Oracle Airlines Data Model (OADM) Overview

Transcription

1 Oracle Airlines Data Model (OADM) Overview May, 2014

2 Content (1) Airlines Business Intelligence Key Trends Airlines Industry Findings Customer Experience Enhancing Customer Experience Oracle Airlines Data Model (OADM) Complete Experience Passenger Data Passenger Data Management Oracle airlines Data Model (OADM) Overview Typical Issues Addressed by OADM OADM Subject Areas OADM Logical Data Model OADM Components Booking Ticketing Check-In Flight Carrier Segment Loyalty Revenue 1

3 Content (2) Oracle airlines Data Model (OADM) Overview Typical Issues Addressed by OADM OADM Subject Areas OADM Logical Data Model OADM Components Booking Ticketing Check-In Segment Loyalty Revenue Complete Oracle Data Warehouse / BI Solution OADM Technical Architecture OADM Components - Layers OADM Components Relationship Flight Carrier 2

4 Airlines Business Intelligence Key Trends Based on Industry Research, Airline will be an area of stronger than average growth compared to other industries Airline is the tenth largest growing segment for Analytics and Information Management 3

5 Airlines Industry Findings Within the trends of today s Airline Market, there are opportunities for growth in Airline Business Intelligence Trend Details BI Opportunity Customer Interaction Growth in the number of vehicles to interact with customers, including airport kiosk displays, e-commerce, call centers, sales, and mobile devices Intelligence to monitor customer interactions with an airline through promotion tracking and segmentation Customer Social Networking Brand loyalty Widespread adoption of social networking to allow customers to provide product reviews and feedback Need to increase brand loyalty and customer retention at a time of increased competition Analytical insight into highly regarded products and services from customers Identify specific customer segments and tailor marketing and brand strategy to serve those customers Customer Behavior Airline IT Budgets Capturing relevant, timely, and granular data on customer buying behavior and to provide one view of the customer Tight margins may inhibit investment in new technologies and processes. Identify customer activity across multiple channels and brands and create a total, 360 view of each customer, identify cross-selling opportunities Develop methodology accelerators to reduce implementation costs and total cost of ownership.. 4

6 Customer Experience Delivering a Superior Customer Experience requires the organization to align around the customer 5

7 Enhancing Customer Experience Enhancing Customer Experience is the Top Priority for Airline 6

8 OADM Complete Experience Only OADM Delivers the Complete Experience meet me, and engage delight me, and serve me Builds on CRM Investments know me, and wow me understand me, and reward me INDIVIDUALIZED SITES GUIDED COMMERCE PERSONAL SERVICE Best-In-Class Customer Apps TARGETED COMMUNICATION NEXT BEST ACTION INTUITIVE SEARCH DEEP KNOWLEDGE Best-In-Class Industry Apps ORDER MANAGEMENT SERVICE MANAGEMENT ADVANCED SERVICE BILLING AND PAYMENTS Complete LOYALTY & UPGRADES CUSTOMER DATA CUSTOMER INSIGHTS 7

9 Passenger Data Passenger Data is the Key to Enhancing the Customer Experience 8

10 Passenger Data Management (1) Passenger Data Drives the Customer Experience 9

11 Passenger Data Management (2) key Challenges When Managing Passenger Data 10

12 Oracle Airlines Data Model (OADM) Overview - Introduction - 11

13 Oracle Airlines Data Model (OADM) Overview OADM is a comprehensive solution framework and is focused on accelerating the benefits of Airline Business Intelligence solution Out-of-the-Box OADM solution includes: Leading industry KPI repository Comprehensive enterprise data warehouse data model Technical and application architecture Sample analytical reports and presentation formats Portal prototype to organize and deliver the information. OADM speeds the development of a data warehouse solution by providing a foundation data warehouse and analytic infrastructure for the Airline operation reporting needs. 12

14 Typical Issues Addressed by OADM How will airline business benefit from using Oracle Airline Data Model? OADM enables airline to turn airline business data into information based on which airline can make decisions. OADM enables business to answer three basic questions What happened? Why did it happen? What will happen? Using pre-built OLAP and Data Mining models to detect hidden information in data repositories, OADM helps airline to answer following questions What is the impact of the fare promotion on booking levels for this origin-destination pair? How do the overbooking levels and load factors compare for flights in this origin-destination pair? What is the number of kiosk check-ins by time of day and day of week at DFW? What is the on-time departure rate for flights out of the Chicago? How many seats did we sell through this alliance partner this quarter? What is the impact on activity levels of our Tier 1 members with our double miles loyalty promotion? What is the open rate for this marketing campaign? What is the promotion acceptance rate? 13

