SAP Analytics Cloud Reviews

The solution is primarily used for dashboarding and planning. My clients come from various industries, including chemical energy, fertilizers, cement, real estate, and telecommunications.

The solution has improved decision-making through its predictive analytics capabilities and effective dashboarding features. It supports better business forecasting and planning.

The product's most valuable features are reporting and planning, driverless planning, business vertical-wise planning, dashboarding, forecasting tools, and business forecasting. 

Real-time reporting is one area that needs improvement for the product. Currently, it operates on a batch-based system, and it would be beneficial if it could update in real-time directly from SAP S4HANA without batch jobs or file uploads.

I have been using SAP Analytics Cloud for three years.

I rate the platform's stability an eight.

I rate the product scalability an eight. 

The technical support services are good. However, the response times need improvement.

The initial setup process has moderate complexity. 

The product is expensive. 

Overall, I rate SAP Analytics Cloud a nine out of ten. 

The most valuable feature of SAP Analytics Cloud for data analysis tasks is its compatibility with the SAP ecosystem. It is easy to integrate with SAP ERP and BW systems, allowing smooth data transfer and live connections without much development. This compatibility makes it stand out, especially compared to other systems like Power BI or Tableau, which often require more adaptation and specific development. Additionally, it supports SAP's push towards cloud solutions, integrating well with SAP BTP and other cloud offerings.

To improve SAP Analytics Cloud, I would like to see more advanced machine learning capabilities, similar to those in SAP BTP. Additionally, better support for connecting to various data sources without needing many workarounds or IT-related activities would be beneficial.

Additionally, in the future, I would like to see improved support for third-party systems, lower cost of acquisition, and better graphics and interface design. These improvements would enhance the tool's usability and presentation capabilities, especially for customer-facing reports.

I have been working with SAP Analytics Cloud for about two years.

SAP's technical support is average. Opening a ticket usually resolves issues, and depending on the support level, response times can be as quick as four hours. Overall, it is pretty standard and I would rate the support as a seven out of ten.

Neutral

The initial setup of SAP Analytics Cloud can be complex, especially with specific requests like multiple users logging in with SSOs. We have faced challenges integrating these scenarios, which reflects some complexities within the SAP ecosystem. While compatibility within SAP is an advantage, these technical challenges suggest it could be even more difficult with third-party systems.

We use SAP Analytics Cloud for real-time decision-making by sitting it on top of our ERP system to track the sales pipeline. It helps the sales and executive teams follow the pipeline stages, from data qualification to cash. We use dashboards to display created sales orders, and PRs in SAP, and send invoices, delays, approvals, and billing requests. It helps the CEO and C-level executives easily see our sales progress, expected revenue for the quarter, and overall performance of our product offerings. So far, it is working well.

SAP Analytics Cloud improves our planning by giving quick visibility into what is happening. For example, we can see which customers are interacting with our demo pages. This feedback helps sales be proactive, asking specific questions and offering help based on customer interest. It helps us respond in real-time.

The integration capabilities of SAP Analytics Cloud have benefited our business systems by making adoption easier. Since it uses familiar SAP interfaces and icons, the transition is smoother, requiring less training. While it may not compete with the design aspects of tools like Qlik or Tableau, its compatibility within the SAP ecosystem reduces the workload, thanks to the standardized icons and interfaces.

For first-time users, I would rate the user-friendliness of SAP Analytics Cloud at around six out of ten. For users already familiar with the SAP environment, I would rate it at eight. While compatibility is a strong point, aesthetics, and UI presentation are weaker areas for SAP.

I wouldn't recommend SAP Analytics Cloud for small to medium-sized companies. The cost of acquisition and support can be steep, and integration with third-party systems might be slower compared to alternatives like Tableau or Qlik, which are cheaper and more suitable for smaller businesses.

Overall, I would rate SAP Analytics Cloud as a seven out of ten.

