Data Integration: Principles and Practice

Abstract

Data integration is the process of combining (also called “merging” or “joining”) data together to create a single unified data object from what were multiple, distinct data objects. The motivation for integrating data is usually to bring together the information needed to jointly analyze or model some phenomena. By producing a single, consistently structured object through data integration, the process of further manipulating those data is vastly simplified, while presumed relationships among the data are clarified.

Data integration is essential for many scientific disciplines, but especially in disciplines such as ecology and the environmental sciences, where processes and patterns of interest often emerge from interactions among numerous complex physical phenomena. Observations of these distinct phenomena are often collected by disparate parties in uncoordinated ways, using different data systems. It is then necessary to gather these data together and appropriately integrate them, to clarify through further modeling and analysis the nature and strength of any relationships among them. Synthesis studies, in particular, often require finding, and then bringing together disparate data in order to integrate them, and reveal new insights.

This chapter describes aspects of data that are critical for determining whether and how data can be integrated, and discusses some of the theoretical considerations and common mechanisms for integrating data.