15 OADM Subject Areas One Common Knowledge Base for Improved Analysis & Decision Making Booking Check-in PNR Coupon Ticket Traffic Flight Segment PAX DOCO/C A/CS Call Center Loyalty Calendar Carrier Revenue 14

16 OADM Logical Data Model A Modular Logical Data Model Foundation Define your own scope for implementation Implement in phases as needed Implement directly with minor modifications Implement during data mart/dw consolidation Use as a corporate data architecture guide Extend with new LOB from merger & acquisition or Your Own Airline specific entities Core Entities Foundation used by all industries 15

17 OADM Components (1) Reference Models the entities (Calendar, Product, etc.) for an airline Translates into Dimensions Base Lowest level PNR details Enables detail data analysis Whole fly history Lookups Holds descriptions for common code lookups Aircraft type, reason, etc. Saves space, don t have to store long descriptions in each transaction record Derived Low level combination of base tables Current state (check-in state, flight state, etc.) Information that can only be obtained by derivation from base Aggregate Summary, Average, etc. of Base and Derived data Enables high level analysis, ROLAP Time Series Forecasting What-if OLAP Data Mining Enables Statistical analysis of transactions Provides for both Supervised & Unsupervised Learning 16

18 OADM Components (2) Cross-Functional Data Models Booking Ticketing Check-In Flight Carrier Segment Loyalty Revenue Reference Base (3NF) Aggregations Booking & Service Class: Booking Class Service Class Carrier Code Effective Dates Status Airport Codes: Airport Code City Code Geo Hierarchy City Region Country Continent Traffic Category: Traffic Category IATA Levels Geo Area Name Market Area Name Calculation Year Calculation Month Flight: Flight Number Flight Type Code Share Type Carrier Code Flight Status Derivations / Data Mining / OLAP Segment: Segment Type Board Point and Off Point Airport Name Board Point and Off Point City Region Country Continent Carrier: Carrier Code Description Carrier Type Legal Name Trading Name Address Status Frequent Flyer: Frequent Flyer No. Card Carrier Airline Member Level Alliance Member Level Gender Date of Birth Address Location Booking Office: Booking Office Code City Code Country Code IATA Code Channel Type Office Type Agent Chain Status 17

19 OADM Components (3) Cross-Functional Data Models Booking Ticketing Check-in Check-In Flight Segment Flight Loyalty Carrier Ticketing Segment Revenue Loyalty Revenue Carrier Reference Base (3NF) Aggregations Booking: Operating and Marketing Flight Agent Class Origin-Destination Frequent Flier Group Seat Details and Preferences Special Requests Check-in: Carrier Check-In Channel Agent Airport Segment Boarding Status Baggage Status PNR: Type Purge Date Group Name Journey Origin/Destination/Return Point Agent Frequent Flier Number GDS Booking TST: Transitional Store Ticket No. Origin Destination Ticket Type Fare Calculation Model Derivations / Data Mining / OLAP Ticket: Primary Number Agent Currency Total Amount Issue Date Creation Date Tax, Payment and Service Fee Coupon: Coupon Number Origin-Destination Agent Ticket Number Coupon Amount Currency Details Flight Details PAX/DOCO/CA/CS: Passenger Nationality Address Travel Doc Type Issue Country Expiry Date Doc Number Gender DOB Passport Hold Indicator Flight Schedule: Flight Date. Flight No. Flight Carrier Code Segment ID LEG ID LEG Aircraft Configuration Code Total Saleable Capacity Nautical Miles 18

