Preso Accenture_INFADAY_2011

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Copyright 2011 Accenture All Rights Reserved. Accenture, its logo, and High Performance Delivered are trademarks of Accenture.

Data quality and data governance,Two strategic pillars of data management

October 6th


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Data Governance

in the Data Management Journey

Global DataManagement

Vision

Governance

Data Quality Xavier Scherpereel

Arturo Salazar

Data Management is a business and technology matter.

Your master data management (MDM) discipline won't succeed unless you implement

effective governance(Gartner mars 2011)

Enterprise Data Model

Master Data Management

2Copyright 2011 Accenture All Rights Reserved.


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Why Data Governance?

Some concrete examples to illustrate the issues

... to NASA, a defect related to the quality of the data (data enteredby mistake in inches while the calculations are performed in the

metric system) caused the loss of one entire mission, causing thecrash of a spaceship a loss of $ 100 million ... Source: ForresterResearch - The Costs of Data and Information Quality Defects

Copyright 2011 Accenture All Rights Reserved.

The quality of customer address data is degraded by 17% per year inthe United States. Source: United States Postal Service

According to a study of the industry of U.S. mail delivery, themail lost due to wrong address cost between $ 6 and $ 9 billionper year.

Source: Forrester Research

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The most common reasons to act

Optimization of the cost of marketing campaigns (mailings)

Potential cost of decisions based on erroneous data in the

reports

Increase revenues

Reduce costs

Targeting and effectiveness of marketing campaigns,

Better understanding of customers by eliminating duplicates


Requirements traceability of products and supplier

Anti laundering-money regulations

KYC (Know Your Customer)

Reputational risks (damage to the brand image to customers)

Operational and Credit riskControl risks

Comply with regulations

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Vision of your peers - Investments in the field of Analytics

Increase

35%Remain stable

62%

Copyright 2011 Accenture All Rights Reserved. 5

Decrease 3%

Source: Information Investments on France, Survey Accenture 2011More than 100 CIO surveys60% enterprises in the TOP 500

Data QualityData Governance

In the next 2 years, investments in the field of Analytics are required to progress for62% of companies (CIO Survey)


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Are you ready for Data Governance?

Governance for CIO Governance for IT Leads

Understand information governanceand MDM

Establish standards and policies forinformation governance and MDM

Identify critical businessprocesses and information

Identify business lines tocosponsor MDM initiatives

2

1

2

1


Use Market standards to assesswhich vendors will likely facilitateinformation governance initiatives

Definition and implementation of theGovernance at the enterprise level

Evaluate which enterpriseinformation will best support MDMprograms

Build a single vision for managinginformation assets and to developa business case

Link information governance to ITgovernance

3

4

Measure the business value ofreusing information across theorganization

5

3

4

5

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Real Cases

Governance Implementation in a Financial Institution

Enterprise Data Governance Implementation Roadmap 2011

Feb Mar Apr May

Validation of roles andresponsibilities

Jun

EDM governance deployment:EDM / Domain / Transversal OpCos

Owners, Managers, Designersrepresentatives defined

Lot 1 : Norms & Standards definition /Cross DomainsAccenture implemented an

approach leveraging the workalready performed and

ensuring a global support tothe Head of Enter rise Data


Run

EDM Opco KickOff 22/01

KPI Lot 1Data Quality* / Business

Performance

Dashboards

On going communicationCommunication plan EDM Communication tostakeholders finalized

EDM OpCo Deliverable

Deliverables Description

Detailed Mapping of roles by domain List of data & process owners, managers and designers by

domain

Norms & Standards definition Data Golden Rules Best practices and Standards to respect across SG CIB

Perform key communication End to end organization of key communications

Performance & Risk Indicators

production

Key performance and risk indicators definition by domain Production of indicators for Opco EDM

Propose tactical solution and target run indicators

Management

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Data Governance by Accenture

DataManagement

DataCreation

Data

Data

Retirement

Data Management FrameworkData Management Framework


DataGovernanceDataGovernance DataStructureDataStructure DataArchitectureDataArchitecture Master Data &MetadataMaster Data &Metadata DataQualityDataQuality

