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updated 7:50 AM, Jun 26, 2020 Europe/Berlin
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Data Modeling

  • 1:M – Link: Modellierung oder Business Rule?

    Auf dem 1. DDVUG Treffen hatten wir ein interessante Diskussion darüber, wo eigentlich die Datenmodellierung aufhört und Business Rules beginnen. Aufgehängt hatte sich dies an meiner Präsentation, in der es um einen Link ging, der eine 1:M (Hub A – (M) Link (1) – Hub B) Relation repräsentiert und über einen bi-temporalen Satelliten den gesteuert (end-dating) wird. So darf für jeden Eintrag im Hub B nur eine aktive Relation im Link existieren. Die Daten für das End-dating des Links kamen im von mir aufgeführten Beispiel bereits aus dem Quellsystem (Blogpost folgt bald).

  • A comment

    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?

  • ADAPT

  • An open and honest feedback on “13 tips …”

    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.

  • Data (Vault) Modeling and Deep Learning @ #XP19

    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?

  • Data Model Scorecard

    Objective review and data quality goals of data models

    Did you ever ask yourself which score your data model would achieve? Could you imagine  90%, 95% or even 100% across 10 categories of objective criteria?

    No?
    Yes?

    Either way, if you answered with “no” or “yes”, recommend using something to test the quality of your data model(s). For years there have been methods to test and ensure quality in software development, like ISTQB, IEEE, RUP, ITIL, COBIT and many more. In data warehouse projects I observed test methods testing everything: loading processes (ETL), data quality, organizational processes, security, …
    But data models? Never! But why?

  • Data Modeling

    All topics around Data Modeling like

    • Conceptual
    • Logical
    • Physical (Data Vault and others)
  • Data Modeling Blogposts

  • Data Modeling Books

  • Data Modeling Zone 17

    After all, I am very happy to be a speaker at this year's Data Modeling Zone in Düsseldorf. Again, like at the Global Data Summit, I'm talking about one of my favorite topics: Temporal data in the data warehouse, especially in connection with data vault and dimensional modeling.

  • Data Modeling Zone Europe 2015

    DMZone2015Flyer

    Do you want to learn something about data modelling with Steve Hoberman? You want to explore new methods like Data Vault 2.0, Anchor Modeling, Data Design, DMBOK and many more? E.g. a keynote where Dan Linstedt, Lars Rönnbäck and Hans Hultgren talks together, and another one with Bill Inmon?

  • Data Models

  • Data Vault - Datenmodellierung noch notwendig?

    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?

  • Data Vault Articles

  • Data Vault im Einsatz beim Gutenberg Rechenzentrum

    Read the full article, I wrote, in BI-Spektrum 05/2014.

    So long
    Dirk

    Data Vault im Einsatz beim Gutenberg Rechenzentrum

  • Data Vault on EXASOL - Modeling and Implementation

    On July 15, Mathias Brink and I ran a webinar about Data Vault on EXASOL, modeling and implementation. The webinar started with an overview of the concepts of Data Vault Modeling and how Data Vault Modeling enables agile development cycles. Afterwards, we showed a demo that transformed the TPC-H data model into a Data Vault data model and how you can then query the data out of the Data Vault data model. The results were then compared with the original queries of the TPC-H.

  • Fact-Oriented Modeling (FOM) - Family, History and Differences

    Months ago I talked to Stephan Volkmann, the student I mentor, about possibilities to write a seminar paper. One suggestion was to write about Information Modeling, namely FCO-IM, ORM2 and NIAM, siblings of the Fact-Orietented Modeling (FOM) family. In my opinion, FOM is the most powerful technique for building conceptual information models, as I wrote in a previous blogpost.

  • General Modeling

  • Knowledge Base

  • Knowledge Gap 2020

    I, Stephan, am very happy that I'm invited to give a presentation at the Knowledge Gap 2020 in Munich.

    My presentation is about advanced techniques in Fact-Oriented Modeling.

    Often data models are built with a technical focus, because they need to be delivered fast or must meet various technical requirements. Therefore, the business aspect and the meaning of objects and relationships are swept under the table. But then the business domain later hardly understands the data model and has problems to work with it in own applications or reports – which often results in a redesign of the data model and renewed time and cost expenditures.