Distinguishing between data and information in seeking knowledge and wisdom

Good practices in service science recommend considering principles encapsulated in data-information-knowledge-wisdom model (DIKW). Our main examples, that we use in clarifying our hypothesis, falls into the domain of information technology. Good practices of this highly knowledge-depending endeavor are repeatedly articulated in different editions of Information Technology Infrastructure Library, a series of volumes that contain explicit referral to DIKW model in thinking about management of knowledge. Focusing on this domain and Library, we acquire various, richly elaborated situations of reporting data, analyzing information, formulating knowledge, and achieving wisdom for decision and policy making. First, I would like to present DIKW model shortly in its original form (Ackoff 1989), then to suggest some viable interpretation of it in focusing on the main question of this paper: do we have a good reason to differentiate between data and information for the practical purpose of being able to gain firm knowledge and wisdom in decision grounded on this knowledge. Drawing definitional lines between data and information is nothing but entering some ongoing debate between theoreticians of the respective domains. As in every “applied” theory, like applied epistemology, applied ethics, etc., we should follow, ideally, a doubled enterprise that can be grasped in the following two questions: Does our particular distinction between something and something else, built up on “purely” theoretical grounding equip us better tools to cope with related particular practical challenges in the concerned purposeful activity? Can a particular practical improvement of a specific practice be analyzed into a vocabulary that provides tribunal for theoretical models? Definitions of theoretical terms like data and information become relevant in the operational practice of a service management when, for example, literacy of the respective field should be trained for people who are responsible of drawing data from databases, constructing reports grounded on available data and, finally, preparing analyses in view of continuous decisional process, with its characteristic stakes and stakeholders. In my paper I compare approaches to and challenges of trainability of data and information literacy. I am concluding that, if we can grasp those differences in abilities, competencies and excellencies that make real differences in the practice between treating, mining, preparing data and establishing and criticizing informational layer emerging from this data, then we may achieve mutual gain for both this very practice and its theoretical codification