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 […]
Implementing Bitemporal Modeling for the Best Value
Click to learn more about author Mike Brody. Bitemporal Modeling is an extremely useful tool for documenting historical data. It allows you to recreate databases as they existed at any point in the past and see whether the records were correct — based on what you know to be true now. This information can not only […]
Bitemporal Data Modeling: How to Learn from History
Click to learn more about author Mike Brody. Have you ever called about a real estate listing only to learn that the house has been taken off the market? Or had to pick up mail that should have been routed to your new home? Sometimes our records don’t reflect reality, and bitemporality exists to keep track […]
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 […]
2018 DATAVERSITY Mid-Year Top 20
It’s halfway through 2018 and time once again to post the Top 20 pieces of content published and consumed by you, the community, within the last year. What are people reading, sharing, and discussing? So, what’s happening on the list? Machine Learning continues to hover towards the top and makes a couple of appearances. No […]
Data Modeling is Dead…Long Live Schema Design!
Click to learn more about author Pascal Desmarets. Reality or not, the perception nowadays is that Data Modeling has become a bottleneck and doesn’t fit in an Agile Development approach. Plus, with NoSQL being “schema-less”, perception often is that there is no need for Data Modeling ahead of coding. You may pretend that it is not […]
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 […]
When Everything in AI is Unique, How do You Solve for it?
Click to learn more about author Paul Barba. Intuition is a core component of human intelligence. But it doesn’t always send us in the right direction. Anyone versed in mathematics will know that sometimes the world behaves opposite to expectations. Things that seem normal or given can actually be outliers. Outliers are the norm. Take […]