de-risking data integration projects

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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

mary@abc.net s.mcminn@def.com jeanm@ghi.net mmend011@jkl.com

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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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Questions & Answers