Design Principles Supporting Data-driven Decisions Platforms

Abstract

The digital transformation of organizations and societies and the increasing availability of big data and analytics make decision-making more complex and dynamic. This challenge is likely to continue and accelerate. Therefore, there is an urgent need for a new scientific approach to facilitate decision-making based on evidence from data. Quite recently, organizations have begun relying on machines to make decisions. So, this leaves us astray about designing data-driven decision platforms to enable humans and machines to collaborate toward organizational decision-making. Incorporating data and algorithms into decision-making addresses existing challenges and brings new ones. Therefore, to enable data-driven decisions, data-driven platforms are needed. However, existing platforms need the principles that ought to exist to foster insight-driven choices in organizations. We argue that a consolidated normative theory must be required for designing data-driven decision platforms. This is problematic because it hinders the ability of organizations to become data-driven concerning how they make decisions. Accordingly, we have posited and evaluated a set of design principles to support data-driven decision platforms, following design science research methodology. Our overarching purpose is to present the posited design principles and the preliminary results from their qualitative evaluation and to contribute to developing design principles, enabling researchers and practitioners to augment them into instantiations of various data-driven decision platforms.