FastChangeCo - an adaptable data warehouse for rapid changes
The fictitious company FastChangeCo has developed a way not only to manufacture smart devices, but also to extend smart devices to clothing and living beings as wearables in the form of bio-sensors. Each of these devices generates a large amount of (sensitive) data, more precisely: through the recording, processing and analysis of personal and environmental data.
Information Modeling in Natural Language
How can I use natural language in my modeling process to achieve high-quality information models?
The answer to your question is Fact-Oriented Modeling (FOM). This is a family of conceptual methods in which facts are modeled precisely as relationships with an arbitrary number of arguments. This type of modeling makes it easier to understand the model, because natural language is used to create the data model. That's what makes FOM fundamentally different from all other modeling methods. This approach sounds new and exciting. However, its basic features date back to the 1970s.
Hybrides Data Warehouse am Beispiel von Data Vault
In der heutigen BI-Welt müssen Anforderungen der Fachbereiche und gesetzliche Vorgaben sowie die Wünsche der Nutzer von Smart Devices an personenbezogenen Daten in Einklang gebracht werden. Anhand des fiktiven Unternehmens FastChangeCo sollen im Folgenden die Herausforderungen an den Schutz der Daten sowie die Umsetzung aufgezeigt werden. FastChangeCo hat eine Möglichkeit entwickelt, nicht nur Smart Devices herzustellen, sondern auch die Smart Devices als Wearables in Form von Sensoren auf Kleidung auszudehnen.
How to successfully torpedo any level of the data warehouse
Are you pampered with success and tired of the eternal pat on the back? You don‘t want to be so successful with your first attempt at implementing a Data Vault project that all your colleagues become jealous?
Here you will find 13 proven tips on how to make your Data Vault project a successful failure.
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