37253949 SAP Data Quality _ Vistex

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Developing a Data Quality

and Integration Strategy

Jonathan G. Geiger Intelligent Solutions, Inc.

April 28, 2010



Sponsor



Copyright © 2010 Intelligent Solutions, Inc., All Rights Reserved

Topics

Complexities

Expectations Setting

Assessment

Improvement

Strategy

3


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Topics

Complexities

Typical Complexities

Why They Exist


Assessment

Improvement Strategy

4


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Data Integration

DataWarehouse

OperationalData Store

Operational Data

ExternalData

InternalData

. . . and a miraclehappens here . . .

Data integration is very complex!



Capture Complexities

Data requirements

Best source of the data

Business rules for capturing the data

Data meaning

External data requirements

History requirements

Currency requirements

Privacy and security requirements

Audit and control requirements

Metadata 7



Cleansing, Transformation &Integration Complexities

Data quality

Data integration

Data transformation

Data enrichment

Error handling



Metadata

8



Load Complexities

Currency



Metadata

9



Copyright © 2010 Intelligent Solutions, Inc., All Rights Reserved10

Why Complexities Exist

Problem RecognitionData deficiencies are often

not recognized

Responsibility Overlaps and gaps

DisciplineData is called an asset but

not managed as such

Benefit RecognitionPeople are getting work

done`




Topics

Complexities


Quality Definition

Governance and Stewardship

Prioritization

Assessment

Improvement

Strategy11




D e

f e c t R a t e

Time

Target

Quality - Definition

Quality is conformance to requirements

Quality is not

.... (necessarily) zero defects




Quality - Definition

Quality is conformance to requirements

Conformance to what?

Whose requirements?

How are requirements set?

What degree of conformance?




Stewardship

A steward is one who is called upon to exercise

responsible care over possessions entrusted to

him or her

(adapted from Webster’s dictionary)

The steward does not own the possessions

The steward has a responsibility affecting the

processes that impact the possessions




Data Steward

Exercise responsible care over the data

resources of the enterprise

The steward does not own the data

The steward impacts processes that affect the dataand its use

Acquisition

Management, maintenance and storage

Dissemination

Disposal




Data Planning Roles

Stewardship responsibilities

Provide input to the subject area model

Provide input to the business data model (business

rules, definitions, etc.) Establish metadata management strategy

Custodianship responsibilities

Develop the subject are model

Develop and maintain the business data model

Establish metadata management strategy




Data Acquisition Roles


Establish the quality expectations

Define business processes for acquiring data

Establish the authority levels for creating, updating,and deleting data

Establish the validation rules for ensuring that the

data meets quality expectations

Custodianship responsibilities Understand the quality of existing data

Provide input to the quality expectations




Data Management Roles

Stewardship responsibilities Establish and monitor data demographic expectations

Establish archival and disaster recovery rules

Provide metadata content

Custodianship responsibilities Transform the business data model into system and

technology models

Establish (technical) data naming standards

Manage metadata

Manage data storage (design, reliability, security,recoverability, archival and restoration, etc.)




Data Dissemination Roles

Stewardship responsibilities Establish privacy and security policies

Define standard query and reporting requirements

Establish capability requirements

Establish quality expectations

Establish policies and guidelines for information use

Provide metadata content

Custodianship responsibilities Ensure adherence to privacy and security policies

Providing input to the quality expectations

Manage and provide metadata




Data Disposal Roles


Establish retention rules

Establish erasure rules

Custodianship responsibilities Provide input to retention rules

Provide input to erasure rules




Making It Real

Two ways to approach stewardship

Data stewards are assigned to a specific data subject

area – customer, product, order, etc.

Data stewards are assigned to a particular function –sales, marketing, finance, etc.

There are benefits and drawbacks to each

approach

In either case, good communication ismandatory




Executive Oversight

Cross-functional committee

Provides authority to the data stewards

Provide resources for data stewardship and

information management

Resolve conflicts




Prioritization

Too many data elements to do at once

Need to categorize data

Criticality

Visibility

Usage

Sanctioned Projects




Topics

Complexities


Assessment Continuous Improvement

Data Profiling

Symptoms vs. Root Causes

Improvement

Strategy24




Continuous ImprovementProcess

25

PLAN

DOCHECK

ACT

Data profiling typically

starts here

Reactive actions

typically start here

Proactive programs

start here

Some companies start

here, following existing

processes


starts here




Data Profiling Framework

Data Cleanup and Business Process AdjustmentData Cleanup and Business Process Adjustment

PLAN

ACT

CHECK

DO

Framework courtesy of SAP




Data Profiling in Context

Diagnostic step to understand data meaning

and quality

Priorities dictate scope

Business data model provides business rules Quality expectations provide perspective

