EZINE:
In this week's Computer Weekly, we talk to PepsiCo's digital director about delivering innovation in customer experience. Our first buyer's guide of 2022 examines hybrid cloud storage. And we find out how Arkwright and Granville from the BBC sitcom Open All Hours are inspiring retailers 40 years on. Read the issue now.
WEBCAST:
Explore with William McKnight the factors involved and how organizations should go about valuing data quality through modeling and taking the right steps to remediate data quality defects throughout the enterprise.
WHITE PAPER:
This white paper describes how IBM's Information Server FastTrack accelerates the translation of business requirements into data integration projects. Data integration projects require collaboration across analysts, data modelers and developers.
WHITE PAPER:
By using the Oracle Exadata Database Machine as a data warehouse platform you have a balanced, high performance hardware configuration. This paper focuses on the other two corner stones, data modeling and data loading, providing a set of best practices and examples for deploying a data warehouse on the Oracle Exadata Database Machine.
WHITE PAPER:
The following white paper explores the top 7 considerations for implementing a data visualization solution in your enterprise. Learn how data visualization can give way to far deeper insights, better decision making, and much more.
WHITE PAPER:
Read on to find details about SAPs BusinessObjects Predictive Analysis, including how the NBA used HANA to help cater to stat-hungry fans.
WHITE PAPER:
This white paper discusses: What is an industry model? What is the value of industry models? Considerations for building or buying data models IBM Industry Models—business and technical blueprints Reducing time to value with IBM Industry Models
PRESENTATION TRANSCRIPT:
This presentation transcript features speaker Evan Levy, partner and co-founder of Baseline Consulting, a professional services firm concentrating on enterprise data issues. This discussion is about data quality and some of the challenges and pitfalls that are seen during data quality implementation.