DATA MANAGEMENT AND EVOLUTION

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Prosper Miles
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2 DATA MANAGEMENT AND EVOLUTION Big data and Operational Analytics 17/October/2017 By: Alan Burgess

3 Implication: Adapt how data is stored, analyzed and exploited Inform decision making at the tactical level Predict potential outcomes Prescribe actions in decision time 3 Data Management and Analytics SITA 2017

Analytics & Condition Monitoring Airport Data and Analytics Platform Queue Analytics Passenger Validation Parking Status E-Tagging Shopping Offers Context Alerts Third parties

4 BIG DATA SCOPE AT AIRPORTS DOTS APIAPI / SDK APIs/SDKs Avg. Q-Wait time Subscribers TRACKING / MONITORING Location Services Baggage Alerts Flight Status Airports AIRPORT OPERATIONS Airside Analytics Passenger Flow Analytics Operations Analytics & Condition Monitoring Airport Data and Analytics Platform Queue Analytics Passenger Validation Parking Status E-Tagging Shopping Offers Context Alerts Third parties DOO BI Pax Flow / Dwell Q Management Airlines Ground Handlers CRS/GDS/ OTAs PASSENGERS Passenger Forecasting AICC CUTE /CUSS BI B2C APP Developers Operations BI Baggage BI Others Patterns / Trends BAGGAGE Analysis / Predict Airport Data Hub (Developer.aero) 4 Data Management and Analytics SITA 2017

availability of data improvements in technology access to algorithms

5 All data matters: big and small (unstructured and structured) Operations analytics opportunities are driven by: increased availability of data improvements in technology access to algorithms Realities: Change is constant Agility is key 5 Data Management and Analytics SITA 2017

6 PASSENGER OPERATIONS Analyse patterns and build profiles Learning to support decision making Multiple profiles for multiple dimensions Maximise knowledge base 6 Data Management and Analytics SITA 2017

7 PASSENGER OPERATIONS Analyse passenger flows Analyse unstructured data from IoT Correlate with structured data, e.g. flight tracking Respond to identified situations 7 Data Management and Analytics SITA 2017

Enjoy continuous visibility of the effect of irregular operations on passenger flows Identify potential processing choke points

8 TERMINAL OPERATIONS Predict, identify and improve Use Flow Prediction algorithms to accurately forecast passenger flows and proactively optimize airport efficiency. Enjoy continuous visibility of the effect of irregular operations on passenger flows Identify potential processing choke points and evaluate alternative solutions with other stakeholders in real-time Analyze relevant flights and identify which events are having the greatest impact on passenger flows 8 8 Data Management and Analytics SITA 2017

9 TERMINAL OPERATIONS Manage the Prediction Rules Apply historical trends Make adjustments based on recent info Add special event parameters 9 Data Management and Analytics SITA 2017

10 TERMINAL OPERATIONS Predict load per Flight Import the flight schedule Add flights to the schedule as required Predict PAX, bags, car park utilization etc per flight 10 Data Management and Analytics SITA 2017

11 TERMINAL OPERATIONS Review Simulation Results PaxFlow Baggage Parking Resource demand 11 Data Management and Analytics SITA 2017

12 TERMINAL OPERATIONS Request resources Identify required manning levels Review and refine the plan Order and negotiate resources 12 Data Management and Analytics SITA 2017

TERMINAL OPERATIONS Manage Day of Operation See the effect of deviations based on real-time flight updates Take proactive steps to rebalance the workload Make sure transfers are made and passengers

13 TERMINAL OPERATIONS Manage Day of Operation See the effect of deviations based on real-time flight updates Take proactive steps to rebalance the workload Make sure transfers are made and passengers are at the gate in time Adjustments to resources and passenger routing are simulated in real-time Simulation uses real-time operations data to provide most accurate prediction 13 Data Management and Analytics SITA 2017

CONCLUSION Big Data and Operational Analytics are relevant for Airport Operations Use data to eneble timely adaptation to changing conditions, including increased demand,

algorithms and continuously refined business rules to prescribe required actions Build confidence to automate prescribed actions and carefully monitor the environment,

14 CONCLUSION Big Data and Operational Analytics are relevant for Airport Operations Use data to eneble timely adaptation to changing conditions, including increased demand, Irregular operations and disruptions Combine knowledge of historical trends with real time data to predict the expected situation if no changes are made Apply well tested algorithms and continuously refined business rules to prescribe required actions Build confidence to automate prescribed actions and carefully monitor the environment, making adjustments as necessary Analyze the effect of changes and actions taken to identify required adaptation of algorithms or business rules 14 Data Management and Analytics SITA 2017

15 Stay on the pulse of your airport operations #AirportMatters Data Management and Analytics SITA 2017

Smart Airport of The Future. Planning for Innovation at Airports

Smart Airport of The Future. Planning for Innovation at Airports Smart Airport of The Future Planning for Innovation at Airports Topics to be covered Single Token SITA Smart Path Day of Travel Airport App Day of Operations Airport Pulse 2 3 SITA Smart Path SINGLE TOKEN