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Managing Risk with Master Data Governance and Controls
David Sentance PwC
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In This Session
• Find out about the risks that businesses are facing due to poor data quality and lack of
appropriate governance processes and understand what organizations are doing in the
face of this challenge
• Hear about simple approaches that can be used to improve the controls over the
maintenance of financial master data
• Learn how to leverage SAP solutions to provide an understanding of the data quality in
your business and how it can be used to track data cleansing and quality improvement
over time
• Understand the SAP solutions that are now available to provide ongoing governance over
master data and some pitfalls to avoid during implementation
• Take home some simple roadmaps for how data quality improvement and master data
governance can be improved within your business and how they can be leveraged to
build the business case for the implementation of master data governance solutions
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What We’ll Cover
• Master Data Governance — Overview
• Master Data Governance — Risks and Challenges
• PwC Master Data Survey Insights
• Case Study — Finance Master Data Assessment
• The Role of SAP Solutions Like Information Steward and MDG
• How to Get Started on Governing Master Data
• Wrap-Up
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What Is Data Governance?
• Data Governance is a repeatable process enabling delivery of standardized, high-quality
data to end users in a timely, auditable, and secure manner. It is used to govern data
architecture, authorize standards, assign accountability, and monitor data quality. Old paradigm New paradigm
“If the system lets me do it, it must be ok!” Prevent the user from being able to create bad data and
begin the migration to a data quality culture.
Business Rules are resident in the experienced
worker’s head and applied when needed.
Define the business rules, edits, referential, and
contextual integrity for consistent application.
Data is local on the desktop or in formats or media
where it is not visible to the Supply Chain.
Capture the data at the source when it is known and
increase the velocity of the data throughout the
enterprise.
Maintain business rules, edits, and integrity in
application code.
Automate business rules, edits, and integrity rules in the
database, workflow, and user interface levels through
configuration.
Manual triggering of business processes via phone,
email, spreadsheets, paper, local DBs.
Integrate workflow and eliminate the need for manual
triggering.
Paradigm Shift
People + Process + Technology = Quality + Accuracy + Completeness
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What Is the Scope of Master Data Governance?
• Master data elements and structure
• Master data processes (to create and maintain master data)
• Master data quality and business rules (for creating and maintaining master data)
• Master data access, delivery, security, and usage
The “Domains” of Master Data
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The Benefits and Outcomes
Benefits Key Outcomes
Establishes a common vocabulary for data across
the organization to ensure access to the right data
Creates a forum and establishes accountability for
decisions that need to be made for data
Improves decision making through increased
knowledge, transparency, and confidence in the
data
Improves data consistency and availability through
implementing controls, processes, and policies
Helps identify upstream data quality issues
Cost avoidance through reducing duplicate or
redundant data efforts
Quicker and less complex data integration efforts
as a result of consistency and better data quality
Better Decision Making: Increases data quality,
makes data more consistent, increases data
availability, and provides lineage so data
consumers know where the data originated;
employees shift focus from compiling (and usually
questioning) data to higher-value activities
Increased Flexibility: Makes IT Operations more
flexible and helps enable service-based architecture
through standardization and common definition
Reduced Operational Friction: Data Governance
reduces data redundancy while improving data
management processes for more streamlined
operational processes and reduced IT costs
Reduced Risk: Fosters control and stewardship of
data, which helps ensure data security and reduces
the risk of legal and regulatory violation
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Data Governance and IT’s Role in Digital Trust
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What We’ll Cover
• Master Data Governance — Overview
• Master Data Governance — Risks and Challenges
• PwC Master Data Survey Insights
• Case Study — Finance Master Data Assessment
• The Role of SAP Solutions Like Information Steward and MDG
• How to Get Started on Governing Master Data
• Wrap-Up
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Cost Value
• Unit cost of storage has decreased dramatically
• But, increasing volume – accompanied by a lack of data
discipline – is driving overall storage cost higher
• Data and BI services costs continue to increase
• Are organizations making better decisions with the
information?
• Unleashing the dynamic – data to information to
analytics to insight to action to value generation
• Operational excellence
Growth Risk
• Huge amounts of information are growing out of control
• Electronic transaction data volumes are skyrocketing
• More information is being digitized
• Online fraud is growing
• Regulatory pressures for greater transparency are
increasing
• Increased executive accountability is acute
Managing Information Is Increasingly Difficult
Are these resonating with you?
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What We See in the Marketplace
• Inconsistent business processes and IT solutions not delivering the benefits
• Why? Impact of Master Data Quality.
