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Data Management at Scale
As data management continues to evolve rapidly, managing all of your data in a central place, such as a data warehouse, is no longer scalable. Today's world is about quickly turning data into value. This requires a paradigm shift in the way we federate responsibilities, manage data, and make it available to others. With this practical book, you'll learn how to design a next-gen data architecture that takes into account the scale you need for your organization. Executives, architects and engineers, analytics teams, and compliance and governance staff will learn how to build a next-gen data landscape. Author Piethein Strengholt provides blueprints, principles, observations, best practices, and patterns to get you up to speed. Examine data management trends, including regulatory requirements, privacy concerns, and new developments such as data mesh and data fabric Go deep into building a modern data architecture, including cloud data landing zones, domain-driven design, data product design, and more Explore data governance and data security, master data management, self-service data marketplaces, and the importance of metadata
Automation, Communication and Cybernetics in Science and Engineering 2009/2010
The book presents a representative selection of all publications published between 01/2009 and 06/2010 in various books, journals and conference proceedings by the researchers of the institute cluster: IMA - Institute of Information Management in Mechanical Engineering ZLW - Center for Learning and Knowledge Management IfU - Institute for Management Cybernetics, Faculty of Mechanical Engineering, RWTH Aachen University The contributions address the cluster's five core research fields: suitable processes for knowledge- and technology-intensive organizations, next-generation teaching and learning concepts for universities and the economy, cognitive IT-supported processes for heterogeneous and ...
Self-Service BI & Analytics
Know-how für erfolgreiche Self-Service-Initiativen Praktischer Leitfaden zur unternehmensweiten Einführung von Self-Service Fokus auf die Konzeption und Governance von Self-Service Mit Impulsen, was bei einer laufenden Self-Service-Organisation zu beachten ist Self-Service im BI- und Analytics-Kontext bedeutet, dass BI-Anwender selbst aktiv werden, um auf bestimmte Daten und Informationsprodukte zuzugreifen. Dabei hängt die Möglichkeit des Self-Service von Umgebungsfaktoren ab, nicht von einzelnen Werkzeugen. Um die Daten nutzen zu können, ist Datenkompetenz bei den Beteiligten erforderlich. Self-Service ist somit als strategischer Prozess zu verstehen, der als Teil der Datenstrategie i...
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