What is your primary use case for SAP Analytics Hub?

I have experience with SAP Business Data Cloud and have been dealing with it for the last two years. I also use SAP Business Data Cloud myself as an architect. Our company is a consulting company, and we have already developed many AI products by our own team. We propose these products to everyone, including our customers, for use. However, when questions arise on the analytics part, we also propose SAP Business Data Cloud to the client. We are preparing a PoV and addressing the general question from clients about converting from BW or BW/4HANA to SAP Business Data Cloud. We offer that PoV and the migration model as advisory leaders.

My primary use case for SAP Business Data Cloud is BW and BDC. It is an enterprise data integration, analytics, and reporting tool. I use it to bring together SAP and non-SAP data sources into a unified business-ready data layer, making it easier to deliver trusted insights across finance, supply chain, and operations reporting. I have integrated and harmonized data from multiple source systems, creating business-ready data models with consistent definitions and governance. As someone with a BW HANA background, I particularly value how SAP Business Data Cloud helps modernize traditional data warehouse approaches by combining trusted SAP business content with scalable cloud-based management and analytical capabilities. My main use case for SAP Business Data Cloud is consolidating SAP and non-SAP data for analytics and reporting. I typically use it to create a governed business-ready data set that supports SAP Analytical Cloud, data dashboards, and provides a single source of truth for decision-making across finance and operations. It helps us streamline data integration, improve data quality, and accelerate insight generation.

We are using SAP Business Data Cloud for reporting purposes. We work on amortization reports for a car brand company where we need amortization data. For this, we require amortization reports, and we also have purchase contract and purchase order reports. We currently use SAP Business Data Cloud for this purpose.

SAP Business Data Cloud's main use case and most powerful capability is strengthening the integration pattern between ERP and Azure Data Lake using BDC Delta Share. This has already been tested and worked end to end. The second use case involves a BW roadmap of migration to SAP Business Data Cloud. The ERP to data lake integration pattern has traditionally involved multiple middleware systems: Managed Data Transfer, GoAnywhere, replicating data via SLT sidecar HANA database, and BusinessObjects Data Services. The integration use case aims to eliminate all of these systems and use BDC Delta Share directly from ERP, replicating via Cloud Connector SLT directly to data lake. This represents the main use case and the basis of TCO comparison. Additional use cases include BW on-prem to SAP Business Data Cloud migration, data lake migration, and Datasphere migration, which has its own greenfield parallel run path. The most powerful use case is the integration piece with Delta Share to eliminate middle layers, followed by People Intelligence app and integration, which has also been tested with reporting on SAP Analytics Cloud dashboard on top of it, followed by BW roadmap migration to Datasphere, and then AIML capabilities, which is a nice to have.

SAP Business Data Cloud enables us to leverage existing BW data and BW models so that the business can continue while developing new software provided by SAP, which is SAP Data Sphere. We are working on a project where we are doing a lift and shift of an existing BW system for a couple of countries, France and Germany. The organization has been using SAP BW for the last 15 to 20 years, so they have their models already created in BW, and they use that for their daily reconciliation or their month-end reportings. At the same time, as we are building similar models in Data Sphere, the data that will reside in Data Sphere will only be the new data after the implementation. Using SAP Business Data Cloud, we pull the data from the existing BW system to Data Sphere and then join both of them so that the organization can get the aggregated data for all the 15-20 years directly from Data Sphere. They do not have to use two different reports: the legacy report for the last 15-20 years of data and the new reports, the SAC reports for the recent data. They can find all their solutions or their analysis within Data Sphere. We are using Data Product Generator to bring in all our transaction data. Similarly, we are bringing in master data so that not only the transaction data, but their attributes for the existing master data can also be leveraged from the legacy system. SAP Business Data Cloud provides flexibility to use or leverage the existing models without having to reload from existing or legacy systems to Data Sphere. For example, we were building a finance report or a group reporting report in Data Sphere. We did not have to create new objects to load the data from legacy to Data Sphere in full for the last 15 years. What we can do is leverage the Data Product Generator which automatically sends data from BW to Data Sphere as and when the business wants to keep BW as well as Data Sphere in parallel. This definitely provides a cost saving of not storing data in multiple places for a very long time. Our ECC to BW landscape is getting migrated to S/4 to Data Sphere. All the transactions from the last 15-20 years are in ECC and in BW only. In S/4, the transactions will be done as soon as it is live. Until 2025, all the data resides on ECC and BW. Starting 2026, transactions which are posted in S/4 are coming only to Data Sphere. If the business has to report or create a balance sheet, trial balance report, or P&L reports for one, two, or three years, Data Product Generator will share the existing BW data along with the transactions as well as master data from BW to Data Sphere. Within Data Sphere they can use the legacy data as well as the new data in the same report. Data Product Generator really helps with this. We are using a use case where we have to send or use, pull as well as push data from multiple third-party applications, which is Anaplan or IBP. SAP Business Data Cloud provides the standard functionality or standard connectors to get data from these third-party systems without creating extensive integrations with a lot of third-party tools or integrators. For multinational companies, for any organization which is in 20 or 30 different countries, not every country will be using the same ERP, whether that is SAP, Google Cloud Platform, AWS, or Oracle. SAP Business Data Cloud helps with this because it is a fabric where we bring in data from all the different sources, join them, combine them, and then create only one report that will show the combined data for all the countries in a single report. SAP Business Data Cloud's fabric architecture definitely helps with the integration. We are using SAP HANA Cloud to get data from different non-SAP third-party systems, for example, Anaplan, and then they are updating data to SAP HANA Cloud and then we are just importing and deploying the tables from the SAP HANA Cloud to Data Sphere. It is fairly straightforward and definitely helps quicken the integration part.

