First of the patterns I see is championed by Product companies like Alation. They do support traditional way of implementing DG, so there you go. But what they afford you to do is to take up a functional area, opportunity, problem, and focus DG effort on it.

Let’s take a quick example; you have Sales reports not working in Microsoft PowerBI, which in turn depends on Snowflake data. Of course, in our fictitious company no report is credible. But, why boil the ocean they would say. Go after this one specific area, and catalog the heck out of it. They enable this pattern by using heavy dose of machine learning helping you to setup metadata repository ground up!

When I say machine leaning, one example is deducing the business glossary automatically from underlying schema; nice!

You can now build on this, assign ownerships and create lightweight operating model and be on your way. Of course, we will leave the good old “People” problem aside; that’s always a headache. But even the smallest of features in these tools, like proposing a top user of a table as a data steward is not bad, comes in handy as technical steward.

When you have plethora of things to do in a DG project, every bit of automation matters, and division of labor becomes easier during implementation.

In summary, I like this agile way of implementing DG, as you can show concrete fruits of your work. Now, arguably this is not applicable to all companies and all domains, but I find it a great tool in my arsenal as I interact with a new customer, especially where there are other factors such as budgets, expectations, patience, sponsorship etc. are at play. One can show a quick win!