DataStorage

DataMovement

sage

Data Ownership

Data Stewardship

Data Policies

Data Standards

Data Ownership

Data Stewardship

Data Policies

Data Standards

Data Modeling

Data Taxonomy

Data Modeling

Data Taxonomy

Data Migration

Data Storage

Data Access

Data Archiving

Data Retirement

Data Migration

Data Storage

Data Access

Data Archiving

Data Retirement

Master DataManagement

Reference DataManagement

Metadata

Management

Master DataManagement

Reference DataManagement

Metadata

Management

Data Profiling

Data Cleansing

Data Monitoring

Data Compliance

Data Traceability

Data Profiling

Data Cleansing

Data Monitoring

Data Compliance

Data Traceability

DataSecurityDataSecurity

Data Privacy

Data Retention

Data Privacy

Data Retention

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Foundational

Diagnostics Thought LeadershipADM-based Estimators

P&IM Offerings & Assets

Focus on some examples


Strategic

Supporting

Executive Portal Enterprise Metrics Management

Electronic Dataand DocumentManagement

Delivery Centers

For access to all P&IM assets and offerings, click:http:// www.accenture.com


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Inconsistencies = 90% of conversion failure

Which data to Cleanse?

Various IS Legacy Sources

Heterogeneity in Source Data

Numerous and Frequent Go-Lives

Recurrent Situations

Quality Control of the extracted data

Tests and validation of the upload

Industrialized approach:

- Standardizations

- Development re-use

- Process automation

Key Expectations

Why Data Quality?

Quality inSAP Data Migration projects

Respects of deadlines

SAP Data Migration Factory (SDMF)

Skilled and dedicated Teams

Proven approach with several customers

IS LegacySources

Target System:SAP

Download Upload

Quality ControlTransformations

Copyright 2011 Accenture All Rights Reserved. 11


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Transformations- Mappings-Transformations

(PowerCenter)

LSMW

Flat Files

IDocs

IS Source

Data Migration SolutionLegacy Sources

1 3 4

SAP Target

2 Quality Checks- Quality Control- Analysis Reports

(IDQ)

Why Data Quality?

Details of the SAP Data Migration Factory

1. Data Extraction from IS source to an intermediate format

2. Quality Control of intermediate data before transformation

3. Conversion of intermediate data into SAP format

4. Load of converted data into SAP

AnalysisReports

UploadTransformation

gra on

Data Base


Download and Cleansing


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90 % of conversion errors = Source Data: formats,duplication, integrity, etc.

Referential and IS Constraints are different betweenLegacies and SAP

Duration of identification and correction of issues

Support and Control the overall process

Successful Go-Live

Main Stakes Identified

Data Quality Tool Analysis

Errors Identification in early stages Quality control Automation

Iterative Correction process

Correction: IS Legacy Sources or Migration Database

Proven approach with several customers

Enhance Quality = Accelerator

Why Data Quality?

Key Differentiator: Enhance Quality of Extracted Data

DataCleansing

ExtractQualityControl

Conversion

MigrationData Base

SAP

Target

DataCleansing

Iterative

IS

LegacySources



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Advantages of new layout: Web-based interface Invalid data can be parsed and corrected

Scorecards can be developed by end-users Users can update source files and re-run

controls until the data is good Faster analysis of data quality checking Version 9.0.1 and above are integrated with

PowerCenter

Why Data Quality?

Sample of Data Quality Reports

Score for each KPI

Details of valid / invalid rows



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SAP Data Migration Factory: Pre-built Developments:

IDQ PowerCenter

SAP LSMW

DB Repository (MS SQL /Oracle)

Ready-to-use environment: Virtual Machine Configured software (INFA,

DB)

Data Quality (IDQ) Data Integration (PWC)

Why Data Quality?

Key Differentiator: Reusable Components

Installed Code

Documentation

Benefits:1. Reduce cost, time and effort2. Reusable components can

be customized for each client

3. Jump Start developers to focusonly on enhancement and/ordeveloping gaps, if any



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Why Data Quality?

SDMF is a complete End to End solution focusing on Quality

People

Accentures Delivery Center

Network (DCN) :-Multiple clients

-Skilled

-Low-Costs

Process

Methodology and Framework:

- Data Quality management

- Conversion Objects in SAP

- Development Framework

ProcessPeople


Technology

Reuse components developed by Accenture in SDMF:

-Prebuilt Developments

-Ready-to-use environments

Technology


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Questions / Comments?

Thank you


Xavier Scherpereel

SDMF Lead for EAME

Phone:+33 6 86 59 96 [email protected]

Arturo Salazar

Data Management specialist

Phone:+33 6 32 89 25 [email protected]

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