Webinar for enterprise data management. One version of the truth is what all business leadership wants from their data.

1. Presented By:
Speaker Firms and Organization:
Danny Miller
Principal & National Solutions Leader
Grant Thornton, LLP Thank you for logging into today’s event. Please note we are in standby
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Chief Technology Officer and Managing Director
HPSquared, LLC Group Registration Policy
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Rocco Maggiotto the event.
Managing Director – Business Advisory Council If you have more than one person from your company attending, you must fill out the
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October 27, 2011

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4. Brief Speaker Bios:
Danny Miller
Danny Miller is a principal in Grant Thornton’s Business Advisory Services group in the Philadelphia office and
is a member of the leadership group for business consulting, which includes technology, cybersecurity and
business consulting for the firm in the U.S. He is also the national solution practice lead for cybersecurity for the
firm in the U.S. He is a member of Grant Thornton’s national Higher Education and Not-for-Profit leadership
group. Danny has over twenty-five years of experience in the Information Technology, Cybersecurity, Business
Consulting and Audit fields.
Experience
Prior to joining Grant Thornton, Danny was a partner for a consulting firm where he was responsible for all client
delivery operations, QA and IT for the firm. Danny has a range of international experience as a director for an
international consulting firm, a software developer, database administrator, a global IT audit manager, and the
CIO of a group of European-based e-commerce companies. He also has experience in data privacy laws and
standards including all ISO standards regarding data governance protection and the European Union’s Directive
95/46/EC on data privacy. He has extensive experience in cybersecurity, privacy, IT strategy and transformation
and business transformation using technology as a catalyst.
Industry experience
Danny has comprehensive experience in a variety of industries, including higher education, not-for-profit,
financial services, banking, oil, gas, consumer and industrial products, Internet, construction, real estate and
manufacturing. He has broad experience in those same industries internationally, including European Union
countries and Southeast Asia.
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October 27, 2011

5. Brief Speaker Bios:
Phil Teplitzky
Phil is a professional with 35-years of experience in the management and operations of information technology
and information technology consulting. He has been involved in the day-to-day management of system
development projects, the establishment of Standards Procedures and Guidelines for software
engineering, quality engineering and change management at major institutions. He has extensive experience in
bringing new technologies and software engineering disciplines into Financial Services.
Previously Phil was the CIO at The Harry Fox Agency and CTO at both TheVitaminShoppe.com, Neighborhood
Pay Services and Mibrary. He has also been a Managing Director of Technology at SHL SystemHouse with
responsibility for its Northeast Region, Vice President of Technology at Citibank and National Director at
Coopers & Lybrand with responsibility for Data Base technology and Architectures.
Phil has been a frequent lecturer and speaker at Data Base, Software Engineering and Architecture
conferences. He has a Master of Science in Computer Systems from the School of Advanced Technology
(Watson School of Engineering) at State University of New York at Binghamton.
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6. Brief Speaker Bios:
Rocco Maggiooto
Rocco Maggiotto is a Managing Director member of HPSquared’s Business Advisory Council and a retired Executive Vice President
and Global Head of Customer and Distribution Management for Zurich Financial Services General Insurance Business. Mr. Maggiotto
was responsible for the development and implementation of Zurich’s customer and distribution management strategies, their global
industry practices, and Chairman of General Insurance's Growth Agenda. Mr. Maggiotto held this position since June 2006, recently
retired and joined HPSquared LLC as a Business Advisory Council Member. As a Council member, Mr. Maggiotto consults with
Financial Services and Insurance Companies.
Prior to joining Zurich, Mr. Maggiotto’s career was divided equally between management consulting and financial services executive
roles. He was a Senior Executive Advisor in Booz Allen Hamilton’s Management Consulting business where he specialized in finance
and risk management, strategic design for client development and integrated client relationship management processes. Rocco’s
previous career has included roles as Chairman of Client Development for the Parent Company of Marsh & McLennan
Companies, Inc. as well as Senior Partner for PricewaterhouseCoopers, where he was a member of their Global Leadership Team
and Global Markets Leader. This role included PwC's global industry practices, their client relationship management programs, and
strategic planning, e-business and marketing and communications functions. Rocco was also a Vice Chairman for the former Coopers
& Lybrand as well as Managing Partner of their New York region, and Chairman of C&L’s financial services industry practice
worldwide. Rocco also developed and managed Coopers & Lybrand’s US Financial Consulting Business. Before that, Rocco was a
Partner with KPMG Management Consulting Practice and, for 16 prior years, held management positions in a New York banking
institution, covering areas of finance, operations, management information systems and corporate services.
