YouTube home #YouTubeRewind


Data Quality Essentials for Data Integration - Powered By Data Virtualization.mp4





The interactive transcript could not be loaded.



Rating is available when the video has been rented.
This feature is not available right now. Please try again later.
Uploaded on Apr 21, 2011

Data quality essentials for data integration. Definition of data quality. Basic data quality problems - null fields, duplicate records, syntax problems, range problems and more. The Queplix automated approach to data quality - faster, easier and how the automation reduces risk and improves results. Automating the push to the data steward. The importance of data governance in assessing data quality. How Queplix data quality scales from integrating any two data sources through the largest global virtual master data management project. Sybase and University of Texas study on the return on investment for data quality. How data normalization manager can help align data quality. The three tiers of data quality - standalone, multi-source integration and production remediation. For more information go to

Comments are disabled for this video.
When autoplay is enabled, a suggested video will automatically play next.

Up next

to add this to Watch Later

Add to

Loading playlists...