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

MONDAY TUTORIALS

T1
Business-Focussed Data Modeling
Unifying Conceptual, Logical, Physical Models
Graham Witt
Independent Consultant

The terms Conceptual and Logical seem to have many different meanings. Many conceptual models of live systems seem to be “rough cut” early drafts of database designs rather than models of business concepts and relationships. Different skills and modes of thought are required for conceptual models compared with those required for database design.

This tutorial itemizes and describes the various artifacts of a conceptual model and provides practical tips for discovery and verification of those artifacts, then describes the process of creating a logical model that reflects both the conceptual model and the requirements of the database platform. The techniques described in this tutorial will equip participants to produce database designs that accurately reflect business information requirements in an efficient and agile manner. Topics include:

• Conceptual classes
• Conceptual relationships
• Conceptual attributes
• Class discovery
• Model verification
• Conversion to logical model.

T2
Developing Better Requirements and Models Using Business Rules

This tutorial will explain how the business rule approach can improve your entire requirements process. Starting with the business model, the speakers identify each relevant deliverable and show where business rules fit in with them. Specifically, they show how business rules address the issues of motivation and guidance – in other words, the question of “why.” They detail how you can use business rules to develop business tactics in a deliverable called a Policy Charter.

Continuing, they focus on the system model and again discuss how business rules fit with each deliverable. Special emphasis is given to how business-perspective rules differ from system-perspective rules, and what you need to do to translate between the two. Finally, practical refinements to system model deliverables are examined, not only to exploit business rule ideas, but also to maintain a consistent focus on validation and communication from the business perspective.

Finally, Mr. Ross and Ms. Lam offer guidelines for how business rules should be expressed, and how they can be managed more effectively. The business rule approach offers practical new techniques to create more effective business solutions, to blueprint the system model, and to speed-up the requirements process. These innovative methods have proven highly successful in organizations of many different types and sizes. This tutorial gives you the opportunity to brainstorm these new techniques with the leaders in the field. This tutorial will show you how to …

• Ensure completeness in business, data and system models
• Exploit business rule techniques
• Communicate more successfully with both the business side and IT
• Accelerate the requirements process
• Use business rules with data and system requirements
• Organize and run a business rules project

T3
Using Information Management to Sustain Data Warehouse
John Ladley
President
KI Solutions

As data warehouse nestles into the information architecture tool kit, many companies and organizations are still grappling with fundamental development challenges and growth issues. In a focused and pragmatic style this class cuts through vendor fog and guru religious wars. Without any preaching, attendees will review the influencing trends and learn practical information-based techniques to ARCHITECT and SUSTAIN their data warehouse. This class is excellent for DW managers, information managers, business users, and experienced DW professionals looking to advance their DW to the next level.

Trends - Defining Data Warehouse in the 21st Century

• What is the value proposition?
• Is there a difference from historical definitions?
• Alignment definition and relevance- How information management and DW are made for each other
• What is the current state of DW and BI technology?

Initiating or Re-initiating a Data Warehouse project

• Creating a manageable project plan
• Understanding data quality and the affect on DW
• Understanding the effect of knowledge cultures on DW
• Assessing the readiness and maturity of an organization to use/sustain the DW

Defining the Data Warehouse Requirements

• Avoiding dumb things that don't work, no matter what it says in the books
• Techniques for business alignment - three key models
• Considering Data Quality
• Engaging the business sustain buy in

Designing the DW

• How to design the data base structure you need, vs. what someone else says
• How to make sure that the DW environment is efficient and cost effective

Define the DW projects

• No, you don't do it in one pass
• Planning DW projects

Sustaining the DW

• Change Management and other Sustaining stuff
• Course Corrections
• How to make sure that the DW environment is efficient and cost effective

Selecting Technology

• What is the current state of DW and BI technology?
• Do you really need at ETL tool?
• How to define the technology requirements

T4
How to Develop an Enterprise Data Strategy
Sid Adelman
Principal
Sid Adelman Associates

Today, if most CIOs are asked about the assets under their control (a primary asset being data), most would be forced to respond that the inventory of data is incomplete or nonexistent, little is known about the quality of data, and “there is no defined strategy for the productive use of this asset.”

