Data Resource Data: A Comprehensive Data Resource Understanding

Are you struggling to gain a thorough understanding of your organization's data resource? Are you finding that your data resource has become quite disparate through lack of understanding? Are you having difficulty developing meaningful meta-data about your data resource, or understanding the meta-data that have been developed? Do you agonize over finding a way to document your data resource that is thorough, understandable, and readily available? If the answer to any of these questions is Yes, then you need to read Data Resource Data to help you understand your organization's data resource. Most public and private sector organizations do not have a formal process for thoroughly documenting the entire data resource at their disposal, in any meaningful manner, that is readily available to everyone in the organization. Most do not even have a formal design for that documentation. The much abused, misused, misspelled, undefined, and incomplete meta-data are not providing a denotative understanding of the organization's data resource, without which a high quality data resource cannot be developed. Data Resource Data provides the complete detailed data resource model for understanding and managing data as a critical resource of the organization. The model presents formal data resource data as a replacement for the relatively ineffective meta-data. It provides an excellent example of a formal data resource model, compared to a traditional data model, that can be easily implemented by any organization. The use of data resource data ensures a thorough understanding of an organization's data resource and the development of a high quality comparate data resource. Like Data Resource Simplexity, Data Resource Integration, and Data Resource Design, Michael Brackett draws on five decades of data management experience, in a wide variety of different public and private sector organizations, to understand and document an organization's data resource. He leverages theories, concepts, principles, and techniques from many different and varied disciplines, such as human dynamics, mathematics, physics, chemistry, philosophy, and biology, and applies them to the process of formally documenting an organization's data resource.