Data Governance Present v 07

8/12/2019 Data Governance Present v 07

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2011 SAP AG. All rights reserved. 1

G

Y

R

R

Client

Project Name

Opportunity Owner

CEM / CP

AE / AM

Date

DATA MIGRATION

DATA GOVERANCE PLAN


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Course ObjectivesDATA MIGRATION TRAINING

Data Governance for Data Migration

Describe the fundamental concepts of Data Governance

Understand the structures and roles in a Data Governance organization

Describe the role of Data Governance in Data Migration

Understand the steps involved in implementing Data Governance in a DataMigration project

DM

SAP

DATA

MIGRATION

After completing this course, you will be able to:


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AGENDA

1.What is Data Governance

2.Data Governance Structure and Process3.Using Data Governance in Data Migration

4.Summary and Questions


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Understand the rationale and business drivers behind Data Governance

Describe the fundamental concepts of Data Governance

Explain the benefits of Data Governance

DG

SAP

DATA

Governance

After completing this section, you will be able to:

What is Data Governance?DATA MIGRATION TRAINING


Time Duration

25 Minutes


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The Data Challenge

Data makes it big in the News

Data slipupsRick Whiting , 10-May-2006

Inaccurate business data lead to botched

marketing campaigns, failed CRM

projects--and angry customers.

A home valued at US$121,900 somehow wound up recorded in

Porter County's computer system as being worth a whopping US$400

million. Naturally, the figure ended up on documents used to calculate

tax rates. By the time the blunder was uncovered in February, the

damage was done.


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The Data Challenge

The complexities of doing business

Business Growth

Finance

CEO

HR

Supply Chain

Sales

Through mergers and

acquisitions, a company will

typically need to integrate

and reconcile data from

different systems.

Individual departments often

run their own systems and

with their own separate data

stores and business

processes.

System upgrades,

multiple systems, data

owners, and duplicate

data results in poor data

quality

Data complexity grows as a business grows

M

erger / Acquisition

Merger / Acquisition

Merger / Acquisition


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The Data Challenge

Does the business know their own data?

Im a professional.

Of course I know

my data!

But, once it leaves my

hands, it is someone

elses problem!

Wow, that transformation is

complex. Are you sure that

is in my data?

Subject matter experts (SMEs) onlyknow their own systems.

But they cant tell you how it changes

and is transformed as it moves from

system to system.

Relationship between systems arecomplex:

SMEs sometimes change jobs.

Im going to start myown consulting firm!


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The Data Challenge

The need for Data Governance

An interest in Data Governance and Data

Quality programs is driven by:

An organization crisis (crises) caused by poor

quality data or a lack of accountability:

Inability to meet regulatory and compliancestandards

Inability to correctly identify financial

expenditures and corporate assets

A need to pull data from multiple, disparate

sources together into one enterprise view, and

to maintain it over time, to support a businessinitiative

A mature, evolving Data Quality, Data

Integration or Business Intelligence Program

proactively moving to enterprise level

management



Data Governance is largely a

communication processit is the policies,

people and processes put in place to

manage and secure enterprise data.

Data Governance is the business process

by which you manage:

Aligning information strategy to yourbusiness needs

Ensuring that you have common

definitions and harmonized data

Integration of data across systems

Implementation and monitoring of dataquality

Data Governance provides the

organizational framework for achieving

Trusted Information

What is Data Governance?

Basic Definition




The Benefits

The implementation of a Data Governance program can result inthe following benefits:

Increased consistency and confidence in

decision making

Data

Governance

Improved Data Security Maximizing the income generation of data

Improved Data Quality

Decreased risk of regulatory

fines

Promotes Data Accuracy

Provides Data Context Improves Data Usability

The treatment of data as an enterprise asset and the delivery of

trusted information are the two key outcomes of Data Governance.



What is Data Governance?Data Governance and Information

Management

Across the enterprise, thereare different types of dataMaster, Transaction, BI, andUnstructured.

