…become this?

9. 9© Copyright 2015 Pivotal. All rights reserved.
How does this…
…become this?
By recognizing this

10. 10© 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

11. 11© Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and
techniques
Sensors &
Actuators

12. 12© Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and
techniques
Sensors &
Actuators
Data Lake

13. 13© Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and
techniques
Problem
Formulation
Data Science for
Building Models
Sensors &
Actuators
Data Lake

14. 14© Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and
techniques
Problem
Formulation
Data Step
Data Science for
Building Models
Sensors &
Actuators
Data Lake

15. 15© Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and
techniques
Problem
Formulation
Modeling
Step
Data Step
Data Science for
Building Models
Sensors &
Actuators
Data Lake

16. 16© Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and
techniques
Problem
Formulation
Modeling
Step
Data Step
Application
Step
Data Science for
Building Models
Sensors &
Actuators
Data Lake

17. 17© Copyright 2015 Pivotal. All rights reserved.
What does it take to build a data-driven models?
Data Cleansing Libraries to Build
Models At-Scale
Feature
Engineering

18. 18© Copyright 2015 Pivotal. All rights reserved.
Treating Patients
What does it take to build a data-driven models?
Data Cleansing Libraries to Build
Models At-Scale
Feature
Engineering
Vaccine Manufacturing Oil Drilling

19. 19© Copyright 2015 Pivotal. All rights reserved.
Treating Patients
What does it take to build a data-driven models?
Data Cleansing Libraries to Build
Models At-Scale
Feature
Engineering
Derive insight from
models to change
processes
Tradeoffs between
model accuracy
and timeliness
Vaccine Manufacturing Oil Drilling

20. 20© Copyright 2015 Pivotal. All rights reserved.
Treating Patients
What does it take to build a data-driven models?
Data Cleansing Libraries to Build
Models At-Scale
Feature
Engineering
Derive insight from
models to change
processes
Tradeoffs between
model accuracy
and timeliness
Vaccine Manufacturing Oil DrillingVaccine Manufacturing

21. 21© Copyright 2015 Pivotal. All rights reserved.
Opportunities for
Data-Driven Decisions
in Pharma

22. 22© Copyright 2015 Pivotal. All rights reserved.
A pipeline of sensors and opportunities for optimizing output
Internet of Things in Manufacturing
Input materials Mix Incubate Filter Centrifuge Final Product

23. 23© Copyright 2015 Pivotal. All rights reserved.
A pipeline of sensors and opportunities for optimizing output
Internet of Things in Manufacturing
Input materials Mix Incubate Filter Centrifuge Final Product
Sensors
Temp
Time
Absorbance
Elution volume
Velocity
Time

24. 24© Copyright 2015 Pivotal. All rights reserved.
A pipeline of sensors and opportunities for optimizing output
Internet of Things in Manufacturing
Input materials Mix Incubate Filter Centrifuge Final Product
Temp
Time
Absorbance
Elution volume
Velocity
Time
• What opportunities exist for intervention, correction?
• Which attributes should be used as features in a model?
• When is the appropriate time to take action?

25. 25© Copyright 2015 Pivotal. All rights reserved.
A pipeline of sensors and opportunities for optimizing output
Internet of Things in Manufacturing
Input materials Mix Incubate Filter Centrifuge Final Product
Temp
Time
Absorbance
Elution volume
Velocity
Time
• What opportunities exist for intervention, correction?
• Which attributes should be used as features in a model?
• When is the appropriate time to take action?
>6 months

26. 26© Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models?
True Potency
PredictedPotency
Input materials Mix Incubate Filter Centrifuge Final Product

27. 27© Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models?Temperature
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
True Potency
PredictedPotency

28. 28© Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models?Temperature
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
• Deriving signal noisy sensor data
requires data cleansing

29. 29© Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models?Temperature
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
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• Deriving signal noisy sensor data
requires data cleansing

30. 30© Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models?Temperature
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
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• Deriving signal noisy sensor data
requires data cleansing