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Big Data and Analytics – Creating
Actionable Intelligence
Shane Benfield – Boeing Global Services Analytics
Wednesday October 17, 2018 - 3:30 pm - 4:30 pm
Key Themes for this session
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• Focus on outcomes before data
• Collaboration is key
• Analytics programs are scalable
Analytics Projects
3
Highly dissatisfied, 3%
Dissatisfied18%
Neutral54%
Satisfied19%
Highly satisfied, 6%
How satisfied are you with results of current analytics projects?
Today’s Reality
Only 25%
Satisfied
4%
16%
26%
27%
27%
Other
Talent /Expertise
Data Access
Culture
Tools
What are your key challenges to achieving improved results from
analytics?
Key ChallengesThe Promise
BigData
Analytics Tools & Platform
Big Value
Source: Boeing Global Services Customer Conference (11/2017)
Our Perspective
4
Real-TimeApplications
Integration & Automation
Prognostics & Optimization++=
Big Data
Analytics Tools & Platform
Aerospace Engineering
Insights++=
Digital
Solutions
AnalyticsProjects
Customer Value Through an Analytics Ecosystem
Completely on your own 7%
Third-party tools/data/
expertise 27%Digital solutions with
embeded analytics 63%
Fully outsourced 3%
Provide Customer Choice
How do you plan to maximize the value of analytics?
+Insights Data
Source: Boeing Global Services Customer Conference (11/2017)
01
01
001
1
00
11
01
01
0
10
01
01
00
1
0
End-to-End Analytics
5
Data and Analytics Platform
Data / Content
Self-ServiceAnalytics/BI
Analytics Consultingand Support
Analytics EnabledDigital Solutions
Domain expertiseData scientistsOEM insights Data
Continuous Optimization
00
10
100
1
00
11
01
01
0
1
01
10
101
0
1
Periodic Projects
CustomerValue
Data
Two Questions
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Question 1: Hype – Not Hype?
A. It is marketing hype, vaporware, an empty promise
B. A lot of hype, but I do see a core that is real and potentially promising
C. Some hype, and my company does do some of these activities and we have had some
success
D. My company uses it everyday to help our operations
E. I have no idea what these terms mean
Big Data, Data Mining, Data Analytics, Artificial Intelligence
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Question 2: To what extent is your operation using
data and data analytics?
A. We have our own in-house analytics team
B. We use outside consultants to handle our analytics needs
C. We use analytics enabled applications in our operations
D. We use consultants and analytics applications
E. To my knowledge we don’t use any analytics
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What does a BA operator care about?
• Safety
• Customer Service
• Demand
• Weather, ATC or other impacts
to day-of-operation
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• Operating Costs
• Maintenance Events
• Fuel Management
• Special Events
Aerospace data generation growing exponentially Digital transformation of aerospace is driving the creation of real-time data growth
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Petabytes
Terabytes
Gigabytes
Megabytes
FuturePresentPast
Time
Aircraft and Operational data from increased
onboard sensors and digitalized operations
• Direct data – sensors readings
• Derived data – calculated from sensors or
other onboard information
• Operational data – all non-aircraft data
from flight ops to maintenance
• OEM and Industry data - like ATC, weather,
and information provided by the
manufacturers and suppliers
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What do we mean by data?
Used for revealing opportunities
Method to improve decision
making
Tools to optimize processes
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What is data analytics? Transforming data into insights
A brief overview of the four analytics
types
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Descriptive Analytics
Diagnostic Analytics
Predictive Analytics
Prescriptive Analytics
What happened?
Why did it happen?
What will happen?
How can we make it
happen?
It is more than statistics, it is a rich set of tools and
methods
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• Statistics
• Network Analysis
• Non-Linear Optimization
• Pattern Recognition
• Classification Engines
• Semantic Search
• Mathematics
• Indexing
• Modeling & Simulation
• Machine Learning
• Text Analytics
• Data Visualization
• Expert Systems
• Artificial Intelligence
• Neural Networks
• Deep Learning
Examples:
In aviation technical operations, data and
analytics represent physical objects and/or
actions; real-world stuff.
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For example…
One method used to create a predictive alertIt takes historical data, data analytics, and domain expertise
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Sensor inputs OEM Knowledge
StatisticalAnalysis
Develop Prediction
Release for Use
Test & Verify
Validate in Ops
Another Question
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Question 3: What are your key challenges to achieving
improved results from analytics?
A. Lack of access or issues with data analytics tools
B. Acceptance by, or implementation within, the operational culture
C. Access to data either internal or external to the organization
D. Access to analytics talent and/or expertise
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Have clarity on business goals
Optimize the system
Identify the technology - applications
Prepare the people and processes
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Approach to maximize value
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It all starts with goals
Understand the desired business outcome or
goals before going after methods, technology, or
data.
• Efficiency
• Performance
• Safety
• Economy
• Schedule
• Reliability
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• What actions will be needed?
• What decisions drive these actions?
• What situations require these decisions?
Then we work back from the desired
results with goals
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This drives the right mix of
analytics and data
This method also supports addressing the people and process issues at a system level
Last Question
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Question 4: Of the issues listed below, which one are
you most concerned about?
A. Increasing Revenue
B. Fuel Spend
C. Improving Fleet Reliability
D. Operational Costs
E. Competitive Pressure
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Example Use Cases
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Analytics applied to Flight Optimization
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One major saved 350 min/day on 4K flights and reduced fuel by 14M lbs./year
• Alerting on opportunities for
optimization not available in a filed
flight plan
• Continuously looking for post-
departure wind-optimal shortcuts
(10%- 20%)
• Monitors surrounding traffic, up-to-
date weather, and airspace
constraints
Eliminating unscheduled maintenance events – reduces delays or cancellations
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Predictive Maintenance
Predictive alert on 777 Integrated Drive Generator avoided an in-service delay or
cancellation and saved up to $300,000 in repair costs.
Fuel analysis saves GOL 20 million kilos of fuelFuel savings of 1% - 3% have been achieved for some operators
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GOL Transportes Aereos using fuel data
analytics
• Identify potential savings and track
savings across all phases of flight
• Benchmark performance and
optimize flight plans
C-17 Lifecycle Optimization ProgramNew analytics applications to increase value and readiness
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In-flight and historical data combined with
maintenance plans and other data helped
increase readiness and lower lifecycle costs
• Fleet performance trending and analysis
• Fuel optimization
• Part failure prevention
• Improved maintenance operations
Key Learnings
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• Focus on achieving a business
outcome
• Data + statistics + tools +
domain expertise = effective
analytics solutions
• Analytics programs are scalable
Data Analytics boosts competitiveness
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