To manage the data, a typicalorganization will differentiateInformation Managementfunctions and initiatives:Information Security, DataQuality, and BusinessIntelligence

In an ideal world, DataGovernance is the overarchingframework and process thatcontrols all the otherinformation managementfunctions.

Information

Quality

Management

Reference &

Master Data

Management

DataWarehousing

& Business

Intelligence

Structured

Data

Management

Unstructured

Data

Management

Data Governance

D

ataStewardship

Information Architecture

Metadata Management

Information Security Management

Master DataTransaction

DataBI Data

Unstructured &Semi Structured

Data

Information Management




The relationship with Master Data Management

Master Data Management (MDM) and Data

Governance are two terms often usedinterchangeably, but there is a difference.

Master data, sometimes called reference data,

is data that is used by multiple business

groups across the enterprise, such as

product or customer data.

MDM is a set of processes and tools that

consistently defines and manages referencedata across different subject area domains.

MDM seeks to ensure that an organization does

not use multiple (potentially inconsistent)

versions of the same master data in different

parts of its operations.

Data Governance is an organizationalframework of information management

initiatives which includes Management Data

Management. Therefore, Master Data

Management supports the overall Data

Governance initiative of having common

definitions and harmonized data across the

enterprise.

Call

Center

Jane Smith4418 N. Str.

Chicago, IL

60611

Part: 2574

SRM

Part: 8975

VENDOR:ABC123

Logistics

VENDOR:

XYZ456

ERP

Jane Peters199, 3rdStreetPalo Alto, CA

Part: B7521

Master Data is Crucial to Your Business

But every department has a different version of it




The different focus areas

Policy, Standards & Strategy

This type of program typically comes into existence because some group within the organization needs support from a

cross-functional leadership body.

Data QualityThis type of program typically comes into existence because of issues around the quality, integrity, or usability of data. It

may be sponsored by a Data Quality group or a business team that needs better quality data.

Privacy, Compliance & Security

This type of program typically comes into existence because of concerns about Data Information Security controls, orcompliance. Compliance, in this context, may refer to regulatory compliance, contractual compliance, or compliance with

internal requirements.

Architecture Integration & AnalysisThis type of program typically comes into existence in conjunction with a major system acquisition, development effort,

or update that requires new levels of cross-functional decision-making and accountabilities.

Data Warehouse & BIThis type of program typically comes into existence in conjunction with a specific data warehouse, data mart, or BI tool.

These types of efforts require tough data-related decisions, so organizations often implement governance to help make

initial decisions, to support follow-on decisions, and to enforce standards and rules after the new system becomes

operational.

Management AlignmentThis type of program typically comes into existence when managers find it difficult to make "routine" data-related

management decisions because of their potential effect on operations or compliance efforts.

Source: http://www.datagovernance.com/Author: Gwen Thomas
http://www.datagovernance.com/http://www.datagovernance.com/http://www.datagovernance.com/http://www.datagovernance.com/


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AGENDA






Understand the structures and roles in a Data Governance organization

Describe the role of a Data Stewards

Understand the best practices to champion a Data Governance program

DGSAPDATA

Governance


Data Governance Structures and ProcessesDATA MIGRATION TRAINING


Time Duration

20 Minutes


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Data Governance Organizations

Structure and Roles

ExecutiveSponsorship

Strategic Data

Governance

Tactical Data

Governance

Data

Governance

Execution

C-Level Executives (CIO, CFO, CTO)

Provides visible senior management support to the adoptionof high-quality data.

Data Governance Steering CommitteeStrategic committee responsible for overseeing the governance

program, approving data governance policies and procedures,

defining priorities and monitoring progress.

Data Governance CouncilAll governance activities are directed and managed by this council. The

Data Council assigns tasks to both LOB and Enterprise Data teams and

ensures data activities have defined metrics and acceptable thresholds

for data quality. The Data Council then analyzes costs; monitors, tracks

and reports on progress against goals and objectives

LOB Data GovernanceThis group consists of Data Architect, Data Stewards, and DBAs that

are responsible for implementing the activities defined by the Data

Council. They provide education to developers and end users on the

importance of data standards and data quality, and participate in system

related projects to ensure the data standards are incorporated.


