Compiled and edited by Tony Shaw, Program Chair, Wilshire Conferences, Inc

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Project Data Architect

Westpac Banking Corporation (NZ)

The presentation was based on experiences covering the last 15 years. It was found that to successfully initiate and sustain IM and Architecture approaches in a changing environment within Westpac, it had been important to:

  • Be aware of the internal and external environment

  • Realise that change is inevitable. Understand the nature of change. Adapt to change, and treat changes as opportunities to add value.

  • Understand that value is about delivering and also about perception.

  • Use an approach that ensured visibility and included directly dealing to organisation-critical changes, and also to culture, progression of the underlying foundation and buy-in.

  • Use a portfolio-based approach to reduce risk

  • Have a foundation that evolved

  • Understand that a critical part of the foundation is the organisation’s relationship capability

  • Fund relationship training for all staff, and encourage opportunities for relationship and network building.

From this came 4 key messages:

    • Change is an opportunity

    • Add value in a visible way

    • Evolve a foundation

    • Don’t forget the importance of relationships.

10 Great Things Which Data Administrators Can Do with Use Cases and Class Diagrams

Susan Burk

Director - Systems Architecture

MassMutual Financial Group

This discussion examined how Data Management can work with use case models and class diagrams to support data definition and logical data modeling, as well as ways for DAs to use their own strengths to support better understanding of business requirements.

#10 Gain Context for operational systems

#9 Define terms

# 8 Find entities and rules

#7 Balance Data and Behavior

#6 Encourage normalization of process

#5 Find data for rules

#4 Gather volumetrics

#3 Use the class model to determine or map to an existing data model.

#2 Use these techniques to re-engineer DA

#1 Get involved earlier in projects.

A Practical Guide for Inventorying Your Data Assets

Perry Benjamin

Data Management Specialist

The Boeing Company

An inventory of data resources can provide the metadata visibility necessary for an enterprise to more easily integrate its computer applications and respond more quickly to business and technological change. The approach taken to develop and populate the data inventory in use at the Boeing Integrated Defense Systems - Long Beach site was presented, including:

  • Scope and organization of the metadata

  • Construction of an enterprise common business object model

  • Metadata capture and change management

  • Components of the inventory system

Are Business Rules Meta-Data In Disguise?

Terry Moriarty



During this presentation, the following topics were discussed:

* What are business rules?

The separation of business concepts, terms and business rules was provided, with examples of how different communities use different terms to refer to the same business concept. Within an enterprise, each community wants to state its business rules in its own vocabulary, but still be able to integrate with all the other communities. A business-oriented business rule classification scheme was provided, using a case study of buying a house within various golf communities. The classification scheme is:

  • Qualification Rules: Do I qualify for this business state? Who is in this business state?

  • Validation Rules: Am I in a Valid State?

  • Configuration/Value Setting Rules: Put me in a valid business state.

  • Price Me. Compute me. Configure me.

  • Process Rules: What do I do next?

* What is Meta-data?

Metadata was discussed using the Data->Information->Knowledge->Wisdom hierarchy. What you consider to be metadata varies based on which perspective you are at. Business Rules fall into the knowledge level where you internalize the rules and just "know" how something is supposed to work. Business rules are part of business data, therefore requiring that metadata achieves the status of business data.

* Aren't Business Concept/Fact models really data models?

To answer this question, the different types of data models were explored: conceptual, logical and physical. If you maintain a model from which the business can be read, it is probably the same as a business concept model. However, if your data model has the characteristics of a dynamic model which allows new data to be supported very rapidly into the information resource, then you probably need a business concept model also. The Zachman Enterprise Systems Architecture framework was used to describe a dynamic model and techniques for mapping an organization's business concepts into a dynamic model's framework was provided.

