Discovering knowledge from data for decision making is dependent on the existence of data relevant to the decision at hand. For decisions in domains that involve many different factors and concerns, such as seaport integration, data may exist across many repositories managed by different organizations with different goals and foci, not to mention different data structures, entities, labels, units of measurement, categories and time periods. To use this data for decision making, approaches to combine the data and handle missing values are two of the problems, among others, that need to be addressed. In this paper we discuss the need for managing micro and macro-level data and our approach to handle missing values.