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Data Stewards are:

Representatives of either the Business or IT

Experts in data content and / or business processes

Policy makers for Data Governance

Data Stewards are responsible for the set of activities

that ensure data-related work is performed according topolicies and practices as established through

governance.

Activities include:

Assessing data quality

Evaluate/Identify data quality metrics

Defining business rules

Prioritizing and identifying resolution to issues

Facilitating data cleansing


The role of Data Stewards


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What is the Difference?

The titles data steward, and data owner, are oftenconsidered to be synonyms, but they are in factdifferent roles and vary depending on the company.

A Data Owner is the custodian of the data from thebusiness who has a direct line of responsibility for afunctional area, i.e. General Ledger, HR, Purchasingetc There will be data owners in every businessfunction throughout the company.

A Data Steward does not own the data, nor do theyhave complete control over its use, but they areresponsible for defining data governance policies, and

ensuring data quality goals are met for their functionalarea.

A Data Steward will work with a Data Owner to ensurethe correct definition and use of data and assist in theidentification and management of data quality issuesfor their area.


Data Stewards vs. Data Owners


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Business Data Stewards

Can speak the language of the business andunderstand the role of the data plays in the companysstrategy

They have a contextual understanding of how the datais used and can explain the business rules behind thedata, i.e. why a data element must be delivered in a

certain form.

They understand the business processes - most of theroot causes of data quality are linked with what thedata goes through before or at the time it is enteredinto an IT system.

IT Data Stewards

Someone who is more technical and understands thevarious operational systems of record, lineage, andformatting for heterogeneous data.

They understand the technical and operational aspectof the systems and can deconstruct data requirementsto determine the optimal source or sources for data.


Business vs. IT Data Stewards


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Characteristics of Maturity

A mature Data Governance Organization has:

Representative government, which is

collaborative, where data stakeholders are

identified, involved, enabled, and accountable

A defined organizational structure and resourcesdedicated to facilitating its success

An information strategy that is aligned with

business strategy

Data usage, quality, and security monitoring and

metrics in placeExecutive understanding and commitment, and

recognition that this is an ongoing process, not a

project.


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Data Governance Programs

Guiding Principles / Best Practices

Data Governance is a long-term initiative which

must be planned and sponsored at the highest levels of an

enterprise.

Data Governance initiatives should be

championed by communicating a compelling reason for

change, setting achievable performance targets, and

allocating resources.

Data Governance is not an IT functionit is owned

by the Business and should be instituted across the

enterprise.

Data Governance must have real authority including

the ability to resolve business issues, approve andfund projects, as well as settle disputes.

A key responsibility of the Data Governance

function is communication. The Data Governance

organization should be known and available to the

data users.


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AGENDA





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Describe the role of Data Governance in Data Migration

Understand the Data Migration Metrics used in the Data GovernanceVisualizations

Understand the steps involved in implementing Data Governance in a Data

Migration project

DGSAPDATA

Governance


Using Data Governance in Data MigrationDATA MIGRATION TRAINING


Time Duration

40 Minutes


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SAP Data Migration Services

Agility and Control

1

ANALYSIS

2

EXTRACT

5

LOAD

64

VALIDATE

Governance and Visualisation

SAP

DATA MIGRATION

SERVICES

Methodology

Templates

Tools

Expertise

SAP Data Migration gives You what You need: A Solution You can Trust.

Standardized and Proven Approach for End-to-End Data Migration

SAP Data Migration Services consist of a framework,

templates, methodology, toolsand expertiseto

analyse, extract, cleanse, validate, upload and reconcile

legacy data into a SAP ERP environment.

SAP Data Migration Services provide a mature

information management infrastructureand enables

data governance best practicesthat live on after the

project.