This tutorial is about developing an enterprise data strategy. It discusses how the key components of a data strategy can make a significant impact on the success of an organization. The tutorial will have exercises that will help the attendee get started developing a data strategy for their own organization. We will address:

• The components of the strategy
• The ROI of a data strategy
• The team to develop the strategy
• Selling the strategy to IT and to the business
• Categorization of Data

T5
Enterprise Messaging Modeling and Data Integration
Bringing Data Skills to Service Oriented Architecture
Dave McComb
President
Semantic Arts

XML, Web services and Service Oriented Architectures are going to play an increasing roll in the development and integration of Enterprise Systems over the coming years. In this tutorial we first lay some groundwork by defining and distinguishing these new technologies and approaches.

We then describe a methodology called "Enterprise Message Modeling" which describes a step by step approach to re partitioning your existing applications and databases into configurations more appropriate for a Service Oriented Architecture, and how to build a shared model of the messages that will provide the traffic between the services, rather than letting them emerge on an ad hoc basis from the uncoordinated activity of many independent application development projects.

The tutorial is based on actual experience with these approaches, and will be interspersed with exercises that will help the participants work through the paradigm changes these approaches entail.

• Integration vs. Decoupling – The Critical Balance
• Message-Based, Service Oriented Architectures
• Application Decoupling and Message Based Architectures
• Developing An Enterprise Message Model
• The data groups role in the new world of message based integration.
• Methodologies and Getting Started
• An overall approach to assessing your current situation, planning a more modern architecture and planning a route to get there.
• Practical insights to help make this happen in your organization.

T6
Enterprise Metadata: The Art and Science of Best Practices
Todd Stephens
Director
BellSouth

This tutorial focuses on the formulation and implementation of an enterprise metadata strategy. Participants will learn techniques to understand the role of metadata in the development of Enterprise Business Intelligence (EBI). The Metadata Services Group within BellSouth has spent the last 4 years developing an enterprise metadata solution based on a solid product line and a customer service focus. This tutorial will develop the attendees understanding of how to develop a successful enterprise metadata implementation. The presenter will be taking four years of metadata experience and condensing that knowledge into this six-hour tutorial. We will examine the principles of marketing, selling strategies, service offerings, product design, architecture, team construction and overall strategy of delivery for an enterprise metadata solution in both the structured and un-structured world. Come see why BellSouth, won the 2003 Wilshire Award for Outstanding Enterprise Meta Data Implementation.

Presentation Content:

• The Enterprise Environment Reveled
• Applying the Principles of Architecture to Metadata
• The Major Components of a Metadata Project
• Selling the Metadata Concepts
• Metadata Success = A Service Oriented Organization

Attendees will learn the following:

• Increase your understanding of which strategy options make the most sense and which do not.
• Identify and compare your organizations metadata strategy with ours.
• Develop metadata solutions and blueprints for a service transformation.
• Recognize which products, architecture and services can create a competitive advantage.
• Discover why enterprise metadata is different than data warehouse metadata
• Apply our lessons learned to your organizations situation
• Better anticipate and prepare for your customers’ changing needs.
• Discover what skills you need within the metadata team.
• Enhance your ability to market and sell enterprise metadata.
• Understand why data architecture can enable the implementation of metadata

T7
Building the Managed Meta Data Environment
David Marco
President
Enterprise Warehousing Solutions, Inc.

Effectively meta data management is no longer an option, but an absolute requirement for corporations and government agencies. Companies have realized that without meta data their IT departments cannot manage their systems and their systems are not providing true value to the business end user. Organizations are implementing managed meta data environments (MME) to provide them an enterprise meta data management solution.

This practical course leverages the lessons learned from companies that have successfully deployed MMEs. The case studies demonstrate the importance of having a methodology for defining meta data requirements, capturing and integrating meta data, MME architectural components, how to calculate ROI, and develop a project plan, advanced meta data architectures, pulse-of-the-market analysis of meta data integration tool vendors, methodology for defining an attainable project scope, presentation of the Data Stewardship Framework, and a granular walkthrough of a detailed meta data model.

