SAP Business Data Cloud Reviews

The usual use cases for SAP Analytics Cloud that I work with are to make it simple, either reporting, or planning, or predictive scenarios.

The capabilities of SAP Analytics Cloud that I consider the most valuable are that all three functionalities are in one tool, and because they are, you don't have to buy another tool to make planning, you don't have to buy another tool to implement prediction. To get some predictive scenarios for your business plan is possible without additional purchases.

I think the areas of SAP Analytics Cloud that could be improved or enhanced are mentioned by users or companies, and they would definitely mention the price in terms of planning licensing. From a consulting point of view, I don't see any major weaknesses. It is reasonably well-developed, and the perfect strength is that innovations are coming on a quarterly basis. It is slightly behind other tools in terms of the number of graphical outputs. Some tools have it faster, but every increment of innovations contains new features. On the other hand, it is perfectly fitting to the SAP environment, with seamless integration to all SAP modules, not only ERP but also SuccessFactors and other tools from the SAP family, for example, integrated business planning for the production planning. This is really a highlight compared to other tools.

I have been working with SAP Analytics Cloud for eight years.

The stability and reliability of this solution are based on the contract with SAP, the cloud tenant contract. The productive environment is stable, with four times a year, there is an upload of the quarterly upgrades, and it is always announced in advance. You have a chance to test it on your test tenant in small increments, so it should not break your productive reports and productive environment.

In terms of the scalability of SAP Analytics Cloud regarding the number of users, you buy the licenses or subscriptions based on some packages, which is differentiated based on the type of license, whether it is just reporting or whether it should cover also planning. A minimum number of planning users is ten, while a minimum number of reporting users is twenty-five, and in some special cases, it can be also only five for demo systems. This varies and needs to be revised with the official SAP pages. Since it is a public cloud service, the consumption of capacity unit goes per the tenant. SAP Analytics Cloud can be used by a small company, and it can be used by a large corporate.

The technical support is a very well-organized service, with a lot of tools for me as a consultant and for the end user on how to contact SAP support and get issues solved. I don't have any concerns about that.

Positive

I do not have experience with Cognos, which is why I was interested in the review, but I am a consultant and I'm implementing SAP Analytics Cloud.

I work for an SAP partner and I am a consultant.

The predictive analytics features of SAP Analytics Cloud include four different tools: regression, time series, and I don't remember the third one now, and the latest edition is Monte Carlo simulation, which works with uncertainties very well. This is also an advantage, and you can implement the result of the prediction to your reporting or to your planning scenarios, doing what-if scenarios.

The interactive data visualization is rather intuitive, and it can work for end users who just run the report and maybe click one filter. You can also create very sophisticated and highly developed dashboards with a lot of scripting and logic behind the screen. It has the tools for every type of user for the analysis.

The process is usually straightforward for me, but the biggest challenge always with the reporting tools is to fit it in the customer's environment. This is always the case with all tools because you need to fit it in their infrastructure and ensure connectivity. All types of connectivity are well described in the connectivity guide of SAP Analytics Cloud. If we struggle with something, it is always on the customer side with their environment, not with the tool itself.

SAP provides enough documentation and enough material for me to find a workaround.

I am not a BI consultant at SAP CZ anymore; I am a consultant in NTT DATA Business Solutions.

I have been working in my current field overall for the past twenty years.

I rate SAP Analytics Cloud as nine and a half on a scale from one to ten, with ten being the best solution and one being the worst.

One of the use cases I implemented for SAP Business Data Cloud involves data migration, specifically handling scenarios with over 1 million records, which exceeds Excel's limits. We needed to transform this massive data for fixed assets, and due to the limitations of traditional tools like Excel and its Power Query, we turned to Data Sphere. We pumped the data into Data Sphere, utilized its computing power, and eventually transferred this to SAP's HANA Migration Cockpit for data migration. Additionally, we used it for data validation, transformation, and intelligent lookup.

Another use case was with S/4 that connected through SAP Analytics Cloud for retrieving summarized data regarding asset classes from simulated depreciation, which is significant in size. We had to stage and transform this data in Data Sphere before moving it to SAC for visualization, planning, forecasting, and budgeting purposes.

I am indeed using the integration between SAP HANA Cloud and SAP Business Data Cloud. This integration greatly enhances our data management processes by enabling seamless data flow from SAP to SAP Business Data Cloud. We can run reports and apply necessary transformations directly in SAP Business Data Cloud, freeing us from the need to execute heavy reports on S/4, thus significantly improving efficiency.

I have utilized the Data Product Studio in SAP Business Data Cloud. The Data Product Studio has facilitated building and managing data products, though the process is intricate and requires multiple steps for activation and monitoring. It varies based on data requirements; while some users find it beneficial, others revert to previous tools due to dependencies on monitoring and stability issues in SAP Business Data Cloud, which struggles with handling large jobs.

SAP Business Data Cloud has aided us mainly in data migration and in areas like planning, budgeting, and forecasting, where it proves helpful. It contributes to preparing dashboards but lacks efficiency in managing operational reports that rely on large datasets. Thus, while it serves well for summarized views, it falls short when dealing with considerable volumes of data.

The main advantage of SAP Business Data Cloud is its seamless integration with SAP sources, allowing for real-time reporting if your backend operates on SAP. The advantage is further compounded by the provision of certain CPEA points for use based on subscription.

I have utilized the self-service analytics feature in SAP Business Data Cloud where we have designated spaces with authorizations. While the setup allows us to analyze the data models, changing them on the fly is not feasible; model modifications necessitate technical expertise for dropping and reloading systems. However, we can effectively manipulate, slice, and dice the data within the existing models.

Overall, having self-service analytics has significantly improved my team's decision-making processes compared to previous methods in Excel.

I would like to see several enhancements in SAP Business Data Cloud, including better integration with APIs, improved job orchestration, and better extraction logics beyond the current model, which lacks a proper delta mechanism. Currently, it extracts all data from tables, consuming resources unnecessarily. Further improvements include better job monitoring capabilities, allowing for automated notifications and exceptional handling, as well as better transformation options. Although the UI/UX is commendable, the limitations in Python libraries and non-customizable intelligent apps hinder functionality.

Improvements in the licensing model are also necessary as its complexity and frequent changes complicate understanding for clients transitioning between platforms. As it stands, SAP Business Data Cloud struggles to compete with affordable platforms like Google or AWS, and a shift towards more contemporary technology is warranted as it still relies on outdated systems.

The effectiveness of SAP AI utilizing our company's official data to provide correct answers is still ambiguous. While Smart Insights reveals existing data, it does not generate new insights, leading to a lack of trust in AI-driven outputs. Thus far, we have relied more on SAP Analytics Cloud's what-if analysis, which is comparable to Excel's Solver, rather than AI for actionable answers, mainly due to AI still being perceived as unreliable for making critical decisions.

I have been working with SAP Business Data Cloud for more than two years, and although SAP Business Data Cloud is a new name, I have experience with its products like Data Sphere and SAP Analytics Cloud (SAC), which I have been working on for at least six years, especially since the onset of COVID. Data Sphere, as a component, has been a focus for the past two years, while my work with BW or BW/4HANA spans approximately eight years.

Regarding stability, the reliability of SAP Business Data Cloud diminishes with larger datasets, leading to difficulties that often require intervention from SAP support. Limited error descriptions complicate problem-solving, resulting in lower reliability due to dependency on SAP's resolution processes.

Connecting SAP Business Data Cloud to platforms like Snowflake or Databricks does change our data management to an extent, allowing for integration and real-time replication. However, the dual costs associated with maintaining investments in multiple platforms, such as Databricks and Snowflake, raise concerns as querying or moving data incurs additional expenses. My reliance on SAP Business Data Cloud mainly revolves around transferring data rather than operating as a comprehensive big data solution, as it fails to effectively handle substantial data volumes.

My experience with SAP tech support is satisfactory; they respond in a reasonable timeframe, although the product requires significant improvements to reach a competitive level against other similar solutions.

Moving our old BW data to SAP Business Data Cloud does not enhance our daily data operations; rather, it introduces limitations as SAP Business Data Cloud lacks integrated reporting tools like BW did. To achieve dynamic reporting, such as changing aging buckets for account receivables, we find ourselves unable to do so within SAP Business Data Cloud, which severely restricts our capability compared to BW.

The setup process for SAP Business Data Cloud is straightforward, with the only complexity arising during port configurations, common across software implementations.

I do not perceive any return on investment with SAP Business Data Cloud, as its performance in that area is exceptionally poor and shows no signs of improvement.