RECONCILE

Data Migration Framework

3

CLEAN


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Look at the data early and often

Get a Data Governance process established

Validation

Transparency

Methodology

SAPDATA MIGRATION

Governance

Data governanceis the businessprocess by which you manage aligning

information strategy to your business needs, ensuring that you have common

definitions and harmonized data, integration of data across systems and

implementation and monitoring of data quality

Data governance is a business driven process The data migration projectis an opportunity to pilot a data governance strategy and incubate the business

processes that support data governance

More Information: Data Migration Framework

Data Governance Visualisation

Project Management Summary Metrics

Track % quality and progress over time

against projectmilestones

Business users/Data Steward reports

Show detailed data exceptions requiring

resolution for each SAP Business Objectse.g. Customer, including reconciliation

exceptions


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Within the context of data migration data governancehas a smaller scope which includes:

Designating accountability for information quality (this is largely an issues management process)

Providing timely and accurate data access for data stewards and subject matter experts

Implementing the needed data security level for sharing and evaluating data as it is migrated to new

applications

The data migration project is an opportunity to pilot a data governance strategy and incubate the business

processes that support data governance


What is the focus?

Data Services

Transformation

Legacy

Data Source

LegacyData Source

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Extract Load

Staging Area &

DQ Metrics DB

Data Services

Migration ETLSAP R/3System

Load

The Data Governance Process in Data Migration


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Accountability for information quality

Legacy source data that supports new business processes

Validated

System of Record

Business rules

SAP configuration

Data cleansing decision making, for example

What is a unique item? A valid customer?

How do we assign material group? Chart of accounts?

What is an acceptable level of quality? There is no perfect

data .

Partnering with the technical team to move forward

Prioritization. There is no perfect data

Business driven data quality analysis and decision making owned

by the data governance team


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Migration Framework StagingCleanse, Validate, Reconcile


Data Access for Data Stewards and SMEs

Data Services

Transformation

LegacyData Source

LegacyData Source




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Extract Load

Staging Area &DQ Metrics DB

Data Services

Migration ETLSAP R/3System

Data Governance Visualization

Project ManagementSummary Metrics: Track

% quality and progress

over time against project

milestones

Business users/Data Steward

reports Show detailed dataexceptions requiring resolution for

each SAP Business Objects e.g.

Customer, including reconciliation

exceptions

Data Content,

Exceptions &

DQ Metrics

Load

SAP Configuration Data

Lookups, valid values, and

business rules that drive

SAP processing.

SAP Business Object Validation Library

Library of DS objects to validate the data pre-load.

The metrics created from this process drive the

Data Governance Engine

As is analysis of legacy data. Direct

access to data in legacy systems

and access to staged data.

Data Profiling


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Data Migration Metrics Pyramid

Tracking Data Quality

Overall

Data Validity

Score

Completeness

Conformity

ReferentialIntegrity

LEVEL TWOWhere are the potential

migration problem areas by category?

Individual Dimension

Level Metrics

Validity

LEVEL THREEWhat metrics have

been recorded within

each dimension?

Data examples/Drill down reports(Metric failure data, Match group reports)

LEVEL ONEWhat percentage of

data is ready to load?

SAP

Configuration

LEVEL FOURData metric failure

data examples

Currency

Data Matching Results

3. Define/Refine

Business Rules

1. Perform Iteration

and Collect metric

results

2. Review, Re-Plan

And Disseminate

Ongoing Monitoring and Process Control


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OverallData Validity

Score

Completene

ss

Conformity

ReferentialI

ntegrity

Individual DimensionLevel Metrics

Validity


SAP

Configuratio

n

DataGov

ernan

ce

Dashb

oard/R

eportin

g

Currency



Data Governance Visualizations


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Overall

Data Validity

Score

Completeness

Conformity

ReferentialIntegrity

Individual Dimension

Level Metrics

Validity


SAP

Configuration

DataGove

rnan

ce

Dashb

oard/R

eportin

g

Currency



Level Two Metrics Defined

Completeness

Do mandatory attributes have a value?

Have optional, key business attributes been supplied?

Conformity

Does data content reflect intended target requirement?

Is the data in the correct format?

Does data fall within permitted domain range or LoV?

Validity

What percentage of customers addresses can be verified?

What source data has poorly populated address data?

Does my material data conform to Industry standards?

SAP Configuration

Is data ready to load based on the configuration information

within the target SAP environment?