Building An XML-Based Data Standard

Bill Hopton

Systems Consultant

Manulife Financial

If the spaghetti of point-to-point interfaces is the wrong way to integrate multiple systems, re-creating the spaghetti inside an EAI tool is not a big improvement. What is needed is a catalogue of standard messages to de-couple the sources from the targets. In our attempt to create such a catalogue (expressed in XML Schema) we had success enriching the messages inside the middleware using a cross-reference database. We also found that some data transformations were so complex that we were unable to achieve 100% de-coupling with the budget we had. However, by making this attempt we feel we have a good start that we can build on in the future, which we hope will involve the ISO15022 financial services standard.

From Afterthought to Asset: The Business Value of Data

Jill Dyche

Vice President and Partner

Baseline Consulting Group

Citing recent research claiming that business data is growing exponentially, author and consultant Jill Dyche discussed why senior business executives are (finally!) turning their heads in the direction of information as a competitive weapon. Dyche explained why yesterday’s practice of “data as enabler” is now ceding to a new and innovative business Zeitgeist of “data as corporate asset.” She explained the impact this phenomenon has had on businesses, as well as on development teams. Dyche introduced the concept of the Information Center of Excellence as representative of data’s evolution, and discussed the role of the ICE as the company’s fulcrum for data delivery.


Tuesday, May 4

11:30 am – 12:30 am

Achieving Data Excellence: Roadmap to Success

Larry Dziedzic

Technology Manager

Johnson & Johnson

Maggie O'Hara

Assistant Professor of MIS

East Carolina University

Data quality, or the lack thereof, has become a hot topic in business. This is not surprising, given that in 2001, The Data Warehouse Institute estimated that dirty data cost US businesses $600 billion. While much attention has been given to defining data quality and to the impact of poor data on the business, less attention has been given to the specific processes involved in improving data quality. Data Quality Improvement (DQI) to achieve data excellence was the focus of this presentation. Using a data life-cycle approach, the presenters demonstrated clear, concrete steps organizations can take to improve data quality at all life-cycle stages: capture, storage, and access.

The Training and Mentoring of Data Analysts

Janet Siebert

Data Architect


Three main theories from educational psychology converge to provide a framework for the training and mentoring of knowledge workers in general, and data analysts in particular. The constructs are Benjamin Bloom’s Taxonomy (1956), Howard Gardner’s Multiple Intelligences (1983), and Mihaly Czikszentmihalyi’s Flow (1990). Bloom argued that thinking skills can be classified into a hierarchy of increasing complexity. Levels of the hierarchy are knowledge, comprehension, application, analysis, synthesis, and evaluation. Gardner argued that there are a variety of different types of intelligence, including linguistic, logical-mathematical, musical, bodily-kinesthetic, spatial, interpersonal, and intrapersonal. Czikszentmihalyi suggests that we are happiest as human beings when we are engaged in challenges well matched to our skills. Successful data analysts leverage multiple intelligences (linguistic, logical-mathematical, spatial, and interpersonal) and operate at relatively high levels in Bloom’s taxonomy. Challenging data analysts requires an awareness of each individual’s intelligences and thinking skills, and the crafting of activities that are appropriate to that individual.

Verbalizing Business Rules

Terry Halpin

Professor and VP (Conceptual Modeling)

Northface University

Although business rules may be implemented in many ways, they should first be specified at the conceptual level, using concepts and language easily understood by the business domain experts who are required to validate the rules. Business rules come in many varieties, such as constraints and derivation rules, and may be specified using graphical and/or textual languages. The focus of this presentation was on expressing a wide variety of business rules in a high-level, textual language that has been designed for expressibility, clarity, formality, generality, and localizability.

  • Using positive, negative, and neutral verbalization patterns for rule validation

  • Relational-style verbalization vs Attribute-style verbalization

  • The use of context for verbalization of complex rules and rule sets

  • Verbalizing rules involving projections over multiple associations of any arity

  • Verbalizing reaction rules

  • Making verbalizations formal and executable

Metadata Management Converges with Business Modeling

Joshua Fox

Sr. Software Architect

Unicorn Solutions Inc.