20 OADM Components (4) Cross-Functional Data Models Booking Ticketing Check-In Flight Carrier Segment Loyalty Revenue Reference Base (3NF) Aggregations Derivations / Data Mining / OLAP Booking: Booking Count by Time, Geography, Segment Booking Count by Channel, Agent, PNR Type, Class Average Fair Booking Status Change Trends Load, Fair, Season Check-in: Total Check in Count Total Group Baggage Count Total Check in Passenger by Passenger Type Total Baggage Count Total Boarded Count No-Show Rate Load Factor Revenue: Issued and Flown Rev. Maximization by Optimization (dimensions) Agent Channel Corporate and Individual Frequent Flyer OD Special service revenue Agent Fraud Analysis: Channel Identification Agent Fraud patterns Duplicate booking Speculative bookings Duplicate ticket numbers Revenue loss Cancellation Fee Unused inventory Frequent Flyer: Loyalty Program Performance Earn/Burn Ratio Partner Performance (Airline and Non-Airline) Tier Movements Promotions Member Churn Analysis Revenue and Liability Analysis 19

21 OADM Components (5) Cross-Functional Data Models Booking Ticketing Check-In Flight Carrier Segment Loyalty Revenue Reference Base (3NF) Aggregations Derivations / Data Mining / OLAP Data Mining: Frequent Flyer Passenger Profiling Non- Frequent Flyer Passenger Profiling Customer Segment Customer Loyalty Classification Targeted Promotion Customer Life Time Value Analysis Frequent Flyer Passenger Prediction OLAP: Booking Count Time Series Analysis (YoY, MoM, Percent Change) Booking Office Ranking Sales Channel Sharing and Ranking Segment Ranking Passenger Feedback Reports Current FF Base Materialization Reports Seasonal Trend Report ASK Time Series Forecast Route Passenger Count Time Series Forecast Call Center Sales Performance Time Series Analysis Customer Satisfaction Growth Trend Sales/Flown Revenue Growth Trend 20

22 Booking Overview The Booking business area provides an enterprisewide, 360 degree view of passenger booking, from all kinds of angles to analysis the booking activity, including Booking Channel, Booking Class, Confirm/Cancellation, Booking Fare. KPIs Booking Counts Cancellation Rate Channel Booking Distribution Promotion Booking Rate Booking Share by Class Booking Forecast FFP contribution Booking Average Fare Booking Pattern by segmentation mining model Selected Business Area Key Logical Entities Reports Booking Segment Analysis Daily Booking Analysis Flight Booking Analysis Booking Forecast Agency Booking Performance Campaign Booking Analysis 21

23 Ticketing Overview The Ticketing business area covers ticket, ticket coupon and sales revenue. This includes the fare information about the ticket, relation between ticket and coupon. The model supports analysis of ticket amount, ticketing fare and coupon analysis, identifying ticketing activity for the customers mining analysis. KPI s Ticket /Coupon Count Ticket Sales Revenue Cancellation Rate After Confirmed Ticket Fare Ticketing pattern after booking Ticketing Payment Method Share Selected Business Area Key Logical Entities 22 Reports Sales Revenue Analysis FFP Customer Mining Ticket/Coupon Reports Channel Performance on Sales Revenue & Ticket passengers Dealer Sales and Commission Monthly Future Plan Drop-out Contract Sum of Future Plans

24 Check-In Overview The Check-In business area provides the basis for post departure analysis. Analysis of flown revenue, flown passenger, no-show and go-show, check-in channel type. It records the actual flown data for each booking. KPI s Flown Passenger Count Flown Revenue Check-in Channel Distribution Through Check-in Count Checked Baggage Count FFP customer mining Selected Business Area Key Logical Entities Reports Flown Revenue Analysis Flown Passenger Analysis FFP Customer Mining Load Factor No-show & go-show 23

25 Flight Overview The Flight business area focuses on analysis of flight capacity flown and operation. Analysis supported includes flight schedule, code share revenue, flight network contribution, flight segment /leg load factor and FFP/F/C revenue contribution. KPI s Load Factor by Flight ASK Flight Flown Revenue Flight Network contribution Flight Spoil Rate Selected Business Area Key Logical Entities Reports Sample reports include Flight ASK & Load Factor Reports Flight Flown Revenue Network Contribution F/C contribution 24

26 Carrier Overview The Carrier business area focuses on carrier O&D market share, Carrier revenue, revenue from alliance and aircraft utilization. Analysis support includes carrier sales and flown revenue, O&D market share, aircraft revenue, alliance revenue and customer interaction. Sample KPI s Sales/Flown Revenue Market Share Carrier Load Factor Customer Survey Satisfaction Customer Complain Call Center Accessible Rate Aircraft Utilization(Hours Per Day) Selected Business Area Key Logical Entities Sample Reports Carrier Sales/Flown Revenue Reports Call Center Performance Aircraft Utilization O&D Market Share Customer Survey Analysis 25

