When something important disappears, it’s natural to start asking questions and looking for answers, especially when that missing piece has had a significant impact on your life.
Similarly, when data that used to exist in your sourcing system suddenly vanishes without any trace, you’re likely to react in a similar way. You might find yourself reaching out to higher authorities to understand why the existing data management system design allowed this to happen. Your colleagues might wonder if better ways to handle such data-related issues exist. Ultimately, you’ll embark on a quest to question yourself about what could have been done differently to avoid the complete loss of that crucial data.
Kimball-style data warehousing already has the idea of type-2 slowly changing dimensions, which allow you to track the deletion of dimensional data by assigning an end date to the row and not inserting a new record with the next start date. It’s a little harder to deal with fact data deletions in that way, though, as there historically is no concept of slowly changing facts.
Read on for some thoughts on the topic from Rayis.