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updated 12:29 PM CEST, Jul 8, 2019
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Data (Vault) Modeling and Deep Learning @ #XP19

  • Published in Conferences

Model driven decision making

During #XP19 you’ll be able to take part in our (Matze and myself) deep dive session about Model driven decision making: Data (Vault) Modeling and Deep Learning. It has been designed to give you a (very) short hands-on and practical guidance.

What is this 15 minute deep dive session about at #XP19?

The fictitious company FastChangeCo has committed itself to a consistent implementation of the information landscape with methods of data modeling. A logical data model maps the necessary business objects as well as their attributes and properties independently of the technology used by a company.

Where, how and with which technology the data of the data warehouse is persisted is not relevant in the logical data model. Only with the development of the physical data models does the technology used become important and thus the physical modeling method. In this constellation, the modeling method Data Vault, in which the data can be modeled across all technologies, and a virtualization technique that unites all the technologies involved as a central instance, are ideal. The central aspect of virtualization is that it is irrelevant to the user where the data actually lies, but the user finds a fully integrated data landscape.

Another aspect is that the data for predictive modeling is already well structured and of high quality. Thus, FastChangeCo is able to use the newly gained knowledge to implement its goals, namely the future-oriented decision and innovative services. Large amounts of data from the data warehouse as well as an enormous temporary computing capacity are required for the creation and training of the required prediction models.

By the way, you can register here. Or get in touch with me and let’s see what deal we can make.

If you can’t attend #XP19 or our deep dive session, stay tuned: We will do a webinar later this year! Subscribe below to my newsletter to stay up to date.

So long,
Dirk

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