Data Modeling for Software Engineers - Scott Sosna - NDC Oslo 2025

Value of the Information & Strength of the Argument

The talk provides valuable insights into the often-overlooked importance of data modeling in software engineering. Sosna’s argument is well-structured, using relatable examples from his experience in medical and mortgage software to illustrate the consequences of poor data design. He effectively makes the case that data is a long-lived asset that deserves careful consideration. The argumentation is persuasive, though it relies on anecdotal evidence rather than empirical data. The speaker’s credibility as an experienced architect strengthens the practical value of the advice.

Scientific Rigor, Source Quality, Title Accuracy

The talk is based on the speaker’s professional experience rather than formal research. While no scientific sources are cited, the content aligns with established best practices in data management. The title accurately reflects the content, and the talk stays on topic. The lack of formal references is a minor weakness, but the practical nature of the advice compensates for this. The speaker mentions standards like ISO 3166 and ICD-10, which are credible references.

The title accurately reflects the content, which focuses on data modeling from a software engineering perspective, emphasizing the importance of data design in modern development.

7/10

The talk is based on the speaker's extensive experience as a software engineer and architect. It provides practical insights and real-world examples, but lacks formal citations or references to scientific literature. The advice is pragmatic and aligns with industry best practices, but is not empirically validated.

Markers derived by PSI from the transcript: the creator did not define chapters.

• 02:00Introduction: benefits of data modeling and the role of data architects in the past.
• 05:00Comparison of traditional data architecture with modern empowered teams.
• 10:00Risks of poor data modeling: lack of understanding, completeness, quality, and usability.
• 15:00Data is forever: data outlives software, making data modeling crucial.
• 20:00Examples of data in APIs, messages, and hardware.
• 25:00Importance of consistency in naming, data types, and structure.
• 30:00Validation and the need to always validate data.
• 35:00Use of international standards like ISO 3166 and 8601.
• 40:00Wrap-up and final thoughts on data modeling for software engineers.

• NDC Conferences — Conference organizer and source of the talk.
• NDC Oslo — Event page for the conference where the talk was given.

• Data Modeling Essentials — Book on data modeling that aligns with the talk's emphasis on importance of data modeling.