Data Driven Action : A Primer on Data Science

Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a
Creative Commons Attribution-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
SPRINGONE2GX
WASHINGTON, DC
Unless otherwise indicated, these slides are © 2013 -2015 Pivotal Software, Inc. and licensed under a Creative Commons Attributio n-NonCommercial license: http://creativecommons.org/licenses/by-nc/3.0/
Data Driven Action: A Primer on
Data Science
Sarah Aerni (@iTweetSarah)
Srivatsan Ramanujam (@being_bayesian)
Jarrod Vawdrey (@jjvawdrey)

Agenda
• Approaches and Open Source Tools for Wrangling and Modeling Massive
Datasets
• Sarah Aerni
• Text Analytics at Scale on MPP
• Srivatsan Ramanujam
• A Scalable Framework For Real Time Monitoring & Prediction Of Sensor Data
• Jarrod Vawdrey
2

Our everyday devices are smart and talk to us

Connected devices take action
to make daily life easier.
But what else?

How can IoT help prevent
accidents like the Macondo
Disaster ?

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

Smart Systems = Sensors + Digital Brain + Actuators
Problem
Formulation
Modeling
Step
Data Step
Application
Step
Data Science for
Building Models
Sensors &
Actuators
Data Lake

How can data drive true, automated action?
How does this…

…become this?
How does this…

• How is data collected?
• Where is it stored and processed?
• Is there real signal or just noise?
• How can we build a predictive model?
• When is the right time to take action?

Critical considerations for successful modeling
How to build a
predictive model
at scale
Data-driven paradigms, data cleansing and feature engineering

How to build a
predictive model
at scale
Tradeoffs
between model
accuracy and
timeliness

How to build a
predictive model
at scale
Derive insight
from models to
change
processes
Tradeoffs
between model
accuracy and
timeliness

How to build a
predictive model
at scale
Use Cases
Oil Drilling Vaccine Manufacturing
Derive insight
from models to
change
processes
Tradeoffs
between model
accuracy and
timeliness
Treating Patients

Drilling into the San
Andreas Fault at Parkfield
California.
Credit: Stephen H.
Hickman, USGS
Data: The New Oil
• Oil & gas generates large amounts of data from sensors enabling data-
driven approaches to improve operations
Predictive maintenance
• 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

How are models built using sensor data?
Integrating
& Cleansing
Feature
Building
Modeling

Integrating
& Cleansing
Feature
Building
Modeling
Integrated Data
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)
Primary data sources

Integrating
& Cleansing
Operator Data
• Failure details
• RPM
ROP
Time

Integrating
& Cleansing
Operator Data
• Failure details
• RPM
ROP
Time
Drill bit changes

Integrating
& Cleansing
Operator Data
• Failure details
• RPM
ROP
Time
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00:00 10:00 20:00 30:00 40:00 50:00 00:00
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Operator Data
• Failure details
• RPM
WOB
Time
Integrating
& Cleansing

Operator Data
• Failure details
• RPM
WOB
Time
Integrating
& Cleansing
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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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Operator Data
• Failure details
• RPM
WOB
Time
Integrating
& Cleansing
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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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Data Driven Action : A Primer on Data Science

Data Driven Action : A Primer on Data Science

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