Metadata repositories help you understand your metadata, and business models help you understand your business. However, metadata repository projects often fail, when the metadata's meaning is ambigious and unclear; modeling projects often fail when developers ignore the model.

The two gain value when they are integrated in a semantic metadata repository. Metadata gains semantics, and models gain a firm grounding in the actual enterprise infrastructure. This results in improved quality and agility in the management and development of information assets. Existing assets can be reused, and redundant assets can be eliminated. The enterprise then unites on a shared understanding of business concepts and processes.

Successful Meta Data Management in Complex Siloed Systems

Sean Goggins

Principal Consultant

Ciber, Inc.

  • Manufacturing, medical research and the emerging “informatics” labeled disciplines are most often substantially concerned with the existence of inconsistently labeled and classified data across 20 or more small “systems” that compose an organization’s complete super-system for data creation and analysis in these areas. It is also common for disparate versions of systems like this to be deployed in 10 or more specific instances across such organizations or, in some cases, customers. Traditional approaches to data management and meta data management provide a strong but incomplete foundation for successfully adding value to systems of this scale. Participants in this session gained a comprehensive, “Big Picture” view of the challenges to consider in the development of a metadata management strategy for organizations with complex systems that support real time manufacturing, clinical research and informatics oriented domains.

XML Tools Comparison: Cutting through the hype - what works for Data Management?

Peter Aiken

Founding Director

Data Blueprint

David Allen

Chief Executive Officer

Data Blueprint

Much of the hype generated around XML comes from and revolves around the various XML tools and XML features of existing tools. This session demonstrated the functionality of various classes of XML tools including:

  • XML editors

  • Translators

  • XML parsing technologies

  • Servers

Database Trends: The DBMS Landscape Circa 2004

Craig Mullins

Director, Technology Planning

BMC Software

Craig Mullins outlined several of the macro trends that are impacting database management and database administration. Chief among these trends were short release cycles of DBMS versions, rising complexity driven by DBMS technology subsuming technology that was previously free-standing, non-stop data volume growth, and the need for non-stop availability driven by Internet-enabling database applications. He spoke about the need to be able to prioritize DBA tasks based on knowledge of the business value of the data being impacted. He proposed the need for intelligent automation through heterogenous database administration using a central console to mask the differences between databases as a solution to the difficulty of managing database systems.


Tuesday, May 4

1:45 pm – 2:45 pm

Using an Enterprise Portal to Drive Global Data Architecture at Citigroup

Denis Kosar

VP Data Architecture


This presentation provided the audience with an overview as to how Citigroup’s Global Data Architecture Group is dealing with the new pressures that the regulators, business drivers, and the economy have imposed on the way we bring ROI back to the business. In other words, “A survival manual for Global Data Architecture in troubled times”. It details examples as to how an enterprise portal can greatly enhance Data Architecture’s ability to perform and communicate its role across the enterprise. The key to its strategy is MENTOR, SHARE and EMPOWER. Providing an advisory role, special services, and a “re-use” strategy are key components for measuring the effectiveness through monthly metrics that can be reported.

This presentation seemed to be right on target judging by some of the questions asked, post presentation dialog, and references made in other presentations. In today’s troubling times we are being asked to do much more with less and I would hope that this presentation has provided the data professional with a game plan and strategy to not only survive but to make a difference to the enterprise.

International Cross-Enterprise Shared Data

A Framework for Business Implementation

Patrick Curry


The goal of securely sharing sensitive data across enterprises and across international boundaries is fraught with security, commercial and regulatory pitfalls. This presentation described the significant progress being made by Defence primes with transatlantic governments support to enable large Defence contracts to benefit from real e-business advantages whilst ensuring compliance of different nations respective national security, data protection, privacy, export control, Sarbanes-Oxley regulations. The description of the Framework and associated guidance highlighted developments in:

- identity management (of personal and assets)