However, I find it quite expensive given its size, especially since hosting it on a cloud platform incurs costs that we did not face with Excel. As data demands grow, the financial aspect becomes crucial, and I also recognize limitations in job reliability compared to traditional SAP BW systems, which offered better orchestration, monitoring, and error handling capabilities. The relatively costly limitation of 200 MB uploads at a time further complicates processes, putting SAP Business Data Cloud at a disadvantage when compared to tools like Talend or Informatica, which are cheaper and more capable. My perspective on this tool is mixed; while it helps, the cost-to-value ratio remains questionable as better options exist.

Technologies like Informatica, Talend, Cloudera, Tableau, and Power BI excel in managing big data, and these platforms effectively handle substantial volumes, unlike SAP Business Data Cloud.

In advising organizations considering SAP Business Data Cloud, I recommend it if their needs focus on planning, budgeting, and forecasting. However, businesses looking for robust operational reporting or big data platforms should consider alternatives. For analytics or data-point movement, SAP Business Data Cloud might suffice. I have provided this review with an overall rating of 6.

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.

It combines traditional data warehouse features with Data Lake formations, positioning BDC as a true central analytics hub to enable one-version-of-the-truth.

The most valuable feature of SAP Business Data Cloud is not really limited to BDC itself. It's the two core applications, which are SAC and Data Sphere. I think seamless planning is the number one feature that is really advantageous to users and really something that stands out for SAP versus everybody else because no other tool has that feature.

In ensuring that data keeps the same meaning and relationships when it moves between different systems in SAP Business Data Cloud, we're using the catalog function with all new implementations. And we're assigning meta tags and additional information, KPI definitions. The catalog feature in SAP Business Data Cloud is what we're ensuring. When you're creating new models, you rely on the lineage and standard features built into SAP Business Data Cloud or Data Sphere specifically.

What I dislike about SAP Business Data Cloud is that the way they position the data products is a little confusing for users because it is fundamentally different whether you're using data products that SAP delivers or customer built data products. SAP built data products are really valuable content to speed up the implementation cycle. Also, one needs to create custom data products to be able to share data from Data Sphere to Databricks or Snowflake. But that is really not the full-fledged data product feature set that SAP has when they offer their own. So the data you're sharing with the other systems is just a table. That is confusing for users or potential customers because they think they can share the same objects you're using for reporting with Databricks, and you cannot. You can only share tables. If SAP was a little more open and transparent about those differences, I think that would be better.

For everything related to SAP Business Data Cloud, there are still a few things that are a little quirky. I would give the entire solution an eight out of ten.

I have been using SAP Business Data Cloud since its system was released around May last year, coinciding with Sapphire. I started using it immediately in a demo and test environment. The first productive use with a client was about nine months ago.

In terms of stability, I have never seen or heard about any lagging, crashing, or downtime related to SAP Business Data Cloud. That is really the number one advantage. I've been working with SAC as one of the primary components of this tool since 2018, and it's the single most stable tool in the analytics space that I've seen from SAP in my 25 years.

SAP Business Data Cloud is very scalable. There's no issue. You can ramp up and down CPU power and availability as needed on the fly. It scales very well.

Regarding the speed and the quality of the technical support for SAP Business Data Cloud, from that being a new tool and being pushed and promoted by SAP, it is very quick. They're very quick to reply. Even with medium-priority tickets, you get a reply usually in one or two days.

If I were to put SAP's support on a scale from one to ten, I always prefer to find my own solutions, so the search is not very intuitive and the organization of the notes is not very intuitive, but once you raise the message and you get a reply, it's usually useful. I would give it an eight out of ten.

I have worked with various  alternatives to SAP Business Data Cloud.

The initial deployment for a new client using SAP Business Data Cloud, I would honestly say is fairly difficult, but SAP is supporting that. For the initial deployment, you need to rely on SAP's assistance. You need to configure SAP for me, you need to have the proper contractual prerequisites in place. You have to integrate to your existing systems. It is a lot of work and there are a lot of steps that need to happen. I would say it's rather difficult.

From what I think about the pricing of SAP Business Data Cloud, this would have to be specific. From an analytics pricing point of view, it's very good and very competitive. From a planning license perspective, SAC on that side is very expensive if you need developer features. That piece is very expensive. From the BDC formation overall, I would say it is priced right, but it is very difficult to estimate the actual cost. SAP uses the consumption unit concept and it has an estimator and a calculator, but with all the features that you may want to use, it's not very transparent and it is very difficult to determine your cost upfront. That is another aspect that I keep hearing from customers where SAP could really improve if they would be more straightforward with what you really need to have to use certain features and what at the end you will pay for it. The pricing transparency is not very good.

I can name a few alternatives I have encountered, including Power BI, Tableau, and Business Objects.

If I were to compare them, it really depends on the feature we're talking about and what the actual requirement is. If you do a direct comparison of what most companies are doing, which is Power BI to SAC, I think SAC is the better solution in the context of SAP data. What it allows you to do is access the data live, and you don't have to load and replicate and duplicate the data, whereas in Power BI, when you use SAP as the backend, you have to load data to Power BI. This results in data duplication, additional cost, additional time, and it's not real-time. A fundamental difference between the two is SAC allows planning and Power BI does not. It's a completely different tool with many more features.

In terms of user-friendliness, every tool has a learning curve. You have to learn it. In terms of stability, I think SAC is more stable. I've used this at a couple of clients with self-refreshing dashboards that you display on a big screen in a warehouse and you just start it up and it runs. It never crashes, never fails, and shows you data in real time, 24/7. It is a very reliable and stable and mature tool.

When it comes to the actual formatting of things and data blending, there's still a place for Business Objects. No other tool allows a user to pixel-perfect format static reports and the ability to create custom formulas and joins, as Business Objects does. It really depends on what feature people are requiring to be able to say which tool is better. But for what most people do, Power BI versus SAC, I would say SAC is much better, if your backend data is SAP.

In SAP Business Data Cloud, we're using Joule and Just Ask on the SAC side, but we haven't used any other AI features yet.

As I'm a consultant and we're not using this on our own, what I see is that when you train the model in SAC, the answers are fairly accurate. It used to be a gimmick, where you have text-to-SQL basically, where the system translates this. This has improved significantly over the past year. It's actually a feature that you're not only showing to your clients that this is possible, but it's actually usable for them. That has improved significantly.

I have not seen users change data models. When I talk to customers about self-service BI, it's always limited to the front end, such as SAC stories. We have not come across customers who really establish this data steward and data space concept where you have an owner within the business for HR, finance, or sales, which SAP is promoting. We have not come across clients that actually use this. Any self-service is limited to the front-end SAC piece.

Whether SAP Business Data Cloud requires any maintenance on my end depends on how you define maintenance. As with every tool, it's more housekeeping where you keep the system clean and don't clutter it with a lot of test or development objects. Beyond that, no. SAP takes care of that.

We have not used SAP Connect to integrate data with external parties or third-party platforms. All the implementations that we have done are SAP data only. The only external data we've connected was from SQL, using an ODBC connection and the SAP tool DP Agent, but not SAP Connect. We have used BDC Connect, which is that component that you need in SAP Business Data Cloud to be able to share data with an existing Databricks environment.

Overall, I would rate this product an eight out of ten.

There are so many use cases, but in my current project landscape, the main important thing that SAP Business Data Cloud is doing is replacing the current ETL flow from Ehana to BODS, from Ehana to IVP we are sharing by CDS, or from any other sources to Ehana we are bringing by BODS. Those ETL flows are the main use case we are replacing with SAP Business Data Cloud using the data flow.

I can give you a quick specific example. In my current project landscape, we have multiple flows where we are sending the data from S/4 to Azure Data Lake using Ehana as a transformation layer. So from S/4 to Ehana and then again Ehana to the Azure Data Lake. But using SAP Business Data Cloud or Data Sphere, what we created, we replaced these pipelines directly with the data flow where in the SAP Business Data Cloud framework, in the Data Sphere framework, we have the source as the S/4 and the target, we directly selected as the Azure Data Lake. There is no need for the BODS tool for creating the pipeline or the Enterprise Hana as a middle layer to send and process the data. We can just directly send data one to one from S/4 to ADLS using the Data Sphere as a framework or SAP Business Data Cloud.

There are a lot of other use cases also where we are planning to expand our SAP Business Data Cloud. For example, we have a lot of historical data coming from EDW or Teradata systems where those data are not required to keep directly in the HANA modeling space. We have a lot of memory issues happening in our enterprise HANA framework, the XSA framework. What we are planning is, if we move to SAP Business Data Cloud, we can keep those historical data in the HANA Data Lake storage file as a parquet file, and whenever required for any modeling purpose or data load purpose, we can bring them from the data lake storage to our HANA modeling space. Also, we are planning to move our entire architecture. We have already started with BW for the different sectors where BW is approaching sunset time; we plan to move those directly to the SAP Data Sphere. For any new implementation, we will definitely do it on SAP Business Data Cloud or Data Sphere only.

