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Ein neues Datenmodell für das Data Warehouse ist notwendig, aber wie modelliert man Daten? Temporale Daten verstehen und dazu Methoden und Techniken erlernen? Oder eine Zertifizierung zur Datenmodellierung?

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A few weeks ago I received a surprisingly open and honest feedback on my recently published article "13 tips ...". I never ever expected that! After a short email exchange, I was allowed to publish the feedback anonymously. Below is the incredible feedback[3]. You see, you are not alone with the challenges of a Data Vault project:

Hi Dirk

Thanks for sending me the English version of the paper. I'm based in […] [1] and Data Vault is not generally established here yet.

We're failing on 8 of these points. […]

My summary:
1) Do without knowledge bases and expert opinions!
We've done DV training but could do with some external support.

2) The Data Vault standard is only a rough guide!
Some people think we can come up with our own version of DV for our company.

3) The understanding of subject matter is overrated!
We're using primary keys from the source systems

4) Conceptual modeling is a waste of time!
We're doing ok on this one. Have done lots of BEAM workshops to base our conceptual models on.

5) Do without the support of your BI department!
Some members of the BI team are on the DV project

6) Always think in whole layers!
Haven't fallen into this trap

7) To do things in a big way
We're trying to implement on a Hadoop platform and are having to make lots of compromises. It would be easier to implement on RDBMS.

8) Too much automation dilutes the quality!
We are trying to build our own automaton engine. Unfortunately, the proof of concept has been hardened with only basic functionality

9) Single Point of Facts? Unnecessary raw data and redundancy!
Some people think we don’t need a Raw Vault since we have a Persistent Staging layer.

10) Never use hubs for transactions!
Ok on this one

11) Save yourself tracking satellites!
Ok on this

12) Allow access only to the Business Vault and never to the Raw Vault!
We're planning to allow access to the data marts on top of DV, not to the DV itself except for advanced users.

13) No raw data for the department!
We think our users aren't capable of querying the DV.

We have certainly learnt a lot along the way. I believe you learn more by making mistakes. The combination of 1) & 2) has led us down a path that's very difficult to get back on track. […]

Thanks so much

Cheers
Mr. Vault, Data Vault [2]

If you have experienced something similar, I would be happy if you share your experiences with me [contact].

So long,

Dirk

[1] On the other side of the world (from my perspective :) ).
[2] Name was changed by me, the author of the email is known to me.
[3] 2019-05-27: Updated 7),9) and 12)

 

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Dirk und Team schaffen es mit diesem Training, ein Spezialistenwissen, durch ihre sympathische und professionelle Art in einer tollen Lern-Atmosphäre zu transportieren.

Meine Erfahrungen über das TEDAMOH Training „Temporale Datenmodellierung“ im Video festgehalten.

Weiterlesen ...

Dirk Lerner wurde damit beauftragt, mich bei der Erstellung eines Data Warehouse unter Verwendung der Data Vault 2.0 Architektur zu unterstützen.Es war ein Vergnügen, mit Dirk zu arbeiten.

Die Beratung hat die Qualität des Designs erheblich verbessert und den gesamten Prozess beschleunigt.

Weiterlesen ...

Dirk hat eine sehr gut strukturiertes Training über temporale Daten durchgeführt.

Sie hat mir sehr geholfen, mein Wissen über dieses anspruchsvolle Thema zu erweitern.

Weiterlesen ...

Ich war sehr zufrieden mit Dirk Lerner's "Temporal Data in a Fast-Changing World" Training.

Die Schulung beantwortete alle meine Fragen zur Bitemporalität, die in anderen Data Vault-Schulungen nicht so deutlich angesprochen wurden.

Weiterlesen ...