POST CONFERENCE FULL DAY TUTORIALS • 1 November 2007

Beyond the Basics - Managing Metadata in the Real World
Peter Aiken, VCU/Data Blueprint

Managing metadata in the real world is a very doable proposition. Today's metadata initiatives must deliver immediate ROI to attract and retain management commitment. This tutorial demonstrates how organizations can and have rapidly and successfully gained value from metadata projects in today's environment in the face of everyday, real-world pressures. Success is achieved by applying a few metadata strategies and concentrating on these fundamentals in a manner that causes the business to recognize metadata as both the cause and the solution to specific organizational challenges. A key underlying assumption is that metadata management requires not a repository to get started but repository-like functionality that can be developed in weeks instead of years. Upon completion, participants will:

• Understand the difference in focus that is required for real- world metadata strategies to work
• Comprehend the complimentary characteristics between metadata and data management practices
• Be able to architect a repository-like solution that can be grown into a more advanced project
• Assess and articulate the business value of proposed metadata projects.

Featured Speaker:

Peter Aiken VCU/Data Blueprint

Building and Growing a Successful IQ Function
C. Lwanga Yonke, Aera Energy, LLC

Successfully tackling the tough challenges caused by poor data quality often seems like an overwhelming and thankless task. Moreover, as awareness about the importance of information quality grows, information quality (IQ) practitioners are increasingly called to tackle a myriad of complex IQ problems. To be successful in the short and long terms, the IQ practitioner must be equipped with a robust foundation deeply rooted in proven best practices and applicable to various IQ efforts such as from CDI, MDM, compliance, governance, data integration, business intelligence, etc.

Drawing from lessons learned at the frontline, this tutorial describes the fundamental components of successful IQ functions and provides practical guidelines on getting started and remaining successful. Several hands-on exercises are used to facilitate learning and promote mastery. This tutorial will be beneficial to those implementing new information quality programs and to those seeking to re-energize or re-focus existing ones. Participants will leave with tangible solutions to many of their toughest IQ implementation challenges. Topics addressed include:

• The fundamental activities of IQ management and improvement
• Developing an IQ strategy
• The best home on the org. chart
• Tools and methodologies
• Measuring IQ Costs and Benefits
• Building a company-wide IQ culture
• Aligning Business and IT for IQ success
• The CXO Perspective
• The attributes of the successful IQ Leader

Featured Speaker:

C. Lwanga Yonke Aera Energy, LLC

New Advanced Data Modeling Topics for the Data Warehouse
Tom Haughey, InfoModel LLC

This presentation is for experienced data warehouse architects and database designers. The presentation will describe the most challenging data warehouse design problems the world of data warehousing has faced. This presentation is not just another dimensional modeling promotion. It will show where dimensional model is and is not applicable. Among the requirements to be addressed in modeling the data warehouse are: handling aggregation, heterogeneous product and transaction types, handling time and history, handling changing dimensions, handling changing facts, handling late arriving data, supporting data with different rates of change and stability, supporting large scale database environments such as MPP (massively parallel processing).

Designing a data warehouse requires different roles and uses of data, a different use of normalization, and new modeling constructs. Key special requirements of the data warehouse focus on time, location, and dimensional aspects of data. These requirements are among the reasons that analytical data modeling demands different skills, perspectives and techniques.

• Data warehouse architectures
• New view of dimensional modeling
• Required snowflakes
• Conforming facts and dimensions
• Handling time and history
• Heterogeneous dimensions and facts
• Changing dimensions and facts
• Mixed changes
• Modeling for different types of time changes
• Late arriving data: facts and dimensions
• Fact to fact joins
• Predicate analysis for star joins
• Do all facts have count, amount; are all dimensions without them
• Factless facts
• Fact or dimension
• Design for parallel
• Multiple roles
• Use of surrogate keys
• Handling multi-valued dimensions
• Handling complex dimensions, such as hierarchical, ragged, multiple dimensions
• Designing aggregates
• Aggregates vs. on-the-fly
• Supporting restatement or aggregates
• Designing for trickle load
• Master data

Featured Speaker:

Tom Haughey InfoModel LLC

Skills for the Advanced Data Modeler - Honing Your Techniques
Alec Sharp, Clariteq Systems Consulting Ltd.

Experienced or “advanced” data modelers don’t all get the same results. Some – the ones we love to hate – develop stable models that are actually used, and make it look easy. Others might have great technical modeling skills, but never manage to engage the business experts or gain the support of business analysts and developers. They end up watching in dismay as their models are consigned to irrelevance or are undone by “new” requirements.

What accounts for the difference? Magic? Luck? Great hair? No – it’s having a well-honed set of frameworks, techniques, procedures, tricks, and other tools that can be used to keep the process moving and keep people engaged. And that’s what we’ll cover in this one-day session – specific, repeatable techniques that you can use to drive your data modeling skills to the next level.

This is an updated version of the top-rated session that Alec delivered at the 2005 DAMA Europe conference.

Some of the topics we’ll cover include:

• Advanced topics – complex rules; generalization vs. literalism; relating recursion, abstraction, and subtyping; history, corrections, “as-of” queries, and Sarbanes-Oxley; using events, services, and use cases, and many more
• Facilitation techniques, and their use in data modeling
• The role of reverse engineering
• The magical number seven, and why we dumb it down to five
• Data modeling in the world of “off the shelf” or legacy applications
• Conducting a data model review presentation
• Finding common ground between E-R and dimensional modeling
• The top ten techniques for humanizing data modeling

Featured Speaker:

Alec Sharp Clariteq Systems Consulting