Informatica Cloud Data Governance, Access and Privacy vs. SAP Data Intelligence

The Informatics vision is already determined and with the relationships already configured, or that plasters any new type of vision or relationship between the Assets. As a result, a pedigree diagram is poor and limited with the availability of information on the screen or in reports for consultation and decision making. It has an approval and maintenance workflow that is also limited to assets already made available only by Informática. It will not be possible to see the connection of a technical, functional, and documentary line in a single flow. It allows the registration of all data referring to a DPIA and LIA, but it does not generate a report that allows the aggregation of this set of information in a single place. The tool makes information available to support a decision regarding the DPIA and LIA, but it does not make it available in a unified way as in a report.

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If you have an SAP products ecosystem in your IT landscape, it becomes a no-brainer to go ahead with an SAP Data Intelligence product for your data orchestration, data management, and advanced data analytics needs, such as data preparation for your AI/ML processes. It provides a seamless integration with other SAP products.

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• automated sensitive field detection
• replace critical data with dummy but real-looking data
• threat detection
• role based access

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• It integrates well with our current ecosystem of SAP products, like HANA.
• It provides end-to-end machine learning operations, with tools for the complete model life cycle.
• It has a simple user interface for novice users, with complex tools also available for power users.
• It builds on SAP Data Hub, providing all the ETL functions of that tool with additional machine learning functionality.
• It can run in the cloud, no on-premise software management needed.
• Many programming languages are supported, it provides a sandbox environment for the user to develop in whichever style they prefer.
• SAP is very actively developing and improving it.

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• High Complexity, requires training.
• As it does not allow customization in relationships such as information separately in visual form and even in other tools of the Informatics group such as EDC.
• Navigation is complex.

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• Data transfer speed tends to be slow when there is poor internet connection since SAP Data Intelligence don’t synchronize data while offline. However, this is not vendor fault, that’s why we have implemented robust wireless technology internet connection in our organization.

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Allow collaborations among various personas
with insights as ratings and comments on the
datasets Reuse knowledges on the datasets for new users Next-Gen Data Management and Artificial Intelligence

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I think the troubleshooting process might be streamlined with improved error recording and tracing. A lot of information about issues and how to fix them is hidden away in the Kubernetes pods themselves. I'm not sure whether SAP Data Intelligence can fix this problem it may be connected to Kubernetes's design, in which case fixing it could need modifications inside Kubernetes itself.

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Initially we struggle to get help from SAP but then dedicated Dev angel was assigned to us and that simplify the overall support scenario. There is still room of improvement in documentation around SAP Data intelligence. We struggle a lot to initially understand the feature and required help around performance improvement area,

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Overall experience with Axon is average and it is a very manual product. Axon is a very good tool for data governance. User-friendly interface and is integrated with multiple systems.

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One of the reasons to pick SAP Data Intelligence is the speed and security it provides, in addition to the excellent support it provides. It is also compatible with all popular databases, which is another reason to choose it.

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• risk mitigation
• data loss prevention
• role based data access

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• Automation of data management slashed tasks by over 60% in most departments for the first 8 months.
• Metadata catalogs have enabled us to categorize data from disjointed sources in one place.
• It runs multiple ML models which enhances flexibility when managing data.

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