I am not happy with the predictive analytics features and smart features offered by the product, making them areas where improvements are required.

Even the search to insights and smart insights and smart discovery in SAP Analytics Cloud are areas where what exactly the variants say is something that is hard to understand, even for a SAP consultant like myself. In some cases, the tool works fine, but it doesn't do so in all cases. The aforementioned area can be considered for improvement in the product.

I have been using SAP Analytics Cloud for more than five years. I work as a consultant for SAP products.

IBM Cognos is a product that I considered against SAP Analytics Cloud.

The tool is useful for real-time decision-making as it is used for BI, reporting, and planning purposes.

I won't be able to describe a scenario where the product helps improve planning processes in our company since we have faced some issues with the planning part. My company implemented SAP Analytics Cloud in 2020 in my current organization, and since that time, we have been behaving using the reporting functionality. However, in 2021, when we started to implement the SAP planning model, we could not see how it could meet the requirements after the project failed.

I didn't feel any added value from the use of SAP Analytics Cloud's features in our company's data analysis tasks, and though SAP claims to have smart features, unfortunately, these features are not designed properly in real time. Only the reporting works fine, but the smart features don't work fine. As far as reporting is concerned, it's easy to develop in SAP. The rest of the features of the product are not up to the mark, especially when it comes to areas like predictions and planning, which were recently implemented in our company. My company is not happy with what SAP has to offer.

The integration capabilities offered by the product are okay and not that complex.

Considering the reporting part, I rate the tool a nine out of ten.

If I speak about SAP Analytics Cloud's planning part, I would say that the whole product is not very successful in our company. I cannot rate it as a whole product. I can rate the product in three parts. For reporting, I rate the tool a nine out of ten. Even if you take care of the visualization part, some of the charts don't get into SAP Analytics Cloud. For the argument and analytics part, I rate the tool a two to three out of ten. For planning, I rate the tool a four or five out of ten since it is successful for some models, while for another module, it is not very successful. If I consider the implementation issues in the product, I rate the tool a five to six out of ten for the planning part and a three or four out of ten for the argument and analytics part.

Unfortunately, the tool is not a good product.

Overall, I rate the tool a six to seven out of ten.

I mostly use it for data insights and industry analytics. There were two use cases that were quite opposite to each other.

One was a truck manufacturing and transportation company. That company both manufactures and runs a logistics company. They use SAP Analytics Cloud for tracking their transportation, to see exactly where the consignment is, where the truck is, what the issue is with the truck, why it failed, where it failed, why it stopped, why the driver is deviating from the defined route, etc.

For the second part, an oil and gas company from the US is using it for basically monitoring their oil wells. They track how much the well is getting explored, which well is currently being used, when it can next be used, etc. So they don't continuously explore a well. They are actually making a rotation between the wells. They keep track of which well is getting explored today versus tomorrow.

SAP Analytics Cloud is coming up with all the algorithms in-built as a function or cloud. We leverage those for predicting different scenarios. SAP has provided a good package for predictive analytics.

We were able to convert everything we came across.  

A few things are industry norms or standards. A data warehouse is available to pull data, change data types, and play with it (manipulate it). All data types don't allow easy changes. For example, if it's numbers, you cannot search with regular expressions. However, some things require exploring or finding regular expressions to show, decide, or conclude. 

In the data warehouse, you're free to make changes. You can make everything text, meaning it's always a string. Even numbers appear as text, giving you the flexibility to work with all columns.

This is a regular feature of a data warehouse, but SAP Analytics Cloud also offers an out-of-the-box visualization package. This is handy for showcasing data, and everything is scalable. You can make changes and codes to see data differently. You can add different visualizations that aren't available in the package, and connect to third-party systems easily.

You have the flexibility of reading metadata. Based on the schema details, you can make necessary changes and resend data while complying with the same schema during syncing. This is useful if you are processing data. Command-line interface (CLI) mode is also available.

