Click to learn more about author Thomas Frisendal. Timely Concerns in Data Models In June I published a blog post called Timely Concerns in Data Models. In summary the concerns that I mentioned in June were: Roles of time (such as Valid Time, Recorded Time, As-Is vs. As-Of, Read timelines, Time Series), The scope of […]
The History of Time in Data Models
Click to learn more about author Thomas Frisendal. In my last blogpost Timely Concerns in Data Models, we looked at the basic challenges of dealing with time dependencies in Data Modeling. I promised to continue this quest by going over the history of these issues. How well have we actually solved these challenges? So, hop […]
Timely Concerns in Data Models
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, […]
Data Models That Build Themselves
Click to learn more about author Mike Brody. Self-service Business Intelligence (BI) is about bridging the knowledge gap that has historically separated business professionals from their data. It’s about doing away with intimate knowledge of information systems as a prerequisite for finding out last quarter’s growth margin. And when it comes to replacing SQL statements with […]
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 […]
Five Tips to Completing Analytics’ Infamous “Last Mile” in 2019
Click to learn more about author Mike Lamble. In 2017, the term “Data Scientist” was LinkedIn’s fastest growing job title; yet, in the same year, McKinsey reported that less than 10 percent of Analytic Models that are developed actually make it to production where they can deliver ROI. The bottleneck lies in what industry insiders call […]
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 […]