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Internet Of Things: How Data Science Driven Software is Eating the Connected World

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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

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Internet Of Things: How Data Science Driven Software is Eating the Connected World

  1. 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. 2© Copyright 2015 Pivotal. All rights reserved. Our everyday devices are smart and talk to us
  3. 3. 3© Copyright 2015 Pivotal. All rights reserved. These devices are now talking to each other
  4. 4. 4© Copyright 2015 Pivotal. All rights reserved. Connected devices take action to make daily life easier. But what else?
  5. 5. 5© Copyright 2015 Pivotal. All rights reserved. How can IoT help prevent accidents like the Macondo Disaster ?
  6. 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. 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. 8© Copyright 2015 Pivotal. All rights reserved. How can data drive true, automated action? How does this…
  9. 9. 9© Copyright 2015 Pivotal. All rights reserved. How can data drive true, automated action? How does this… …become this?
  10. 10. 10© Copyright 2015 Pivotal. All rights reserved. How can data drive true, automated action? How does this… …become this?
  11. 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. 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. 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. 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. 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. 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. 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. 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. 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 $150,000/incident (billions annually)* •  Goals –  Early warning system –  Insights into prominent features impacting operation and failure –  Reduction of non-productive drill time –  Reduced incidents *http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry
  20. 20. 20© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? Integrating & Cleansing Feature Building Modeling
  21. 21. 21© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? Integrating & Cleansing Feature Building Modeling Integrated Data Primary data sources Operator Data ( thousands of records ) •  Failure details •  Component details •  Drill Bit details Drill Rig Sensor Data ( billions of records ) •  Rate of Penetration (ROP) •  RPM •  Weight on Bit (WOB)
  22. 22. 22© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? 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  23. 23. 23© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? 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  24. 24. 24© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? 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  25. 25. 25© Copyright 2015 Pivotal. All rights reserved. How are models built using sensor data? 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● ● ● ● ● ● ● 00:00 10:00 20:00 30:00 40:00 50:00 00:00 101520 df$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)

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