eLearningCurve

The data resource is one of the four critical resources in an organization, equivalent with the financial resource, real property, and the human resource. Proper management of all four critical resources is necessary to effectively and efficiently manage a successful organization. Yet most organizations fail to manage the data resource with the same priority, discipline, and attention that is applied to the other three critical resource. The time for formal and disciplined management of the data resource is long overdue.

Most public and private sector organizations face many challenges with burgeoning quantities of disparate data. These disparate data are not well understood, have high redundancy, are not consistent, have low quality, and fail to adequately support the organization’s business information demand. The only way to resolve this situation is to thoroughly understand how and why disparate data are created, and how those problems can be resolved. Only then can organizations begin the transition to a high-quality data resource.

Best Practices in Data Resource Management, taught by data management veteran Michael Brackett, begins with common definitions of data disparity and data quality, and their impact on the organization. The course describes the bad habits and good practices related both to the architecture and governance components of data resource management. Each component has five sets of best practices that provide a problem statement, problem examples, a solution statement, good practices that resolve the problem, and examples of those good practices.

• How to define and identify disparate data.
• How to identify the impact of disparate data on the business.
• How to define, identify, and manage data resource quality.
• The common problems with the architecture and governance of the data resource.
• The best practices to solve these architecture and governance problems.

• Anyone who has responsibility for the architecture or governance of the data resource.
• Data resource quality practitioners at all levels.
• Business executives and managers who struggle with the business impacts of poor quality data.
• IT executives and managers who are challenged to deliver reliable and trusted data to support the business information demand.
• Data and information system architects who need to break the cycle of disparate data creation.