Looking back, then forward, is a traditional exercise by year-end. Which data concerns are important enough to worry about in 2024? Which of those do we stand a chance of doing something good for in 2024? Needless to say, money (budget and costs) is an issue. But even more needless to say, solving real business […]
Generative AI and Semantic Compliance
Only CPT and its peers know how many statements have been made based on results from generative AI. But there are loads of them. My background as a data modeler over many years makes me shiver a little bit, because what the friendly AI helpers help us produce is subjected to cognitive processes, where we, the readers, process […]
Modeling Modern Knowledge Graphs
In the buzzing world of data architectures, one term seems to unite some previously contending buzzy paradigms. That term is “knowledge graphs.” In this post, we will dive into the scope of knowledge graphs, which is maturing as we speak. First, let us look back. “Knowledge graph” is not a new term; see for yourself […]
2023: Mitigating Data Debt by Knowing or by Guessing?
One of the newer data buzzwords is “data debt.” Actually, it is approximately 10 years old, and it became popular ever since agile people realized that postponing things creates not only technical debt, but certainly also data debt. Will we, in 2023, be better at not creating so much data debt, and will it be […]
It’s All About Relations!
The new ISO 39075 Graph Query Language Standard is to hit the data streets in late 2023 (?). Then what? If graph databases are standardized pretty soon, what will happen to SQL? They will very likely stay around for a long time. Not simply because legacy SQL has a tremendous inertia, but because relational database paradigms […]
Say Hello to Graph Normal Form (GNF)
You thought you knew all normal forms? (And possibly also some abnormal …) Well, think again: There is also “graph normal form (GNF).” The diagram below is a fifth normal form graph concept model, which is just a few steps from GNF, so hang on: Where GNF comes from GNF is based on serious mathematics, […]
What Kinds of Data Languages Will We Need in the Future?
IBM had a pole position on the Database Management Systems (DBMS) market by developing “DL/I” in the 1960s as a means for defining and using hierarchical databases. Under the product names of DL/I and IMS (Information Management System) this dominated the database market for many years. Everybody, except for IBM followers, called the product “D-L-1,” […]
Tales of Data Modelers
Reading Larry Burns’ “Data Model Storytelling” (TechnicsPub.com, 2021) was a really good experience for a guy like me (i.e., someone who thinks that data models are narratives). I agree with Larry on so many things. However, this post is not a review of Larry’s book. Read it for yourself – highly recommended. Reading it triggered […]
Quick, Easy, and Flexible Data Model Diagrams
Click to learn more about author Thomas Frisendal. Many of us have a lot to do. And we have short delivery cycles, sprints, and a lot of peers to share data models with. In search of something lightweight, which is quick and easy, and may be produced (or consumed) by other programs? Stay with us on a […]
What’s in a Name? (aka Data Modeling What?)
Click to learn more about author Thomas Frisendal. This is a summer special, on the lighter side, but addressing a simply overwhelming issue at times. What Are You Talking About? Sometimes the obvious is not that … obvious. Many people know that I am on the graph-y side of the house. But explaining a simple matter like […]