The best feature of SAP Business Data Cloud is that it's a cloud-based solution, making it software as a service. It's no longer confined to only the database, allowing flexibility to work with your application layer. This advantage that BW used to give isn't available in SAP enterprise modeling. The most interesting thing I've faced is the data lake storage; it has enormous data lake storage capability and is already optimized for use. Any data source can just be dumped here, and when required, you can query the necessary part of the data lake for your modeling purpose. You don't need to store everything in HANA disk as we used to do in SAP Enterprise HANA modeling; that was a waste of HANA disk memory. You can use the data lake feature, and the data flow feature also gives flexibility to move data around from any source to any destination without needing any ETL tool. From a flow perspective, this is a very good tool.

In my current landscape, I mentioned that we had to send files from S/4 tables to Azure Data Lake; it used to require considerable effort with BODS, which also involves a licensing cost. Now, with SAP Business Data Cloud, we save on the BODS license and avoid the costs associated with the Enterprise HANA 48 terabyte box. We have saved costs due to the data lake framework. Moreover, with SAP Business Data Cloud, we can easily store petabytes of data, which will help save on costs going forward. We have minimized our BODS reliance and the need for traditional ETL tools. Further integration of tools like SAP IBP within the Data Sphere will enable further removal of existing ETL pipelines, translating to more significant cost savings.

The integration options are quite good, but the interface can be tricky. For example, you have two storage spaces: the data lake file space and the HANA modeling space. In connection settings when creating the HANA cloud connection, you must mention the association with the HANA data lake space. This interface might seem hazy to newcomers. As for performance, I haven't worked with huge data handling in the manner we did in Enterprise HANA. That's a new learning area for me since I haven't explored performance aspects deeply. Integration options are good; I worked on transformation flows, data flows, and replication flows. The replication flow is particularly effective for sending SLT-enabled data using SAP landscape transformation, allowing flexibility with your ABAP CDS or S/4 tables. The data flow is good for sending data from one system to another without needing any ETL feature, enabling the use of Data Sphere directly, while transformation flow is crucial for transferring large datasets within SAP Business Data Cloud or the Data Sphere framework.

I have used SAP Business Data Cloud for the last one year.

In my experience, I rate SAP Business Data Cloud a 7 out of 10. It sometimes behaves unpredictably, but overall it is good and stable.

We are still relatively new to SAP Business Data Cloud in our organization. Our Enterprise HANA box is 48 terabytes, associated with high server costs, and we have NSEs as well. We are planning to fully implement SAP Business Data Cloud in our enterprise landscape, utilizing the substantial storage area of the data lake. Not all data needs to be stored directly in HANA disk, so we plan to separate the data into cold, warm, and hot storage types. Warm data will reside in our regular HANA modeling space while cold data can be efficiently stored in the file. For warm data, we aim to leverage SQL on file or Dremio for HANA Delta Lake capabilities to retrieve data rapidly. This segregation of cold, warm, and hot data using SAP Business Data Cloud will improve how we currently manage all data in HANA disk or memory.

Customer support is good. I have had positive experiences with customer support.

Previously, we used Enterprise HANA and BW as our old solutions before switching to leverage the cloud capabilities and new features aligned with SAP's roadmap.

There were no other options available for consideration.

From a learning perspective, there needs to be better support from SAP. The primary drawback is that while SAP provides access to the Data Sphere as basic learning, it only grants access to the HANA Cloud space, not the HANA Data Lake space. Furthermore, access to custom data product creation or installation is limited. Improving access for learners and customers wishing to understand the tools is essential. The learning documentation and software are not as robust as I would hope from SAP's side.

My advice for those considering SAP Business Data Cloud is to fully leverage its capabilities. Do not focus solely on the Data Sphere; explore SAC, utilize Data Bricks, and employ the HANA Data Lake capabilities for enhanced data models. Engage with innovations including data mesh for unstructured or streaming data.

Regarding SAP Business Data Cloud's AI capabilities, I do not have much idea, but from what I have learned, its AI capabilities mainly reside within the S/4 side, specifically Joule, and also in SAC with natural language processing features. However, I haven't explored much; they target user perspectives by allowing users to create their reports without requiring predefined reports. Users can easily build data models and generate reports through simple queries.

I have worked on some NLP features; the outputs focus on creating basic dashboards along with Joule for S/4. From my experience, they are fairly basic, and I would rate the AI capabilities about 5 out of 10.

In maintaining that data keeps the same meaning and relationships while moving between systems, we ensure that if we create data for sharing to DataBricks or other applications within the SAP Business Data Cloud framework, the data includes proper metadata and semantic information. For instance, we always assign a unit to revenue quantities or currency data to maintain meaning. When sharing data products between systems including DataBricks for machine learning, we ensure all dimensions and measures maintain proper semantic associations.

We haven't fully moved the old BW data to SAP Business Data Cloud yet. We have utilized the data product generator for brownfield implementation when moving traditional BW objects to SAP Business Data Cloud. For new sectors, we are forgoing BW in favor of greenfield implementations.

I have observed challenges with SAP Business Data Cloud, mainly in terms of database direct access. You have to navigate through the administration cockpit for database queries. In SAP BW, we lacked direct database access, which I feel should change in SAP Business Data Cloud going forward. Users should have proper access to HANA cloud, allowing easy usage of the graphical view and checking performance or functioning of calculation views or table functions. The performance-checking aspect lacks a bit due to the need to filter or assess at a node level. There is a tricky interface challenge in working with the HANA cloud and HANA data lake. The custom data product creation feature also seems complex, requiring a data sharing cockpit profile and more.

I rate SAP Business Data Cloud an overall 7.5 out of 10.

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.

The best feature of SAP Business Data Cloud, in my opinion, is the flexibility it gives me to have business content identified and installed. The good thing is that any customizations can be outsourced to BTP, making any necessary changes or migrations much easier. Since the data and everything is hosted by SAP, it reduces my dependency on allied teams regarding business dependency. For authorizations, I have more control; I can manage my own space and access. Additionally, the part of Business Data Cloud that includes Data Sphere comes with SAC as a bolt-on, making my base reporting much more accessible, even though I might need additional licenses for planning, but that is acceptable.

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.

SAP Business Data Cloud could be improved by publishing broad guidelines on how to handle it correctly, perhaps specifying certain things not to do. For instance, my current structure requires extracting, transforming, and loading data, but Business Data Cloud operates more in BW modeling in HANA mode. You still need your SQL joins, but you only need to persist data when necessary. If it can introduce agents to accelerate data modeling or aid development, that would provide extra flexibility. For example, if I need to make customizations, I could define specifications, and an agent could develop that on BTP and integrate it with my BDC, giving me additional flexibility and a better time to value from the development cycle while adhering to SAP's recommended coding standards.

I would not honestly give any product a perfect rating. However, one area where Snowflake has gained traction is dynamic workload management. I am uncertain if SAP Business Data Cloud provides that feature, but if I have certain reporting demands and the system is at its limit, jobs can fail. It should offer automated workload management, and while SAP can charge for it, such a feature would prevent higher workloads from causing system failures and disruptions.

I have been in this field for almost 18 to 19 years.

As far as stability and availability go, the current landscape hosted in AWS has generally met expectations. Barring a few human errors, we have not seen significant availability issues. In a typical quarter, the availability has been over 99%; in a few cases, it dropped to around 98.5%, but overall, I do not anticipate availability being an issue.

Since SAP Business Data Cloud is completely hosted, I believe scalability should not be a problem. Based on the need, additional licenses or resources can be procured, so I do not see scalability as an issue.

In my experience, I would rate SAP support as two to three out of five.

To get accurate help, I have noticed that the first-level support staff usually do not devote much attention to the case. For example, when I provide a set of instructions, they often simply ask me to implement those steps without checking whether they are applicable. If I need a response on the same day, I am 99% sure that it will not be resolved within that timeframe.

I have not used the self-service analytics features extensively. I am more focused on the data modeling side. However, I have seen that the self-service analytics or the ad-hoc analytics available for B4HANA has not been leveraged much. I have noticed people moving to Snowflake or Power BI for self-service analytics. From what I have read about SAP BDC, I think that it could be very helpful and could help keep businesses on board with the SAP ecosystem rather than moving away to other analytics systems.

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.

