  In recent years, rather than enclosing data within a single organization,
exchanging and combining data from different domains has become an emerging
practice. Many studies have discussed the economic and utility value of data
and data exchange, but the characteristics of data that contribute to problem
solving through data combination have not been fully understood. In big data
and interdisciplinary data combinations, large-scale data with many variables
are expected to be used, and value is expected to be created by combining data
as much as possible. In this study, we conduct three experiments to investigate
the characteristics of data, focusing on the relationships between data
combinations and variables in each dataset, using empirical data shared by the
local government. The results indicate that even datasets that have a few
variables are frequently used to propose solutions for problem solving.
Moreover, we found that even if the datasets in the solution do not have common
variables, there are some well-established solutions to the problems. The
findings of this study shed light on mechanisms behind data combination for
problem-solving involving multiple datasets and variables.
