Beschreibung:
<jats:title>Abstract</jats:title><jats:p>Data and information are considered today as the “new oil” or the “new gold” in almost all aspects of life and economic domains, such as industry, healthcare, education, entertainment and more. The so‐called 4th Industrial Revolution<jats:sup>1</jats:sup> is based on the digital transformation derived from the Big Data revolution, through the capability of storing huge amounts of data and performing very sophisticated analytics.</jats:p><jats:p>In this paper, we present opportunities for Systems Engineering (SE) to evolve towards a data ‐driven and evidence ‐based discipline, thereby making better systems and engineering decisions. We discuss how systems engineers can apply data‐driven characteristics through systems engineering processes and programs. The classical Model‐Based Systems Engineering (MBSE) approaches are presented here as powerful tools to collect, generate, and analyze data on systems under development. In addition, the “Digital Twin” concept is presented here in the context of system design. We highlight the challenges for systems engineering to become an evidence‐based discipline. Moreover, we empha‐size research and development in systems engineering processes using statistical techniques in the design and analysis of systems testing, and Model‐Based Systems Engineering (SE) as a source for evidence‐based engineering decisions. The success of data driven SE in organizations depends on the information and data analytics infrastructure in these organizations. An information quality frame‐work is proposed for evaluating organizational information infrastructure. In addition, it is proposed to assess the data analytics maturity level in organizations. The level of data analytics is the basis for planning implementation programs of data‐driven and evidence‐based systems engineering. The paper concludes with a case study based on a real‐life complex project, and lessons learned for ef‐fective data analytics implementation.</jats:p>