I have performed two POCs over SAP Business Data Cloud. My core expertise is in DataSphere and it was a core part of this initiative. We integrated data from S/4, ECC, and Alteryx. We transformed the data models into a traditional analytical model and created Insight apps for reporting.
Lead Analyst at a tech vendor with 10,001+ employees
MSP
Top 20
Apr 30, 2026
I have worked with SAP Data Sphere, and Business Data Cloud is a recent introduction because I am currently working on a critical S/4 transformation. Currently, the analytics landscape is on B4, and they are moving to Business Data Cloud, so that is where I am working with SAP Business Data Cloud. Although I have worked on different parts of it like SAP Data Sphere previously, it has been more than two years since I have worked on it. When I talk about Business Data Cloud, I have been an out-and-out SAP analytics person, and I have worked on almost all of the versions of BW that were available, seeing both the good and the bad sides of it. A lot of it is basically expectation management. Earlier, when SAP introduced improvements, it primarily focused on its process orientation and how this process orientation can be put into data used for analytics to provide a cross-functional and in-depth view of business functions. However, many big enterprises do not realize that when this product is sold to them, they might be promised certain features, but technology has its own limitations. For example, when I was working with something called TREX, which was BW Accelerator, people put in huge amounts of data into just processing that. When the transition to HANA happened, there were many gaps regarding how you can have a powerful engine in a Ferrari, but you cannot use a Ferrari to tow a truck. My point is that if your data model is poorly designed, no matter how good the processing is, it will not support it, and that is supposed to fail. You cannot expect HANA to run five years of data at once and process everything; that is not possible. So it is important to understand that at the end of the day, while it does have a lot of processing power, it is just a technology system. You should focus a lot on the design aspects before you embark on a journey to implement any newly designed product or newly introduced product in the market for your reporting or analytics requirements. You need to understand what to do and what not to do with that product. For example, when HANA came into the picture, one report designed for financial leadership faced issues because they executed many different variants at the same time using a single query from an existing workbook. That is expected to fail no matter how good the product is. Your data and your best practices are non-negotiable when you are designing or implementing; having qualified people on-ground with thorough design evaluation is essential while embarking on that journey. So if I look at SAP Business Data Cloud compared to the traditional data warehouse, you can have a data lake or a data warehouse, whatever the case may be. SAP Business Data Cloud sort of eliminates the need for extensive technology integration; you do not have to build ETL pipelines. It is sitting on one single cloud, essentially a product as a service, and it integrates directly with HANA in native tables, handling all data replication and availability for you. Compared to BW, you can trust the data products from Business Data Cloud because the data comes directly from your book of records, such as S/4HANA or any functional system you are referring to. So it represents a paradigm shift from how BW or traditional warehouses worked with SAP. Business Data Cloud provides added functionality where you do not need reconciliation; you just need to ensure that your KPI definitions are on point and broadly aligned with your various analytics requirements, so you can trust your numbers. Within SAP, there is a lot of focus on trusting your numbers, as sometimes downstream errors can skew the overall reporting. For example, I worked with a client where a copy-paste error inflated their overall inventory drastically. Here, you can trust your numbers more effectively; you can set different priorities and identify outliers, which simplifies analytics. You need to have a deeper understanding of your processes, so the time to value increases. Time to value has significantly increased because there is a lot less dependency on your traditional IT organization. If I am working with finance leadership, I can have my own person managing a universe on top of finance data, define the requirements for them, and they can generate reports. The integration with AI and ML makes my life much easier, and while I have not explored Databricks in depth yet, whatever I have heard about it providing add-on capabilities is a game changer. For now, we are still in the evaluation and setup process of Business Data Cloud. Those evaluations continue, but definitely, the integration with AI and ML capabilities would provide more flexibility in adding business value. For example, you can schedule predictive maintenance based on your existing data and define heuristics for automating order fulfillment, managing order cost dynamics and inventory according to the requirements. This gives a much better flexibility and predictability to proceed.