Referential Integrity

Are all billing transaction connected to a valid customer?

Do all my customers have addresses?

Currency

How up-to-date is the data. Does the data fall within acceptable

time line?

The metrics and business rules included within the six data migration categories are:

The Data Migration Framework can cater for Custom categories if required

D t A f D t St d


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Data Access for Data Stewards

Why is this Data Governance?

Data Profiling

Management Dashboard

and Detailed Reports

SAP ConfigurationData Validation

Data Migration Data Governance

Management tracking of actual status

Visibility to actual DQ metrics

Business driven process to

Identify cross silo data definitions

Accurately identify SOR

Map source to targetPrioritize data quality issues

Plan of action for resolving DQ issues

Fix

At source

New business rule

Data enrichmentAssign to other business areas

New or improved process

Configuration changes

D t G f D t Mi ti


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Examples of sensitive Data includes:

Human resources detail

Social security numbers

Phone numbers

Financial details

Internal employee data

Credit Cards


Defining Security for Data Migration

Data Migration

Team Member

Data Steward

The business

defines the security

access for the Data

Migration Team

Access Rights

Birth Date

Social Security

Salary

Criminal History


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Data governance is largely a communication

process

Following the initiation every task is iterative

The objectives and targets are key to the success of

the team

The team should create a vision for the post

production state of data governance as part of the

initial planning


Implementation Steps

Initiate the projectCreate the data

governance

team

Plan data

migration

governance

Manage the

data migration

Key Points

Manage the

data migrationManage the

data migration

Post production

data governance


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Initiate the Project


governance

team

Plan datamigration

governance

Manage the

data migration

Key Points

Analysis of current governance and cultureDefine scope and objectives

Executive sponsorship

Internal project plan

Team structure, roles and responsibilities

Key milestones

Initial communications plan

Map governance to the business needs

Define team structure

Executive Sponsors, Data Migration Lead, Data Governance Lead, DataStewards, Data Migration Specialists, and maybe a BI Report Developer

Manage the


data migration

Post production

data governance


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Create the Data Governance Team


governance

team

Plan datamigration

governance

Manage the

data migration

Postproduction data

governance

Key Points

Leverage existing team or pilot data governance at the project levelIdentify data stewards

Gain resource commitment

Assign data steward to SAP application modules, business objects, or custom

subsets

Communication and task planningSAP configuration team interface

Tool install and training

Manage the


data migration


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Plan Data Migration Governance


governance

team

Plan datamigration

governance

Manage the

data migration

Key Points

Review business needs

Data quality metrics workshops

Review out of the box metrics

Identify and design custom metrics, refine set structure

Set targets and thresholds (requirement to track changes)

Design custom visualizations and exception reporting

Define data access security requirements

Detailed communication, workflow, and task planning

Issue management process planning

Cross-silo approach by business object

Prioritization process (roles and responsibilities)

Manage the

data migration

Post production

data governance


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Manage the Data Migration


governance

team

Plan datamigration

governance

Manage the

data migration

Key Points

Initial analysis and mapping

SOR validation, gap analysis, legacy data profiling

Source to target mappingIterative loads

Metric visualization and exception reporting reviewManagement dashboard

Trend analysis for iterative loads

Record level analysis

Issue managementPrioritization

Refining and validating business rules

Legacy system data fixes

Data enrichment

Reconciliation

Post production

data governance


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When needed, iterate back to planning and objectives

Team and communication changes

Issue resolutionLegacy fixes

Data quality metrics updates

Business rule updates


governance

team

Plan data

migration

governance

Manage the

data migration

Post production

data governance


Performing Iterations

Key Points


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Post production Data Governance


governance

team

Plan datamigration

governance

Manage the

data migration

Post production

data governance

Key Points

Validation and reconciliationOngoing data governance and data quality issue management

Planning for subsequent phases

Maintenance of integration


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AGENDA


2.Data Governance Structure and Process

3.Using Data Governance in Data Migration



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Thank You!

Contact information:

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Title

Address

Phone number

Data Governance Present v 07

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