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Imagination at work.
Matt Denesuk!Chief Data Science Officer!GE Software!February 2014!
Compounding Business Value Through Big Data & Advanced Analytics:!"An Industrial Perspective"
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
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 !
!
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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
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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
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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.
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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!
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Industrial Data
Science
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“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!