Top 8 Data Governance Tools
Collibra Governanceerwin Data Intelligence (DI) for Data GovernanceSAS Data ManagementAlation Data CatalogInformatica AxonMicrosoft Azure PurviewSAP Data HubBigID
It's incredibly easy to use.
I like Collibra's flexibility. I like to be able to modify things for our own use. For example, we've chosen to use Collibra also as a knowledge management tool, even though it is not designed to be a knowledge management tool. That's the beauty of it. It can serve as a knowledge management tool by creating some custom assets specifically for knowledge management.
The metadata manager and the mapping manager are valuable. We use the metadata manager to document our tables and columns within our data stores, and we use the mapping manager for ETL specifications. These features are helpful because our major focus is on just documenting the movement of data. We don't focus on the other modules within the product, so we just never decided to use them.
The tool is reliable, quick, and powerful.
The product offers very good flexibility.
Given the relatively low level of maturity, Alation's most relevant feature at the moment is a user interface that's easy to navigate, which helps us find and understand the data. So while Alation has a lot more functionality, our pain point right now is being able to easily find, understand, and trust the information to use it.
The solution is stable.
The feature of auto-onboarding of the assets, enterprise assets via EDC is good.
Has a good interface and is reasonably priced.
The most valuable feature is the S/4HANA 1909 On-Premise
Its connection to on-premise products is the most valuable. We mostly use the on-premise connection, which is seamless. This is what we prefer in this solution over other solutions. We are using it the most for the orchestration where the data is coming from different categories. Its other features are very much similar to what they are giving us in open source. Their push-down approach is the most advantageous, where they push most of the processing on to the same data source. This means that they have a serverless kind of thing, and they don't process the data inside a product such as Data Hub. They process the data from where the data is coming out. If it is coming from HANA, to capture the data or process it for analytics, orchestration, or management, they go to the HANA database and give it out. They don't process it on Data Hub. This push-down approach increases the processing speed a little bit because the data is processed where it is sitt
The features that I have found most valuable are the user experience, the credentialing, and that BigID is user friendly. Additionally, you can deploy to several other Microsoft platforms and you can use it for other things, like a bigger element or a report.
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What are key differences between MDM and Data Governance? What are the practical differences in which each of these solutions is applied?