IQ Product Sessions

Quality customer data is essential to any organization that cares about its customers and customer relationship management. But measuring the quality of your data can be a challenge. How do you verify how good your data quality is? If you do not have an objective benchmark for your data quality, how can you accurately track whether it's improving or deteriorating? And if you implement initiatives to improve your data quality, how can you measure their effect? Using case studies and real-life examples, this interactive presentation will provide you with a clearer understanding of how to benchmark your organization's data quality.

Speaker's Bio: Ms. Kramer is a Product Manager with Innovative Systems, Inc. Innovative Systems is a world leader in providing data quality solutions, with applications in 21 countries. Services include industrial-strength data quality and data linking tools, systems integration, and customer-centric databases. Ms. Kramer has planned and coordinated the creation and maintenance of customer information files for clients ranging from major financial institutions to international hotel, retail and e-commerce firms. She is an authority on pre- and post-conversion strategies, conversion needs specification, and data management staff training

An industry-wide trend is for CRM, Data Warehousing, and other enterprise systems projects to be late, over budget, and return diluted value due to poor data quality and Customer Data Integration (CDI) that result in an incomplete, distorted, and inaccurate "single customer view". The scope of this problem includes the tremendous opportunity cost and a direct, negative impact on the bottom line of the Total Cost of Ownership (TCO) and Return On Investment (ROI) of these enterprise systems. Group 1 Software's Enterprise Data Quality Solution enables you to successfully deploy CRM, Data Warehousing, and other enterprise systems by reliably providing a flexible, comprehensive, and scalable 3rd-generation solution for enterprise-wide data cleansing, standardization, augmentation, and Customer Data Integration (CDI). It accomplishes this by tailoring its performance to meet each customer's unique needs for creating, and then maintaining, the most accurate single customer view possible. This presentation will provide an overview of this unique, 3rd-generation solution.

Speaker's Bio:

Ed Allburn has over 13 years experience with Business Intelligence, Data Quality, and Customer Data Integration (CDI) in both technical and product management roles. Prior to Group 1 Software, he has worked for such companies as Customer Insight Company, a Metromail/Experian company, the IBM TJ Watson Research Center, and Firstlogic (formerly Postalsoft). Ed has also raised funding and led two e-commerce startup companies, one of which as was twice a FORTUNE Magazine "25 Cool Companies" candidate.

When you need to combine multiple, error-filled data feeds into a single, highly accurate database, the hardest problem is matching corresponding records. How do you match, for instance, "Andrew Borthwick" with "Andy Borthwich"? We present an innovative, accurate system that employs a powerful, patent-pending, machine learning technique to determine the probability that two database records correspond to the same person or company. Machine learning techniques link records more effectively because:

• Machine learning techniques easily use many database fields to make match decisions
• You train the system with a small number of pairs of human-matched records, so the matching mimics your organization's decision-making process
• We report the match result as a probability, simplifying your management decision with regard to reviewing match decisions

We present a case study in which ChoiceMaker's de-duplication system helped New York City solve a severe duplicate record problem in a database of 10 million child immunization records and link the database with children in a lead exposure database. ChoiceMaker's system employed many clinical and demographic database fields to arrive at a highly accurate estimate of the probability that two records referred to the same child.

Speaker's Bio:

Dr. Borthwick holds a Ph.D. in Computer Science from New York University. As President and CEO of ChoiceMaker Technologies, he is the architect of ChoiceMaker's core record-matching technology. The National Science Foundation awarded him a Small Business Innovation Research Grant to further ChoiceMaker's research on applying modern machine-learning techniques to database de-duplication and record linkage.

How SSA's new Identity Systems Solution (IDS) is tailored for an IQ market demanding faster, more compatible, WEB-enabled products that minimize customer development and programming requirements and significantly lessen time-to-market cycles.

Speaker's Bio:

Information Impact Home