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© 2015 Experian Information Solutions, Inc. All rights reserved. Experian and the Experian marks used herein are trademarks or registered trademarks
of Experian Information Solutions, Inc. Other product and company names mentioned herein are the trademarks of their respective owners.
No part of this copyrighted work may be reproduced, modified, or distributed in any form or manner without the prior written permission of Experian.
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De-risking data integration projects Enabling early business involvement to minimize downstream cost and time
Rishi Patel Strategic Technical Manager, Experian Data Quality
Michael M. Ott Data Integration Expert and Thought Leader
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Agenda
How current environments introduce additional complexity
An advanced approach to overcoming these challenges, including:
A data integration project checklist
Questions & Answers
Historical challenges to data integration projects
User collaboration
Early business-user involvement
Producing accurate specifications prior to coding
Early prototyping
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Speakers
Michael M. Ott Data Integration Expert and
Thought Leader
Rishi Patel Strategic Technical
Manager
Experian Data Quality
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Historical challenges to data integration projects
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Data
Target
System(s)
Analyze Design Build Test
Run over time and/or budget
Errors in source data, lack of
accurate docs, complexity of
sources
Poor:
Documentation
Collaboration
Investigation
= partial truth
80% of data integration projects fail
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Data
Target
System(s)
Analyze Design Build Test
Poor:
Documentation
Collaboration
Investigation
Rework x10
Analyst User IT
= partial truth
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“An error detected in the project testing phase
can cost up to 100 times more to correct than the
same error found during the design phase”
--The Quality Assurance Institute
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Current environments introduce additional complexity
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Today’s complex environments
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Data
Target
System(s)
Analyze Design Build Test
Rework x10
Analyst User IT
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Advanced approach to overcoming these challenges
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Data
Target
System(s)
Analyze Design Build Test
Automated Data Analysis
Proactive discovery
Based on 100% of your actual
data = the whole truth
Rework x10
Analyst User IT
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CIF Key
100% of your current data
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100110010100010101001
1010101101001
1010011011101010100100101010
101001101010100010001010010
01010010010100
101010110010010101
10100110010100101010010010
10100110
1010011011101010100100101010
1010101101001
724-555-5678 724-555-6789 724-555-3456 724-555-4578
Mary Masters Susan McMinn Jean Menendes Maria Menendez
1425673-323 1435675-344 1445682-356 1455690-389
[email protected] [email protected] [email protected] [email protected]
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Data
Target
System(s)
Analyze Design Build Test
Target
System(s) Analyze, Design
& Prototype Build Test
Data Prototyping
Validated
Results
Specifications
Time with Advanced Approach
Time with Traditional Approach
Analyst User
Perfect Spec &
Test Data
IT
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Screen shot
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Screen shot
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Collaborate
? ? ? ? ?
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Screen shot
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account
customer officer
partner
address
prospect
organization
shareholder
product
Process A
Process Z
data steward
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Reference master data
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Data integration project checklist
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Pre-integration planning
► Assess
► Identify and inform business and IT stakeholders
► Create workflows and documentation
Project initiation
► Adjust plan and policies
► Communicate plan with stakeholders
► Resolve security issues, gain access to legacy datasets, define hardware and software requirements
Data integration project checklist
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Landscape analysis
► Build data dictionary
► Define data quality management process
Solution design
► Create mapping specs
► Create data quality management specifications
► Agree to integration SLAs
Data integration project checklist
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Build and test
► Build and document integration logic
► Test integration
► Define fall-back policy
Execute and validate
► Execute integration
► Log SLA progress
► Validate integration success
Hand over ownership
Data integration project checklist
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Summary
The complexity of today’s environments add even more challenges
An advanced approach to overcoming these challenges includes:
Use a data integration project checklist as a guide
Historical challenges have caused 80% of data integration projects to fail
User collaboration
Early business-user involvement
Producing accurate specifications prior to coding
Early prototyping
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Experian Public.
Questions & Answers
