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 […]
Data Governance and Data Architecture: There is No Silver Bullet
In terms of a market perspective, Data Governance has increased in visibility partly because of the increase in security breaches, data security issues, and compliance requirements for various industry regulations. To secure and manage data properly, it helps to manage it at a higher level and know which of your data is sensitive in the […]
Data Modeling in the Machine Learning Era
Machine learning (ML) is empowering average business users with superior, automated tools to apply their domain knowledge to predictive analytics or customer profiling. The article What is Automated Machine Learning (AutoML)? discusses a prediction that by 2020, augmented analytics capabilities will play a key role and be a “dominant driver” in the growth (and purchase) […]
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, […]
Sigma Announces Visual Data Modeling, SQL Runner and One-click Snowflake Integration
According to a recent press release, “Today at the inaugural Snowflake Summit in San Francisco, Sigma, an innovator in cloud business intelligence (BI) and analytics, announced its forthcoming release of a visual data modeling capability, SQL Runner, and one-click Snowflake integration, allowing anyone to explore data in cloud warehouses and generate insights in minutes. Sigma’s […]
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 […]
Embracing Data Silos: Semantic Search and Analytics Innovation
Walk around any large organization and hear people groan about finding the right data to do their work. In the typical organization, data sits in multiple places, lost behind technical and functional boundaries. These isolated systems, referred to as “data silos,” have often existed for good purposes and reasons such as helping each business function […]