Devart Excel Add-ins vs. SAP Data Intelligence

For a mid size company it is a perfect tool like IT application companies but if companies having large Data like Banks it is less appropriate

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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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• Source control
• Unit testing
• Schema comparisons

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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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• It has room for improvement in terms of security and making it possible for large organizations like us to use it in a more secure way with integrating with our in-house security systems.
• Pricing could be reduced further for it to be adopted world-wide.
• it isn't foolproof. Excel has no means of checking for human error during data entry, which means that the wrong information can skew all the results - sometimes severe.
• Users unfamiliar with Excel syntax may also find entering calculations and calling up other functions a bit frustrating until they get a solid understanding.

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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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It automated the whole reporting and excel. Our team can generate pivot, dashboards and multiple other reports in various formats and update them with one click. My Sales team need to update more then 1500 leads status on daily basis. By using this tool now they can do this in few minutes.

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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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Microsoft Excel is the most used tool in the software universe. As such, asking Microsoft Excel to be the tool used post data acquisition makes a ton of sense. Almost everyone we hire has some idea on how to use this tool, which decreases the time spend training new employees.

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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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• It had a positive ROI as we used it on its strengths. We could save time and make some of our processes simpler.
• It does help us up to a certain limit - but it acts as a part of our general IT infrastructure. It cannot solve our direct business objectives.
• It cannot be expanded in all directions as we need a lot more native coding and IT infrastructure.

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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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