SAP provides an excellent framework for business reporting and regulatory requirements, considering those factors in their solutions. You can comply with regulatory frameworks much better because SAP imposes strict controls, where every change is audited. You can audit and keep track of everything going on. When discussing regulatory frameworks, SAP BW typically forms part of SOX audits, meaning it is a regulated system with proper processes in place. However, if you take data out of SAP, while it offers more flexibility for analytics, it also creates risks—particularly concerning data integrity, such as HR or core finance related data, which could lead to information leaks.

I rate this product an 8.5 out of 10 overall.

I am an end user of SAP Business Data Cloud. I develop enterprise planning solutions using SAP Business Data Cloud together with SAP Datasphere and SAP Analytics Cloud. I use SAP Datasphere, which is a core capability within SAP Business Data Cloud, to integrate S/4HANA data through CDS Views and create financial planning solutions for CAPEX, OPEX, and HR modules, as well as P&L, Cash Flow, and Balance Sheet reporting.

I want to explore similar use cases with another client like MakeMyTrip. I also want to understand what additional capabilities SAP Business Data Cloud provides compared to standalone SAP Datasphere.

When using SAP Business Data Cloud, I integrate data from S/4HANA using CDS Views and bring that data into Datasphere. Depending on the business requirement, I use replication flows or remote access concepts. I perform mappings while creating dimensions and model the data using standard data warehousing concepts such as star schema. I create reusable dimensions and fact tables using graphical views, joins, and semantic modeling. I typically perform left outer joins wherever required and integrate the dimensions with fact tables based on business keys. I am implementing these concepts independently within SAP Business Data Cloud.

The concepts are similar when integrating data from S/4HANA, but I am trying to implement them using SAP Business Data Cloud architecture. The features are quite helpful and user-friendly, allowing me to understand the implementation better. They also help me debug and perform lineage analysis. While creating graphical views, joins, and semantic relationships, the system helps me understand cardinality and data relationships across the model.

I have not personally used BW Bridge. However, another team on a different project worked on integrating SAP BW data into SAP Datasphere and SAC for an organization in the Kingdom of Saudi Arabia. They exposed BW data through CDS-based models into Datasphere, where Datasphere acted as the unified semantic layer containing SAP and third-party data. The team created analytical models and directly exposed them to SAC. Most calculations, transformations, and calculated dimensions were implemented within Datasphere itself. Very minimal business logic was implemented in SAC, as the transformation and semantic modeling were handled completely within the Datasphere layer.

One of the most valuable capabilities of SAP Business Data Cloud is the layered architecture it provides through SAP Datasphere. The integration layer, semantic layer, business layer, and presentation layer make the overall solution much easier to develop, maintain, and troubleshoot. When I work on financial planning models or reporting solutions, I can easily identify where the data is coming from and trace it through the lineage until it reaches the final dashboard. This significantly reduces the effort required during reconciliation and debugging.

The semantic layer has had the biggest positive impact on our implementation. Instead of repeatedly creating business logic in multiple reports, we define the business semantics once and expose reusable analytical models for SAP Analytics Cloud. This improves consistency across planning models, executive dashboards, and financial reports.

Another significant benefit is the ability to work with trusted business data. Through SAP Business Data Cloud and Datasphere, we can integrate data from S/4HANA while preserving business context. This allows finance users to perform planning, forecasting, and reporting using business-ready datasets rather than spending time preparing data manually.

One concept that I found particularly valuable is seamless planning. While I am still exploring all of its capabilities, I understand it as the ability to perform planning on trusted and up-to-date business data with minimal data movement. Compared to traditional approaches where periodic refreshes and multiple ETL processes were required, Business Data Cloud simplifies the overall architecture and reduces the dependency on duplicated datasets.

From a business perspective, this has enabled us to build financial planning solutions for CAPEX, OPEX, HR planning, P&L, Cash Flow, and Balance Sheet reporting more efficiently. Since most of the transformation and semantic modeling are performed within Datasphere, SAP Analytics Cloud can focus on planning, visualization, and executive reporting rather than heavy data preparation.

Overall, SAP Business Data Cloud has improved data governance, simplified architecture, reduced manual reconciliation efforts, and provided a more scalable foundation for enterprise planning, executive dashboards, and future AI-driven analytics.

The most valuable features of SAP Business Data Cloud (with SAP Datasphere at its core) are its layered architecture, semantic modeling capabilities, and seamless integration across the SAP ecosystem.

The integration layer allows me to connect to S/4HANA through CDS Views, replicate or federate data depending on the business requirement, and integrate it with other SAP and non-SAP sources in a governed manner. The semantic layer enables me to model reusable business entities, dimensions, measures, hierarchies, and business logic with proper relationships and calculations. The business layer helps organize and expose analytical models as reusable data products, while the presentation layer enables secure consumption in SAP Analytics Cloud for planning, reporting, and executive dashboards.

This layered architecture makes the platform very user-friendly and transparent. When I build a model, I can easily trace the data from the source system to the final report using data lineage, impact analysis, and dependency views. This makes debugging much easier, helps validate data, and simplifies reconciliation between the front-end reports and the integration layer. Compared to other tools I have worked with, this level of visibility significantly improves troubleshooting and governance.

The semantic layer has had the biggest positive impact on my projects. Once business logic, calculations, hierarchies, and currency conversions are modeled in Datasphere, I can reuse the same analytical models across multiple reports, dashboards, and planning stories. This improves consistency, reduces duplication of effort, enhances performance, and simplifies long-term maintenance.

Another key benefit is seamless planning using trusted and up-to-date business data. Instead of relying on multiple ETL processes and periodic data refreshes, I can work with current business data through live connectivity or appropriate replication strategies based on the use case. This enables faster planning and forecasting for CAPEX, OPEX, HR, P&L, Cash Flow, and Balance Sheet reporting while improving business agility and decision-making.

Additionally, capabilities such as data products, business catalog, semantic modeling, row-level security, and integration with SAP BTP services provide a strong foundation for building scalable, governed, and AI-ready analytics solutions. As SAP Business Data Cloud continues to evolve, these capabilities will further strengthen enterprise planning and intelligent decision support.

Overall, SAP Business Data Cloud has helped improve data governance, reduced manual effort, accelerated time-to-value, enabled trusted business data for analytics and planning, and provided a scalable architecture for real-time and future AI-driven business insights.

SAP Business Data Cloud can be improved in several areas, especially around user experience, automation, data quality, and operational reliability.

While modeling in SAP Datasphere, creating and maintaining dimensions—especially large, complex dimensions—can be time-consuming. Auto-generation of dimensions from source metadata, smart recommendations, and reusable templates for commonly used dimensions would save significant time and effort. Inline data quality validations, relationship checks, and modeling best-practice recommendations (such as key selection, cardinality, join conditions, and semantic validation) would help prevent errors much earlier in the development cycle.

If the platform could provide proactive validations and meaningful warning or error messages during the initial modeling stages—such as "Are you sure you want to proceed?", "This relationship may impact downstream objects", or "This join may result in duplicate records"—it would help new users avoid rework and improve the overall development experience.

Another area for improvement is connection management and operational reliability. In one of our projects, we created multiple connections for different business modules. During the transition from Development to UAT, we discovered that some of the connections had expired or required revalidation. This resulted in rework on dependent transformations, data flows, and analytical models, causing project delays and additional effort. Better lifecycle management of connections would significantly improve implementation stability.

It would be beneficial if SAP Business Data Cloud enhanced connection lifecycle management by providing features such as automatic credential validation, connection health monitoring, expiry notifications, auto-renewal where supported, and easier transport of connections across Development, QA, and Production environments. Centralized monitoring with detailed diagnostics and actionable error messages would also simplify troubleshooting and reduce implementation effort.

Additional capabilities that would further strengthen the platform include metadata-driven modeling, enhanced data profiling and data quality dashboards, richer out-of-the-box business content, improved data lineage and impact analysis, and deeper integration with SAP BTP services such as SAP Integration Suite, SAP Build, AI Core, and SAP Business AI. As SAP Business Data Cloud continues to evolve, these capabilities will make it even more powerful for enterprise planning, analytics, and AI-driven business scenarios.

Overall, improvements in automation, proactive validations, connection reliability, metadata management, and operational monitoring would make SAP Business Data Cloud more efficient, stable, and user-friendly while providing a stronger foundation for trusted business data and enterprise analytics.

I have been using SAP Business Data Cloud for about six months.

I have not explored the AI features of SAP Business Data Cloud. However, I have tried using other features of SAP Business Data Cloud, such as Business Application Studio, which is part of BTP. Business Application Studio allows me to create an app that I can run to get calculations by clicking a button when I create a button. I can feed in those calculations by entering HTML code. I can get those calculations done and assume that this is AI-generated calculation compared to what I enter manually when performing a budgeting activity. I can compare manual, humanly entered budget data versus AI-generated calculations.

