Finally: The book is here!

My book "Design Thinking Business Analysis - Business Concept Mapping Applied" is now available! Learn how to solve the conceptual modelling challenge the right way (concept mapping) on the right side (the business side)! Read More...

Semantics vs. Natural Language Processing

I have argued that formal semantics are useful, but they are also, ehm, formal. Which disqualifies them from broad usage in business modelling. But things are getting better. Read More...

Schisma #2 - Business Rules!

One of the things you learn when you model information is that sometimes you have to stop drawing, because you get into a lot of details. It could be for instance certain facts about a specific group of products. And other groups of other products have their own specialties as well... Been there? What you do is that you move such details into a list of "Additional rules". So you end up having diagrams and (long) lists of (business) rules. Skill and experience is what makes you stop in time.... In reality the whole diagram is in itself also a representation of a set of business rules!

Fortunately good people from the business rule community (steadfast and sincere people) have been working under the auspices of the OMG (Object Management Group) on something called: Semantics of Business Vocabulary & Business Rules (SBVR). It seems to be much needed. Fortunately it has been developed in such a manner that it integrates with RDF and OWL. Unfortunately we are still missing some good, simple, business oriented tools for visual diagramming of "Business Vocabularies" (what is wrong with Concepts?).

So, maybe the business rules perspective is what it takes to get business concept modelling done right?

By the way SBVR, is also heavily inspired by the ORM modelling technique, which I like a lot. But, which also is too complex for business level analysis and modelling.

Modelling Schisma #1 - Less is more!

There is a mismatch between the needs of a good business information model and the traditions employed by it data modellers. This is a great divide. Read More...