The 17th Information Quality Conference 2005 Houston, TX,USA September 19-23, 2005   Information Quality Track 1

2:45-3:45 No Session
     TBA

Wednesday, September 21:
10:00-11:00 Manageable Chasms: Moving from the 2nd to 3rd Level of IQ Maturity
     Chris Carroll & James Mann, IQ Team Leader / IQ Technical Architect, American Power Conversion

11:15-12:15 Total Data Quality: An Important Part of Every Organization
    Len Dubois, Vice President, Worldwide Marketing, Trillium Software, a division of Harte-Hanks

1:30-2:30 Establishing a Global Customer Data Quality Management Program
    Richard Goldberg, Senior Manager, Enterprise Customer Information, IBM Corporation   

2:45-3:45 Using Data Monitoring to Manage Data Quality Over Time
    Jeff Monica, Manager of Data Quality, StorageTek

4:00-5:00 No Session

This presentation demonstrates key principles for starting an Information Quality function and establishing its value. You learn "how to sell" information quality concepts, determine return on investment, leverage internal politics, align IQ initiatives with organization goals, and develop an organizational structure to meet business needs. An IQ proposal template and example provide you with project plans and tools necessary to 'hit the ground' running once an IQ function is approved. Implementing this saved PHS $5 million on one project alone!

• Beginning an Information Quality department
• Starting in a positive direction with quick wins

Creating business data definitions, business process rules, logical and physical data models, entity relationships, database mapping and ETL designs are often performed in isolation. This makes it difficult to integrate and keep this information meaningful and useful. This requires proactive change management to maintain high quality in a dynamic information environment. This presentation reviews an approach to integrate a complex information resource data environment and demonstrate the results:

• A Business Information Resource Data Model
• Basic tool requirements for data management
• Demonstration of IBM's pilot integrated metadata environment

Quality in and quality out can feel like an impossible goal until the path is broken into manageable "chasms." With a foundation from the 2004 IQ Conference, APC's small IQ team set up a simple IQ infrastructure to mature from Awakening to Enlightenment. Efforts focused on:

• Finding starting points and tools for first metrics, monitoring, and IQ certification
• Balancing maturing activities with upstream and downstream clean up activities
• Aligning IQ communications with corporate tools and "corporate speak"

Total Data Quality provides the ability to deliver information that is fit for all business purposes-however, wherever and whenever
an organization needs it. Total Data Quality is achieved through a combination of products, people and processes. Guiding principals to achieve Total Data Quality:

• Comprehensive
• Dynamic
• Intelligent
• Measurable
• Seamless

You learn how to achieve Total Data Quality with examples from successful companies. Total Data Quality delivers the data you
need today and the foundation for data that you need tomorrow.

To effectively execute your CRM, Marketing and preferred routes to market processes, it is critical that your customer information is of the highest quality and it is recognized as a strategic enterprise-wide asset. Specifically, this session will:

• Demonstrate how IBM established a global customer data quality management project infrastructure
• Provide guidance on the challenges and lessons learned about developing a customer data quality environment
• Identify how to mobilize, lead and sell to Executive Management a consistent global customer data quality environment

This presentation describes StorageTek's experience building a Corporate Information Factory (CIF), consisting of a data warehouse and multiple data marts. The CIF integrates data from over 60 operational systems and data sources. To build high-quality data within CIF, we established a set of data management and quality procedures using corporate protocols.

• Establishing processes to inspect, standardize, correct and validate information
• Establishing metrics for data quality with business data stewards
• Controlling data quality over time through monitoring