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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?


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?

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The Data Doctrine

Message: Thank you for signing The Data Doctrine!

What a fantastic moment. I’ve just signed The Data Doctrine. What is the data doctrine? In a similar philosophy to the Agile Manifesto it offers us data geeks a data-centric culture:

Value Data Programmes1 Preceding Software Projects
Value Stable Data Structures Preceding Stable Code
Value Shared Data Preceding Completed Software
Value Reusable Data Preceding Reusable Code

While reading the data doctrine I saw myself looking around seeing all the lost options and possibilities in data warehouse projects because of companies, project teams, or even individuals ignoring the value of data by incurring the consequences. I saw it in data warehouse projects, struggling with the lack of stable data structures in source systems as well as in the data warehouse. In a new fancy system, where no one cares about which, what and how data was generated. And for a data warehouse project even worse, is the practice of keeping data locked with access limited to a few principalities of departments castles.
All this is not the way to get value out of corporate data, and to leverage it for value creation.

As I advocate flexible, lean and easily extendable data warehouse principles and practices, I’ll support the idea of The Data Doctrine to evolve the understanding for the need of data architecture as well as of data-centric principles.

So long,

1 To emphasize the point, we (the authors of The Data Doctrine) use the British spelling of “programme” to reinforce the difference between a data programme, which is a set of structured activities and a software program, which is a set of instructions that tell a computer what to do (Wikipedia, 2016).

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Reflections on Data Natives conference, October 2016

A conference for the data-driven generation!

It’s late October 2016, an incredible crowd of young data-driven peeps are on their way to Berlin, looking forward to meet many other peeps with the same attitude at the Data Natives conference: Doing business with data or seeing a huge value in using data for the future. Besides the crowd I was not only impressed by the location but also by the amount of startups at the conference.

The schedule for two days was full packed with talks and it wasn’t easy to choose between all these interesting topics. So I decided not to give myself too much pressure. Instead I cruised  through the program, and stumbled on some highlights.

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Sketch Notes Reflections at TDWI Roundtable with FCO-IM

Sketch Notes Reflections at TDWI Roundtable with FCO-IM

Our 25th anniversary roundtable in Frankfurt with FCO-IM was a great success. Almost 90 registrations and more than 60 attendees is an unexpected outcome for a topic that is almost unknown in Germany. If you want to know what happened at the roundtable, read about it in my previous blog post.

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Follow Up Data Vault EXASOL Webinar

In July 2016 Mathias Brink and I had given a webinar how to implement Data Vault on a EXASOL database. Read more about in my previous blogpost or watch the recording on Youtube.

Afterward I became a lot of questions per our webinar. I’ll now answer all questions I got till today. If you have further more questions feel free to ask via my contact page,via Twitter, or write a comment right here.

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  • Data Vault
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  • TDWI Roundtable

    Blogposts around the TDWI Roundtable Frankfurt.

  • Data Architecture
  • FOM

    Fact-Oriented Modeling (FOM) stands for a family of fact-oriented conceptual modeling methods. FOM facilitates easier communication about the conceptual model between the modeler and the domain expert by verbalization of concrete examples in the language of the domain expert, a design process as a guide for creating the model and the focus on elementary facts. The most popular methods in this family are Cognition Enhanced Natural Language Information Analysis Method (CogNIAM), Second Generation Object Role Modeling (ORM 2) and Fully Communication Oriented Information Modeling (FCO-IM).

  • General Modeling
  • Bitemporal Data

    If everything would happen at the same time, there would be no need to store historic data. We, the consumers of data, would know each and everything at the same instant. Beside all the other philosophical impacts, if time wouldn’t exists, is data still necessary?

    (Un)fortunately time exists and data architects, data modelers and developers have to deal with it in the world of information technology.

    In this category about temporal data I will collect all my blogposts about this fancy topic.

  • Data Modeling Tools
  • Data Modeling Certification