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For the last 20 years, enterprise architecture management (EAM) was primarily an instrument for harmonizing and consolidating IT landscapes and is lived as a transformation and governance discipline. It, however, is rather related to IT strategy than aligned to the actual corporate strategy and the work of the enterprise architect is characterized by tasks like prescribing, monitoring, documenting, and controlling. As digital transformation continues apace, companies are facing new challenges that lead to a volatile, uncertain, complex, and ambiguous (VUCA) world. To face these challenges, vision, understanding, clarity and agility allow to anticipative and implement necessary changes. This, of course, has implications for the role of the enterprise architect. S/he needs to start actively supporting innovation and taking more of an advisory role instead of just being driven by the current state of the enterprise architecture. This paper investigates the role of the enterprise architect in the VUCA world. Based on current literature and expert interviews, a survey was conducted among consultants who work as (or with) enterprise architects. Survey results include the evaluation of statements on current tasks of enterprise architects, their influence on projects and companies as well as future requirements on the roles of the enterprise architect. The results from the survey were synthesized with the findings from literature to derive the roles, tasks and skills of enterprise architect in the VUCA world.
Künstliche Intelligenz ist in aller Munde. Immer mehr Anwendungsbereiche werden durch die Auswertung von vorliegenden Daten mit Algorithmen und Frameworks z.B. des Maschinellen Lernens erschlossen. Dieses Buch hat das Ziel, einen Überblick über gegenwärtig vorhandene Lösungen zu geben und darüber hinaus konkrete Hilfestellung bei der Auswahl von Algorithmen oder Tools bei spezifischen Problemstellungen zu bieten. Um diesem Anspruch gerecht zu werden, wurden 90 Lösungen mittels einer systematischen Literaturrecherche und Praxissuche identifiziert sowie anschließend klassifiziert. Mit Hilfe dieses Buches gelingt es, schnell die notwendigen Grundlagen zu verstehen, gängige Anwendungsgebiete zu identifizieren und den Prozess zur Auswahl eines passenden ML-Tools für das eigene Projekt systematisch zu meistern.