27 Segment Overview The Segment business area provides reporting and analysis on segment sales and flown revenue/passenger, segment overbooking and spoil, network contribution and segment O&D market share. KPI s Load Factor YoY, MoM by Segment Revenue by Segment Spoil & Overbooking by Segment Average Fare by Segment No-show and go-show by Segment O&D market share by segment Selected Business Area Key Logical Entities Reports Segment Revenue Analysis Network contribution analysis Segment booking analysis Market share analysis forecasting 26

28 Loyalty Overview The Loyalty business area focuses on analysis of loyalty programs, tier 1 &2 membership, account of FFP and non-ffp, FFP contribution, FFP development and life cycle value forecasting. KPI s Tier 1 & 2 membership count/increase Points earned Points redeemed Loyalty program performance Average Fare by FFP Mining model to develop high value non-ffp Selected Business Area Key Logical Entities Reports Sample reports include Active FFP analysis Mining on development of non-ffp Points & Redeem analysis Alliance analysis 27

29 Revenue Overview The Revenue business area focuses on types of revenue analysis. Analysis support includes average fare analysis, segment revenue analysis, flight revenue analysis, F/C contribution, outbound and inbound revenue, ancillary revenue and product revenue KPI s Code Share Revenue Ancillary Revenue increase against flight revenue Network Revenue Contribution Outbound & Inbound Revenue Share Revenue forecasting Reports Channel & Agency revenue analysis Flight & Segment revenue analysis Revenue share by ancillary, code share, flight Selected Business Area Key Logical Entities 28

30 In-Database Analytics Complete Oracle Data Warehouse / BI Solution Oracle Big Data Appliance Optimized for Hadoop, R, and NoSQL Processing Oracle Big Data Connectors Oracle Exadata System of Record Optimized for DW/OLTP Oracle Exalytics Optimized for Analytics & In-Memory Workloads Hadoop Open Source R Oracle NoSQL Database Oracle Event Processor Oracle Big Data Connectors Oracle Data Integrator Oracle Advanced Analytics Airlines Data Model Oracle Database Oracle BI Applications Oracle BI Tools Oracle Enterprise Performance Management Oracle Endeca Information Discovery Oracle Real-time Decisions Times Ten Stream Acquire Organize Discover & Analyze 29

31 Data Collection and Transformation OADM Technical Architecture Data Sources Point of Sale E-Commerce Partner Channels Merchandising Loyalty/CRM Financial ERP Allocations Social Media Competitive Supply Chain Derived Tables Foundation Layer Analytic Layer Presentation Layer Exalogic Big Data Appliance Exadata Exalogic Exalytics 30

32 OADM Components - Layers OADM is designed and pre-tuned for Oracle data warehouses, including the Oracle Exadata Storage option. With pre-built data mining and On-line Analytical Processing (OLAP) models, it provides clients with industryspecific metrics and insights that they can act on immediately to improve their bottom line Layers Foundation Analytic Presentation 31 Description Data in 3 rd Normal Form, including transaction detail storage, Logical and Physical models Dimensional models (both ROLAP and OLAP) that: Describe the industry specific structure of airline business areas Provide focused measures and key performance indicators (KPIs) for the airline industry. Data mining objects that: Enable statistical analysis of transactions Can identify critical information that may be hidden in your data, providing for both supervised and unsupervised learning Pre-built, industry-specific reports Pre-built dashboards for summary information, or as a launching pad for further analysis Oracle Airline Data Model Foundation Layer Derived Tables Analytic Layer Presentation Layer

33 OADM Components Relationship Relationship between each component of the OADM product Oracle Airlines Data Model Source Data - OLTP System - Data Marts 32 - MDM, etc. Landing Zone - Tables / Views - CSV Files, etc. Staging Area - Data Quality - ETL Rules - Interface, etc. ORDM Foundation & Reporting Layers Master data is stored in Reference and Lookup tables Base tables stores only transactional data (3NF) Transactional Reporting Fact data is stored in Derived and Aggregated tables Analytical Reporting

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