This report compiled and edited by Tony Shaw, Program Chair, Wilshire Conferences




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НазваниеThis report compiled and edited by Tony Shaw, Program Chair, Wilshire Conferences
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CONFERENCE SESSIONS

Tuesday, April 29

11:30 am – 12:30 am


Data Ownership, Governance and Stewardship in a Multinational Company

One size does not fit all


Larry Dziedzic

Technical Manager

Johnson & Johnson


Ron Lemezis

Senior Information Architect

Johnson & Johnson


Ron Lemezis and Larry Dziedzic presented on the impossibility of having a single governance model for a large multinational company. While only a few of the attendees had any formal governance projects in action, there was good interest in where and what J&J had done to that point, what they would be doing in the next steps. And importantly, what where the issues and obstacles that would have to be overcome to make everything happen for successful implementation.


Metamodels for ER, ORM and UML: a Critical Review


Terry Halpin

VP, Conceptual Modeling

North Face Learning


Metamodels for the following data modeling approaches were reviewed: ER (Barker and Information Engineering versions); ORM; and UML class diagrams. The analysis clarified the commonailities and differences between the approaches, as well as their strengths and weaknesses, and illustrated the following main points:


- Non-derived constraints on derived associations facilitate

complex constraint declaration.

- Multiplicity/cardinality in UML, IE and Barker/ER does not scale properly for n-ary associations.

- ORM's graphic notation for data modeling is more expressive and orthogonal than UML, IE, or Barker ER.

- An attribute-free notation is probably needed to provide high expressibility without high complexity.

- ER, ORM, and UML each have their strengths, with ORM providing an ideal basis for creating ER or UML class diagrams.


Knowing our Customers: Initiating Customer Rule Management at AT&T


Frank Cunningham

District Manager - Customer Data Integrity

AT&T - Consumer


Every business needs to know who their customers are... This is accomplished by defining and obtaining concurrence on the necessary terms (data) and business rules that meet the needs of the business. Starting an effort like this from scratch can seem daunting, but with strong business sponsorship, a well-defined process and a clear sense of scope and priority, it will be a success!


Meta-Data: It’s Not Just About Data Anymore


Robert Seiner

Publisher, TDAN.com

Founder & Principal, KIK Consulting


This presentation defined meta-data as "Data recorded in IT tools that improves both business & technical understanding of structured and unstructured data and data-related processes." The presentation included walk-throughs of four conceptual meta-models developed by the presenter to support structured data (tabular, delimited, ...), unstructured data (artifacts - content, knowledge, documents, ...), data stewardship and business intelligence programs. Also covered were concepts beyond traditional meta-data stores including the concepts of business data directories, business report directories and business integration directories.


Business Intelligence: From Theory to Reality


Shaku Atre

President

Atre Group


Business Intelligence was defined as: “Business success realized through rapid and easy access to actionable information through timely and accurate insight into business conditions and customers, finances and markets.

Where and how to start with BI:

- Step A: Identify your business requirements

- Step B: Develop business intelligence strategy with the knowledge workers in mind

- Step C: Do you have an enterprise data architecture? Is it developed by the business community and IT jointly?

- Step D: What is in place as far as the infrastructure is concerned?

- Step E: Plan on implementing “small steps” with “big successes.” Build business community support to have solid sponsorship

- Step F: Build BI applications for growth of data and growth of knowledge workers’ use

- Step G: Think “out of the box” for the future


New Approaches to Customer Data Integration


Chandos Quill

Vice President, Strategic Marketing

Experian


Jeff Canter

Vice President of Operations

Innovative Systems, Inc.


Customer data integration is one of the top priorities of marketing and sales organizations today. In this session we heard from two different approaches from two leaders in the field.


Referenced-Based Integration

Integrating customer data is, by nature, a reference process. Knowing whether data is accurate or not requires a picture of reality to which data cleansers and integrators can compare records. This presentation detailed new reference-based data integration methods to achieve dramatically better results - methods that go beyond mere matching formulas to compare customer data to historical customer reference repositories.


Data Synchronization

Jeff Canter’s talk on Data Synchronization offered an alternative view of CDI, including the following:

* A "single customer view" is really a myth and insufficient even if achieved.

* Enterprise customer systems need to support multiple, "purpose"-driven views of the customer that are synchronized rather than merely integrated.

* The five basic challenges to successful synchronization are:

- Ease of data access

- Availability of source data documentation

- Level of data quality

- Database architecture

- Ability to leverage synchronized data


XML Tools Explained and Demonstrated


Srinivas Pandrangi

Architect

Ipedo


Denise Draper

Chief Technology Officer

Nimble Technology


Steve Hamby

IT Architect

Software AG, Inc.


This unique session included XML tool demonstrations, so as to better understand the functionality and maturity of the toolsets on the market. This session included three of the leading XML vendors -- Software AG, Nimble Technology and Ipedo -- to show three different XML tools for data management.


As the amount of information in marked up in XML grows, enterprises need a strategy for XML data management. Ipedo's XML Information Hub provides a platform to integrate, manage and deliver XML content. With powerful features like views, virtual documents, schema manager and native XML storage and management, the XML Information Hub can create a robust foundation for an XML data management architecture for enterprises.


Please note: Wilshire Conferences and DAMA are vendor-neutral, and do not endorse these or any other products. Product demonstrations are included in this session for educational purposes and to demonstrate general tool functionality, and not to advertise or promote the sale of products or services.


CONFERENCE SESSIONS

Tuesday, April 29

1:45 pm – 2:45 pm


Get "Focused" Before Data Modeling


Steve Farrell

Senior Business Analyst

Advanced Strategies, Inc.