Some algorithms are complex and can't be processed by just tagging the data and expecting results. Perhaps SAP could add documentation that data needs to be preprocessed before processing.  

I have been using it for four years now. 

There are two things to consider:

When we talk about SAP Analytics Cloud, it has two parts. 

• One is hardware, which you were calling the data warehouse.
• And second one is the software with machine learning algorithms, packages, or functions.
• There's a third component for coding: either the coding IDE, the in-editor environment, or the code-level integration interface (CLI). 

Both modes are available. From that perspective, it's stable.

From a development perspective, there are enough algorithms available for almost all scenarios. Where it's not available, you're free to add imports, so you're not limited. 

The hardware part is largely supported, and SAP is largely using AWS, so it's okay.

Regarding scalability, I'm referring to what we can do and update and whether there are limitations. So, limitations only apply to the service size you're on. Based on that, we can process data. But that's the hardware part, which SAP doesn't provide as part of the cloud offering. 

On the cloud, SAP allows you to scale up the server size, and then it should work. That's what we observed from a cloud standpoint.

From a scenario-capturing standpoint, I was able to capture almost every scenario I've encountered. There was no issue, even with the complex situation at the hackathon. We simulated a load of around 2,050 and were able to make that possible in the cloud, which was a good use case. 

Besides us, other companies like Accenture, IBM, Deloitte were there. We were the only small-sized company. We are small compared to other big companies participated there.

The customer service and support are okay. The response time is faster. 

Positive

I have worked with Azure as an alternative for two of our clients. They also offer cloud analytics functionality. Their prices are lower, and you might save 40% to 60% compared to SAP Analytics Cloud.

Price is the main part. The second part is their integration with Excel, since it's their own product. Because of Power BI, they have an advantage. They can enable everything they manipulate on the cloud in Excel as well. 

Although SAP also provides integration with Excel, it takes a longer route since Excel isn't an SAP product. SAP might say that Excel and Power BI licenses are subjective. Azure offers a package that includes everything, so you don't count Excel as an additional cost.

The deployment is easy and quick. You don't have to worry much. The steps are provided, and if you follow them religiously, you're done. It doesn't take much time to enable. It's actually the client side (because of their approval protocols), like user enablement, authorizations, etc., that takes more time than SAP's part (as the document and steps are quite elaborate).

If all authorizations are in place, installation takes around a week maximum. It can be done earlier, but some things take time to enable. Last time, it took one working week to enable us, and then around a week to create the tenant and environment (with integration to Backend server). So, it was a two to three-week job.

For deployment, typically, one person handles everything. We added a second person for support when the first person was unavailable. 

One person cannot be available all the time, so we added a backup. Typically, one person should be enough, but a backup should be provisioned. For continuity, a team of two would be good enough.

SAP has out-of-the-box connectors and adapters that allow us to connect to virtually all data sources. SAP has over 350 connectors and adapters combined. So it's really easy to integrate this product with other data sources and systems that we currently use. 

The ROI was faster for the oil and gas company, achieved within 18 months. For the logistics company with analytics (refer to the use case Q/A above), the ROI was achieved within 23 or 24 months. Anything within three years is considered a good investment.

SAP pricing tends to be premium, so it is slightly higher. However, in this case, the premium charges are justifiable because it comes with out-of-the-box connectors and adapters and can still communicate with your on-premise or any other system. It creates a common interface for gaining data insights.

The only suggestion I have is regarding enabling people on the platform, which requires an individual license. This is a concern among clients because even for just viewing, a license is needed since an S-user ID or P-user ID would be created (S-users are usually direct employees, and P-users are contractual employees). Only then can that person access the cloud and the analytics. This can be a bottleneck because clients need to decide who should be granted access.

For example, in a use case where vendors were considered for access to a common solution, the solution met their expectations, but they limited access to only a few vendors. It would be beneficial if this could be adaptable for certain portions, such as viewing only. Maybe a package could be offered for that, which might help SAP expand further.