• What’s been done? Implement Technology.
• Yet one thing stands out – Majority of master data initiatives have enjoyed only limited
success
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Limited Success
Why? Common Themes
• Countries, multiple currencies, and
inconsistent regulations
• Diverse product/service offerings
• Multiple customer touchpoints
• Complex processes
• Master data defined in different ways
• Organizational complexity
• Inconsistent data quality and access
• Lack of standardization
• Multiplicity of tools
• IT optimization
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What’s Missing?
ERP
Products and
Services
Customers
Suppliers
Business
Warehouse
Management
Accounting
Financial
Consolidation
Demand
Management
Reporting Structures
External
Internal
Tax/
Legal Entity
• Take the data out of the
legacy applications
and manage it in MDM/
MDG where we can
automate the rules and
gain efficiencies
• Distribute the data
from MDM to all the
systems that need it
Master Data Management/Governance
(SAP MDM/MDG)
“One version of the truth” enables “One place, one number”
Finance
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How Do You Establish Ownership and Accountability?
• Ownership and accountability are created within and across an enterprise in three ways:
Initiated by defining clear roles and responsibilities for a governance organization
Standardized by defining a set of policies to help ensure ownership and accountability
Implemented by defining a set of procedures to support various activities as related to
governance and by establishing metrics to measure performance
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Four Master Data Pain Points — Material/Product
• Multiple lines of products – duplicates, obsolescence, classification/SKU proliferation
• No visibility to inventory across worldwide sourcing/excessive inventory levels tying up
working capital
• Inaccurate BOMs
• Frequency that changes need to be made results in operational inefficiencies
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Four Master Data Pain Points — Customer
• No visibility to Product/Customer combination of data across the enterprise
• Different discounts, payment terms across companies in the same organization resulting
in lost opportunities for profit maximization
• Understanding your Customers from a global organization perspective
• Product delivery challenges due to poor address information/inability to take advantage
of USPS discounts
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Four Master Data Pain Points — Supplier
• No visibility to Product/Vendor combination of data across the enterprise
• Different discounts, payment terms across companies in the same organization resulting
in lost opportunities for cost reduction
• Understanding your Vendors from a global organization perspective
• Fraud – Who creates and maintains/Segregation of Duties in procure to pay
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Four Master Data Pain Points — Finance
• Acquisitions/Divestitures creating redundant master data
• Internal re-organizations creating redundant master data
• Finance master data not fully understood so accounting entries are made incorrectly/
without the right attributes, which impacts financial reporting
• Intercompany reconciliations and impact on financial close
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What We’ll Cover
• Master Data Governance — Overview
• Master Data Governance — Risks and Challenges
• PwC Master Data Survey Insights
• Case Study — Finance Master Data Assessment
• The Role of SAP Solutions Like Information Steward and MDG
• How to Get Started on Governing Master Data
• Wrap-Up
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PwC Global Study of Master Data Management
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Success Factors for Master Data Management
Key Finding:
Successful MDM should not
be viewed as a purely
technology issues
4 factors:
• Management commitment
• Structured and goal-
orientated governance
• Process optimization
• Time and budget
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Data Quality and Governance
Key Findings:
Central governance leads to
a higher level of data quality
due to the management
processes in place
These processes are
relatively simple
Knowledge and experience
can be consolidated and
continuously improved
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Main Problems Affecting Data Quality
Key Findings:
Companies often have
master data that is old or
has not been properly
maintained
The most important aspect
is keeping data complete
and up-to-date
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What We’ll Cover
• Master Data Governance — Overview
• Master Data Governance — Risks and Challenges
• PwC Master Data Survey Insights
• Case Study — Finance Master Data Assessment
• The Role of SAP Solutions Like Information Steward and MDG
• How to Get Started on Governing Master Data
• Wrap-Up
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Case Study — Finance Master Data Assessment — Objectives
• Improve the visibility and transparency of the financial, statutory, and management
consolidation process
• Shorten closing and reporting cycles
• Simplify intercompany reconciliations
• Manage multiple accounting standards
• Support regulatory change
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Case Study — Approach and Timeline
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Case Study — Key Considerations
• What is the path forward:
To Improve month-end financial consolidation and management reporting?
For controlling areas and fiscal year variants?
• What options are available to address Global Costs within the existing structures?
• What structure options exists to support reporting for Region, Line of Business, etc.?