Consultant-SAP GRC at a tech consulting company with 201-500 employees
Real User
Top 10
Apr 30, 2026
My main use case for SAP Business Data Cloud is to connect multiple systems and derive data products using customized as well as standard solutions. This includes a data transition to use BW/4HANA and other legacy systems to transfer data to SAP Business Data Cloud PC, private cloud edition, and generate data products based on this. SAP Business Data Cloud can be used fundamentally in the same way as Databricks where we can send the data to zero-copy sharing data with Databricks and perform machine learning, and then write back data into SAP Business Data Cloud and utilize it in SAP Analytics Cloud. The challenge is based on machine learning that we cannot implement in another tool, so we can use SAP Business Data Cloud for this purpose.
Principal Architect at a tech consulting company with 501-1,000 employees
Real User
Top 5
Apr 24, 2026
My main use case for SAP Business Data Cloud involves installation of data products and creation of data products, and sometimes ingestion of data to non-SAP data cloud such as AWS S3 as well as ADLS Gen2 and sometimes GCP as well. The process of creating and installing data products in SAP Business Data Cloud is very simple; I just have to find out the use case for which I am trying to find the data product. If I find any data product that is related or matching to my requirement, I just have to search in the catalog and then click a button to find the data package. Then I install it and automatically, the data gets stored in the underlying object store and if I want, I can proceed further in the data sphere and process whatever is required. Apart from that, sometimes I use the data products from the BWPC, such as generating the data products on the BW objects using the data product generator, which is also one of my use cases. For one of my clients, I am implementing that use case.
Director & Co Owner at INFRABEAT TECHNOLOGIES PVT LTD
Real User
Top 5
Sep 1, 2025
I left my review on Qlik Analytics Platform and SAP Analytics Hub. We are only using SAP products currently. I have been using SAP Analytics Hub for 20 years now. I have been using it as a partner, specifically as a partner integrator. We are using SAP Analytics Hub for two reasons: one is for cataloging and one is for sending out data from SAP products to data lakes and others.
SAP Analytics Hub serves as a platform for reporting and creating dashboards. It offers powerful capabilities for cross-functional reporting and dashboard creation across various modules, making it a valuable tool.
SAP Business Data Cloud (SAP BDC) is a unified, intelligent data platform — part of the SAP Business AI Platform — that governs SAP and third-party data through a business data fabric. As an evolution of our industry-leading data, analytics and planning solutions, Business Data Cloud brings together Datasphere, Analytics Cloud, and Business Warehouse with a unified experience that delivers transformational insights across all lines of business. By harmonizing mission-critical data with the...
I have performed two POCs over SAP Business Data Cloud. My core expertise is in DataSphere and it was a core part of this initiative. We integrated data from S/4, ECC, and Alteryx. We transformed the data models into a traditional analytical model and created Insight apps for reporting.
I have worked with SAP Data Sphere, and Business Data Cloud is a recent introduction because I am currently working on a critical S/4 transformation. Currently, the analytics landscape is on B4, and they are moving to Business Data Cloud, so that is where I am working with SAP Business Data Cloud. Although I have worked on different parts of it like SAP Data Sphere previously, it has been more than two years since I have worked on it. When I talk about Business Data Cloud, I have been an out-and-out SAP analytics person, and I have worked on almost all of the versions of BW that were available, seeing both the good and the bad sides of it. A lot of it is basically expectation management. Earlier, when SAP introduced improvements, it primarily focused on its process orientation and how this process orientation can be put into data used for analytics to provide a cross-functional and in-depth view of business functions. However, many big enterprises do not realize that when this product is sold to them, they might be promised certain features, but technology has its own limitations. For example, when I was working with something called TREX, which was BW Accelerator, people put in huge amounts of data into just processing that. When the transition to HANA happened, there were many gaps regarding how you can have a powerful engine in a Ferrari, but you cannot use a Ferrari to tow a truck. My point is that if your data model is poorly designed, no matter how good the processing is, it will not support it, and that is supposed to fail. You cannot expect HANA to run five years of data at once and process everything; that is not possible. So it is important to understand that at the end of the day, while it does have a lot of processing power, it is just a technology system. You should focus a lot on the design aspects before you embark on a journey to implement any newly designed product or newly introduced product in the market for your reporting or analytics requirements. You need to understand what to do and what not to do with that product. For example, when HANA came into the picture, one report designed for financial leadership faced issues because they executed many different variants at the same time using a