sap data quality

8/8/2019 SAP Data Quality

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Developing a Data Quality

and Integration Strategy

Jonathan G. GeigerIntelligent Solutions, Inc.

April 28, 2010


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Sponsor


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Copyright 2010 Intelligent Solutions, Inc., All Rights Reserved

Topics

Complexities

Expectations Setting

Assessment

Improvement

Strategy

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4/58Copyright 2010 Intelligent Solutions, Inc., All Rights Reserved

Topics

Complexities

Typical Complexities

Why They Exist


Assessment

Improvement Strategy

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Data Integration

DataWarehouse

OperationalData Store

Operational Data

ExternalData

InternalData

. . . and a miraclehappens here . . .

Data integration is very complex!


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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

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Load Complexities

Currency



Metadata

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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`


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De

fectRate

Time

Target

Quality - Definition

Quality is conformance to requirements

Quality is not

.... (necessarily) zero defects


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Quality - Definition

Quality is conformance to requirements

Conformance to what?

Whose requirements?

How are requirements set?

What degree of conformance?


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Stewardship

A steward is one who is called upon to exerciseresponsible care over possessions entrusted tohim or her

(adapted from Websters dictionary)

The steward does not own the possessions

The steward has a responsibility affecting theprocesses that impact the possessions


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Data Steward

Exercise responsible care over the dataresources 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


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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


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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 andtechnology models

Establish (technical) data naming standards

Manage metadata

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


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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


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Data Disposal Roles

Stewardship responsibilities

Establish retention rules

Establish erasure rules

Custodianship responsibilities Provide input to retention rules

Provide input to erasure rules


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Making It Real

Two ways to approach stewardship

Data stewards are assigned to a specific data subjectarea customer, product, order, etc.

Data stewards are assigned to a particular functionsales, marketing, finance, etc.

There are benefits and drawbacks to eachapproach

In either case, good communication ismandatory


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Executive Oversight

Cross-functional committee

Provides authority to the data stewards

Provide resources for data stewardship andinformation management

Resolve conflicts


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Prioritization

Too many data elements to do at once

Need to categorize data

Criticality

Visibility

Usage

Sanctioned Projects


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Topics

Complexities


Assessment Continuous Improvement

Data Profiling

Symptoms vs. Root Causes

Improvement

Strategy24


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Continuous ImprovementProcess

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PLAN

DOCHECK

ACT

Data profiling typicallystarts here

Reactive actionstypically start here

Proactive programsstart here

Some companies starthere, following existing

processes



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Data Profiling Framework

Data Cleanup and Business Process AdjustmentData Cleanup and Business Process Adjustment

PLAN

ACT

CHECK

DO

Framework courtesy of SAP


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Data Profiling in Context

Diagnostic step to understand data meaningand 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 actions27

Root Cause Analysis is


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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

%#


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Topics

Complexities


Assessment

Improvement

Data warehouse implications

Upstream implications

Strategy

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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

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Continuous Improvement


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PLAN

DOCHECK

ACT





processes



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Topics

Complexities


Assessment

Improvement

Strategy

Major components

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Framework & Business Drivers

Relate to Enterprise Quality ManagementApproach

Formal or informal

Goals

Understand business drivers and needs

Business intelligence / operational systems

Strategic / tactical / operational

Declare strategy

Mission statement

Guiding principles

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Subject Area Model

Business Data Model

OperationalSystem Model

Data WarehouseSystem Model

Technology Models

Data Models

Continuous Improvement


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PLAN

DOCHECK

ACT





processes



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Data Profiling Framework

Data Cleanup and Business Process AdjustmentData Cleanup and Business Process Adjustment

Framework courtesy of SAP


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Roles and Responsibilities

Executive Oversight

Data Stewardship

Data Custodianship

Data Providers

Data Users

T l d T h l


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Tools and Technology

Database management system

Data modeling

Data profiling

Metadata management

D Q li M i


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Data Quality Metrics

Data usage

Data quality improvement

Benefits attained

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T i


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Topics

Complexities


Assessment

Improvement Strategy

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Ab t I t lli t S l ti


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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 CRM

courses BI and CRM Consulting services

Mentoring Assessment and Planning Management

Design and Implementation

International client base in all industry verticals

SAP Solution OverviewEnterprise Information Management


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Enterprise Information Management

Kristin McMahonDirector, Enterprise Information ManagementSAPApril 28, 2010

Poorly Managed InformationLeads to Inefficiency and Risk


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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.


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Build an Information Driven Organization


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Provide all users with data thatis complete, accurate andaccessible

Improve business insightand decision making

Provide high quality data to allbusiness processes

Increase operationalefficiency and reduce costs

Enhance informationgovernance via policy-baseddata management

Meet compliance andregulatory requirements

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


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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 / Page 49

What Are the Sources of Bad Data Problems?


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SAP AG 2010 / 50

EnterpriseInformation

EmployeeData Entry

CustomerSelf-Service

DataMigrationProjects

ITApplication

Updates

Purchasedor RentedExternal

Data

The Data Quality Framework


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SAP 2009 / Page 51

CONTINUOUSMONITORINGMEASURE

ANALYZE

PARSE

STANDARDIZE

CORRECT

ENHANCE

MATCH

CONSOLIDATE

YOUR DATA

Data Assessment

Enhance Data Cleansing

Match &

Consolidate

Continuous Monitoring

Data Quality ApproachThe Three Rules of Data Quality


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SAP AG 2010 / 52

Rule 1: Analyze your data

Profile, query, extract and in every other waybecome intimately familiar with data content at adetail level. If you take a high-level approach todata quality, you will waste time discussing whatthe data might look like.

Rule 2: Define your scope

All data quality projects uncover hidden issues.Be very clear about what is, and is not, relevantto your current effort.

Rule 3: Cleanse your data and track yourresults

Data quality is not a one-time process. It is anongoing process of monitoring and correctingyour data. You should know that: 1) new qualityneeds are being met and 2) new businessprocesses are being monitored.

What is the definition of clean data?

Who defines clean?

Who owns it over time? Which entities have the most issues? Where are the issues originating from?

Which business processes are affected?

What business benefit can be achieved? How clean does it need to be? People, process, and tools?

Define stakeholders to analyze and clean Define processes to clean, monitor and

maintain cleanliness Acquire necessary tools to assist

Business and IT collaboration through

visualizing information governance metrics


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SAP 2009 / Page 53

Business users can easily see howtheir information measures upagainst information governancerules and standards

IT can easily share data qualitymetrics to business users andinvolve them in owning the dataproblem

Building a Roadmap for Enterprise Information

Management is Key for Success


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1. DataREADINESS

4. DataGOVERNANCE

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. DataCONSOLIDATION

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

Value

Cleanse Cleanse Cleanse

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


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END-TO-END DataManagementFull EnterpriseCOVERAGE

1. DataREADINESS

4. DataGOVERNANCE

2. DataINTEGRATION &CLEANSING

3. DataCONSOLIDATION

Dash Quality

MDG

MDM

Dash Quality

MDM

Dash Quality Data Quality

People & Process Maturity

Value

Dash Integrator Dash Integrator Dash Integrator

SAP 2009 / Page 55

Time to Value: Fast and cost-effective integration with existing

Why SAP?The Best Choice for EIM


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SAP and non-SAP systems

Proven Customer Value: Matureoffering and large install base ofcustomers supporting criticalbusiness scenarios

Market Leadership: Analystrecognition and customerimplementation success

Comprehensive Solutions for EIMStrategyOne-stop for end-to-end informationgovernance and management

SAP 2007 / Page 56

Q ti ??


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Questions??


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