Data Science Case Studies: The Internet of Things: Implications for the Enterprise

2 © 2015 Pivotal Software, Inc. All rights reserved. 2 © 2015 Pivotal Software, Inc. All rights reserved.

Internet of Things: Implications for the Enterprise

Rashmi Raghu, Ph.D. Principal Data Scientist

3 © 2015 Pivotal Software, Inc. 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

Billions of Data Points

Oil Exploration

Video Surveillance

OIL RIGS GENERATE

25000 DATA POINTS PER SECOND

Medical Imaging

Mobile Sensors


Implications for the Enterprise �  Organizational –  Vision –  Preparedness –  Execution

�  Technical –  Data quality & completeness –  Heterogeneity of data sources –  Technology architecture



�  Technical –  Data quality & completeness –  Heterogeneity of data sources –  Technology architecture

Issues in any of these have implications for data science approaches and their effectiveness


Case Studies

Oil Drilling Telecommunications Predictive Maintenance Customer Micro-segmentation


Case Studies



Data: The New Oil �  Oil & gas exploration and production activities generate

large amounts of data from sensors

�  What opportunities exist for data-driven approaches to improve operations?

Drilling into the San Andreas Fault at Parkfield California. Credit: Stephen H. Hickman, USGS

*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry


Data: The New Oil �  Oil & gas exploration and production activities generate

large amounts of data from sensors

�  What opportunities exist for data-driven approaches to improve operations?

Drilling into the San Andreas Fault at Parkfield California. Credit: Stephen H. Hickman, USGS

*http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry

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


Predictive Maintenance for Drilling Operations

Integrating & Cleansing

Feature Building Modeling


Primary Data Sources



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) …


Primary Data Sources: Challenges



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) …

Challenges •  Failure instances not clearly labeled •  Labels may be embedded in reports or comments

Implications •  Dependent variable generation also becomes a

machine learning exercise •  Accuracy of failure prediction impacted by

accuracy of failure label derivation


Primary Data Sources: Challenges

Well ID Depth Comment Event flag

1 1000 equipment not responding 1

2 2000 TOOH to bit. rubber pieces seen 1



•  Dependent variable generation – a machine learning exercise •  Text analytics pipeline needed to convert failure reports or comments to event flags


Complex Feature Set Across Data Sources



•  A failure occurred at the end of this run

•  Taking a window of time prior to failure, what features could we extract (e.g. variance of RPM, max bit position velocity)?

Bit

posi

tion

RPM

RO

P W

OB


Complex Feature Set Across Data Sources

•  Depth •  Rate of Penetration •  Torque •  Weight on Bit •  RPM •  …

•  Drill Bit details •  Component

details etc. •  Failure events •  …

Features on Time

Windows

•  Mean •  Median •  Standard Deviation •  Range •  Skewness •  …

Final Set of Features on

Time Windows

•  Leverage GPDB / HAWQ (+ MADlib, PL/X) for fast computation of hundreds of features over time windows within billions of rows (or more) of time-series data

Operator data

Drill Rig Sensor

data


Predictive Maintenance App Pipeline

Data Lake

Ingest

Business Levers

Early Warning System Rig Operator Dashboard

Models •  Elastic Net Regression •  Cox Proportional

Hazards Regression •  Decision Trees

Initial data cleansing filters

Wells with failure scores and early

warning indicators

Feedback loop for continuous model improvement Domain

Knowledge

Oil Rig Operator

HAWQ

GPDB PL/X

MADlib

R Python

C Java Perl

Spark + MLlib


Case Studies



State of Data at Telco Company

Customer Segments

New Data Sources

Multi-Gadget Families Affluent Matures

Thrifty Families High Tech Singles

Budget Singles Seniors

Internet Deep Packet Inspection TV Consumption (Linear)

Video On Demand Consumption


Native Services

Video On Demand TV Internet

Internet Devices

OTT (Over The Top) Services

What is the level of engagement with client’s products (TV, VOD, Internet)?

What are the patterns of device usage behavior?

What is the level of OTT engagement, by segment, and by bandwidth?

Understanding Subscriber Behavior


Newly Identified Behavior-Based Segments S

ubsc

riber

s

Moderates

OTT & Data Heavyweights

Portable OTT Entertainment Seekers

iPhone Heavy

Android Heavy

iPad Heavy

In-Home OTT Entertainment Seekers

In-Home Native Content Seekers

VOD Heavy

TV Heavy


Moderates

OTT & Data Heavyweights

In-Home OTT Entertainment Seekers

Portable OTT Entertainment Seekers - iPhone Heavy

Portable OTT Entertainment Seekers - Android Heavy

Portable OTT Entertainment Seekers - iPad Heavy

In-Home Native Content Seekers - VOD Heavy

In-Home Native Content Seekers - TV Heavy

Cross Behavior-based and Existing Segments New Behavior-Based Segments

Customized Micro-Segments!

Existing Segments

Multi-Gadget Families

Affluent Matures

Thrifty Families

Budget Singles

High Tech Singles

Seniors


Heterogeneous Data Sources �  Prevalence of new data sources was

limited but increasing –  Rich usage data available on a

subset of the subscribers –  Leads to limited applicability of

micro-segments

�  Lack of data may be alleviated by expanding data science efforts –  Leverage micro-segmentation model to

score a different subset of subscribers (who we have limited data on)

New Data Sources

Internet Deep Packet Inspection TV Consumption (Linear)

Video On Demand Consumption


Driving New Business Value

Upsell and Cross-Sell New Product Offerings Data Monetization



�  Technical / Data –  Data quality & completeness –  Heterogeneity of data sources –  Technology architecture

•  Data quality & completeness: •  Data capture mechanisms can have a lasting impact on ability to solve a

business problem •  Heterogeneity of data sources:

•  Existence of legacy systems & devices may limit the applicability of new models unless that is taken into account ahead of time

•  Feedback to spur upgrading of equipment wherever possible


Implications for the Enterprise �  Creating value from IoT requires organizational and technical alignment

�  Impacts of these considerations on data science efforts and outcomes are non-trivial

�  Specific impacts of data issues include: –  Longer time to realization of value –  Model accuracy issues –  Limited applicability of results –  And more …


For further information, checkout … �  Pivotal Blog @ http://blog.pivotal.io

�  Pivotal Data Science Blog @ http://blog.pivotal.io/data-science-pivotal

�  Pivotal Data Product Info, Docs and Downloads @ http://pivotal.io/big-data

�  Oil & Gas Use Case Webinar: –  Video: https://www.youtube.com/watch?v=dhT-tjHCr9E –  Slides: http://www.slideshare.net/Pivotal/data-as-thenewoil

�  Blogs: –  Oil & Gas Use Case:

http://blog.pivotal.io/pivotal/case-studies-2/data-as-the-new-oil-producing-value-for-the-oil-gas-industry

–  Time Series Analysis: http://blog.pivotal.io/tag/time-series-analysis

Data & Analytics

Data Science Case Studies: The Internet of Things: Implications for the Enterprise