1. Imagination at work.
Matt Denesuk!
Chief Data Science Officer!
GE Software!
February 2014!
Compounding Business Value Through Big
Data & Advanced Analytics:!
"An Industrial Perspective"
© General Electric Company, 2014. All Rights Reserved.
Contact: matthew.denesuk@ge.com!
RETHINK TECHNOLOGY
Transforming IT Systems, Data and
Technology Operations
Four Seasons Hotel
East Palo Alto, CA
February 13, 2015

2. What’s this all about? "
Industries that are all about
data & IT see outsized
productivity & performance
gains!
• Telecom, financial srvcs,…!
2
Making industrials all about data
& IT will transform how the world
works!
• Power, water, aviation, rail, mining, oil
& gas, manufacturing, …!
And Big Data + Physics is the enabler!

3. 3 GESoftware.com | @GESoftware |
#IndustrialInternet
The Value to Customers is Huge!
Efficiency and cost savings, new customer services, risk
avoidance – 1% improvements cuts $276B in waste across
industries!
Aviation
Power
Healthcare
Rail
Oil and Gas
Industry Segment Type of savings
Estimated value
over 15 years
$66B
$30B
$63B
$27B
$90B
Commercial
Gas-fired
generation
System-wide
Freight
1% fuel savings
Exploration and
development
1% fuel savings
1% reduction in
system inefficiency
1% reduction in
system inefficiency
1% reduction in
capital expenditures
Note: Illustrative examples based on potential one percent savings applied across specific global industry sectors. Source: GE estimates

4. Example: Wind Farm in Analytics Age!
(20 TB/yr"
for 250
wm farm)"

5. 5 GESoftware.com | @GESoftware |
#IndustrialInternet
Internet"
of things!1SW-defined !
machines! 2 Big Data &
Analytics!3Deep domain
capability! 4Active network"
of machines, data,"
and people!
Adaptable nodes
to enable system
flexibility. !
Employing deep
physics, engineering,
and expert models to
understand the data
and build actionable
models. !
Scaling and
dramatically
accelerating time to
value. !
Critical ingredients:!
“Industrial Data Science”!

6. Cornerstone of the Transformation is
Software-Defined Machines (SDM’s)"
!
! CONSUMER"
COMMERCIAL &
INDUSTRIAL"
Device behavior has to be adaptable!

7. Entertainment
digitized
© General Electric Company, 2014. All Rights Reserved.
Connectivity!
What happened when 1B people became connected? !
Social
marketing
emerged
Communications
mobilized
IT architecture
virtualized
Retail & ad
transformed
Consumer
Internet
][
][
][
][
][

8. Industrial
Internet
Brilliant
Power
Brilliant
Factory
Logistics
Optimization
Factory
Optimization
Smart
Grid
Hospital
Optimization
Real-time
Network
Planning
Intelligent
Medical
Devices
Connected
Machines
Brilliant
Hospital
Brilliant
Rail Yard
Now what happens when 50B Machines
get connected? !
Employees increase productivityOT is virtualized Analytics become predictive
Machines are self healing & automated Monitoring and maintenance is mobilized[
[
© General Electric Company, 2014. All Rights Reserved.
Shipment
Visibility

9. What do we need from Data Science? !
9

10. 10
Three basic components of Industrial Data
Science"
Physics/engineering-based models"
• Need much less data!
• Powerful, but difficult to maintain and scale!
!
Empirical, heuristic rules & insights"
• Straightforward to understand !
• Captures accumulated knowledge of your experts!
!
Data-driven techniques – machine learning,
statistics, optimization, advanced visualization, …"
• Often not enough data in the industrial domain!
• Bias: limited to regions of parameter space traversed
in normal operation!
• But easiest to maintain and scale !
!

11. 11
Industrial Example: improving rule based systems!
Many equipment operators have a system something like this, with rules
derived based on experience and intuition.
Rule sets
implemented in
Analytics Engine
Produce alerts
Low-latency
operational
data
Alerts

12. 12
Industrial Example: improving rule based systems!
Rule sets
implemented in
Analytics Engine
Produce alerts
Low-latency
operational
data
Pattern, sequence,
association mining, etc.
Outcome
data
Combine ML plus rule-based
alerts with outcome data to
produce better alerts
More
actionable
alerts

13. 13
Sensor Data
Another Industrial Example: use advanced physical
models to create new features for ML approaches!
Predicted Values
and Δs"
Variety of Machine
Learning
Techniques
Outcome
data
Using as ML features the:
1. Deviations from
expected physics, &
2. Inferred or hidden
parameter estimates
provides much richer and
effectively less noisy
data, resulting in much
stronger predictions and
models.

14. 14
Capability / Impact Ramp"
Data completeness, breadth, quality
DataScienceComplexity
Basic
Reporting
Advanced
Reporting
Anomaly
Detection
Rules
augmentation
Predictive
analytics
Prescriptive
analytics
Operational
optimization
Alerts
Highly-
actionable
management
info
High-value
guidance
Sophisticated, optimized
management of business
operations

15. Optimizes the design &
operations of complex
business and physical
systems, extracting more
value at lower risk
Broad range of deep Data Science capabilities
needed
Innovates new ways of
performing reliability
analysis, statistical
modeling of large data,
biomarker discovery and
financial risk management
Focuses on developing
algorithms and systems for
real time video analysis
Research in algorithms and
software systems that analyze &
understand images to produce
actionable insights
Develop scalable and cross-
disciplinary machine learning
& predictive capabilities to
derive actionable insights from
big data
Modeling complex system and
noise processes to detect subtle
deviations and estimate critical
system parameters
Employing deep physical and
engineering understanding of
equipment and processes to
generate normative models.
Sensor &
Signal
Analytics!
Delivering data and
knowledge-driven decision
support via semantic
technologies and big data
systems research
Knowledge!
Discovery!
Applied
Statistics!
Physics &
expert-
based
Modeling!
Machine!
Learning!
Computer!
Vision!
Image
Analytics!
Optimization &
Management
Science!
15
Industrial
Data
Science

16. 16
“Industrial Data Science” "
Outcome-oriented application of mathematical & physics-based
analysis & models to real-world problems in industrial operations. !
Tools & processes needed to do that continually & at scale. !
Improve the performance of industrial operations, e.g.,"
• Higher equipment uptime, utilization, !
• Lower maintenance/shop costs, longer component life!
• Fleet level optimization & trade-offs!
• Business optimization (linking to financial & customer data)!
Combination of :"
• Physical & expert modeling experience & depth!
• Installed base of industrial equipment and data. !
• Big Data, Machine Learning, and statistical capabilities!
What
is it? "
Why do
we do it!
What’s
needed"
Industrial
Data
Science