I quoted a case study where I did a revenue model. I created a quality and rate calculation by revenue, and those calculations fetched results. To get the growth percentage, I entered the growth percentage in an application I created. For example, when I entered 10 percent, the revenue would go from 100 to 110. However, AI comes up and suggests that by looking into the data set, the revenue is more viable and can be generated more or less. These kinds of responses from AI by looking into the data set are what I have explored in BTP, which is integrated with Business Application Studio. Another feature is SAP Build, where I can automate the budgeting activity by approval. For example, a user or planner creates a budget, and that budget should go to stakeholders, CFO, and directors for confirmation that the budget is correct. Those manual back-and-forth communications can be automated by this feature in SAP Build. I am not sure whether these are part of SAP Business Data Cloud or are a separate part of BTP. I have not explored any GenAI or other features of SAP Business Data Cloud.

I have not used SAP BDC Connect to integrate data with external parties or third-party platforms because I need more understanding on connections. I am trying to use the data fabric concept with third-party tools. I have the integration suite and other applications, but I have not worked much in that area. The integration suite, I have only tried to replicate that with S/4HANA, but not with other third-party tools or non-SAP data sources. However, I have to do this in the near future. I have a project where I need to use it for MakeMyTrip-like scenarios where I have bookings and different external data sources from websites. Those data have to be integrated with SAP Business Data Cloud, and those are non-SAP data sources. Definitely, SAP Business Data Cloud Connect would be needed there.

SAP Business Data Cloud is quite scalable. The features that I have not explored will limit certain areas only. If I widely make use of all the components and all the features that SAP Business Data Cloud has, then it would make me more scalable and more diversified into different requirements. The limitation is that certain areas are unexplored, and SAP is coming with those evolutions and I need to adapt to that.

I have moved on from that project to a new one, so as of now I do not communicate much with the technical support of SAP Business Data Cloud. However, when I was on board on the project, I created a lot of SAP tickets and I was part of all the evolutions that SAP Business Data Cloud has undergone in the initial phase. We started using SAP Business Data Cloud from the initial stage itself, and then this project went on parallel where evolution was taking place on SAP Business Data Cloud and our development as well. That is when I was interacting much with the technical support team.

I would rate the technical support of SAP Business Data Cloud as a seven, though there are a lot of opportunities and improvement required in those areas. The support needs to be quick. I cannot hold on for certain tickets that have been raised, and those are being worked upon over a lot of time. I become answerless on every stand-up call. Stand-up calls being on a weekly basis, even if they move from a daily basis, I go ahead unanswered with the same solutions being repeatedly given to the clients. The clients get frustrated more than I do.

Based on my experience with SAP Business Data Cloud and its aspects, I cannot rate features I have not explored. The areas that I have had experience with were quite helpful. The features are coming hand-in-hand with toggling between SAC and SAP Business Data Cloud or Datasphere, so I need to take some evolutions which have been taken up in SAC and build those features also on SAP Business Data Cloud. Certain features like the correlation errors that come up and those help notes, a little more help is needed. This has to be in the very starting phase, so it should be more educative to the users so that they do not make any mistake in the first place itself and should not discover answers when they go to the SAC stage rather than finding the answers in Datasphere itself. Since this is data-related and has got analytics features, I think those analytics features can be more helpful when I try to understand data quality. But right now I see an area of improvement.

Before SAP Business Data Cloud, I primarily worked with traditional data warehousing concepts and SAP Analytics Cloud. I have also worked with other analytics and reporting tools in different projects. SAP Analytics Cloud has been my strongest area, where I used modeling features extensively for enterprise planning, reporting, budgeting, and forecasting.

As the complexity of enterprise data grew, I realized that performing large-scale data integration, transformation, and semantic modeling directly in SAC has its limitations. SAC is primarily designed for analytics and planning, whereas SAP Business Data Cloud, together with SAP Datasphere, provides a much stronger foundation for enterprise data integration, semantic modeling, governance, and reusable business data products. By moving most of the data preparation and transformation into Datasphere, SAC can focus on planning, analytics, and executive dashboards, resulting in better performance and a cleaner architecture.

I decided to move towards SAP Business Data Cloud because SAP's strategic direction is centered around Business Data Cloud, Business Data Fabric, and AI-driven business applications. It provides a unified platform to integrate SAP and non-SAP data, preserve business semantics, and build scalable enterprise analytics solutions. It also offers better governance, lineage, semantic modeling, and tighter integration with SAP BTP services compared to traditional approaches.

As organizations continue to adopt RISE with SAP and cloud-first architectures, I believe SAP Business Data Cloud provides a future-ready platform for enterprise planning, reporting, analytics, and AI-enabled business scenarios.

I did not participate in the initial setup of SAP Business Data Cloud. I would love to understand and get more knowledge in those areas. I have a clear disconnect because an external expert did that setup. However, I have to do this setup again for other projects. I would love to learn that. If there are help notes or tutorials available, that would help.

SAP Business Data Cloud is deployed on cloud in my organization.

I have not used the self-service analytics features in SAP Business Data Cloud. I am learning from others that it has self-service analytics features. I have seen that SAP Business Data Cloud has analytics features, but I have not used them yet.

I would love to use the machine learning capabilities of SAP Business Data Cloud. However, I have limited exposure to Python or other ML languages. What I have read about it is that not much coding is required apart from JavaScript or other languages. However, I think ML does not require JS scripts. That is where I have not explored it. I would love to explore it and would definitely want to discuss it further. If guidance or more ideas are provided, I can understand that I need to do more research and learning on SAP Business Data Cloud Connect, Machine Learning integration, Application Studio, SAP Build, and Integration Suite. I have not worked with Integration Suite. When someone says SAP Business Data Cloud Connect, I need to understand if it is a separate component altogether on SAP Business Data Cloud or if it refers to the four different components such as Integration Suite, BW Bridge, Application Studio, and SAP Build, or if it refers to creating connections with other third-party sources.

I understand that SAP Business Data Cloud's Data Product Studio is also a component of SAP Business Data Cloud. I need to understand what Data Product Studio is and why I should use it.

Based on my experience with SAP Business Data Cloud and its aspects, I would rate SAP Business Data Cloud as a software and advancement as an eight.

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.

The best features I appreciate in SAP Business Data Cloud are that SAP is now offering a single platform subscription. Previously, if you wanted data engineering, you had to pursue DataSphere or BW, or if you wanted to extend for reporting, you had to buy another subscription for SAC. Instead, you can take a single subscription of SAP Business Data Cloud and implement whatever you need.

Another advantage is the subscription-based model with pay-as-you-go pricing. This is beneficial instead of having to buy a fixed amount of memory that you have to pay for even if you are not using it. The flexibility is quite good.

The large scale of integration is significant. Through the open cloud connector, we can integrate many systems. Previously, it was a closed SAP environment. Now we can integrate with different platforms across the board and transform data across the platforms, with SAP Business Data Cloud at the center. This makes it easy to convince clients and business stakeholders that they should purchase the subscription.

SAP Business Data Cloud ensures that data keeps the same meaning and relationship when moving between systems in quite insightful ways. Because we are integrating with cross-platforms, many clients now want to move from their on-premise systems to the cloud and gain some footing in AI. When considering the size of data for big companies or organizations in the energy sector or manufacturing sector, which have multiple landscapes across their business, everyone wants to integrate everything. In that scenario, SAP Business Data Cloud is quite helpful for live data reporting, replication, and data transformation.

From my POC experience, I can say we can assume definitely around 30 to 40 percent time saving.

With SAP's AI capabilities in SAP Business Data Cloud, there are some parts integrated, but I am not convinced or impressed as much as I am with traditional data warehousing. For example, there was one component called data generator available in SAP Business Data Cloud. It was transforming a previously built data model in BW to the DataSphere model. However, we have seen some disturbances where the data model built on custom functional modules needs human dependency. It was not transforming exactly as much as our requirement. That is one point.

The second point is about the Insight app; I am not that happy about this. It can be improved. For the Insight apps, they need to be shared through other reporting platforms because of client requirements. One of my clients was from the manufacturing sector and wanted to try the Insight app. They wanted some reports in SAC and some reports in Power BI. However, the Insight app is not available; we cannot share this Insight app to Power BI. That was the issue we faced. This is a limitation.

In SAP Business Data Cloud, to help different AI assistants stay in sync and share the same business rules so they do not give conflicting information, I feel SAP still builds all these AI capabilities into a closed system. If you compare other data engineering stacks, they are openly integrating and partnering with other platforms. They are much more advanced and much more ahead of their time in comparison to SAP Business Data Cloud.

