Friday, October 10, 2003

Understanding Data to Create Metadata

Information Quality, Liability, and Corrections "Nonetheless, information on its own is neither inherently good nor bad. It is often a sequence of events that leads to the consequences of what we simplistically refer to as bad information. One striking example is the Johns Hopkins clinical trials case, in which an insufficient search in PubMed resulted in a death [1]."

Five ways information can go wrong: incorrect fitness or "quality", ambiguous or fradualent, biased, incomplete and out of data.

Semantic Information Architecture: Creating Value by Understanding Data "Enterprises should consider capturing data semantics for two main reasons. Tactically, semantics saves time by capturing the meaning of data once. Without semantics, each data asset will be interpreted multiple times by different developers as it is designed, implemented, integrated, extended and decommissioned. This independent interpretation will be time-consuming and error-prone. With semantics, the data asset is mapped and interpreted only once. Moreover, any new assets can be generated from the information model so that they use official business terminology from the outset.

The second and most significant benefit of semantics is a strategic one. Semantics can turn hundreds of data sources into a single coherent body of information. This single body can then provide a common understanding showing where data is located, what it means and how it can be managed systematically. This keeps the data consistent and well defined and removes redundancy. Privacy and security policies may be applied uniformly based on the business content of the data."
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