Data observability is a new discipline on the rise within data management. As with many new disciplines everything is not new, though. There are several capabilities that come with a data observability solution that have been known for decades within Master Data Management (MDM) and not at least Data Quality Management (DQM).
The brief reason of being for data observability is to prevent data issues at scale. Compared to MDM and DQM you will usually utilize a data observability solution more upstream and have more data sources in scope. The emphasis of data observability is to early and continuously identify data issues. MDM and DQM is geared towards resolving the issues.
Below is a short walkthrough of the common capabilities you can deploy as part of the triangle of data observability, MDM and data quality.
Data Matching
Implementing a data observability solution will usually not extend to data matching capabilities. These capabilities will still reside in the intersection of MDM and data quality.
Data Discovery
Data discovery has been an adjacent part of many MDM solutions as touched on in the post How Data Discovery Makes a Data Hub More Valuable.
You will probably find a better home for data discovery in a data observability solution as this is better deployed for multiple upstream data flows.
Data Profiling
In Data Quality Management (DQM) solutions data profiling has often been seen as a one-off exercise that precedes data quality improvement and data matching, data migration and other data management initiatives.
With a data observability solution, you will be able to implement continuous data profiling and related monitoring.
Metadata Management
Metadata management is essential for data observability, MDM and data quality respectively and over essential for getting the full return of investment in a triangle of data observability, MDM and data quality solutions.
Liliendahl on Data Quality 
Thank you Henrik for the excellent article. What key dimensions should we consider for the data observability
Thanks Shanker. If you are thinking about data quality dimensions, then completeness and timeliness (via freshness) are the most obvious ones that you can monitor by using a data observability solution. There are though a couple of other dimensions that we usually have not conidered as data quality dimensions, but that are essential to data quality and underpins some of the traditional data quality dimensions. Anomaly detection hints into data accurarcy for example and schema drift hints into data consistency as another example.