Meta data role?

Question & Answer
, ,
1

I want to understand how meta data plays a role for QuickSight Q
Eg relation with Collibra data management ?

2

Hi @Annick - Welcome to the community. Thanks for the question.

Q Topics are collections of one or more datasets that represent a subject area that your business users can ask questions about.

With Amazon QuickSight automated data prep for Q, you get an ML-powered assist to help you create a Q topic that is relevant to your end users. The first process begins with automated field selection and classification, something like this:

  1. Automated data prep for Q chooses a small number of fields to include by default to create a focused data space for readers to explore.
  2. Automated data prep for Q selects fields that you use in other assets like reports and dashboards.
  3. Automated data prep for Q also imports any additional fields from any related analysis where a topic is enabled. It identifies dates, dimensions, and measures, to learn how fields can be used in answers.

This automatic set of fields help the author quickly get started with natural language analytics. Authors can always exclude fields, or include additional fields, as needed by using the Include toggle. This section of the userguide explains Quicksight Q in detail. Does this help?

1 Like
3

Thanks for this info - very useful.
To be honest, could you clarify just a bit more? As a Collibra user, I am documenting quite some (business) metadata in Collibra. For instance, in the glossary we define what specific business terms mean, how they relate to each other, what are synonyms…
Also in datasets, we define in business language what different columns mean.

In Quicksight, I understand quite some time needs to be spent defining the “Topics” (even with the ML powered assistance)… So it feels like quite some double work in adding a semantic layer on top of the data…
I was wondering if other customers have explored the link between Collibra and Quicksight Q (especially in terms of re-using the extensive business vocabulary we are adding on top of the data).

Thanks!