Wissen
Bereich(e): Whitepaper
Today's business environment requires data models, which are resilient to change and enable the integration of multiple data sources. More and more organisations consider Data Vault when modelling data warehouse platforms as a part of their...
INTRODUCTION AND METHODOLOGY
Digital transformation will never mean exactly the same to every organization but there are certain common elements that will always play a part in any successful digital transformation project. Most common are:
- Customer...
Imagine a world where incident alerts arrive 30 minutes before problems even begin — you’d actually have the power to prevent outages and deliver a truly seamless experience to your customers. Sound impossible? Think again — the right AIOps...
Adidas CEO Kasper Rorsted recently said, "We've become a digital company." For example, 90% of the marketing budget is now given over to digital. According to Rorsted, Adidas is now gaining critical insight from Big Data and digital analytics.
Adidas...
Das Internet der Dinge (Internet of Things, IoT) verbindet nicht nur alles mit allem überall und jederzeit, es hat auch die Grundlage für die nächste industrielle Revolution geschaffen.
Vernetzte Geräte gehören zu den wichtigsten Errungenschaften von...
In most applications we use today, data is retrieved by the source code of the application and is then used to make decisions. The application is ultimately affected by the data, but source code determines how the application performs, how it does...
Wie können Sie Maschinendaten nutzen, um auf die Datenschutz-Grundverordnung der EU vorbereitet zu sein?
Wir haben drei Anwendungsfälle zusammen gestellt, die Sie bei der Implementierung der Datenschutz-Grundverordnung unterstützen und unabhängig...
You need a 360 view of your business
You have more customer data coming from more sources than ever before. But chances are, it is spread across silos and stored in relational data models, including registration databases, fulfillment and CRM systems,...
The original data lake’s architecture has two severe drawbacks. One relates to the physical nature of the data lake which may kill the big data project entirely because it can be “too big” to copy to a central environment. The other relates to the...
In a big data environment, the notion of data quality that is “fit for purpose” is important. For some types of data science and analytics, raw, messy data is exactly what users want. Yet, even in this case, users need to know the data’s flaws and...
