It is 2026 and the technology world is all about AI. The underlying architecture, definition and classification of your data is just as important as it ever was. There are several reasons for this:
- a good data model is the foundation for a good application
- when data leaves your application its meaning must be understood
- we are generating and consuming more data than ever before, a small problem quickly becomes a big problem
- AI agents derive meaning from data classification (think column name) and use it without asking you
Be very specific about what your data contains and very clear about its sensitivity classifiction and your future self will thank you many times over.
Anecdote one: A sale is a sale, right? In retail, particularly online, there are many ways to measure sales. Experiences in retail show that the web marketing team measure using web stats from browsers. Fulfilment measure based on confirmed orders. Finance measure shipped, net of returns. Merchandise planners will combine confirmed sales with returns. At the end of the year targets and bonuses are based on the finance figure but that isn’t helpful to through the year planning.
There is a certain skill in looking away from your data definition and looking back imagining it is a year later and can you tell exactly what the data is, what it means and what expected values will be in there.