SAP Business Data Cloud does support AI or ML enabled with new use cases in our organization. There are some apps we want to develop, and it is not only limited to SAP Business Data Cloud. We are trying to integrate Joule capabilities into SAP Business Data Cloud. For example, if we are doing a greenfield implementation, on top of these tables, we have to create the CDS view for optimized extraction. In that case, we are trying to do this CDS extraction and CDS code writing using Joule AI automation. That is something SAP could directly integrate into SAP Business Data Cloud. It will save us time.

Second, they have given the product generator, so remodeling is a bit easy. The third point is about reporting. The Insight app is not something I am happy with overall. The Insight app concept was not giving end-to-end functionality, and there is a limited scope of customization into that pre-built Insight app. SAP can work on improving this.

I have been using SAP Business Data Cloud for almost one year. Since it was launched, I started reviewing and exploring the possibilities of what we can implement over it.

I would rate the stability at seven out of ten, with ten being the best.

You can take scalability as a nine, definitely nine.

I rate the technical support at eight point five out of ten, with ten being the best.

On the pricing point, I would say it is quite high compared to other solutions that are available in the market. Unless the client's priority is performance and costing is the secondary priority for them, the client is not willing to buy SAP subscriptions.

Comparing SAP Business Data Cloud with other solutions or other vendors, with the open connectors and cloud connector, it is quite easy. There are also OData services and JDBC drivers. There are many ways to integrate, but we mainly use the standard approach. If it is an SAP-based system, then we use a cloud connector. If it is non-SAP systems, like Azure, then we use the open connectors. For example, Qlik Sense or Alteryx.

I have used the self-service analytics in SAP Business Data Cloud, and they help change our data models quickly. It is quite good. They still need some human intervention while we are doing this, but the capability is present. Essentially, we can shorten the development team in this process, definitely. It also reduces our development time. We did not need to develop a requirement again and again. We can reuse those models or Insight apps.

Moving our old data to SAP Business Data Cloud has made our daily operations faster or easier. I have mentioned that many clients have old on-premise systems. Some of them still have 7.5, some moved to BW for HANA. They can now take the private cloud edition. If they move to the private cloud edition, it is very easy. We did not need to do a greenfield implementation. Instead, we can do the brownfield approach. Over there, as I mentioned earlier, there are some disturbances we have seen for custom function modules, but that is quite manageable. Instead of getting a big team of developers, with the help of experts on a particular platform, we can shorten this duration.

Connecting SAP with platforms like Snowflake, Google, or Microsoft has changed the way my team manages and moves data. Many customers are using different landscapes like Databricks, and Databricks is kind of leading organizational data into their AI capabilities. We can now directly work on something, develop some AI capability, or work on AI capabilities or some AI solution that the client requires. With zero delta sharing, it is a bit easy. Instead of storing the data in the traditional way where we used to push the data into another system, we can directly share and do this on our AI agents or generative AI components for direct development in Databricks. That is quite helpful. The zero delta share copy is quite helpful and is also saving a lot of money and is cost-effective.

I am using the integration for SAP HANA Cloud and SAP Business Data Cloud.

This integration affects my management processes as I am working with one manufacturing client who wants a single platform where they want to decommission all their previous different landscapes over the region and want a single global region landscape. In that case, we proposed SAP Business Data Cloud because whatever data we are getting across the platform, we store in a single landscape. We can consolidate and transform into a single landscape. Furthermore, this live replication gives us an edge over traditional data warehousing solutions like BW. This affects things such as time-divided regional divisions due to time zone constraints. Previously, traditional on-premise systems were hosted on on-premise servers. Now, SAP is hosting in the cloud, so it was quite easy to integrate all landscapes into a single platform. This reduces the complexity of the organization.

With SAP, I am using Data Product Studio with SAP Business Data Cloud.

The benefits I have seen in using these two products together are that it is saving time of rebuilding. Suppose someone had already built something according to my requirement. Then, going through the data marketplace, instead of developing everything, I can directly get that data product from the data marketplace. It was saving my implementation and development cost for the project and also time. It is quite helpful.

SAP Business Data Cloud is mostly deployed in the cloud. Now clients want to move to the cloud itself. Most of them want to upgrade to a cloud solution itself. I did not work on a hybrid model solution, but I have heard from my excellence team that they are also trying to implement the hybrid solution as well.

I have worked on the integration with the S3 bucket and Alteryx system. Apart from that, I did not work on other integrations. Most of the clients want to buy the S3 bucket itself because its costing is comparatively very low in the market. Most clients want to store their historical data into the S3 bucket itself.

Most of my clients are from either the energy sector or the manufacturing sector. They are huge clients. I have told you, unless they do not have the priority of performance, then most clients, for the ERP system, are buying an S4 system, but for the integration and for the data engineering, they used to buy or choose other platforms. For this, one section over SAP can work on pricing for the smaller scalar organization.

SAP Business Data Cloud does require some maintenance in that the admin team or Basis team used to take care of these things. Mostly now it is moved to a cloud solution, so it is easy. Prior to the on-premise system, it is quite a bit easier to install the updates right now. The main point is that SAP has really worked on reducing the complexity of installing the updates and on these things, so that was the great part.

I would rate the overall solution as an eight because there are some platform limitations that need to be worked on. Sometimes, the SAP support team itself will give a direct statement that it is standard functionality. That is why I have cut two points. Otherwise, it is quite good.

If the data volume is huge and the priority is performance, then I would recommend SAP Business Data Cloud. I am in consulting myself. I used to recommend to clients that if they want to take performance as their number one priority and they want to get into new technology like AI as well as they want to get into the cloud, then I assure them that they should get into SAP Business Data Cloud. But if cost is their first priority, then they can still take the private cloud edition and transform their old legacy systems in a phase-wise manner over time. However, costing is something that is impacting. From my overall experience, costing is something where other platforms get an edge over SAP.

I would rate this review as an eight out of ten overall.

SAP Business Data Cloud serves multiple solutions. The primary use case is dashboarding and analytics. Second, customers can use it to create an enterprise lake or lakehouse solution on SAP. Additionally, there is an agentic AI solution, which means on top of the dashboarding or enterprise warehouse, I can run machine learning and AI, generative AI directly on those analytics as well. These are the use cases for which a customer would choose SAP Business Data Cloud.

As a partner, I believe the biggest advantage in the product is that SAP context is maintained. If I don't use SAP Business Data Cloud and I bring SAP data outside to a hyperscaler data lake, I lose the SAP context. Here, SAP context is maintained, and that is the biggest advantage. Additionally, SAP has standard APIs for integrating SAP Business Data Cloud with all SAP products, so I get out-of-the-box dashboards, which means out-of-the-box content, and the APIs are pre-delivered by SAP.

The product helps to eliminate silos between agents with a shared understanding of the business. SAP Business Data Cloud has a unique feature called Delta Share. For customers who have partial data inside SAP and some data outside SAP, they don't need to replicate the same data twice. I can share whatever is already there in SAP Business Data Cloud with hyperscalers and vice versa. If I have some data in Amazon and some data in SAP Business Data Cloud, I can share both of the data with each other bi-directionally, which prevents silos.

The concept of Universal Business Context capability in the product helps to maintain data meaning and relationships across systems. Context is maintained through Data Sphere, which is part of SAP Business Data Cloud, where I can maintain new entity relationships. There is also a feature called knowledge graph, where I can add my own semantics on top of it.

SAP Business Data Cloud has SAP BDC Connect for data integration, which enables the sharing and Delta Sharing of data that I mentioned. The product has support for AI and ML, which helps with integrations to Snowflake or Databricks. Inside SAP Business Data Cloud, I don't have the AI or the machine learning part, but it uses that as a joint offering via Snowflake's machine learning capabilities or Databricks capabilities, or I can reuse hyperscaler capabilities like Amazon, Azure, and Google. I share the data using Connect, and then I run machine learning and pass back the results.

SAP Business Data Cloud includes SAP Analytics Cloud, which is very similar to Tableau and Power BI. The analytical agility feature in the product does impact my ability to work efficiently. Data Product Studio helps to build and manage data products. SAP has been releasing data products from their side, and I can install and activate these data products using the studio. It is created as a plug and play. I can go and select the data product and activate it, which activates end-to-end the entire pipeline, making it fast.

I use SAP BDC Connect with all new partners like Snowflake, Google, and Microsoft. Every version comes out, and today, it is only available for Databricks. By June, I will get Azure and Snowflake, and by October, I will get Google, with every quarter a new partnership being announced.

I use integration between SAP HANA Cloud and SAP Business Data Cloud, but that is the standard integration available between all SAP products, not just HANA Cloud. I can get integration with even the LOB solutions of SAP like Ariba.

