This is especially true when dealing with environmental data. For example, crop boundaries, usually defined by field edges, rarely match the boundary between types of soil. If you want to determine the most productive soil for a particular crop, you need to overlay the two data sets and compute crop productivity for each and every soil type. In principle, this is like laying one map over another and noting the combinations of soil and crop productivity. A GIS can perform all these operations because it uses geography, or space, as the common key between the data sets.
Information is linked only if it relates to the same geographic area.
Consider a situation where you have two data sets for the same area, such as yearly income by county and average cost of housing. Each data set might be analyzed and mapped individually. Alternatively, they can be combined to produce one valid combination. If, however, you have 20 data sets for the county, you have over one million possible combinations. Combining them adds value to the database.