Mr. Maggiotto holds a Bachelor of Arts degree in Political Science and a Masters in Business Administration in Finance. He is also a
certified systems’ professional. He is on the Boards of the Ronald McDonald House of New York, The Weston Playhouse Theatre
Company, and the Council of Governing Bodies of New York States private colleges and universities. He is also on the Board of
Directors of inXpay, a private company specializing in matching of commercial invoice and payment vouchers; and Lucid Inc. Lucid is
a medical device and information technology Company that develops, manufacturers, markets and sells FDA cleared non-invasive
diagnostic confocal imagers for accessing skin lesions suspicious for skin cancer.
For more information about the speakers, you can visit: http://knowledgecongress.org/event_2011_Data_Management.html
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October 27, 2011

7. What would you say if you found out that most of the reports, dashboards and other information that you
were using to run your business are incorrect? Do you think it might impact your ability to competitively run
your business, or even make good, sound decisions? If you don’t have confidence that you are making
decisions on the right data, you are not alone. Many businesses are awash in data and are unable to
state with certainty that the information that they use daily is correct. That calls into question how we think
about data and how it's used.
Enterprise Data Management (EDM) is the management of an institution’s fundamental data that is shared
across multiple business units, everything from project budgets to donor contacts to employee contact
information. You can think of master data as all of the enterprise data (people, places, things and
activities) that the institution needs to conduct its business.
The goal of EDM, consequently, is to ensure the accuracy, consistency and availability of this data to the
various business users.
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8. In the webcast, we will discuss what Enterprise Master Data Management (EMDM) is and how strategies
built around the EMDM framework can benefit businesses. We will also discuss several key concepts of
data management, including:
• What business problems do EDM help address
• Data maturity models, their meaning and value to an organization
• Data quality strategy and practices
• Applying EDM in data warehouse situations
• Security considerations when it comes to enterprise master data
We will discuss real life examples of actual companies in multiple industries that have been impacted by a
lack of enterprise data management and how implementing EDM standards and practices has improved
their businesses.
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October 27, 2011

9. Featured Speakers:
Danny Miller Phil Teplitzky
Principal Chief Technology Officer and Managing
Grant Thornton, LLP Director
HPSquared, LLC
Rocco Maggiotto
Managing Director - Business Advisory Council
HPSquared LLC
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10. Topics we will cover
• Business Drivers – Why EDM is important for Business, Information and Technology Leaders
• What is EDM – What comprises an EDM Framework
• Benefits of EDM – What do enterprises realize from an EDM Framework
• Guiding Principles of EDM – What are the keys for success
• Convergence of EDM and Business – Information Leaders (Finance and Risk), Technology and
Business need to work in concert
• Reference Models – There are proven models that can be adapted
• Summary
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11. Business and Market drivers that are elevating the importance of
Enterprise Data
• Market (identity and cross sell), serve, and know your customer better
• Improve competitive position
• Reduce technical complexity and cost
• Meet Regulatory requirements
• View data as a valuable enterprise asset
• Manage the explosive growth in data
• Leverage advancements in technology and software
• Align and share fragmented storage – across silos
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12. A View of Some Executive Drivers
CEO/CFO
 Signoff on financial statements, quality of the data and the systems that produce them.
CIO
 Balance the need to support business growth with cost. Balance founded in efficient
architectures, synergistic investment in technology, and availability of good information
CRO
 Siloed approach to compliance is no longer acceptable
CMO/BU Leader
 Leverage customer relationships, manage distribution channels, develop more responsive
products and services, grow profitable revenues and meet regulations of new economy
 Support rapid change not supported by older databases, and
 Overcome silo behaviors for cross sell & up sell.
