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

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 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

Example: Wind Farm in Analytics Age!

(20 TB/yr" for 250 wm farm)"

5 GESoftware.com | @GESoftware | #IndustrialInternet

Internet "of things!1 SW-defined !

machines! 2 Big Data & Analytics!3 Deep domain

capability! 4 Active 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”!

Cornerstone of the Transformation is Software-Defined Machines (SDM’s)"!! CONSUMER" COMMERCIAL &

INDUSTRIAL"

Device behavior has to be adaptable!

Entertainmentdigitized

© 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

] [

] [

] [

] [

] [

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 productivity OT is virtualized Analytics become predictive

Machines are self healing & automated Monitoring and maintenance is mobilized [ [

© General Electric Company, 2014. All Rights Reserved.

Shipment Visibility

What do we need from Data Science? !

9

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

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

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

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

Capability / Impact Ramp"

Data completeness, breadth, quality

Dat

a S

cien

ce C

ompl

exity

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

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

“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