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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© Copyright 2015 Pivotal. All rights reserved.
Our everyday devices are smart and talk to us
3© Copyright 2015 Pivotal. All rights reserved.
These devices are now talking to each other
4© Copyright 2015 Pivotal. All rights reserved.
How can connected devices in
our home be smart enough to
make daily life easier?
5© Copyright 2015 Pivotal. All rights reserved.
How can we know a tree has
fallen on a power line before
the residents complain?
6© Copyright 2015 Pivotal. All rights reserved.
How can we use data
to help prevent
accidents like the Macondo
Disaster ?
7© Copyright 2015 Pivotal. All rights reserved.
How does this…
8© Copyright 2015 Pivotal. All rights reserved.
How does this…
…become this?
9© Copyright 2015 Pivotal. All rights reserved.
How does this…
…become this?
By recognizing this
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© Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and
techniques
Sensors &
Actuators
12© Copyright 2015 Pivotal. All rights reserved.
To realize this opportunity requires the right tools and
techniques
Sensors &
Actuators
Data Lake
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© 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© 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© 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© 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© 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© 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© 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© Copyright 2015 Pivotal. All rights reserved.
Opportunities for
Data-Driven Decisions
in Pharma
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© 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© 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© 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© 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© 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$wob
True Potency
PredictedPotency
28© Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models?Temperature
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520
df$ts_utc
df$wob
•  Deriving signal noisy sensor data
requires data cleansing
29© Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models?Temperature
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520
df$ts_utc
df$wob
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•  Deriving signal noisy sensor data
requires data cleansing
30© Copyright 2015 Pivotal. All rights reserved.
How can noisy data create meaningful models?Temperature
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
101520
df$ts_utc
df$wob
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•  Deriving signal noisy sensor data
requires data cleansing
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science
Business Impact From IoT? Just Add Data Science

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Business Impact From IoT? Just Add Data Science

  • 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. 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  • 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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cleansing
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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● •  Deriving signal noisy sensor data requires data cleansing