DAMA INTERNATIONAL SYMPOSIUM & WILSHIRE META-DATA CONFERENCE
May 2-6, 2004 – Century Plaza Hotel, Los Angeles, CA USA

NIGHT SCHOOL

Information Assurance

Ann Moore
Data Governance Chairperson
Nationwide Mutual Insurance

Ronald Borland
Data Architect
Nationwide Mutual Insurance

This is a high level presentation on implementing a formalized Data Management Program for finance and insurance industries. Information Assurance provides a blueprint for a coordinated, cross-functional approach to improving data quality and associated processes. The presentation provides detail on organizational structures, roles and responsibilities, and team interaction models, as well as, the guiding principles and best practices. The goal is to provide each attendee with information sufficient to evaluate the suitability of Information Assurance implementation in his/her organization and to prepare implementation proposal(s) if warranted.

• Information Assurance Defined
• The Nationwide Approach
• The Results of our Information Assurance Efforts
• How you can benefit from an Information Assurance initiative

Data Management Practice Maturity Survey - Do you know where your meta data is?

Peter Aiken
Founding Director
VCU/Data Blueprint

Burton Parker
Principal
Paladin Integration Engineering

How well does your organization manage its one resource (described by Brackett) that it cannot use up, and is designed to be reusable? Chances are - not as well as it could. Over the past two years, the Institute for Data Research has surveyed more then 40 organizations of differing sizes - from both government and industry. The results of this survey are permitting the development of a model that can help organizations assess their organizational data management practices. Good data management practices can help organizations save the 20 - 40 % of their technology budget that is spent on non-programmatic data integration and manipulation (Zachman). This talk describes the Data Management Practice Maturity Survey and presents the results to date. Participants will be equipped to generally assess the state of their own organizational data management practices. Architecting classes of problem engineering-based solutions instead of more expensive, point-to-point solutions! Many applications that have been seen as very complex can now be successfully implemented.

Introduction to ORM

Terry Halpin
Professor and VP (Conceptual Modeling)
Northface University

Effective business rules management, data warehousing, enterprise modeling, and re-engineering all depend on the quality of the underlying data model. To properly exploit database technology, a clear understanding is needed as to how to create conceptual business models, transform them to logical database models for implementation on the chosen platform, and query the populated models. Object-Role Modeling (ORM) provides a truly conceptual way to accomplish these tasks, facilitating communication between the modeler and the domain expert. This night school provides an overview of the ORM approach for modeling and querying information systems, and compares it with other data modeling methods such as class modeling in the Unified Modeling Language (UML), and Entity-Relationship (ER) approaches.

Lies, Damn Lies, and Enterprise Integration: A Fair and Balanced Perspective

Evan Levy
Senior Partner
Baseline Consulting

A recent Morgan Stanley study cited integration as the number one priority of the CIOs surveyed. Clearly, without a comprehensive approach, most integration attempts will fail. ERP, EAI, EII, & Data Warehousing vendors all claim to provide integration solutions, however their products and strategies in reality are not comprehensive. This session separates the facts from fiction for enterprise integration from the various vendor perspectives and suggests alternatives for achieving true enterprise integration. It illustrates how to use proven architectural frameworks to handle integrating critical enterprise components such as metadata, data, and applications and how you can leverage your investment in ERP, EAI, EII, & Data Warehousing implementations.

• Incomplete metadata,
• Limited ETL,
• Proprietary APIs and communications protocols,
• Incompatible security models,
• Closed data models
• Lack of centralized administration.

Metadata and Grid Computing

Frank Kowalkowski
President
Knowledge Consultants, Inc.

Clyde Laakso
President
Lake, Vesta and Bright

Grid computing is getting a lot of press today as the ‘next big thing’ that will impact the enterprise. It is true that grid computing exists today in several variations such as the Seti project and platform load-sharing in an enterprise that offloads processing capabilities across machines and takes advantage of unused capacity to run applications. However, making the concept useful, practical and a ‘value add’ for running applications on an external grid for the enterprise requires some significant convergence of technologies and disciplines. To make the grid idea useful within an enterprise we need an extended grid concept. Moving the extended grid concept to the business landscape and more specifically to the enterprise environment requires four important capabilities to make the grid viable; clean and standardized metadata and metadata services, a bill of materials for an application, a routing list for execution and a costing mechanism for billing.

Semantics in Business Systems

Dave McComb
President
Semantic Arts

Semantics is the study of meaning, and as such is the discipline that undergirds most of what we do as data modelers, data administrators and systems integrators. We all practice semantics, but we rarely study it or even give it any thought. This situation is changing as initiatives such as the Semantic Web, and the attention that Middleware vendors are now putting on Semantic Integration are raising the level of awareness and the urgency of the conversation. This presentation sets up a framework for thinking about Semantics more rigorously and incorporating Semantic thinking into your projects. We will also briefly cover, because it is so topical, the Semantic Web and how it is likely to affect our thinking about Enterprise Systems.

