SITA IT Summit 2013

Operational visibility through deep analytics How big data methods improve aviation profitability

Joshua Marks, CEO +1 703 994 0000 Mobile josh@masflight.com

W W W . M A S F L I G H T . C O M

SITA 2013 IT Summit

Big data methods unlock new profitability gains

$13.5

$22.6

$32.5 $36.1

$40.1

2009 2010 2011 2012 2013e

Unbundling Revenue (USD Billions) Global aviation profitability has

depended on ancillary revenue. But those gains are slowing. Aviation must use productivity to sustain growth – and invest in IT platforms that merge and link data

Source: Amadeus/IdeaWorks

SITA 2013 IT Summit

Today: Critical data trapped in IT silos, crippling big data

Flight Schedule and Fleet Data

Revenue and Passengers

Airport and Operations

Finance & Accounting

Different Vendors & Silos Different Users Manual Integration

Revenue

Flt Ops

IT/Web

Finance

FEED

Collect Data, Merge Tables Build Databases

Obtain data from the web or internal PCs,

integrate by hand

FEED

FEED

FEED

SITA 2013 IT Summit

Operational visibility through deep analytics

Validated information and task-specific applications are critical for aviation planning and management.

Forecasting Partner analysis Post-ops review Benchmarking

Schedule design Hub connectivity Maintenance planning Airport operations

SITA 2013 IT Summit

Foundation of Big Data: Integrated, Managed Information

Schedule Sources

FLIFO Sources

Weather Sources

Radar & Flt Plan

Airport & Gate Info

Fleet & Tail Info

Other Sources

FL

EE

T

AIR

LIN

E

SY

ST

EM

FL

IGH

T

FILED & FINAL SCHEDULES

GATES AND AIRPORT INFO

TAIL NUMBER & FLEET INFO

GATE DEPARTURE & TAKEOFF

LANDING & GATE ARRIVAL

ORIGIN & DEST WEATHER

FLIGHT PLAN FILED & FLOWN

ENROUTE WEATHER

MARKETING CARRIER OPERATING CARRIER

R E A L T I M E D A T A S O U R C E S

C L O U D D A T A W A R E H O U S E

SITA 2013 IT Summit

Example: Improving Schedule Accuracy

Block planning is an art based on review of: Taxi and flight history One-time factors

Big data enables a more scientific approach with: Departure and arrival gates Intra-seasonal weather Tail number differences

0

50

100

150

200

250

5 15

25

35

45

55

65

75

85

95

105

115

125

135

145

155

165

175

185

195

205

215

225

235

Cou

nt o

f Flig

hts

Minutes After Gate Departure

Gate Out Landing Time Gate In

Modal Taxi Out 23 min

Modal Gate Arrival 2h 28m

Delta: All 2012 New York LGA to Atlanta Distribution of Taxi and Flight Times

SITA 2013 IT Summit

Example: Identifying Airport Operational Improvements

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

Data from 2012 All UA SFO Operations

West Base Domestic (Gates 72-75)

21.0 min taxi-out

SITA 2013 IT Summit

Example: Operational Disruption for High-Yield Passengers

Delta Air Lines 2012 New York to Los Angeles

13% 11%

10% 8% 8%

9%

ATL DTW MSP

Misconnect % Pax > $500

15%

8% 8% 8% 7% 6%

15% 18%

DTW MSP ATL SLC

Misconnect % Pax > $500

14% 12% 12%

9%

14%

7%

11% 11%

ATL MSP SLC DTW

Misconnect % Pax > $500

Blue: Flights A+30

and Cancelled

Red: % of NY-LA

O&D > $500

Compare connect points and O&D

traffic

From JFK via: From LGA via: From EWR via:

SITA 2013 IT Summit

Cloud + Big Data: Visibility without legacy constraints

Management

Linked data Full archives

Powerful retrieval

Aggregation AUTOMATED DATA

COLLECTION & LINKING

Visibility

Lower IT investment, more flexibility and new insight

SCALABLE STORAGE ARCHITECTURE

FEED ANALYTICS AND DASHBOARD SYSTEMS

Multi-source feeds Auto correction Linked tables

Ops & Revenue Real-time monitor

Predictive Analytics

• Profitability depends on finding new efficiencies in operations and revenue

• Linked, cloud-hosted data combines low acquisition cost with flexibility and power

• Big data analytics fundamentally changes how planning can reduce variability

• Dashboard and monitoring systems also change day-of and predictive management

Investment Case & ROI

Organizational Insight & Value

SITA 2013 IT Summit

Conclusions for Cloud-Based Big Data

SITA 2013 IT Summit

For more information

• Demonstrations • Data samples • Trial accounts • White papers • Research

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