single query from an existing workbook. That is expected to fail no matter how good the product is. Your data and your best practices are non-negotiable when you are designing or implementing; having qualified people on-ground with thorough design evaluation is essential while embarking on that journey. So if I look at SAP Business Data Cloud compared to the traditional data warehouse, you can have a data lake or a data warehouse, whatever the case may be. SAP Business Data Cloud sort of eliminates the need for extensive technology integration; you do not have to build ETL pipelines. It is sitting on one single cloud, essentially a product as a service, and it integrates directly with HANA in native tables, handling all data replication and availability for you. Compared to BW, you can trust the data products from Business Data Cloud because the data comes directly from your book of records, such as S/4HANA or any functional system you are referring to. So it represents a paradigm shift from how BW or traditional warehouses worked with SAP. Business Data Cloud provides added functionality where you do not need reconciliation; you just need to ensure that your KPI definitions are on point and broadly aligned with your various analytics requirements, so you can trust your numbers. Within SAP, there is a lot of focus on trusting your numbers, as sometimes downstream errors can skew the overall reporting. For example, I worked with a client where a copy-paste error inflated their overall inventory drastically. Here, you can trust your numbers more effectively; you can set different priorities and identify outliers, which simplifies analytics. You need to have a deeper understanding of your processes, so the time to value increases. Time to value has significantly increased because there is a lot less dependency on your traditional IT organization. If I am working with finance leadership, I can have my own person managing a universe on top of finance data, define the requirements for them, and they can generate reports. The integration with AI and ML makes my life much easier, and while I have not explored Databricks in depth yet, whatever I have heard about it providing add-on capabilities is a game changer. For now, we are still in the evaluation and setup process of Business Data Cloud. Those evaluations continue, but definitely, the integration with AI and ML capabilities would provide more flexibility in adding business value. For example, you can schedule predictive maintenance based on your existing data and define heuristics for automating order fulfillment, managing order cost dynamics and inventory according to the requirements. This gives a much better flexibility and predictability to proceed.
My main use case for SAP Business Data Cloud is to connect multiple systems and derive data products using customized as well as standard solutions. This includes a data transition to use BW/4HANA and other legacy systems to transfer data to SAP Business Data Cloud PC, private cloud edition, and generate data products based on this. SAP Business Data Cloud can be used fundamentally in the same way as Databricks where we can send the data to zero-copy sharing data with Databricks and perform machine learning, and then write back data into SAP Business Data Cloud and utilize it in SAP Analytics Cloud. The challenge is based on machine learning that we cannot implement in another tool, so we can use SAP Business Data Cloud for this purpose.
My main use case for SAP Business Data Cloud involves installation of data products and creation of data products, and sometimes ingestion of data to non-SAP data cloud such as AWS S3 as well as ADLS Gen2 and sometimes GCP as well. The process of creating and installing data products in SAP Business Data Cloud is very simple; I just have to find out the use case for which I am trying to find the data product. If I find any data product that is related or matching to my requirement, I just have to search in the catalog and then click a button to find the data package. Then I install it and automatically, the data gets stored in the underlying object store and if I want, I can proceed further in the data sphere and process whatever is required. Apart from that, sometimes I use the data products from the BWPC, such as generating the data products on the BW objects using the data product generator, which is also one of my use cases. For one of my clients, I am implementing that use case.
The usual use cases for SAP Analytics Cloud that I work with are to make it simple, either reporting, or planning, or predictive scenarios.
I left my review on Qlik Analytics Platform and SAP Analytics Hub. We are only using SAP products currently. I have been using SAP Analytics Hub for 20 years now. I have been using it as a partner, specifically as a partner integrator. We are using SAP Analytics Hub for two reasons: one is for cataloging and one is for sending out data from SAP products to data lakes and others.
We use the product for analytics across various business processes and sectors. It helps us analyze SAP ERP data.
Our primary use cases include analytics and integration planning, dashboarding, and data reporting.
SAP Analytics Hub serves as a platform for reporting and creating dashboards. It offers powerful capabilities for cross-functional reporting and dashboard creation across various modules, making it a valuable tool.
We use Analytics Hub primarily for consolidating different kinds of reports and queries into one, single window.
We are a solution provider and the SAP Analytics Hub is one of the products that we implement for our customers.