  In the past decades, massive efforts involving companies, non-profit
organizations, governments, and others have been put into supporting the
concept of data democratization, promoting initiatives to educate people to
confront information with data. Although this represents one of the most
critical advances in our free world, access to data without concrete facts to
check or the lack of an expert to help on understanding the existing patterns
hampers its intrinsic value and lessens its democratization. So the benefits of
giving full access to data will only be impactful if we go a step further and
support the Data Analytics Democratization, assisting users in transforming
findings into insights without the need of domain experts to promote
unconstrained access to data interpretation and verification. In this paper, we
present Explainable Patterns (ExPatt), a new framework to support lay users in
exploring and creating data storytellings, automatically generating plausible
explanations for observed or selected findings using an external (textual)
source of information, avoiding or reducing the need for domain experts. ExPatt
applicability is confirmed via different use-cases involving world demographics
indicators and Wikipedia as an external source of explanations, showing how it
can be used in practice towards the data analytics democratization.