Data profiling reveals conditions

Analysis determines actions Expectations adjustment

Corrective actions

Preventive actions

27

R t C A l i i



Copyright © 2010 Intelligent Solutions, Inc., All Rights Reserved 28

Root Cause Analysis isPerformed

Major Cause Major Cause

Major Cause Major Cause

Characteristic

AB CD OTHERS

%#




Topics

Complexities


Assessment

Improvement

Data warehouse implications

Upstream implications

Strategy

29




Data Warehouse Implications

Data handling options

Accept

Reject

Fix

Adopt default value

Error handling options

Suspend data awaiting correction

Transmit correction to source

Transmit need for corrections

30

Continuous Improvement





31

PLAN

DOCHECK

ACT


starts here

Reactive actions


Proactive programs

start here



processes


starts here




Topics

Complexities


Assessment

Improvement

Strategy

Major components

32




Framework & Business Drivers

Relate to Enterprise Quality Management

Approach

Formal or informal

Goals

Understand business drivers and needs

Business intelligence / operational systems

Strategic / tactical / operational

Declare strategy

Mission statement

Guiding principles

33




Methodology




Subject Area Model

Business Data Model

OperationalSystem Model

Data WarehouseSystem Model

Technology Models

Data Models

Continuous Improvement





36

PLAN

DOCHECK

ACT


starts here

Reactive actions


Proactive programs

start here



processes


starts here




Roles and Responsibilities

Executive Oversight

Data Stewardship

Data Custodianship

Data Providers

Data Users

T l d T h l




Tools and Technology

Database management system

Data modeling

Data profiling

Metadata management

D t Q lit M t i




Data Quality Metrics

Data usage

Data quality improvement

Benefits attained

40

St t C i ti




DATAQUALITY

STRATEGY

DATAQUALITY

STRATEGY

Strategy Communication

T i




Topics

Complexities


Assessment

Improvement Strategy

42

Ab t I t lli t S l ti




About Intelligent Solutions

Founded in 1992 by Claudia Imhoff Received outstanding recognition for client satisfaction

(Dun and Bradstreet survey of our clients)

Internationally recognized industry expertise

Full line of Corporate Information Factory and CRMcourses

BI and CRM Consulting services Mentoring

Assessment and Planning

Management

Design and Implementation

International client base in all industry verticals



Poorly Managed InformationLeads to Inefficiency and Risk



Leads to Inefficiency and Risk

Over 51% of organizations estimate datarelated issues cost their company over

Forbes Insight

“

90% of all businesses still do not havesufficient policies in place to meet data

governance regulations.”IT Policy Compliance Group

“

$5 million.



Build an Information Driven Organization



Provide all users with data that

is complete, accurate and

accessible

Improve business insightand decision making

Provide high quality data to all

business processes

Increase operationalefficiency and reduce costs

Enhance information

governance via policy-based

data management

Meet compliance andregulatory requirements

SAP Provides Best-In-Class EIM SolutionsDeliver Information That Is Complete, Accurate, and Accessible



p

Data Integration & Quality Management:

SAP BusinessObjects Data Services

SAP BusinessObjects Data Federator

SAP BusinessObjects Text Analysis

SAP BusinessObjects Data Insight

SAP Data Migration services

Master Data Management:

SAP NetWeaver Master Data Management

SAP Master Data Governance for Financials

SAP Data Maintenance by Vistex

Enterprise Data Warehousing:

SAP NetWeaver Business Warehouse

SAP NetWeaver Business Warehouse Accelerator

SAP BusinessObjects Rapid Marts

SAP BusinessObjects Metadata Management

Content & Information Lifecycle Management:

SAP NetWeaver Information Lifecycle Management

SAP Extended ECM by Open Text

SAP Document Access by Open Text

SAP Archiving by Open Text

© SAP 2007 /

What Are the Sources of Bad Data Problems?



© SAP AG 2010 / 50

EnterpriseInformation

EmployeeData Entry

CustomerSelf-Service

DataMigrationProjects

ITApplication

Updates

Purchasedor RentedExternal

Data

The Data Quality Framework



© SAP 2009 /

CONTINUOUSMONITORINGMEASURE

ANALYZE

PARSE

STANDARDIZE

CORRECT

ENHANCE

MATCH

CONSOLIDATE

YOUR DATA

Data Assessment

Enhance Data Cleansing

Match &

Consolidate

Continuous Monitoring



Building a Roadmap for Enterprise Information

Management is Key for Success



1. Data

READINESS

4. Data

GOVERNANCE

Understand what data

assets you have andhow they are beingused

Deliver trustedinformation repeatableand reliably at the rightform, to the right place atthe right time

2. DataINTEGRATION

CLEANSING

3. Data

CONSOLIDATION

Understand

Govern

Consolidate

Understand

Consolidate

Understand Understand

Consolidate diversemaster data landscapesand increase trust andreliability in information

Technology enablingpeople to implement arepeatable process tomanage the use, qualityand lifecycle of

information

People & Process Maturity

V a l u e

Cleanse Cleanse Cleanse

Data Quality Provides Value ThroughoutPortfolio maps to the People and Process Maturity



END-TO-END DataManagement

Full EnterpriseCOVERAGE

1. DataREADINESS

4. Data

GOVERNANCE

2. Data

INTEGRATION

CLEANSING

3. Data

CONSOLIDATION

Dash Quality

MDG

MDM

Dash Quality

MDM

Dash Quality Data Quality

People & Process Maturity

V a l u e

Dash Integrator Dash Integrator Dash Integrator

© SAP 2009 /



Q ti ??



Questions??

Contact Information



Co tact o at o

If you have further questions or comments:

Jonathan G. Geiger, Intelligent Solutions

[email protected]

Kristin McMahon, [email protected]

37253949 SAP Data Quality _ Vistex

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