 Merge external data for total view of customer.
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13. What Is Enterprise Master Data Management?
MDM is the management of an institution’s fundamental data that is shared across multiple
business units, everything from project budgets to employee contact information. You can
think of master data as all of the enterprise data (people, places, things and activities) that the
institution needs to conduct its business.
The goal of MDM, consequently, is to ensure the accuracy, consistency and availability of
this data to the various business users.
All organizations would benefit greatly from creating a strategy for MDM and implementing an
MDM program in light of its current state and an organization's future data and information
needs.
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14. What Is Enterprise Master Data Management?
• Master Data is the common business data that need to be organizationally agreed and then shared
globally inside.
• Companies are awash in data, but which data is the right data to use? Data grows by 50%+ each
year.
• Company leadership needs "one version of the truth" on dashboards, reports and in analytical
datasets.
• Financial, Internal Audit and Compliance departments should be concerned about controls,
availability, integrity and quality of data.
• Conceptually:
 Data and information are valuable corporate assets and should be treated as such
 Data must be managed carefully and should have quality, integrity, security and
availability addressed.
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15. To satisfy the Business and Market drivers the Enterprise needs to:
• Align information (data), technology and business strategies
• Clarify roles and responsibilities for enterprise data management
• Develop a common data management language for business and technology
• Identify data uses, values and interdependencies
• Prioritize data improvement efforts with its value, align with existing project priorities, and capture
short wins for momentum
• Make relevant, accurate and useable information (data) available to support decision making
and business processes
• Ensure data is shared and appropriately secured in our IT systems as define by law and market
demands
• Leverage information (data) in business decisions, processes and relationships
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16. Common EDM Issues Found Across Many Enterprises
• Discovery – can not find the right data
• Redundancies – can not create value due many versions of the same data exist in different hands
• Business Intelligence:
 Integration – can not access, manipulate and combine the data
 Integrity – can not reconcile data across the enterprise
 Insight– can not extract value and knowledge from the data
 Collaboration – can not leverage and share data for market facing activities
• IT Leadership – can not manage or control data growth
• Management – can not make confident business decisions
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17. Pragmatic Business Benefits for the Enterprise
• Assurance that common data reconciles across systems and the organization
• Improved data quality across the enterprise
• Reduced complexity in the management of data through standards
• Ability to trace flow of data across systems and the enterprise
• Can scale to meet future business volume – increasing data volumes
• Meet the needs of any project and can extend across the wider enterprise
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18. Keys of a successful EDM Strategy
Accommodates
• Changing business requirements
• Delivery of tactical projects
• Progressive changes in technology
Aligns with other strategic initiatives
• Consistent frameworks, blue prints and roadmaps
• In touch with organizational culture
• Allow for parallel activities
Improves data management competency across enterprise
• Integrate data management metrics across activities
• Data governance
• Solutions which integrate conceptual, logical and physical to insulate for change
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19. A Good EDM Strategy Must Include:
• Vision which aligns business to technology and strategic to tactical
• Executive /C-Level Sponsorship
• Data Governance & Data Stewards for relevant subject areas
• Solutions which are architected versus systems that are build
• Standards, policies, and procedures
• “Data Management” Organization
• Technology solutions which are open, based on common standards, flexible and promote re-use
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20. What Is Enterprise Master Data Management?
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21. Data Quality
Pre-Governance Governance Provides
• Overly complex IT infrastructure • Uniform communications with customers,
• Silo-driven, application area-centric solutions suppliers, & channels due to veracity & accuracy of
• Slow-to-market delivery of new or enhanced key master data
application solutions • Common understanding of business policies &
processes across LOBs & with business
• Inconsistent definitions of key corporate data assets partners/channels
such as customer, supplier, & pricing masters • Rapid cross-LOB implementation of new apps
• Poor data accuracy within & across business areas requiring shared access to master data
• LOB-focused data with inefficient or nonexistent ability • Singular definition & location of master data &
to leverage information assets across LOBs related policies to enable transparency &
auditability essential to regulatory compliance
• Redundant IT initiatives to re-solve data accuracy • Continuous Data Quality improvement as Data
problems for each individual LOB Quality processes are embedded upstream rather
than downstream
• Increased synergy for cross-sell & up-sell.
