EPISODE · Oct 2, 2009 · 22 MIN
Best Practices in Data Resource Management
from eLearningCurve · host Mike Brackett
Public and private organizations face a common challenge in resolving inconsistent and redundant data, which is often spread across departments, business units, and other divisional lines. While this problem is typically acknowledged and at least partially understood by those working within the organization, it typically takes IT and business specialists applying experientially informed skills to rectify the systems creating the bad data and create a high-quality data resource. Former DAMA President and Lifetime Achievement Award Recipient Mike Brackett has precisely the expertise and experience needed for successfully enacting Data Resource Quality Improvement projects. In this episode of eLearningCurve’s Webcast series, Mike gets down to brass tacks on the identification, definition, and impact of disparate data, as well as common architectural and managerial mistakes and best practices in pursuing Data Resource Quality Improvement.
What this episode covers
Public and private organizations face a common challenge in resolving inconsistent and redundant data, which is often spread across departments, business units, and other divisional lines. While this problem is typically acknowledged and at least partially understood by those working within the organization, it typically takes IT and business specialists applying experientially informed skills to rectify the systems creating the bad data and create a high-quality data resource. Former DAMA President and Lifetime Achievement Award Recipient Mike Brackett has precisely the expertise and experience needed for successfully enacting Data Resource Quality Improvement projects. In this episode of eLearningCurve’s Webcast series, Mike gets down to brass tacks on the identification, definition, and impact of disparate data, as well as common architectural and managerial mistakes and best practices in pursuing Data Resource Quality Improvement.
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Best Practices in Data Resource Management
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