Hello Trish,
this is an excellent question. The key differences for Data Fabric compared to traditional approaches are:
1) Self-service Data and AI Model Marketplace: Business users are empowered to find and work with data across the enterprise. The ability to move data, etc. needs to be simplified to the point that business users can do it with a couple of mouse-clicks without a dependency on the IT department.
2) To allow 1) critical roles such as Chief Information Security Officers (CISO), Chief Data Officers (CDOs), Chief Privacy Officers (CPO), etc. need to be able to still sleep well at night. This is only possible if the Data Fabric solution automatically enforces end-to-end aspects of access, privacy, data placement, retention, etc.
3) A Data Fabric learns and advises the users
pro-actively: If you make a change in a data model, an intelligent Data Fabric provides the user immediate feedback on downstream applications, e.g. ETL jobs which would be broken if you make the model change. Or if you are a Data Steward resolving data quality issues, once you did this a certain number of times for similar task, a machine learning model in the background should be able to discover the pattern of resolution and take care based on predicting the action on future, similar tasks automatically.
These are just a couple of examples of some of the fundamental ideas - there are many more. But I hope this gives you a first idea that a Data Fabric is quite different from old approaches of data management.
Kind regards,
Martin
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Martin Oberhofer
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Original Message:
Sent: Tue February 22, 2022 12:59 PM
From: Trish Smith
Subject: Questions for AMA: Data Fabric
What's different about a data fabric vs. traditional approaches to integrate, govern, and access data?
Thanks,
Trish
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[Trish] [Smith] [MBA, BMath, Mom]
[Content Developer]
[IBM]
[Ottawa] [ON]
[613-356-5435]
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