• Six Architectural Components of the Managed Meta Data Environment (MME)
• Meta Data Extraction Layer
• Meta Data Integration Layer
• Meta Data Repository
• Meta Data Management Layer
• Meta Data Marts
• Meta Data Delivery Layer
• Real-World MME Case Studies
• Understand How to Construct Your Own Meta Data Model
• Analyze Meta Data Tool Vendors
• Developing a MME Architecture
• Understand How to Model Meta Data
• Universal Meta Model

T8
XML Database and Metadata Strategies
James Bean
Chairman and Co-founder
Global Web Architecture Group

XML has rapidly become the metadata language du jour. As a self-describing metadata technology, XML presents numerous opportunities, capabilities, and advantages for describing enterprise application integration and e-commerce transactions. However, there still remain many questions as to how XML should be incorporated with enterprise database and metadata strategies. This tutorial will take the practitioner through the process and will provide specific techniques and examples.

At every turn, data architects are challenged as to how, where, and why XML should be stored in a relational database. Questions such as "Should I store the XML as a complete document?", and "Should I decompose and store the individual XML elements and attributes?" require a best-practices based strategy. Further, the XML metadata schemas (e.g. vocabularies) that describe the data presents an entirely different, yet related set of complexities. This tutorial will leverage provide insight into a best practices and common sense approach to XML, databases, and metadata.

• XML and XML Schemas
• XML Database Storage Strategies
• XML Schema Data Types vs. Database Data Types
• XML Schemas - Storing your XML Metadata

T9
Mastering Reference Data
Malcolm Chisholm
President
Askget.com Inc.

All databases contain reference data, and the same reference data is often implemented in many databases across an enterprise. Failure to manage reference data properly can cause major problems for sharing data, passing transactions from one system to another, interpreting data, and implementing business rules. Successful management of reference data requires understanding how reference data is different to other classes of data. This presentation explores the unique tasks needed to manage reference data successfully, the capabilities that must be put in place to carry out these tasks, and the risks that have to be mitigated. Additionally, the management of reference data life cycles is examined, along with the many special requirements of the different subclasses of reference data. The organizational requirements for managing reference data, and ways in which these can be justified are described. The infrastructure that must be created to manage reference data is examined, with special emphasis on a central repository and distribution mechanisms. Particular emphasis will be given to:

• A description of what reference data is, and the various categories that comprise it
• The management techniques that are applicable to the various categories of reference data. This will include a facilitated exercise for the audience to categorize reference data categories in a data model
• The resources needed, and techniques available, to manage reference data centrally within an enterprise
• Options for distributing reference data within an organization, and also how to implement a central reference data server environment
• The special metadata that is associated with reference data and how to manage it
• The management of the reference data life cycles
• Dealing with the governance of reference data

T10
Data and Databases
Joe Celko
Professor and VP of Relational Database Management Systems
Northface University

This is a technical class conducted in two parts. Part one will examine scales, measurements and how to encode data for a database. Part two will examine how to apply the theory and techniques of part one in an SQL database environment.

Part I: Data Encoding

Scales & Measurements

Types of scales
- Nominal - Categorical - Absolute - Ordinal - Rank - Interval - Ratio
- Scale Conversion

Encoding schemes

Bad Encoding Schemes

Types of encodings:
- Enumeration Codes - Chronological - Procedural - Physical - Alphabetical or numerical order

Encoding types:
- Measurement - Abbreviation Code- Algorithmic - Hierarchical - Vector - Concatenation

Guidelines for designing encodings

Part II: Putting Data into a Database

Keys & Identifiers
-Basic Idea - Simple and Compound Keys

Types of keys:
- Natural - Artificial - Surrogate - "Unique-ifiers" - Superkey - Primary - Foreign Keys

Generating Keys:

Referential Constraints

Data quality

Statistical distributions

Correlations

Scrubbing Data

Data Redundancy
- Redundancies other than Normal Forms
- Transitive dependencies hidden in computed columns
- Redundancies among attributes that do not in the same table
- Redundant tables

Attribute Splitting

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