A step foreword historical data governance in information systems

Abstract

From major companies and organizations to smaller ones around the world, databases are now one of the leading technologies for supporting most of organizational information assets. Their evolution allows us to store almost anything often without determining if it is in fact relevant to be saved or not. Hence, it is predictable that most information systems sooner or later will face some data management problems and consequently the performance problems that are unavoidably linked to. In this paper we tackle the data management problem with a proposal for a solution using machine-learning techniques, trying to understand in an intelligent manner the data in a database, according to its relevance for their users. Thus, identifying what is really important to who uses the system and being able to distinguish it from the rest of the data is a great way for creating new and efficient measures for managing data in an information system.