Why operationalize?

Consistently bringing data in a timely manner to the right stakeholders is what data/analytics operationalization is all about. It looks deceptively simple, but only about 1% of AI/ML projects truly succeed, and the main reasons are a lack of scale, a lack of trust, and a lack of governance, meaning that not all the compliance boxes are checked to deliver the project within the window of opportunity. The key areas that need attention to enable this include getting complete datasets, including unstructured data, which is the hardest to tame, accelerating the development process by improving means of collaboration between data personas and having a well-defined governance and deployment framework.

By now, the medallion architecture should be a familiar architecture blueprint construct. It is to be noted that in the real world, several producers, several pipelines, and several consumers criss-cross. Each pipeline transforms and wrangles data based on the requirements...