Predictive Analytics for Accelerating Business Advantage
TDWI Speaker: Fern Halper, TDWI Research Director
Date: Tuesday, January 14, 2014
Time: 9:00am PT, 12:00pm ET
Webinar Abstract
Predictive analytics is quickly becoming a decisive advantage for achieving desired business outcomes, including higher customer profitability, stickier websites, more relevant products and services, and more efficient and effective operations and finances. Predictive analytics involves methods and technologies to help organizations spot patterns and trends in data, test large numbers of variables, develop and score models, and mine data for unexpected insights. Sources for predictive analytics are expanding to include machine data and semi-structured and unstructured data, making it important to include text analytics and mining in technology portfolios.
Organizations across industries and market sectors are seeking ways to make predictive analytics more than just a specialized activity done by an elite few at only the largest organizations. The growth in volume and variety of big data is a major driver behind its spreading impact. Organizations want to realize their return on investment in big data by using the power of analytics to adopt a predictive and proactive approach to customer behavior, business events, and other significant changes to their environments.
Join this TDWI Webinar and discover how predictive analytics can enable better business decisions and actions. This Webinar is based on TDWI’s recently released Best Practices Report, Predictive Analytics for Accelerating Business Advantage, which brought together insights from an extensive research survey and interviews with users and industry experts. The Webinar will help you plan and execute the development, deployment, and implementation of predictive analytics methods and technologies.
You will learn:
- How peer organizations are implementing predictive analytics to meet business objectives
- The skills needed for predictive analytics
- How predictive analytics fits with BI and big data
- Role of in-memory and in-database processing to support predictive analytics
- Best practices for obtaining value from predictive analytics
Fern Halper, Ph.D.