The 17th Information Quality Conference 2005 Houston, TX, USA September 19-23, 2005   Information Quality Track 2

Tuesday, September 20:
10:15-11:15 Can Information Quality and Process Re-Design Coexist?
     Carl Fisher & Eric Nielsen, Data Steward & Sr. Member of Technichal Staff, Schneider National, Inc.

1:30-2:30 Leveraging Information Quality in Data Migration
     Diana Joseph, Architect, Information Quality Management, Old Mutual, South Africa

2:45-3:45 No Session

Wednesday, September 21:
10:00-11:00 Enforcing Data Quality through Data Lineage
    David Plotkin, Data Quality Manager, Wells Fargo Bank

11:15-12:15 Securing Enterprise Data Assets: Data Quality Technologies & Best Practices
     Chuck Schardong and Jeff Canter, Innovative Systems INc.

1:30-2:30 Data Quality and the Process of Change, from Obstacle to Opportunity
    Mannie Goldberg & Robert Rich, Director, Data Resource Management/Director, Product Marketing, NSTAR Electric & Gas/Ascential Software

2:45-3:45 Information/Data Quality and the Regulatory Climate
    Stuart Roth, Managing Director, MPI Professionals

Presentation Descriptions

In real estate the three most important things to remember are location, location, location. In Information Quality the most important things to remember are process, process, and process. Schneider National, the nation's largest truckload carrier, will share its successes, failures, and watch-outs experienced during implementation of an enterprise Information Stewardship program while also re-designing its core processes.

• Gaining Senior Level Support for Information Stewardship
• Information Stewardship Implementation
• Process Design to Support Information Quality

When you have a sound information quality methodology, it can be applied in numerous ways: conversions, new systems, data warehouses or data integration. You learn how a data quality methodology integrated with business rules is applied to data migration to increase success and improve information quality as it moves from the source to target environment(s). This presentation includes elements of a case study and:

• Overview of our DQ methodology
• Integration of Data Quality and Data Migration Methods
• Measuring success

Understanding information quality often involves knowing where data came from, the value chain to its destination, such as a data warehouse, and its transformations. You learn how to document data "lineage," including a metamodel for recording it. You learn processes to record and implement business rules and how to segregate data that fails the rules. You learn the process and cultural implications of implementing rigorous IQ through metadata management.

• Tracking data lineage across its value/cost chain
• Specifying IQ rules
• Working with analysts to gather the specifications

This presentation describes the ever-expanding scope of data quality technologies, and how industry-leading companies are integrating advanced data profiling, data quality and data integration solutions into their enterprise data management practices in order to improve overall business operations. The session:

• Identifies next-generation data quality technology advancements, particularly the integrated convergence of data profiling and traditional data quality capabilities
• Illustrates how specialized data quality technologies are being successfully employed to comply with regulatory compliance mandates
• Highlights how both data quality technologies and data management best practices are essential to the success of enterprise customer data integration initiatives
• Shares real-world examples of data quality best practices at leading companies such as Abbey National, Protective Life, Wachovia and others

Data quality improvement requires changes in attitudes, believes, and behaviors. These changes require leadership. In this case study, you learn how to improve your ability to lead DQ initiatives. You learn about the factors that proved critical to the success of one initiative, and established a pattern of success for future initiatives. They'll learn about the underlying functional architecture that support the initiatives.

• Picking the right data quality initiatives
• Aligning the organization
• Establishing a trusted data source
• Functional architecture to support quality initiatives

Issues surrounding Data Quality in the Regulatory climate are now taking shape. With Sarbanes Oxley and Basel II, many Financial Services firms are forced to review their Data Architecture and Data Strategy to meet increasing Government regulations. Firms that get ahead of the DQ curve will best prepared to meet immediate and future challenges.

• Why Regulatory Compliance is not enough without Data Quality processes
• What does it mean to get to a 99% Data Quality level? How do I achieve and sustain that goal?
• Why must the "business" be involved in Data Quality and not just Technology?

This presentation describes how Intel applies Information Quality principles to its value chain.

• Creating a culture for IQ as a key component to the success of value chains
• The importance of the business model and Information Architecture from a IQ value chain perspective
• Designing IQ into information architecture
• Value Chain stakeholder involvement
• Single version of the truth for highly shared information
• How cross-functional stakeholders drive highly shared information
• How to organize, lead and govern IQ across the value chain

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