Abstract

The outcomes and quality of organizational decisions depend on the characteristics of the data available for making the decisions and on the value of the data in the decision-making process. Toward enabling management of these aspects of data in analytics, we introduce and investigate Data Readiness Level (DRL), a quantitative measure of the value of a piece of data at a given point in a processing flow. Our DRL proposal is a multidimensional measure that takes into account the relevance, completeness, and utility of data with respect to a given analysis task. This study provides a formalization of DRL in a structured-data scenario, and illustrates how knowledge of rules and facts, both within and outside the given data, can be used to identify those transformations of the data that improve its DRL.

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