Got Bad Data? Part 3

Who should attend
Business analysts, data analysts, data stewards, data and database administrators, and warehouse managers who want to ensure the quality and reliability of their organizations’ information.

Why you shouldn’t miss this Webinar

Invest a little time in this Webinar to hear how data quality influences the effectiveness of predictive analytics and forecasting. You’ll learn how to:

• Go beyond just name and address matching to realize the potential of numerical data quality.

• Overcome insufficient yet relevant data from your predictive and forecasting models.

• Drive data acquisition and generation processes for future modeling and forecasting use.

With reliable, high-quality data at your fingertips you can derive more value from your data and significantly improve business performance.

The big picture 
Achieving true data quality is a key component of the platform for SAS^® Business Analytics, which:

• Provides an effective infrastructure for managing the growing appetite for intelligence.
• Helps you derive more value from existing technology and information assets.
• Supports sustainable growth of your organization through innovative use of technology and information.

Presenters

Gaurav Verma
Business Analytics Marketing Manager
SAS

Gaurav Verma drives SAS’ global business analytics product marketing strategy and positioning. Verma’s key areas of focus are persona-based marketing, awareness and messaging for SAS' business analytics and information management solutions. He also serves as a spokesperson to press and analyst communities.

Verma, who has more than 10 years of experience in marketing, came to SAS from Cognos, where he served as the Financial Services Industry Marketing Director. Verma has also served as Director of Corporate Marketing for Information Builders and as Principal Analyst and Program Manager for Doculabs.

Tina Ritterhoff
SAS Americas Business Analytics Practice

Tina Ritterhoff works with customers to design solutions for a wide array of business challenges. Ritterhoff has extensive experience in a wide variety of industries, including finance, energy, manufacturing, transportation, high-tech and government. Ritterhoff believes that enterprise data architecture is a foundational requirement for business analytics, and to that end, her primary focus areas revolve around data management, data governance and business analytics.