This next year demands quicker data-driven decisions, better automation, more integration, and more data, requiring optimized Data Modeling. Data Modeling refers to documentation describing core business rules and definitions around data. Business and technical shareholders benefit from Data Modeling by seeing complex data concepts in an intuitive, visual way. Throughout 2020, Data Modeling will need […]
Modeling Misfit Types: Why Type Inheritance Is Not a Good Fit in Data Models
Click to learn more about author Thomas Frisendal. “Complete Consistence” Drives Temporality, … And What Else? In August I published a blog post called The Future History of Time in Data Models. The short version of that story is that if you aim for “Complete Consistence for Temporal Extensions”, you need to work on the […]
Data Modeling in an Agile World
Data Modeling creates a model for storing and processing data that works in a predictable, consistent manner. It includes the visual presentation of data structures, while enforcing business rules and government policies. A data model focuses on the needed data and its organization, rather than the operations performed on the data. Data Modeling is done […]
The Future History of Time in Data Models
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 Modeling vs. Data Architecture
In the second edition of the Data Management Book of Knowledge (DMBOK 2): “Data Architecture defines the blueprint for managing data assets by aligning with organizational strategy to establish strategic data requirements and designs to meet these requirements.” Another way to look at it, according to Donna Burbank, Managing Director at Global Data Strategy: “Data […]
Data Architects and Data Modelers: The SQL/NoSQL Debate is Dead
Karen Lopez says that when it comes to surviving as a Data Architect, “Hybrid is the future.” It’s no longer enough to speak only one language or stay attached to one set of technologies. According to Lopez, “purely relational (SQL) databases don’t exist any longer,” since most applications developed now use various types of database […]
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 […]