If we speak about potential improvements or areas for improvement, the primary concern is cost. The main missing element right now is content in all areas. The content is highly limited right now for most of the data products, with only three or four available at this point. Data products are not available for everything, and all are being developed. If I start a project, I have to create it myself or wait for SAP to release it sometime in the future. The most important missing feature is that not all the data products are ready as of today. If the cost could be slightly reduced to make it much more competitive compared to what is available, it would also help.

Cost is quite high when I look at the market and competitors. If I compare the cost with SAP competitors, I find that when creating an enterprise data lake, if I don't use SAP Business Data Cloud and make it directly in the hyperscalers, it is pretty much cheaper than doing the same in SAP Business Data Cloud.

I have been working with SAP Business Data Cloud for approximately one and a half years as a partner with the vendor.

In terms of stability and reliability, there is some downtime, but I find it pretty reliable. The downtime I have experienced occurred only once, and it was down for maybe a few minutes. SAP Business Data Cloud is pretty reliable, and I don't see a major issue there.

SAP Business Data Cloud is scalable. There are no limitations in terms of scale, so it fits both SMB customers and enterprise customers.

Regarding customer support and the technical team, that is a broader topic that doesn't only apply to SAP Business Data Cloud. Since it is fairly new, there is a good amount of support available right now. In SAP's policy, for the first couple of years, the product team is also part of the support team. Currently, I have decent support and no complaints regarding poor service. Judging on my experience, I would rate support from zero to ten points as about eight.

Regarding installation, I would say it is quite straightforward as compared to the competitors. From a complexity point of view, I don't think it is too complex; it is good.

I have observed some ROI with SAP Business Data Cloud. There are advantages, but it is early days because it is just one year out in the market. Once the entire solution is ready, meaning by the end of this year, I will start seeing ROI. While the product is ready and good, I still have to develop all the content. Once the content is available, then it is a faster go-live with lesser cost in implementation, and that would give an ROI.

Setup cost doesn't affect much because I still have to manage the pipelines, but that is about it. Cost is quite high when I look at the market and competitors. If I compare the cost with SAP competitors, I find that when creating an enterprise data lake, if I don't use SAP Business Data Cloud and make it directly in the hyperscalers, it is pretty much cheaper than doing the same in SAP Business Data Cloud.

I am building my own solution similar to SAP Business Data Cloud, so I use both, depending on the customer choice. If customers want to use SAP Business Data Cloud, I sell that. If they don't want to go for it, then I use my own solution on Amazon Data.

A solid rating for SAP Business Data Cloud would be eight points, which reflects my overall assessment of the product.

My main use case for SAP Business Data Cloud involves developing solutions on the cloud by taking data from our in-house BW system, bringing it to the Data Sphere, and planning to conduct reporting on SAP Analytics Cloud. SAP Business Data Cloud is a consolidated solution that combines Data Sphere, SAP Analytics Cloud, and is managed by SAP BTP, which provides AI and machine learning features.

A specific example of how I am using it in a real project involves our SAC standalone system, which has the limitation that we cannot consume a very high volume of data. To address this issue, we are conducting a POC on SAP BW. In SAP Business Data Cloud, we are bringing data from our SAP BW system into SAP Data Sphere, wrangling this data, and planning to create a dashboard in SAP Analytics Cloud while incorporating AI features supported by SAP Business Data Cloud platform.

Regarding my main use case, one challenge we faced was the high volume of data, which led to poor performance in standalone mode, and data loading took considerable time. We have a requirement to consume a high volume of data, meaning billions of data points, not just millions. This capability allows us to provide the business with reports and dashboards that can start at a very high-level company code and drill down to the specific document level.

SAP Business Data Cloud offers a Data Sphere platform for data orchestration and storage, providing excellent features while being connected with BTP and integrating SAP AI, machine learning, and general integration.

The platform's data orchestration and AI integration make a significant difference for my team by featuring native connectivity with Databricks. We can store data in our Data Sphere, utilizing the zero-copy approach to use Databricks machine learning features and bring back data for reporting in SAC platform. We are currently working on a POC for this scenario, where we will consume data in Databricks without making copies and will use it for our dashboarding and reporting.

SAP Business Data Cloud has positively impacted my organization, although it is not fully implemented yet as we are currently conducting the POC. Our business requires using AI and machine learning features, and this tool has advanced capabilities which we want to leverage to provide value and fulfill current and future requirements for the business.

Improvements for SAP Business Data Cloud are needed, especially regarding connectivity, as loading the hierarchy is somewhat complex. A smooth transition is something we want, where we can easily use what we have developed in our SAP BW within SAP Data Sphere or SAP Business Data Cloud platform, as data is an integral part of SAP Business Data Cloud.

Regarding improvements, we need native integration with our existing SAP BW system, which is quite old and has seen investment over the last ten years. There is not an easy way to migrate from our current BW reporting to SAP Business Data Cloud solution, so maintaining both solutions in parallel is necessary. In the future, we hope to find a path to easily migrate from SAP BW to SAP Business Data Cloud, which will be quite helpful. Currently, we are utilizing this solution for new business requirements and not planning to migrate our existing BW to SAP Business Data Cloud yet.

In terms of features, the commenting feature when having a live connection with the Data Sphere is not exceptional. In standalone SAC, when you store data, the commenting feature is excellent, allowing comments at data point layers or multiple layers. I wish they would include commenting with the live connection to the Data Sphere with SAC stories and dashboards in the future.

I have been working in my current field for the last fifteen years.

SAP Business Data Cloud is quite stable.

SAP Business Data Cloud's scalability is good because it is a cloud solution, allowing for scaling based on requirements, which is a beneficial attribute of the software-as-a-service model.

Customer support from SAP is very good; they are assisting us with the POC to ensure successful implementation in the future, providing excellent support.

Previously, I used standalone SAC. Now that it has been repackaged with Data Sphere and BTP, I am moving to a more integrated SAP Business Data Cloud platform.

I have used self-service analytics features in SAP Business Data Cloud to quickly change our data models, which has significantly improved our team's speed, as it is a very good feature—part of SAC and also part of SAP Business Data Cloud—allowing even business users to utilize this functionality effectively.

I have not seen a return on investment yet, but I believe it will provide more features for the business, particularly in AI and machine learning, helping to optimize workloads. While we cannot measure benefits currently, I am optimistic that it will be beneficial in the future, potentially reducing the need for resources.

My experience with pricing, setup costs, and licensing indicates that it is slightly on the higher side, but we are still in the assessment phase and are finding value for the money. If we do not, we may request SAP to provide a discount or reduce the price.

Before choosing SAP Business Data Cloud, we evaluated other options such as Google, but since our data resides in SAP systems, we thought the integration would be much better with SAP, which understands our data's meaning. This allows us to utilize our previous investments by using SAP Business Data Cloud platform.

I believe SAP Business Data Cloud could positively impact my team once fully implemented by providing faster insights and better scalability, as it is a fully cloud solution managed by SAP. Scaling can happen at any time, requiring just a change in some settings and an increase in payment. We currently have licenses for a limited number of users, but in the future, more users will be required. Since this is a software-as-a-service solution, these aspects are crucial.

Regarding SAP Business Data Cloud's AI capabilities, I feel that its governance and security are well-maintained since we have our authorization control via the company code. This ensures that the business cannot access any data outside their permissions, as AI cannot query data outside user access, making it quite well-controlled.

The accuracy and reliability of SAP Business Data Cloud's AI output can depend on the context. Sometimes, when I ask a question, the system interprets it differently and provides an unexpected answer. It can be challenging for the system to understand what I am saying clearly. Our business has high expectations and often compares our features to ChatGPT or Copilot, and while SAP Business Data Cloud is good, it does not quite match those GenAI tools.

SAP's AI uses my company's real official data to provide correct answers. This is the first product where we are utilizing AI and machine learning. Previously, our use of SAC AI within BTP was not effective. With the new SAP Business Data Cloud platform, we are optimistic about achieving more accurate and business-friendly AI and machine learning capabilities.

I have not connected SAP Business Data Cloud to any other AI agents, and I do not believe it is possible for other AI to integrate easily due to security concerns. We have not included any AI agents in our SAP landscape.

We use SAP Business Data Cloud for reporting and dashboarding, ensuring we provide the right context of the data when moving it from our SAP BW system because that data is highly contextualized and not raw. This allows the business to understand the data for their self-service reporting.

Currently, we are not using SAP Business Data Cloud Connect for data sharing as the platform is new, and we do not have that much data to share.

We have not connected SAP Business Data Cloud to external platforms such as Snowflake, Google, or Microsoft, so our data management and movement remain unchanged.