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22. Data Quality - Governance
• Establish institutional data standards
• Identify and resolve data disputes
• Implement necessary changes to data standards and policies
• Communicate actions to the organization as appropriate
• Ensure accountability of institutional data policies and standards
• Escalate issues to Governance Team as necessary
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23. Data Quality
• Definition: timely, relevant, complete, valid, accurate, consistent
• Role of Data Quality
• How to measure Data Quality
• Poor data quality and its cost
• Process efficiency impact as a result of poor data quality
• Potential benefits of new systems not be realized because of poor data quality – if you don't address it
up front, you will pay for it!
• Decision making is ultimately negatively affected by poor data quality – many examples available
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24. Data Architecture
• Is the transformation of the Data:
 Requirements
 Operational Characteristics and Attributes
 Data Base and Data Structures
 Standards and Frameworks
Into a Physical instantiation – the Data Ecosystem
• All Architectures are based on the universal truth that Form follows Function and in this instance it is
the physical Form of the Data Ecosystem Dimensions – which are defined in the Data Maturity Model
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25. Data Operations: Security & Privacy
• Information Risk Management (security) is a process that identifies risk to all information assets and
provides an approach to control and mitigate risks.
• Need to consider impact of loss, alteration or exposure of information to the organization.
• Privacy of Personally Identifiable Information (PII)
• To start the process, perform an analysis of risks by:
 Identifying all information assets that are important (data classification)
 Assign a value and important (data classification)
 Looks at threats and vulnerabilities
 Measure the risk to assets
 Come up with a game plan that is economically feasible to protect assets
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26. Data Operations: Data Warehouses
• What is a Data Warehouse?
• Issues that can occur in Data Warehouses and attempting to apply EDM principals
• Role of EDM in the creation of Data Warehouses
 Consistency of Semantic and Syntax
 Translation and normalization
 Common language, edit and validation
• What should I do?
 Create a Data Warehouse Data Maturity Model
 Asses your level of Data Maturity at both the:
 Operational
 Data Warehouse level of abstraction
 Create an Action Plan to mitigate identified issues and deficiencies
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27. EDM requires Business & Technology to Work in Concert
• Information/data management is a shared responsibility between data management professionals in
IT and business data owners representing the interests of the data producers and data consumers
• Business data owners are concerned with:
 Definition and value of the data
 Data quality (data is useable at deferent times and different degrees of accuracy)
 Data stewardship (roles and responsibilities vary)
 Availability and sharing of the data
• IT is the custodian of the data and responsible for the systems which store, maintain, process and
deliver the data
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28. Enterprise Data Management Reference Model
• Model is based upon multiple
dimensions
• Core areas for the evaluation
process include and are not
limited to:
1. Data Governance and
Strategy
2. Data Platform
3. Data Operations
4. Quality Management
• Each business will have a specific set of dimensions and definition of target long term maturity
levels, as industry information management needs vary.
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29. Data Maturity Reference Model
Data Maturity Model
Level 5 The Data Maturity Model (DMM) is
Optimized
an Industry accepted model support
by the Software Engineering Institute
Level 4 (SEI) from Carnegie Mellon.
Level of Maturity
Managed
DMM provides an auditable
Level 3 framework and methodology for
Defined defining the specific components at
the business – process level required
Level 2 for effective Data Management.
Reactive
Full DMM defines best practices and
Level 1 provides a framework for assessing
Initial and measuring capability.
