Patrick VERGNE Database Manager

Christophe BLANC Senior DBA

AIR France: Stratégie BigData et Use Cases

Big Data: the ambition of the IT, and the way to reach it.

AF/KL Businesses can get value from quick implementation of

BIG DATA technologies by AF/KL IT

This is a key driver of the digitizing initiative in the Group

Implement a consistent technical landscape (capabilities, skills, process, tools…)

Enable Big Data projects Experiment and learn

through POC construction delivering value to the Business

7

Big Data : Where are we now ?

Data Governance key roles and process are defined

•  Data Officers appointed on Customer Data

and KLM Operations domains

•  On going discussions in Operations and E&M

•  Data Management communities under implementation

Several Big Data projects are on-going

In several domains as Commercial, Digital,

Revenue Management, E&M, Cargo…

A consistent Big Data technical landscape is implemented

•  All aspects of Big Data are covered: storage, distribution, in memory processing, data streaming, noSQL Data Base, Data Visualization…

•  Relying on up to date open source standard solutions

•  Almost 10% of our Linux processing power dedicated to Big Data

Big Data skills are available

•  Data Scientists by Operational Research Department

•  Big Data skills deployed in the IT organization to support projects

8

Many Big Data opportunities

Big Data can bring value in various domains:

Ø  Customer

Ø  Operations

Ø  Aircraft maintenance

Ø  Cargo

Ø  Corporate (HR, Finance…)

9

And already some major achievements

•  Several Big Data initiatives already achieved, or on-going :

§  Passenger Revenue Management §  Customer 360° View §  Destination Recommendation §  Predictive Maintenance for E&M §  Real Time Performance Tool for KLM Operations §  Cargo Revenue Management §  Data warehouse offload to Big Data Platform §  Proof of Concept LIDO Flight data analytics for KLM Flight Operations §  Proof of Concept for AF Crew rosters analytics §  Proof of Concept for AF Crew reports processing (machine learning)

5

10

CRM Program is key to serve AFKL ambition, …

“ After our significant investments in product we will invest in customer intimacy by excelling in customer attention and customer service in an individual manner”

•  Get at par with level of industry leaders ü  Operational excellence ü  Product leadership

•  Be number 1 for Customer Intimacy

Three different competitive strategies

Strategic direction of AF/KL

13

Zoom on OCP : Business vision : the Sine

The Sine is the logo of customer centric thinking and acting. Time is positioned on the X-axis, on the Y-axis you find the level of customer involvement with our brand. Being located up the Sine means high customer involvement, down the Sine sub-consequently means low customer involvement. The Sine Model emphasizes the long term of our activities and the value of customer relations. The Sine is a continuous journey, not a circle. Every new journey tells us more about our traveling customers. During our shared journeys we are endlessly tuning our efforts and solutions in order to fit customer needs and desires. There never is an end to it. So come along, and follow the Sine.

OCP follows the Sine …

… Travel ‘n’ Travel ‘n+1’ …

14

Zoom on OCP : CRM Program vision : deliver 360° contextual data to all touch points in real time

Customers are facing multiple AFKL touch points. For each of those customer experience , customer has a goal/reason to get in touch …

CRM program vision is to capture / restitute on the fly all customer activities data, aside customer individual data…

… and, once the customer is identified, define the most probable reason that brings the customer in front of our touchpoint before the customer has to tell it … allowing

ü  Customerin,macyü  Highlytouchpointpersonaliza,on

Deliver to all our staff the 360° vision for optimal interactions…

15

Our ambition is to deliver true customer centricity available for all our touchpoints

à A clear objective: the creation of a real-time and complete 360 degrees customer view

Oscar

. . .

16

Use case example

10:00 10:07

Customer navigation on booking tool /!\ Payment error page

10:09

Customer call to Call Center

10:11

On the fly log capture

Aggregate / Filter / Identify / Store

Customer Experience

CALL : Mr JOHNS Platinium

WHY : PNR : XU9I6 on EBT CDG - JFK - CDG

2 min ago ERROR at issuance “ Invalid Credit card entry”

PAST : - 10FEB flight delayed 20‘ - 05JAN upgrade Y->J

- 15DEC lost bag

WebService provideCustomerActivities

Contextualized reply

19:00 10 days later 22:00

Travel

Due to its previous bad experience,

customer upgrade is suggested.

Customer Experience

Web Experience Call center Experience Travel Experience

17

Aggregation Correlation Rules

OCP Architecture overview and … BigData technical challenges

Customer Experience 360° vision

Operational Customer exPerience platform / CRM domain

Capture Normalization Aggregation Correlation

Rules Storage

DataSource

DataSource

DataSource

DataSource

Realtime

Services data API/WBS

OCP Data Services

Events

File Extracts

Capture

Normalization Storage

AFKL O.R.

engines

HBASE/HIVE

18

Pourquoi NoSQL ?

•  Couvrir des besoins ou le SQL-Relationnel serait trop complexe ou couteux

•  Source de données hétérogène

•  Pas de modèle prédéfini des données

•  Volumes => architecture distribuée (« scalable »)

•  Simplicité et agilité pour le développement

•  Haute disponibilité par le biais des « Replica Set »

19

Planning (Grandes Etapes)

2014 :

•  Etude architectures AF/KL sur les solutions noSql du marché : choix MongoDB •  Premiers POC domaine ecommerce KLM

2015 :

•  Février : Première réalisation MongoDB à KLM pour le projet E-Commerce •  Printemps 2015 : les projets Eureka et OCP pour Air France sont lancés •  Investissements infrastructures MongoDB à Amsterdam en Juillet 2015 •  Ete 2015 : MongoDP ’Operationnal-Product’ à AF/KL •  Fin 2015 : OCP en production à AF sur périmètre call-center.

2016 :

•  Nice : 3 applications en production (OCP, Eureka, Tracking-PNR) •  Toulouse : En pré-production/POC : Maintenance A830 / Occitane / Ground Services / Near Real Time Agent Tasks •  Amsterdam : InfFlightEntertainment, triPlanner, LoungeRest, myWeb

20

Infrastructure (Ex OCP)

Nice

Toulouse

SITE# 1

A P P L I C A T I O N S

Réplication Réplication Réplication Réplication

SITE #2

Ø 3 localisations régionales : AMS, TLS, NCE

Ø Chaque localisation : 2 sites régionaux (HA et DRP)

21

Nom du projet / 14/10/16

MongoDB : Points forts / Points d’attention

+++ :

•  simplicité de prise en main •  robustesse, fiabilité •  administration aisée grâce à OPS Manager •  support efficace

Points d’attention :

•  Sauvegardes gros volumes et copies inter-environnements •  Maitrise des couts d’infrastructures •  Montée en compétences des équipes (Dev-Ops)

22

Questions

http://www.airfrance.fr/ http://www.airfrance.fr/ http://www.airfrance.fr/FR/fr/local/achat-reservation/meilleures-offres/tarif-promotion-vol.do http://Corporate.airfrance.com/

Patrick VERGNE Manager Database on Linux (Engineering & Support) La Barigoude / 5 avenue Maxwell / F-31109 Toulouse cedex 9 email : pavergne@airfrance.fr

Christophe BLANC Senior DBA Database on Linux (Engineering & Support) La Barigoude / 5 avenue Maxwell / F-31109 Toulouse cedex 9 email : chblanc@airfrance.fr

787 Dreamliner J-90