  In the future, competitive advantages will be given to organisations that can
extract valuable information from massive data and make better decisions. In
most cases, this data comes from multiple sources. Therefore, the challenge is
to aggregate them into a common framework in order to make them meaningful and
useful. This paper will first review the most important multi-criteria decision
analysis methods (MCDA) existing in current literature. We will offer a novel,
practical and consistent methodology based on a type of MCDA, to aggregate data
from two different sources into a common framework. Two datasets that are
different in nature but related to the same topic are aggregated to a common
scale by implementing a set of transformation rules. This allows us to generate
appropriate evidence for assessing and finally prioritising the level of
adoption of analytical tools in four types of companies. A numerical example is
provided to clarify the form for implementing this methodology. A six-step
process is offered as a guideline to assist engineers, researchers or
practitioners interested in replicating this methodology in any situation where
there is a need to aggregate and transform multiple source data.
