Data modeling is a wide concept that exists since long and encompasses allpossible ways to interpret the information associated with a process, analyticalmeasurement or set of related parameters that presents a systematic variation.Data modeling can follow the path of knowledge and be based on first princi-ples or can focus on measurements and empirical models. These differentapproaches are known as hard- and soft-modeling, respectively.It seemed to us very appropriate to dedicate this article to Paul Gemperline, aperson who has significantly contributed to the worlds of hard- and soft-modeling, presumably acknowledging the value of looking at data from allpossible perspectives.The following pages do not intend to be an extensive review, but a personalperspective on values, milestones and progress of hard- and soft-modeling andon the necessary existence and valuable combination of both ways to interpretchemical information.