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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?

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?

  • Geschrieben von Dirk Lerner
  • Zugriffe: 2211

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,
Dirk

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).

  • Geschrieben von Dirk Lerner
  • Zugriffe: 1874

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.

  • Geschrieben von Dirk Lerner
  • Zugriffe: 968

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    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).

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