- secure cross enterprise logical information architecture,

- multi-national export control compliance business rules,

- applying DRM

Enterprise Data and Process Modeling for the Telecommunications Industry - A case study using the E-TOM model

Robert Mattison


Excellence In Telecommunication

In this presentation, Rob Mattison shared his experience in the deployment of an enterprise data model for a start up telco in Asia. By making use of the telecom industry standard E-TOM (Telecommunications Operations Model), and gaining top management commitment to doing it "right the first time", the organization was able to deploy a highly effective and flexible data model (and supporting database structure) that is helping them to attain and maintain market leadership in all operational areas. E-TOM was utilized to create an entire enterprise reporting, data warehouse, data mining and operational process quality infrastructure.

The Role of Metadata Management in the Construction of a Successful Data Warehouse

James Montague

IT Manager-Data Architect-Enterprise Information Management

3M Company

3M’s large and diverse Data Warehouse requires a robust, active metadata (MD) repository. It's critical to its success to store MD, supporting the entire construction process: data design, analysis, development, and testing & quality assurance. 3M’s MD solution is a best practice in many ways:

  • Manages data legacy from source system through each transformation.

  • Captures subject areas data structures.

  • Audits the DW, detecting undocumented changes.

  • Provides web reports to DW developers and business users.

  • Generates data models.

  • Manages ‘canonical formats’ for internal ETL.

  • Manages business rules and error conditions.

  • Defines tolerance: data completeness and accuracy.

Creative Metadata on a Shoestring: Inexpensive Metadata Tools

Bonnie ONeil


Westridge Consulting

This presentation provided an overview of several very helpful, inexpensive tools:

  • Infoselect: (price range: $50 to $250)
    Unstructured Data tool: Helps organize and search through very large documents and find a text string

  • MindManager: (price range: $50-$300)
    Brainstorming software: Helps organize thoughts gathered from meetings; also useful for source to target mappings for data migrations.

  • KnowledgeDriver: ($5K)
    Inexpensive data profiling software

  • Teamroom: Comes with Lotus Notes; other software can do this too.
    General functionality: Allows you to post documents and allows users to post and subscribe, and create conversation threads. Very useful for obtaining user feedback from geographically distributed users.

  • BRSRuleTrack: ($5K)
    Metadata Repository for Business Rules.

Metadata: How Far Can it Take Us?

Doug Lenat



After a near 20-year effort to convert human common sense into a form that computers can understand and use, the Cyc Knowledge Base (pronounced “psyche”) has recently emerged into the commercial world. Cyc is like a “super-metadata” engine – providing layer upon layer of additional understanding and context to the specific metadata already in your systems. These extra layers of metadata then open the door for all sorts of new and exciting applications. Cyc is a significant technological achievement that will likely change the metadata landscape forever. This session described:

  • What Cyc is and the CYC Knowledge Base?

  • How Cyc provides you with metadata?

  • How this is qualitatively different from what metadata usually provides?

  • How the layering of more and more metadata helps? What changes occur as we add these additional layers?

  • Application areas in data management:

  • Integrating disparate databases

  • Data quality

  • Better search and querying

  • Semantic web

  • Security and compliance

Assessing BI Suites and Platforms for Performance Excellence

Jonathan Wu

Senior Principal

Knightsbridge Solutions

Business intelligence (BI) provides:

* The ability to easily access information for analysis, reporting and decision-making purposes without having to understand the associated complexities of the structured data, technical architecture or schemas from transactional systems.

* Access to information ranging from sophisticated cross-functional enterprise analytics to trend analysis and potentially transactional data

  • Market share

  • Customer retention

  • Operational efficiency

* Access to information to monitor business activities and performance

BI suites and platform technologies have significantly evolved over the last 5 years. This presentation reviewed the functionality of the leading BI suites and platforms, and addressed the following:

  • Leading product vendors

  • Technical architectures that provide optimal performance

  • Approach to software selection

  • Matching needs with the corresponding technology


Tuesday, May 4

3:15 pm – 4:15 pm

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