Click to learn more about author Thomas Frisendal. The Component Parts of Data Models Back in March 2019 I published a post here on DATAVERSITY® titled The Atoms and Molecules of Data Models. The objective was to scope ”a universal set of constituents in data models across the board”. I used this classic data model, […]
Modeling Sets of Data
Click to learn more about author Thomas Frisendal. Remember? People of my age were taught set algebra at high-school (in my case in the late seventies). Today it is elementary school stuff. And it is indeed a useful tool with applications in many real-life situations. Why did Set Algebra not Become More Popular? In retrospect, […]
The Atoms and Molecules of Data Models
Click to learn more about author Thomas Frisendal. I realized that I needed to know what the constituent parts of data models really are. Across the board, all platforms, all models etc. Is there anything similar to atoms and the (chemical) bonds that enables the formation of molecules? My concerns were twofold: As part of […]
2019: Full Scale Schema Modeling
Click to learn more about author Thomas Frisendal. Using Concerns to Navigate Data Architectures Welcome to 2019! This is the year that offers us a unique opportunity to re-architect the way we think schemas, data models and Data Architecture. We do indeed need to do some things better. The real world is full of concerns, […]
Monetizing Information? Show Me Your Data Model
Click to learn more about author Thomas Frisendal. I recently (finally) had the opportunity to read Doug Laney’s fine book about Infonomics. I have followed his work on this for years, because we share the ambition that data and information should be recognized as assets, just like factory equipment and trucks. Because data keep the […]
Next and Prior: Pointing in Data Models
Click to learn more about author Thomas Frisendal. Pointers have been in and out of data models. From the advent of the rotating disk drive in the 60s and until around 1990, pointers were all over the place (together with “hierarchies”, which were early versions of aggregates of co-located data). But relational and SQL made them […]
The Emergence of ”Metadata Science”? Using Graph Technology for Data Modeling
Click to learn more about author Thomas Frisendal. Building Data Models from Meta Models Recently, I worked with a government client on a Knowledge Graph kind of project. Being government, much of their data is public information. And, consequently, so are the data models. Instead of diagramming the same data all over again, I decided […]
Design Thinking Data Models
Click to learn more about author Thomas Frisendal. I seriously believe, and I also know from professional experiences that Design Thinking is the secret sauce for creating high quality Data Models. This sounds contra intuitive, so we will have to do some debunking: ”Design Thinking – isn’t that for designing good looking products, like watches, […]
Detecting Data Models
Click to learn more about author Thomas Frisendal. Did you ever dream about becoming a famous detective? A new (teenage, possibly) Sherlock Holmes? Well, in the area of Data Modeling you now have good chances of showing off how good you are at detecting. What? Well, detecting the data models from evidence, of course. This […]
Whither Data Modeling Education? (The Future of Data Modeling)
Click to learn more about author Thomas Frisendal. Machine Generated Looking at machine generated data makes you wonder about the Data Modeling of such. Whodunnit? Actually, the whole Big Data tsunami was caused by ‘machine generated’ data coming in in great numbers. Unstoppable. We all know that ‘machine generated’ is not really a proper description […]