At least it is free? Seriously we have been doing this since 2012 and are ever perfecting the format
Data Ed Online is my monthly data management webinar that focuses on how to unlock business value through foundational data management practices and techniques
All webinars consist of a 60-minute presentation, followed by a 30-minute interactive Q&A session - come join us!
Produced in conjunction with our long time partners at Dataversity who have the finest collection of data management educational resources on the web.
Yes - all registrants will recieve a link to the recording a couple of days after each event
Good data is like good water: best served fresh, and ideally well-filtered. Data management strategies can produce tremendous procedural improvements and increased profit margins across the board, but only if the data being managed is of a high quality. Determining how data quality should be engineered provides a useful framework for utilizing data quality management effectively in support of business strategy. This, in turn, allows for speedy identification of business problems, delineation between structural and practice-oriented defects in data management, and proactive prevention of future issues. Organizations must realize what it means to utilize data quality engineering in support of business strategy. This webinar will illustrate how organizations with chronic business challenges often can trace the root of the problem to poor data quality. Showing how data quality should be engineered provides a useful framework in which to develop an effective approach. This in turn allows organizations to more quickly identify business problems as well as data problems caused by structural issues versus practice-oriented defects and prevent these from re-occurring.
– Help you understand foundational data quality concepts
based on the DAMA DMBOK, as well as guiding principles,
best practices, and steps for improving data quality
at your organization
– Demonstrate how chronic business challenges
for organizations are often rooted in poor data quality
– Share case studies illustrating the hallmarks and
benefits of data quality success