Airline and Airport Big Data: Impact and Efficiencies

How Big Data Changes Aviation Efficiency Routes 2014 Presentation Joshua Marks, CEO

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www.masflight.com

Airlines and airports generate tremendous amounts of data Legacy technology limits what we can log, merge and use Big Data unlocks the value of ambient data

The cloud and “Big Data” tools transform how we collect, merge and analyze data, opening new frontiers of capability

• Material change in operations and commercial capability

• Highly disruptive to global aviation – winners and losers

• Changes the industry’s profit horizon and long-term

Slide 2

Efficiency and optimization

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www.masflight.com Slide 3

Lots of useful information

Bookings & Transactions

Loyalty Programs

Supporting Information – Weather, Fleet, Revenue, Social Media, Etc.

Seats sold Prices paid Elasticity

Route demand Points of sale

Ancillaries

Customer name Demographics

Location Travel history Preferences

Offline activities

Airport Operations

Flight Operations

Facilities used Time on gate

Checked bags Carry on bags Above-wing Below-wing

Flight plan Fuel loaded

Weight/balance Taxi times Flight path

Resources used

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What’s the problem?

Critical info is trapped in silos, crippling big data Needs structure, standardization and validation to be useful

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Unified platforms are essential

Single Data Slice

Blended Data Sets

Retrospective Predictive

Excel Access

MySQL Oracle

Today’s Modeling

Tools

Core Value for Aviation

• There are great visualization tools to improve planning and analysis, but what data do you feed them?

• How do you ensure the integrity and reliability of data collected if you fully automate analytics?

• How can you access large enough volumes of historical data to gamble on predictive analytics?

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Big data feedback loop

Cloud infrastructure Virtualized, on-demand resources with infinitely extensible processing, bandwidth and storage

Data pooling & query platforms Connect data & create structure by merging, conditioning streams and archived data

Predictive analytics Automated analytics integrated into workflow that unlock data value and improve profitability

Business intelligence Data mining and visualization software that reveals trends and useful information

DRIVING EFFICIENCY GAINS

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Changing attitudes

Limited by usable data and computational power Use past transactions and isolated data slices to guess what the future looks like

Today

Tomorrow Robust data foundation with computational power Real-time analytics observe and compare to historical trends automating/improving decisions

Commercial example: Real-time demand monitoring

Current systems: Past transactions reflect when supply matched demand, but don’t track abandoned purchases

New approach: Track search and profile info on public websites to identify both completed transactions and abandoning users

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Airports: comparative metrics

Big Data illustrates each airport’s operational, commercial advantages • Demographics – wealth, demand,

drive times from local communities

• Commercial – flight connectivity, checkpoint crowds & vendor traffic

• Operations – delays and congestion

• Gates – availability and utilization

Unlock differentiators that attract airlines, customers on multiple axes

AVERAGE TAXI-OUT TIME (MINUTES)

BWI CLT DCA EWR IAD PHL

American 16.8 19.2 16.4 23.2 16.6 19.6

Delta 19.3 23.1 19.4 21.8 18.5 20.8

United 14.4 19.3 17.3 22.1 17.2 18.3

US Airways 17.1 19.4 22.1 19.6 19.7 19.4

Southwest 14.0 15.7 20.1 12.4 15.2

10.3 9.5 8.5 8.5 8.4 8.3 7.9 7.5 7.5 7.2

BWI LAS OAK DEN DAL LAX MCO HOU MDW PHX

Major U.S. Airline: Daily Departures per Gate

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Airports: operational variability

West International (Odd gates 91-99)

23.5 min taxi-out

East International (Even gates 90-100)

21.3 min taxi-out

East Base Domestic (Gates 68-71)

18.1 min taxi-out

Outer Domestic Pier (Gates 76-77 and 80, 82, 84, 88)

18.6 min taxi-out Inner Domestic Pier

(Gates 81, 83, 85, 87, 89)

20.7 min taxi-out

masFlight Data - All UA SFO Operations

West Base Domestic (Gates 72-75)

21.0 min taxi-out

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The data flood is coming

Future applications will require robust histories & perspectives Imperative to invest in data platforms that create the foundation

Infinite storage Inexpensive cloud options, no bandwidth restrictions

and an ecosystem of apps

Freedom from legacy IT constraints – collect as much data as you can

Mobile engagement Pervasive, connected,

and location-aware through GPS, WiFi and Beacons

Personalized interaction employees & customers and profile data too

Connected aircraft Real-time connectivity and tracking – commercial and

operational implications

High fidelity visibility into aircraft health, location

and customers on board

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Conclusions

• We already live in a sea of data – collect it and leverage it – Commercial, operational, and social sources

– 3 billion passengers, 35 million flights, trillions of data points annually

– Critical to store every aspect of customer interaction

• Applications are moving to the cloud – they need data – Full transition in coming years to cloud-based apps and data sets

– IT systems must be open architecture with easy data input/output

– Link and pool data to create valuable structured information

• Prioritize data collection as foundation for future efficiency gains

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www.masflight.com

4833 Rugby Avenue, Suite 301, Bethesda, MD 20814 www.masflight.com � +1 (888) 809-2750

@joshmarks linkedin.com/in/joshuabmarks

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