Maturity Criteria
Data Governance & Strategy, Data Operations, Quality Management,
Data Platform
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30. Data Maturity Interaction Model
• data key resource for process improvement – enterprise asset
• semantic metadata and business rules actively managed Levels of Maturity
Level 5
• data management process – continual improvement
Optimized
• automated processes : data consistency, accuracy, reliability • Level 1 - Initial : Data management
• central metadata repository - synchronization processes are mostly disorganized and
• data policies well documented and enforced Level 4 generally performed on an ad hoc basis
Level of Maturity
•business active in data strategy - Stewards Managed • unified data
strategy exists
•data recognized – enterprise asset • Level 2 – Reactive : Fundamental data
•meta data repository exists
management practices are
•Workflow not linked to data flow Level 3
Defined •data models exist in isolation established, defined, documented and
•limited controls exist repeatable
•central platform for managing data
•data functions
•data integrated point to point
at local level Level 2 • Level 3 – Defined : Business analysts
Reactive • data models and definitions at application level begin to control the data management
• risk high – lack of integration, consistency, standards process with T playing a support role
•processes not repeatable – not well defined
Level 1
Initial • data - general purpose – no consistent formats & definitions • Level 4 – Managed : Data is treated as
a critical corporate asset and viewed as
•data stored redundantly in unconnected databases -siloed
equivalent to other enterprise wide
assets ( e.g.
capital, resources, technology)
Maturity Criteria • Level 5 – Optimized : the organization
Data Governance & Strategy, Data Operations, Quality Management, is in continuous improvement mode
Data Platform
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31. Data Maturity Interaction Model
Maturity Criteria Level 5
Optimized
Level of Maturity
 Data Governance & Strategy Level 4
• Strategy, goals & scope definition Managed
• Data content and coverage
Level 3
• Sponsorship Defined
• Governance Operating Model
Level 2
 Data Operations Reactive
• Data policies & procedures Level 1
• Data procurement & sourcing Initial
• Business precedence and data validation
• Data distribution & entitlement
• Archive, retention, security & privacy Maturity Criteria
• Business process & workflow Data Governance & Strategy, Data Operations, Quality
• Hierarchies & linkages Management, Data Platform
• tewardship & ownership
• Mapping and cross referencing
• Extensibility and reuse
 Data Platform
 Quality Management • Common data model
• Data quality strategy & objectives • Loading and application integration (ETL and EAI)
• Quality assurance & audit • Semantic and definitions
• Data cleansing, enrichment and validation • Format standards and messaging model
• Change and exception management • Transformation rules
• Inventory, traceability and surveillance • Data repository standards
• Classification • Architecture framework (SOA)
• Quality measurement and benchmarking • Metadata Repository
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32. Standards and Meta Systems
• Standards are ubiquitous and necessary components of all civilizations – they are the warp and
woof of civilized life
• Types of standards in everyday life:
– Dictionaries
– Grammars
– ANSI / ISO
You use them every time you to the Super Market, the Auto, Plumbing and Electrical Supply store
Every time you go to the Bank, Use a Credit Card or use the WEB or play a song on you iPod
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33. Standards
• Where do standards come from?
• They come from groups of people who need to communicate and make themselves understood
• For economic reasons
– Trade across countries
– Trade across companies
• Examples:
– Samuel Johnston and the English Dictionary
– Otto Von Bismarck and DIN
– UCC (Uniform Commercial Code)
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34. Summary
• Today’s increasingly complex business environment is placing greater data needs on the
enterprise e.g.
– 360 view of the customer
– More demanding regulatory and compliance reporting
• Addressing these needs requires an enterprise to manage its data as a cross organization asset
• A strategy which combines business and technology to develop and deploy a holistic Enterprise
Data Management framework
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35. Q&A:
Danny Miller Phil Teplitzky
Principal Chief Technology Officer and Managing
Grant Thornton, LLP Director
HPSquared, LLC
Rocco Maggiotto
Managing Director – Business Advisory Council
HPSquared LLC
You may ask a question at anytime throughout the presentation today. Simply click on the question mark icon located on the floating tool bar on the bottom right side of your screen. Type
your question in the box that appears and click send.
Questions will be answered in the order they are received.
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October 27, 2011

36. Notes:
*** Participants in this webcast are given a special discount of $50 in
all upcoming Knowledge Congress’ events in 2011 by applying
discount code “kcwebcast88” on the second page of the
registration form. ***
To view the list of our upcoming events, please visit:
http://www.knowledgecongress.org/events.htm
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October 27, 2011

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