• What alternate views of – BPC vs. ECC vs. BW can enable management reporting?
• What alternatives are available for managing the Chart of Accounts for the transactional
system?
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Case Study — Key Findings
Key Findings:
• High level of
redundancy due to
historical
acquisitions and
divestitures
• No processes to
close out finance
master data that was
no longer required
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Case Study — Key Findings (cont.)
Key Findings: (cont.)
• Inconsistent usage of
GL accounts when
posting financial
transactions
• Many examples of
incorrect postings
with manual resolution
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Case Study — Recommendations
Controlling Area Reduction • Align Fiscal
Variants/Op Concerns
• Controlling Area Merge to 1
Data Cleansing
• Simplification
• FMD Usage
Leveraging SAP Leading Practice • Alternative
Hierarchies
• Expansion in the use of BPC
• Expanded Functional Areas
• Internal Orders
Master Data Governance
• Empower the current Data Stewards
• Formalize an MDG Organization
• Implement an MDG Portal
• Implement a Data Quality Dashboard
Financial Reporting
Vision (See next slide for
details)
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Case Study — Example Financial Reporting Vision
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What We’ll Cover
• Master Data Governance — Overview
• Master Data Governance — Risks and Challenges
• PwC Master Data Survey Insights
• Case Study — Finance Master Data Assessment
• The Role of SAP Solutions Like Information Steward and MDG
• How to Get Started on Governing Master Data
• Wrap-Up
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Data Analysis and Quality — Use of SAP Tools
SAP Table
Extraction Data
Interrogation Analytics Outcomes
Data Services Information Steward
Leverage an agile, reliable data foundation to move,
govern, improve, and unlock value from your enterprise
information
Understand and analyze the trustworthiness of your
enterprise information, and get continuous insight into
the quality of your data
Access relevant information regardless of data type,
domain, or source
Improve information trustworthiness and reduce the risk
of propagating bad data
Improve data quality for more effective decision making
and business operations
Consolidate, integrate, and audit your metadata from all
relevant sources
Save time and money with a single solution for complete
and accurate information
Define data validation rules against data sources to
continuously monitor quality
Connect to SAP, Text Files, Reporting Databases, other
ERPs
Create a metadata business glossary and build a central
location for organizing them
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Data Quality Analysis — SAP Information Steward
1. Profile
2. Drill down to investigate
3. Review records for context
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Data Quality Analysis — SAP Information Steward (cont.)
1. Area of Review
2. Quality Dimension 3. Overall Position
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Data Quality Analysis — SAP Information Steward (cont.)
Drill down into the scorecard to reveal Individual Metrics and associated scores
1. Domain 2. DQ Dimension 3. Validation Rule 4. View failed data
1 2 3
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Data Quality Analytics — SAP BusinessObjects BI Including Web Intelligence, Lumira
Vendor A
Vendor B
Vendor C
Vendor D
Vendor E
GREECE
ITALY
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SAP MDG — Centrally Govern Master Data Source: SAP
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SAP MDG — Simplified Process Flow
Maintain to Approve
Can be performed
before it becomes
available for use in
ECC
Source: SAP
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SAP MDG — Capabilities to Address the Governance Challenges
• Process
• People
Source: SAP
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SAP MDG — Familiarity for SAP Users
• ERP-Vendor-like UI
Goal: Support data maintenance in a way
being very similar to ERP Vendor Master
Every new BP automatically becomes an
ERP Vendor
Display ERP Vendor number and select
account group instead of BP number and
BP grouping
Identifying data (name, address, bank
accounts, etc.) and general vendor data
(Control data, Company codes, Purchasing
organizations, etc.) on one screen
Source: SAP
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What We’ll Cover
• Master Data Governance — Overview
• Master Data Governance — Risks and Challenges
• PwC Master Data Survey Insights
• Case Study — Finance Master Data Assessment
• The Role of SAP Solutions Like Information Steward and MDG
• How to Get Started on Governing Master Data
• Wrap-Up
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Get Started with Data Quality
• Find a data issue that is causing a business issue
E.g., Significant number of master data changes
or adjustment journals
• Recommend data cleansing and process
improvement actions
Fix the problem
Stop it from happening again
• Monitor and follow up
Leverage data analytics to
provide trackers, etc.
