In recent weeks I have read so many pessimistic and negative articles and comments in the social media about the state of data modeling in companies in Germany, but also worldwide.

Why? I don't know. I can't understand it.

I know many companies that invest a lot of time in data modeling because they have understood the added value. I know many companies that initially rejected data modeling as a whole, but understood its benefits through convincing and training.

Isn't it the case that we (consultants, managers, project managers, subject-matter experts, etc.) should have a positive influence on data modeling? To support our partners in projects in such a way that data modeling becomes a success? If we ourselves do not believe that data modeling is a success, then who does?

In der Vergangenheit hatte ich immer wieder Bedarf an großen Datensätzen, um Datenlogistikprozesse oder eine Datenbanktechnologie für Data Vault zu testen.

Wie bereits in meinem Blogpost Modellierung oder Business Rule beschrieben ist es notwendig sich bei der Datenmodellierung über Geschäftsobjekte, die Wertschöpfungskette, fachliche Details und die Methodik des Modellierens einige Gedanken zu machen.

Oder doch nicht? Kann ich mit Data Vault einfach loslegen? Schließlich ist Data Vault auf den ersten Blick ganz einfach. Drei Objekte: HUBs, LINKs und SAT(elliten), einem einfachen Vorgehensmodell und ein paar wenige Regeln. Brauche ich für Data Vault noch die Datenmodellierung?

The Smartest Way To Deal With The Data Integration Challenges

Authored by Dario Mangano
Edition: 1.0

 Data Warehouse projects fail.

As an industry we have been battling with this phenomenon for decades. Though we have been getting better over the years, as an industry we still have a long way to go.

Fortunately some people have found ways to beat the odds. By thinking out of the box, formulating new ideas, and creating new innovative approaches these people have each somehow unlocked the secrets of successful DW programs.

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