Metadata harvesters, data catalogs, and business glossaries help standardize data and create transparency
What is our primary use case?
Our clients use it to understand where data resides, for data cataloging purposes. It is also used for metadata harvesting, for reverse engineering, and for scripting to build logic and to model data jobs. It's used in multiple ways and to solve different types of problems.
Pros and Cons
"erwin has tremendous capabilities to map right from the business technologies to the endpoint, such as physical entities and physical attributes, from a lineage standpoint."
"Another area where it can improve is by having BB-Graph-type databases where relationship discovery and relationship identification are much easier."
What other advice do I have?
It is a different experience. Collaboration and communication are very important when you want to harvest the value from the humongous amount of data that you have in your organization. All these aspects are soft aspects, but are very important when it comes to getting value from data. Data pipelines are really important because of the kinds of data that are spread across different formats, in differing granularity. You need to have a pipeline which removes all the complexities and connects many types of sources, to bring data into any type of target. Irrespective of the kind of technology you…