Identify Business Data Issue
Execute Data Profiling and Analysis
Perform Root Cause Analysis
Recommend Data Cleansing and Process Improvement Actions
Monitor and Follow Up
Quick Wins and Benefits – Create the business case for MDG
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Develop the MDG Strategy and Governance Framework
Data Governance • Governance Organization Model • Roles and Accountability • Policy • Framework of Processes and Procedures
Data Management
Technology Architecture • Fit/GAP Analysis • Software Re-Use • Future State Technology Architecture • Data Flow
Strategy: • Data Management Strategy • Deployment Scenarios • Implementation Sequencing • Business Case
Data Quality Profiling Results
Data Management Strategy
Strategy & Roadmap
DM Technology Architecture
Business & Data Process Design
Data Standardization
Data Governance
Governance Organization Model
Data Management Survey: • Current issues with quality, accuracy, and
completeness • Stakeholder Interviews • Vision for Data and Information Management
Process Design: • Business Process Maps • Data Management Processes • Reconciled Business Process to Data Processes • Source System Strategy
Develop Data Standards: • Data Standardization Workshops • Logical Data Models by domain • Functional Requirement Specifications • Data Quality Profiling
Linear Progression To
Managing Data:
“One Place, One
Number”
Data Standards
Governance Framework
Implementation Sequence
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•Conduct system integration testing
•Conduct UAT testing
•Train testing resources
•Conduct end-user training
•Start blueprint/design for the first data management
project
•Develop the data governance organizational model,
policy roles and responsibilities, and framework
•Develop physical data scheme by domain
•Develop technical design spaces
•Evaluate data management tools
•Make the tool decisions
•Procure tools and hardware platform
• Install data management tools
Implement Master Data Governance
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6
5
10
9
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•Conduct governance process
•Manage data quality metrics and issue
• Implement data governance program
• Implement data management tools
•Develop performance metrics
•Configure new data management tools
•Construct physical database(s)
•Develop custom functionality where needed
•Unit test
Strategy and Requirements Implementation
10 9
7
6
5
4
3
2
8
1
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Five Keys to Successful Enterprise Master Data Governance
Executive Buy-In
• Gain and sustain alignment with executive management on the benefits of harmonized and accurate master data
See the Future
• Develop a practical vision, strategy, and roadmap that support the business’s priorities
Change Agent
• View as a means to change business processes and organizational culture, not just as a technology function
Alignment
• Link capabilities to strategic-growth and cost-management initiatives and leverage their momentum
Demonstrate the Value
• Utilize a “building block” approach to execute the program to manage complexity and expectations while delivering quick wins
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What We’ll Cover
• Master Data Governance — Overview
• Master Data Governance — Risks and Challenges
• PwC Master Data Survey Insights
• Case Study — Finance Master Data Assessment
• The Role of SAP Solutions Like Information Steward and MDG
• How to Get Started on Governing Master Data
• Wrap-Up
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Where to Find More Information
• www.pwc.de/en/prozessoptimierung/studie-stammdatenmanagement.html
Marcus Messerschmidt and Jan Stuben, “Hidden Treasure: A global study on master data
management” (PwC, September 2011).
• www.pwc.com/us/en/ceo-survey/finding-risks.html
Dennis L. Chesley, Erik Skramstad, and Dietmar Serbee, “2016 US CEO Survey – Faster
information flows create volatility and, for some CEOs, opportunity” (PwC, 2016).
• http://go.sap.com/solution/platform-technology/enterprise-information-management-eim.html
Enterprise Information Management (EIM) Solutions
• http://scn.sap.com/community/mdm/master-data-governance
SAP Master Data Governance on SCN
• http://scn.sap.com/community/information-steward
SAP Information Steward on SCN
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7 Key Points to Take Home
• Data Governance – Repeatable process enabling delivery of standardized, high-quality
data to end users in a timely, auditable, and secure manner
• People + Process + Technology = Quality + Accuracy + Completeness
• Pain points within the business are indicators of poor data governance
• Information Steward and SAP MDG can be leveraged to improve data quality and provide
controlled master data governance processes+ Completeness
• Digital Trust needs well-governed data to help ensure confidence in the areas of Data,
Business Systems, and Transformation
• The Five Keys to Successful Enterprise Master Data Governance:
Executive Buy-In, See the Future, Change Agent, Alignment, Demonstrate the Value
• The roadmap to master data governance is:
Start with Data Quality, Develop the Strategy, Implement Master Data Governance
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Your Turn!
How to contact me:
David Sentance
Email: [email protected]
LinkedIn: www.linkedin.com/pub/david-
sentance/1/99b/ab5/
Twitter: @DavidSentance
Please remember to complete your session evaluation
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