Monday, May 3, 2004
5:00 pm – 6:00 pm

One Purdue Information Factory

Brad Skiles
Director, Data Services and Administration
Purdue University

Purdue is preparing to migrate from diverse legacy applications, some of which are over 30 years old, to an ERP environment. Prior to this move, Purdue will be implementing a new data architecture called the One Purdue Information Factory (OPIF). This presentation presents how the OPIF attempts to imbed quality in Purdue data through the practical application of these three components. Specifically, Purdue's plan to:

• implement a scalable and integrated architecture based upon the Bill Inmon information factory architecture
• capitalize on a robust metadata engine
• assess and correct data content in the legacy systems
• implement a state-of-the-art business intelligence tool suite

Bare-handed Data Modeling: A Polemic

John Schley
Senior Data Administrator
Principal Residential Mortgage, Inc.

A polemic is an argument designed to stir up controversy. This session will do just that by shining a cold light on the deliverables of a data modeling effort. Attendees will look at data models, definitions and DDL—three commonly understood products of data modeling—and see just how little value these can be to the business. We will discuss the real value of data modeling: as a starting point for data management. Participants will learn of some alternative ways of doing data design that not only yield better solutions, but will enable co-workers to appreciate the value provided by data management. We will see how this approach ties in with Zachman’s Framework and finish up with a list of what’s hot/what’s not in data analysis. This session is targeted at those data professionals who feel pigeon-holed as “database people” or work in environments where chairs are assets but data is not.

What is Why? What is How? And What is What?
Distinguishing Business Rules From Data And Processes

Tom Yanchek
Manager-Corporate Repository/Architecture
United Parcel Service

Distinguishing a business rule from the data it supports or a process it enforces can be tricky and often times confusing. This presentation will discuss:

• How to overcome common roadblocks when identifying business rules
• How to separate data, process and rules without losing their dependencies
• How the importance of employing an organized Business Rule Strategy helps build better enterprises
• How the importance of employing an organized Business Rule Strategy helps enhance or possibly consolidate existing portions of an enterprise
• Tips and Techniques for identifying a business process, a business rule or business data
• How to measure the success of the approach and how to identify, employ and implement quality improvement procedures
• How to minimize or significantly reduce development costs and maintenance costs – in some cases 25-35%
• How to maintain business processes, a business rules and business data for verification, acceptance and reasonability

Meta Data Starts with an Enterprise Architecture Data Model

John Sharp
Principal Consultant
Sharp Informatics

Collecting, analyzing and reviewing your organization’s information status and future goals are required for successfully implementing an Enterprise Architecture. This presentation will help you to do this by properly establishing the rules to make your Enterprise Architecture project successful. The basic model will contain major objects such as principles, technology, standards, business drivers, processes and applications. The relations between these objects can be expressed as simple sentences. A simple procedure will be shown for extending the EA Data Model to include additional knowledge by converting simple true statements into valid fact types. Examples of improvements in the quality and quantity of collected data using the application created from an EA Data Model will be presented. The basic model will be available for review and possible use.

Data Management Using Metadata Registries

Judith Newton
Computer Specialist
NIST

The adoption of XML as the information interchange format for the Web presents a set of challenges and opportunities for data managers. While XML makes it easy to describe the format of information objects and the relationships among them, it has limited provisions for conveying the semantic content of the objects. Metadata registries based on the ISO/IEC standard 11179 supplement XML schema descriptions and all other data applications with descriptive and relational metadata, allowing all representations of information to be mapped together in a single resource. The new version of 11179 introduces a Metamodel that allows incorporation of conceptual and logical level objects along with multiple representations of these objects. This makes it a vessel uniquely suited to store metadata for multiple applications. Principles developed for the establishment of standardized names through naming conventions can be used to administer namespaces and the objects contained in namespaces.

Integrating Unstructured Data

Michael Reed
Principal Consultant
Enterprise Warehousing Solutions, Inc.

Metadata is a key component in an unstructured data environment. The task becomes even more complex if queries will span both structured and unstructured data in the same request. This session will present some of the major metadata standards in this area, including Dublin Core, RDF, ISO 11179, and JMI. Methods of integrating both structured and unstructured data into a data warehouse will be discussed. Key concepts presented will include: Entity Extraction, Content Management, Knowledge Management, Data Relevancy, Text Indexing, Tagging, Storage Considerations

Conducting Database Design Project Meetings

Gordon Everest
Professor
University of Minnesota

Dr. Everest provides a synthesis of his experiences in gathering database design requirements through interviews and conducting database design project meetings. Reflecting on the lessons learned yields a set of best practices which should be considered by anyone attempting to lead a database design project.

• Interviews vs. facilitated group sessions
• Picking the users to interview or invite to the table.
• Preparations before the interviews or before the meetings
• Accelerated (one meeting possibly over several days, e.g., JAD) vs. extended series of meetings
• Findings of an experiment which compared the two approaches to group sessions
• Lessons learned
• Highlighting the best practices.

To Laugh or Cry 2004: Further Fallacies in Data Management

Fabian Pascal
Analyst, Editor & Publisher
Database Debunkings

A lot of what is being said, written, or done in the information management field by vendors, the trade press and "experts" is increasingly confused, irrelevant, misleading, or outright wrong. The problems are so acute that, claims to the contrary notwithstanding, knowledge, practices and technology are actually regressing! This presentation exposes some of the persistent misconceptions prevalent in the information/data management field and their costly practical consequences. Test yourself on your ability to see through the former and avoid the latter.