I have been working with SAP Business Data Cloud for a while now, and the main use cases for it are to streamline data integration and automate processes to improve efficiency. I use SAP Business Data Cloud to ensure that data keeps the same meaning and relationships when it moves between different systems.

Initially, I used the standalone modules first, starting with SAP's Analytics Cloud and then Data Sphere. Once SAP Business Data Cloud emerged, I transferred to the newer technology immediately to see what difference it might offer. SAP Business Data Cloud has data validation features present when you're creating models. You can see what happens with the data and observe the transformation either through your actual analytic models where you can see your SQL queries and figure things out, or through the automated validation features that are there. When you click on it, it automatically detects what's wrong or what might be a concern, even if it's not necessarily something wrong, but something that looks a bit odd and that you might need to check out before you deliver a final data model. I love SAP Business Data Cloud's integration. I appreciate that it's an integration suite into the analytics realm and offers a big data lake environment where you can access everything—the data, the models, the dashboards. It's all in one place with a more unified front and experience. When you don't have the standalone modules and only have outdated tools that are not as sophisticated as the SAP ecosystem, the difference is noticeable right away when you get on to SAP Business Data Cloud. You notice the much more refined experience when using this module. Since I used Data Sphere and Analytics Cloud as standalone platforms before, it was interesting to see them in an all-in-one platform where everything is integrated, giving a tighter integration between the analytics side and the planning side. It definitely has better governance with handling data specifically because it has one data layer. It's not duplicated models and data scattered in various places. I also noticed how useful it was to handle non-SAP data as well as SAP data, which was more efficient than the standalone modules Analytics Cloud and Data Sphere. That's why I made the shift.

As an SAP consultant, my use case typically depends on what the client is asking for. We're seeing more and more with SAP Business Data Cloud that it is used in conjunction with S/4 upgrades where companies are moving from ECC to S/4, and at the same time, they're modernizing their BI stack. They have BW in the past, and instead of keeping that and connecting that to S/4, they know they need to modernize anyway, so they modernize this with SAP Business Data Cloud at the same time. I've seen that with three different clients over the past year.

My main use case for SAP Business Data Cloud is primarily with SAP Datasphere, but I also help customers connect SAP Business Data Cloud with Databricks. Currently, customers are transitioning from SAP Business Warehouse into SAP Business Data Cloud, so I am helping customers navigate through that transition. There are other POCs that customers are trying right now, specific to Databricks, but soon we're going to do it with Snowflake as well regarding my main use case and how I am helping customers with SAP Business Data Cloud.

SAP Business Data Cloud serves as a pass-through solution for data in my project engagement. Whenever data needs to be fed from both SAP and non-SAP data sources, I use a functionality called Datasphere, which is now part of SAP Business Data Cloud, to pass the data through and expose it to different types of visualization tools, which could be both SAP and non-SAP. In a particular scenario, the client needed historical data maintained in an Excel file, which is a non-SAP data source, along with actual data maintained through SAP S/4HANA standard tables. I created a pipeline and an analytic model in Datasphere that makes a union of data coming from the Excel file. I created a CDS view to obtain the actual data from SAP's standard extractors and standard tables. The CDS view and the data coming from Excel are unioned together, resulting in an analytic model that contains both historical and actual data. The data is passed through to SAC reports through a live connection, which provides real-time insights into this data. SAC stands for SAP's analytics cloud tool, and the data could alternatively be passed to non-SAP tools such as Power BI or Snowflake.

SAP Business Data Cloud serves as my primary solution when I need to integrate data. I use SAP Business Data Cloud for data integration, analytics, and reporting to help consolidate data from multiple SAP and non-SAP systems into a centralized platform, enabling user access to trusted data for reporting and dashboarding. It also provides advantages in analytical performance monitoring and generation of reporting, supporting decision-making across the organization. One specific example of how my team uses SAP Business Data Cloud in a real-world scenario is by creating a centralized executive dashboard for business performance monitoring, where we integrate data from our ERP, sales, and finance systems into a single platform, replacing the previous manual reporting generated from multiple sources, which was time-consuming and led to inconsistent data. For analytics, we can use the data from SAP Business Data Cloud to get a comprehensive view across systems and provide a trusted foundation for business planning and decision-making.

My primary use case for SAP Business Data Cloud is understanding and analyzing business data from multiple sources to generate insight for decision-making. As a part of my certification learning, I focused on how SAP Business Data Cloud integrates data, maintains business context, and supports analytics through datasphere and SAP Analytics Cloud. For example, in one scenario, I explored how sales, customer, and operational data from different systems can be unified into a trusted data layer, which can be used to create multiple dashboards and business reports. This helped me understand how organizations can improve reporting accuracy and make faster data-driven decisions.

SAP Business Data Cloud serves as a central data platform while integrating with the data sources across both cloud and on-premises systems. This gives me the flexibility to work with the data regardless of where it resides.I use SAP Business Data Cloud in a hybrid environment, and my primary cloud provider is Microsoft Azure. The platform integrates data from multiple sources, and Azure has worked well for scalability, connectivity, and data management requirements. I have used SAP Business Data Cloud Connect to integrate data with external partners for data sharing. The biggest benefits have been faster access to information, fewer manual steps, and more consistent data across systems. Overall, it has made data sharing more reliable and efficient. The integration between SAP HANA Cloud and SAP Business Data Cloud has helped simplify data management by making data more flexible and consistent across platforms. It reduces manual effort, improves data quality, and supports faster reporting and analytics. I have used the Data Product Studio in SAP Business Data Cloud to help organize and manage data products more efficiently. It provides better visibility, governance, and consistency, making it easier for teams to find and use trusted data.

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.