Everyone knows that "scope" plays a huge factor in a data modeling effort. However scope is just one of several related constraining factors that are crucial for success. Together they add up to a total "focus" statement that will provide guidance to the modeling effort. These factors include:

* Scope - including beginning and ending events and other items

* Perspectives - points of view that must be reflected in the model

* Depth - distinguishing between framing models and full detail models

* Universality - how generic a solution is desired

* Deployment - where the solution will be implemented

* Time Frame - how long the model is expected to be stable and extensible

* Scope of Integration - touch points at the edge of scope that might require integration


If these focus questions are not answered at the start of a modeling effort, a tremendous amount of time and energy can be wasted.


Enhancing Information Quality Management Practices at HUD


Andres Perez

Senior Information Management Consultant

IRM Consulting, Ltd. Co.


During this session, the speaker presented lessons learned in achieving a new level of Information Quality. This case study helped in explaining how an organization can apply the TIQM® Methods to improve information quality and to help move an organization from a reactive, data correction approach to a proactive information quality improvement approach based on continuous process improvement. Also, it provided ideas on how to change an environment of no-accountability for information quality to one of management accountability. Finally, it provided some insight on how to move from a system focus to a business process focus.


Master Reference Data: A Real-Time Data Architecture for Today's Enterprise


Martin Dunn

CEO

Delos Technology


Master Reference Data (MRD) describes the core entities within a business such as Customer, Supplier, Product, Parts etc.

- The symptoms of poorly managed MRD are duplications and misclassifications.

- Poorly managed MRD destroys ROI in CRM, SCM, ERP and Analytics.

- Any real-time enterprise (RTE) architecture must include meta data, a cross-reference, historic data and business rules.

- We discussed how MRD can be managed within the major real-time architectures from IBM, Oracle and Microsoft.

- MRD comprises many different data elements each of which needs to be managed independently.

- We introduced a methodology to define the MRD data elements.

- We discussed some of the implications of this MRD framework.


Enterprise Information Management - Identifying the Solution


Mark Abramson

Senior Meta-Data Architect

JP Morgan Chase


Blockbuster's Enterprise Data Warehouse (EDW) team provides hundreds of statistical analysts, marketing managers, and buyers at Blockbuster's Texas headquarters with next-day access to complete information on customers, retail sales, rental patterns, inventory, shipments and forecast. In this presentation, Mark discussed the tools and methods used to integrate terabytes of data between Blockbuster's retail stores and headquarters each night. He showed how the 15-person EDW team has created a best-practices customer relationship management solution that includes a global operational data store, a five terabyte data warehouse, and an associated meta data management solution. That associated meta data management solution tracks hundreds of FTP and Java file transfer jobs, more than 500 ETL and data quality transforms, and report generation for more than 12 different reporting applications. The resulting meta data information provides business users and developers alike with complete desktop access to all business or technical meta data for the entire EDW solution.


Business Performance Management: The Future of Analytics?


Seth Grimes

President & Principal Consultant

Alta Plana Corporation


Seth Grimes spoke on Business Performance Management and Analytics. BPM melds statistical analysis and business intelligence techniques with strategic management methodologies and process-focused views of business operations. While software implementations from BI, ERP, and BPM vendors are compelling, they are immature, limited by unmet data integration challenges and the complexity of the methodologies.


XML Data Architecture – A Introductory Roadmap for Establishing an XML Data Architecture


Brian Magick

XML Data Architect

Hewlett Packard


Adoption of XML in the Data community has been slow as the number of XML technologies, specifications, and standards are common sources of confusion. This presentation focussed Data Management professionals on the aspects of XML that will help move XML adoption in the right direction, focusing on the data.


Attendees learn some basics of XML such as its purpose and design goals, and some XML myths that are simply not true. XML Schema and DTD, the core of an XML Data Architecture were defined, as well as the limitations of the DTD and the concepts of well-formed versus valid XML. The concept of “what is an XML Data Architecture” was presented and a common XML validation framework will be stepped through. A further in-depth look at the XML Schema was presented suggesting that the Schema is a data model, contract, and a data dictionary.


Raising the Quality Bar in Database Administration


April Reeve

Data Architect

Wyeth Consumer Healthcare


How does a database administration manager implement a controlled DBA function for a new or, even harder, less controlled organization? For a new organization the problem is easier, because we can deal with the issues in a life cycle order, starting with development or software assessment. For development we can use ideas from software process improvement methodologies plus deal with particular issues for database administration and, of course, deal with issues concerning packaged software implementation and development tool metadata. For an existing organization we frequently first have to deal with a chaotic and stressful production situation.


Raising the maturity level, or service quality, or control levels of an organization is not achieved overnight. It is not an activity that can be done in isolation but requires the involvement of the application development teams, the data center and server support staff, and sometimes even the end users. It will not happen without a sustained effort to make it so. Yet, leaving a database administration capability uncontrolled will only result in miserable DBAs, unhappy developers, and untrusting users.


CONFERENCE SESSIONS

Tuesday, April 29

3:15 pm – 4:15 pm


Data Modeling Panel:

"Have Things Really Changed?"


Davida Berger (moderator) Scott Ambler

Terry Halpin

Len Silverston

Dave Hay

Terry Quatrani

Graham Witt


In many organizations, the role of the traditional data modeler has changed..Data modelers have been required to learn more skills and in some organizations take on new roles. This panel of modeling experts addressed the changes in the role of today's data modeler and provide advice to help today's data modeler adjust to this change.


Topics included:

* How has the role of the modeler changed?

* Are the agile methodologists correct when they say one needs to be more than a modeler?

* Is the modeler now more of an analyst modeling interfaces for packages rather than modeling the business

* Does the agile methodology and software development result in poor data design?

* What new skills are needed for the data modeler?

* ER vs UML, and where does Object Role Modeling fit in?

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