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Transcript of 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
Data Governance for Data Migration
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
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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
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What is Data Governance?
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.
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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
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What is Data Governance?
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
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What is Data Governance?
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
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 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
After completing this section, you will be able to:
Data Governance Structures and ProcessesDATA MIGRATION TRAINING
Data Governance for Data Migration
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
Data Governance Organizations
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 Governance Organizations
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.
Data Governance Organizations
Business vs. IT Data Stewards
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Data Governance Organizations
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
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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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
After completing this section, you will be able to:
Using Data Governance in Data MigrationDATA MIGRATION TRAINING
Data Governance for Data Migration
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
Data Governance for Data Migration
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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Data Governance for Data Migration
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 Governance for Data Migration
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
Data examples/Drill down reports(Metric failure data, Match group reports)
SAP
Configuratio
n
DataGov
ernan
ce
Dashb
oard/R
eportin
g
Currency
Data Matching Results
Data Migration Metrics Pyramid
Data Governance Visualizations
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Overall
Data Validity
Score
Completeness
Conformity
ReferentialIntegrity
Individual Dimension
Level Metrics
Validity
Data examples/Drill down reports(Metric failure data, Match group reports)
SAP
Configuration
DataGove
rnan
ce
Dashb
oard/R
eportin
g
Currency
Data Matching Results
Data Migration Metrics Pyramid
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
Data Governance for Data Migration
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
Data Governance for Data Migration
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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Data Governance for Data Migration
Initiate the Project
Initiate the projectCreate the data
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 migrationManage the
data migration
Post production
data governance
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Data Governance for Data Migration
Create the Data Governance Team
Initiate the projectCreate the data
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 migrationManage the
data migration
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Data Governance for Data Migration
Plan Data Migration Governance
Initiate the projectCreate the data
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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Data Governance for Data Migration
Manage the Data Migration
Initiate the projectCreate the data
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
Initiate the projectCreate the data
governance
team
Plan data
migration
governance
Manage the
data migration
Post production
data governance
Data Governance for Data Migration
Performing Iterations
Key Points
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2011 SAP AG. All rights reserved. 42
Data Governance for Data Migration
Post production Data Governance
Initiate the projectCreate the data
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
1.What is Data Governance
2.Data Governance Structure and Process
3.Using Data Governance in Data Migration
4.Summary and Questions
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8/12/2019 Data Governance Present v 07
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Thank You!
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