SAP Business Data Cloud is quite stable.

My advice for others considering SAP Business Data Cloud is to start with smaller solutions if you plan to move from SAP BW and see how it fits into your current landscape. It is not a big bang transition. Starting with smaller projects allows for a smoother integration, with the prospect of more capabilities from SAP in the future for migrating fully from SAP BW to SAP Business Data Cloud.

My additional thoughts about SAP Business Data Cloud are that it is a promising, future-oriented product with substantial AI and machine learning features, and I believe that the product is still evolving, so more improvements will come for its components such as SAC, Data Sphere, and BTP. I would rate this product a seven out of ten.

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 data spheres 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.

What stood out to me most was the capability to work with a unified business data cloud while maintaining business context. Instead of looking at isolated data sets, the platform helps connect information from different sources, which makes reporting more meaningful and easier to understand.

I also appreciated how dashboards can provide a single view of key business metrics, making it easier to identify trends and support decision-making. From a learning perspective, the integration between data management and analytics was particularly impressive. One challenge I faced initially was understanding the overall architecture and how components such as SAP Data Sphere, data models, and analytics fit together.

Since I am relatively new to this platform, there was a learning curve, but once I understood the data flow, it became easier for me to work with it.

To some extent, my experiences are mainly through training and learning scenarios, but SAP Business Data Cloud helped me understand AI and machine learning that can be applied to trusted business data rather than isolated data sets. Given my background in AI, NLP, and data analytics, one of the use cases that stood out to me is combining business data with predictive analytics to support forecasting and trend analysis.

For example, in my sentiment analysis and market forecasting project, I could see how a platform such as SAP Business Data Cloud could provide governed, high-quality data to improve reliability on AI models and business insights. Another use case was executing reporting and decision support where AI-driven insights can help identify patterns, anomalies, and opportunities more quickly than traditional reporting methods.

What I found most valuable was the connection between AI capabilities and trusted enterprise data. It reinforces the idea that successful AI projects depend not only on the model itself but also on the quality, governance, and business context of the underlying data.

Overall my experience has been positive, but I think SAP Business Data Cloud could be further improved on onboarding experience for new users, because the platform brings together multiple concepts such as data integration, governance, modeling, and analytics. There can be a learning curve for people who are new to the SAP ecosystem.

I would also appreciate more guided tutorials and examples and learning resources focused on real-world business scenarios. That would help new users become productive more quickly. Another area of improvement could be simplifying the configuration and modeling processes, especially for users coming from a non-SAP background. Making these workflows more interactive would improve adoption and reduce the time required to get started.

One area that could continue to evolve is AI experience. SAP Business Data Cloud already provides a strong foundation with trusted business data, but additional AI-driven recommendations, automated insights, and natural language analytics could make it even easier for business users to extract value from the data.

I also think expanding and simplifying integration with a wider range of third-party platforms and data sources would be beneficial, especially for organizations operating in a hybrid environment with both SAP and non-SAP systems. Overall, the platform is very strong in data management and analytics. The improvements I would appreciate are mainly around making advanced capabilities more accessible and reducing complexity for new users.

I have been working in the current field for twelve months. I have been building experience in analytics, AI, and cloud technologies for approximately a year through internships, projects, and certification, including SAP Business Data Cloud.

SAP Business Data Cloud was stable in my experience. During my training and hands-on exercises, I did not experience any significant downtime or reliability issues. Most challenges were related to learning the platform rather than platform performance.

Based on my training and understanding of the platform, SAP Business Data Cloud appears highly scalable. Its cloud-native architecture allows organizations to grow their business and analytics capabilities while maintaining governance, performance, and business context.

I did not directly interact with SAP Business support because my experience was all through certification training. However, I found documentation and learning resources helpful and comprehensive. More beginner-focused examples and real-world scenarios would make the learning experience even better.

Before learning SAP Business Data Cloud, I primarily worked with SQL, Python, Tableau, Excel, Google Cloud, and AWS-based solutions for analytics projects. I was not migrating from another enterprise data platform. SAP Business Data Cloud was my first-hand experience and exposure to a comprehensive enterprise data management and analytics platform.

I do not have production ROI metrics because my experience was in a training environment. However, it improved my efficiency in understanding data integration and analytics workflow, reduced the time needed to understand complex data architecture, and strengthened my abilities to work with data-driven projects.

I do not have direct experience with pricing or licensing because I have used SAP Business Data Cloud in a training environment. From my understanding, it is an enterprise-focused solution where costs can depend on usage, scale, and required capabilities. I was not involved in evaluating and negotiating licenses.

I was not involved in the formal evaluation process. I was familiar with solutions such as Snowflake, Google BigQuery, and Azure Data Services, but I chose to learn SAP Business Data Cloud because of its strong focus on enterprise data integration, governance, and analytics within the SAP ecosystem.

Since my experience with SAP Business Data Cloud has primarily been through certification training and learning projects, the biggest impact has been made on my understanding of enterprise data management and analytics.

One specific benefit was gaining a clear understanding of how organizations can integrate data from multiple sources into a single trusted environment for reporting and decision-making. It also improved my knowledge of data governance and data modeling and analytics workflow. From my project perspective, my background in data analytics and AI has become easier to connect with real data use cases. For example, I was able to better understand how business data could be structured and analyzed to support forecasting, trend analysis, and executive reporting.

While I do not have production metrics to share, it has significantly improved my ability to work with data-driven solutions and enterprise analytics concepts.

It definitely increased my confidence in handling enterprise analytics concepts. Before learning SAP Business Data Cloud, I mainly focused on the technical side of data analytics, such as data processing, machine learning, and visualization. Through SAP Business Data Cloud, I gained a better understanding of how data is managed and governed at the enterprise scale. As a result, I now approach projects with a stronger focus on data quality, integration, and business context, rather than just analytics itself. This helped me think more strategically about how data supports business decisions.

In terms of efficiency, having a structured framework for understanding data flow and data analytics processes reduced the time I spend on trying to connect different pieces of information. It also improved the quality of my work because I became more aware of the importance of trusted and well-governed data when generating insights.

The features that stand out the most to me are the ability to unify SAP and non-SAP data while preserving business context. This helps me create a trusted data foundation for analytics and reporting. Another feature I found valuable is the integration with SAP Data Sphere, which makes data modeling, governance, and access management more streamlined.

I also appreciate the data analytics capabilities through SAP Data Cloud and SAP Analytics Cloud, where users can create dashboards and gain business insights from a single platform. Finally, the platform's support for AI and advanced analytics is very promising because organizations can use high-quality, governed data for forecasting, planning, and intelligent decision-making.

From my experience, the overall process was fairly straightforward. Once I understood the platform architecture and the relationship between SAP Data Sphere and the analytic layer, the interface is designed to help users organize and model data in a structured way. The main challenge for me was the initial learning curve. Since SAP Business Data Cloud includes concepts such as data modeling, governance, semantic layer, and data integration, it took some time to understand how everything connects. However, after working through training exercises and other use cases for creating data models and building dashboards, it became much easier.

One additional feature I would highlight is the governance aspect. Having trusted, well-managed data is extremely important for analytics and AI use cases. SAP Business Data Cloud does a good job of maintaining data quality and business context across different sources.

One concept I found most valuable during my SAP Business Data Cloud training was its focus on preserving business data context and data segments. Rather than simply moving data from one system to another, the platform helps maintain meaningful relationships and business definitions associated with that data. In my training scenario, I learned how SAP Data Spheres and the business data fabric approach ensure that the data remains consistent when it is integrated from different sources.

It reduces the risk of different teams interpreting the same data in different ways. What I found particularly helpful was the emphasis on data modeling, governance, and semantic layers. These capabilities help me maintain consistent business definitions and relations across systems, which is important for accurate reporting, analytics, and AI-driven insights.

Although my experience was in a training environment, I saw the value of connecting SAP Business Data Cloud with external platforms because it helped me reduce data silos and create a more unified view of business information. The biggest benefit is the improvement in data sharing, integration, and access to analytics across different systems.

On a scale of one to ten, I would rate SAP Business Data Cloud an eight out of ten. The platform provides strong capabilities for data integration, governance, analytics, and maintaining business context. I particularly appreciate its unified approach to enterprise data and its potential for AI-driven insights. The reason I would not give it a higher score is the learning curve for new users and some complexity around the set modeling. Overall, it is a very capable platform with a lot of value for organizations managing large-scale business data.

My advice is to start with a clear business objective and invest time in learning the platform architecture and data governance concepts. The learning curve is manageable with training, and the platform offers strong capabilities for data integration, analytics, and AI when used efficiently. I rate SAP Business Data Cloud an eight out of ten overall.