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Presented By:

October 27, 2011

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Danny MillerPrincipal & National Solutions Leader

Grant Thornton, LLP  

Phil TeplitzkyChief Technology Officer and Managing

DirectorHPSquared, LLC

 Rocco Maggiotto

Managing Director – Business Advisory CouncilHPSquared LLC

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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. 

ExperiencePrior 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 experienceDanny 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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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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Brief Speaker Bios:

► For more information about the speakers, you can visit: http://knowledgecongress.org/event_2011_Data_Management.html

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.

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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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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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Featured Speakers:

Phil TeplitzkyChief Technology Officer and Managing DirectorHPSquared, LLC

Danny MillerPrincipalGrant Thornton, LLP

Rocco MaggiottoManaging Director - Business Advisory CouncilHPSquared LLC

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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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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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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 LeaderLeverage 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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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.

What Is Enterprise Master Data Management?

What Is Enterprise Master Data Management?

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• 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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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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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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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

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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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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What Is Enterprise Master Data Management?

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Data Quality

• Overly complex IT infrastructure • Silo-driven, application area-centric solutions• Slow-to-market delivery of new or enhanced application

solutions• Inconsistent definitions of key corporate data assets

such as customer, supplier, & pricing masters• Poor data accuracy within & across business areas• LOB-focused data with inefficient or nonexistent ability

to leverage information assets across LOBs• Redundant IT initiatives to re-solve data accuracy

problems for each individual LOB

• Uniform communications with customers, suppliers, & channels due to veracity & accuracy of key master data

• Common understanding of business policies & processes across LOBs & with business partners/channels

• Rapid cross-LOB implementation of new apps requiring shared access to master data

• Singular definition & location of master data & related policies to enable transparency & auditability essential to regulatory compliance

• Continuous Data Quality improvement as Data Quality processes are embedded upstream rather than downstream

• Increased synergy for cross-sell & up-sell.

Pre-Governance Governance Provides

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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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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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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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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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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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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

Enterprise Data Management Reference Model

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• 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 Platform3. Data Operations4. 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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Data Maturity Reference Model

Level 1

InitialLevel 1

Initial

Lev

el o

f Mat

uri

ty

Maturity CriteriaData Governance & Strategy, Data Operations, Quality Management,

Data Platform

Level 2

ReactiveLevel 2

Reactive

Level 3

DefinedLevel 3

Defined

Level 4

ManagedLevel 4

Managed

Level 5

OptimizedLevel 5

OptimizedThe Data Maturity Model (DMM) is an Industry accepted model support by the Software Engineering Institute(SEI) from Carnegie Mellon.

DMM provides an auditable framework and methodology for defining the specific components at the business – process level required for effective Data Management.

Full DMM defines best practices and provides a framework for assessing and measuring capability.

Data Maturity Model

Data Maturity Interaction Model

Level 1

InitialLevel 1

Initial

Lev

el o

f M

atu

rity

Maturity CriteriaData Governance & Strategy, Data Operations, Quality Management,

Data Platform

Level 2

ReactiveLevel 2

Reactive

Level 3

DefinedLevel 3

Defined

Level 4

ManagedLevel 4

Managed

Level 5

OptimizedLevel 5

Optimized

Levels of Maturity

• Level 1 - Initial : Data management processes are mostly disorganized and generally performed on an ad hoc basis

• Level 2 – Reactive : Fundamental data management practices are established, defined, documented and repeatable

• Level 3 – Defined : Business analysts begin to control the data management process with T playing a support role

• Level 4 – Managed : Data is treated as a critical corporate asset and viewed as equivalent to other enterprise wide assets ( e.g. capital, resources, technology)

• Level 5 – Optimized : the organization is in continuous improvement mode

• meta data repository exists

• data - general purpose – no consistent formats & definitions

• data stored redundantly in unconnected databases -siloed

• processes not repeatable – not well defined

• data functionsat local level

• data integrated point to point

• data models and definitions at application level

• risk high – lack of integration, consistency, standards

• data recognized – enterprise asset

• limited controls exist

• Workflow not linked to data flow• data models exist in isolation

• central platform for managing data

• business active in data strategy - Stewards • unified data strategy exists

• data policies well documented and enforced• central metadata repository - synchronization

• automated processes : data consistency, accuracy, reliability

• data key resource for process improvement – enterprise asset• semantic metadata and business rules actively managed

• data management process – continual improvement

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Data Maturity Interaction Model

Level 1

InitialLevel 1

Initial

Lev

el o

f M

atu

rity

Maturity CriteriaData Governance & Strategy, Data Operations, Quality

Management, Data Platform

Level 2

ReactiveLevel 2

Reactive

Level 3

DefinedLevel 3

Defined

Level 4

ManagedLevel 4

Managed

Level 5

OptimizedLevel 5

OptimizedMaturity Criteria

Data Governance & Strategy• Strategy, goals & scope definition• Data content and coverage• Sponsorship• Governance Operating Model

Data Operations• Data policies & procedures• Data procurement & sourcing• Business precedence and data validation• Data distribution & entitlement• Archive, retention, security & privacy• Business process & workflow• Hierarchies & linkages• tewardship & ownership• Mapping and cross referencing• Extensibility and reuse

Quality Management• Data quality strategy & objectives• Quality assurance & audit• Data cleansing, enrichment and validation• Change and exception management• Inventory, traceability and surveillance• Classification• Quality measurement and benchmarking

Data Platform• Common data model• Loading and application integration (ETL and EAI)• Semantic and definitions• Format standards and messaging model• Transformation rules• Data repository standards• Architecture framework (SOA)• Metadata Repository

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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 storeEvery 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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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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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

► 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.

Q&A:

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Phil TeplitzkyChief Technology Officer and Managing DirectorHPSquared, LLC

Danny MillerPrincipalGrant Thornton, LLP

Rocco MaggiottoManaging Director – Business Advisory CouncilHPSquared LLC

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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