sabre: master reference data in the large enterprise

LEADING IN TECHNOLOGY

24 HOURS A DAY

7 DAYS A WEEK

365 DAYS A YEAR

14,000OPEN SYSTEMS SERVERS

AND VIRTUAL MACHINES

1.5 BILLION INCOMING API

DATA REQUESTS DAILY

410+ MILLION

XML TRANSACTIONS

VIA WEB SERVICES DAILY

1.1 TRILLIONMESSAGES

PROCESSED IN 2013

$ 100s

of MILLIONSANNUAL INVESTMENT

IN TRAVEL TECHNOLOGY

100,000MESSAGES PER SECOND

11 YEARSCONSECUTIVELY RANKED

ON INFORMATIONWEEK 500

MOST INNOVATIVE

TECHNOLOGY COMPANIES

No defined process for maintaining standards

Changes maintained manually by departments

No one responsible for master data governance

Technology StandardsMaintenance

ProcessChange Process People

Lack of focus on master data beyond current initiatives

Lack of definition of master data standards







ProcessChange Process

People

How did we move forward?

Identify master reference data development priorities as part of technology strategy.








People


Establish master reference data governance standards







Process

Change Process

People


Create stewardship process and workflow for maintaining master reference data standards








People


Define change process for master reference data to ensure consistent usage across the enterprise







ProcessChange Process People


Identify governance roles to ensure master reference data integrity throughout process, databases, applications, BI and reporting

Hello Houston We Have a Problem

• 20,763 Reference tables in over 292 applications

• No central system of record for shared data

• Duplicate research, maintenance, translations, and storage

• Big Data Scientists are spending valuable time looking for authoritive sources of common reference data.

• Data is duplicated across the enterprise without broad accessibility and contextual knowledge

If we spent 10 hours a year on only 50% of the tables, maintenance cost can

exceed $7,786,125….I believe we have their attention!

Focus on one pain point

One

• One trusted source

• Very quick implementation (rapid prototyping, model-driven approach)

Two

• Governance enforcement to provide for better accuracy / higher quality data

• Reduced release cycles for data integration-reducing coding effort

Three

• Automated processes to streamline update of consuming systems

• Standardized “Codeless” GUI maintenance website

Four

• Improved information quality by standardization of sources, value and translations

• Centralizes / standardizes master data distribution

MDM Momentum

Find the most dramatic example

identify the pain point

and teams affected

Follow the Money

get help from financed

Tell the Story

illustrate the problem

and solution

Take Action

Assume success and

have the plan, roadmap

or strategy ready to go

Challenge

Underestimated the ETL process from legacy systems

Learning :

Understanding the data formats and data anomalies

Solution:

Introducing a Data Engineer who can move easily from subject areas and is not intimidated by the legacy structure of the data

Challenge

The data is poorly organized and understood

Learning :

Engage the subject matter experts

Solution:

Begin data discovery as thorough as possible as early as possible to provide a common understanding among all the teams

Challenge

MDM was un familiar to the organization

Learning :

Communication strategy to the organization

Solution:

Create a road show and demo that would resonate with the business and showcase the tool and process potential

Challenge

Data process migration from legacy application to MDM

Learning :

How to promote from one environment to another for the migration path to MDM

Solution:

Engage the same team to be responsible to maintain legacy and the migration for some interim time

Challenge

Data Stewards and Data owners from Multiple teams located across the globe

Learning :

How to Inspire and lead the teams to a single solution

Solution:

Initiated a two day training summit surrounding the new processes and procedures

Challenge

Customization requirements for our environment

Learning :

Partnership with Orchestra Network to enable customized solutions

Solution:

Implement a real-time replicate to down stream systems and partition the data by role

confidential 27

• GUI auto-generated from the MDM data

model

• One data governance front-end for the

business

• Browser based

• Validation entry rules in model

• Data integrity enforced through primary /

foreign key constraints

• Governance is enforced through

embedded workflow model

• Workflows isolate changes not approved

from the “current” set of data

• Sophisticated data management

features such as versions,

inheritance..etc.

MDM GUI/

Workflows

Data Steward/Owner

Data

Stewards

Data Owner

confidential 28

• The MDM tool runs and deployed like

any other Tomcat WebApp

• Development team writes Maven

packaging scripts

• Includes model, workflow

• Java triggers

• Check in to source control

• Provides out of box web services for

data distribution through SOAP / HTTP

(later projects)

• Bulk import/export capable out of box

• Provides Trigger mechanism for

distributing change events

• Events published to MQ via Java

Trigger

• Subscribers listen on MQ queue

Trigger

Event on

Change

MDM Server

confidential 29

• Standard Data Service capability out of

the box

• Wsdls published as static URL to

Tomcat server

• Client SOAP request based on wsdl

• MDM tool repository stored in Oracle

• Separate MDM Oracle instance for long-

term separate management

• Redundant Oracle instances for high

availability

MDM Repository

No Pressure No Diamonds