SAP BW/4HANA vs. SAP Data Intelligence

SAP BW/4HANA is certainly an excellent choice when it comes to setting up a data warehousing platform within a landscape of existing SAP systems ecosystem for realizing quick time to value and ROI over the long term. Many non-SAP analytics tools such as Power BI , Qlik also now offer connectors to leverage SAP BW/4HANA data as additional options to deploy third party visualization tools which make it a more open ended platform of choice. But with the future roadmap of SAP and industry in general being focused towards cloud based offerings (SAC and Datasphere) infused with AI capabilities one has to make a judicious choice among these depending on the use case, capabilities of platforms and existing systems landscape and the overall long term IT strategy. SAP BW/4HANA may not be a good platform of choice if starting a native greenfield data warehousing implementation with cloud first approach which would require data integration across SAP and Non-SAP sources and one should consider other SAP cloud offerings which however still have some time to reach a level of maturity as SAP BW/4HANA has reached over the years.

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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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• With ODP based extraction, SAP BW/4HANA does well to integrate the data from various source systems like S4HANA , S4C.
• With composite providers having the ability to integrate HANA calculation views, we are able to make the best of use of both application layer and data base layer(HANA) for mixed modelling scenarios.
• with data tiering optimization higher data volume are effectively handled as we have the option to control the amount of data that is being stored in HOT , Warm and cold storage. With data volume grows over the year, DTO comes in handy solution to automatically move the delta to appropriate area based on temporature.

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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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• Better integration with HANA layer with BW/4HANA application
• Flexibility to provide more secure API's to interact with non-sap Data bases
• Integration with Gsheets as well like Excel(AFO) and introduce better reporting integration with various reporting tools.
• Evolution to provide better data lake , data brick options like in BDC

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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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SAP BW/4HANA requires specialized skillsets around data warehouse modeling and the access to data, however the modeling capabilities are intuitive and have now become accessible to both SAP and non-SAP data warehouse specialists. This new model allows for Interchangeable skillsets and access to a broader pool of experts throughout the industry, as well as easier access to data.

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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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SAP Max Attention and general support for SAP BW/4HANA is broadly available. Truth to be told, MaxAttention has a premium cost but brings great engineers. So this being an enterprise capability and extremely critical, we did not consider cost as the main factor since support works well. Just keep that in mind when making your selection.

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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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SAP BW / 4HANA and SAP IQ are both used for warehouse; with quick consultations for business analysis and that allows us to obtain dashboards and KPIs efficiently. SAP IQ is columnar and SAP BW / 4HANA immemorial. SAP BW / 4HANA was selected for the response speed of the queries since it saves the information in cache.

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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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• SAP BW/4HANA has been immensely helpful in eliminating alternate silos of data creation by business users thereby ensuring data integrity and consistency across the organization. The focus is now more on data analysis as opposed to data preparation in spreadsheets saving users hours of laborious manual work in collating data.
• We have been able to quickly integrate and leverage data from multiple SAP systems into SAP BW/4HANA to drive holistic decision making within the organization including streamlining the month end close process using BPC.
• Excel friendly reporting tools such as Analysis for Office have made it much easier for user adoption and training making change management much easier and change the culture within the organization.

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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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