So, the individual license requirement could be there. Perhaps something for only visualizing or viewing with no changes could be offered at a lower cost or bundled. This would allow companies to enable their ecosystem partners on the platform.

Overall, I would rate it a nine out of ten. It's a good product. I would definitely recommend using it. 

SAP Analytics Cloud is typically based on machine learning algorithms, which are loosely called AI-enabled. AI has three components: machine learning, deep learning, and then AI. AI isn't widely used, but even for machine learning components, we generally call it AI.

Anything beyond machine learning components, almost all algorithms are available. If, for any specific reason, you need something not available, it can be included. So, from an AI perspective, it is very compatible and ready out of the box. All of the data insights are supported by AI algorithms.

People often refer to machine learning as AI, even though AI is typically machine learning or deep learning enabled. Both are present in SAP Analytics Cloud, so it is already AI-enabled.

We primarily use the solution to create financial dashboards. 

It is a good tool with a friendly interface. It is not really complicated.

The solution can scale.

Technical support is helpful. 

It's easy to set up.

We find the product to be reliable. 

The pricing is pretty good.  

Sometimes there are a few problems. For example, the direct model has two kinds of models to connect your data. There is the import data and the direct connection data.

If I want to create some calculated dimensions, it is not possible to do it when we work on the direct model. You have to import the data into the cloud in order to create some calculated dimensions. However, when we work with SAP Analytics Cloud, we can't create the calculated dimensions.

In the live connection model, we can't create calculated dimensions, however, in the import model we can create them. I would like to have this functionality in the SAP Analytics Cloud live model, also.

I've been working on the solution for three months. 

The stability is excellent. I would rate the reliability nine or ten out of ten. 

It is scalable. There are no problems. When we work on the import mode, all is good.

I'd rate the scalability eight out of ten. 

There are around 40 people using the solution, between business intelligence and EPM. 

Technical support is good. I've never had a problem with them. 

Positive

I have used other tools, however, only in the capacity of academic research. I've used many solutions. For example, I was working with Microsoft Business Intelligence, SSCS, SSIS, and Power BI. And I was working on Alteryx, as well as SAP BW 4HANA and Tableau.

SAP Analytics Cloud is the tool that we use for dashboarding, so I can't compare it to the SSCS, SSIS, or Alteryx. It is comparable to Tableau and Power BI. All three tools are scalable and they are not difficult to use.

There is just a little problem with SAP Analytics Cloud when we are working on the live models. Otherwise, all is good.

I'd rate the initial setup eight out of ten. It's a very good tool that is easy to set up. It has a user-friendly interface which makes it pretty easy.

I work on the SAP Analytics Cloud Business Intelligence license.

There is another license for the EPM, enterprise performance management. I'm working only on the Business Intelligence license, not on the EPM license.

I'm not sure of the exact pricing, however, my understanding is that it is pretty good. 

We are SAP partners. 

I am certified in SAP Analytics Cloud. I got the certification last year.

I'd recommend the tool to others. 

In the other reporting tools, we don't find, for example, the EPM or the smart data functionality or the machine learning functionality. In SAP Analytics Cloud, it's not only a tool for creating a dashboard and a report. We can also create some machine learning applications and some EPM dashboarding. I recommend others to use itas it is a very rich tool and it is updated often. Every two or three months there is a new update.

I'd rate the solution nine out of ten. The only issue we've had is working with the live connection. Other than that, it's great. 

My VP asked me to work with my plan number to figure out how to increase and support our sales region. On the planning admin, I created a model and then imported actual and forecasted data. From that, I was able to determine the variance. We managed to plan this with small amounts of data. 

We created a planning model and enabled it to make a planning checkmark that works with the time category in the tab dimension, in order to bring it down to the lowest granularity, by month, start date, and end date. 

We created an organization dimension. A responsible person to monitor each region is required. Then you can drag and drop the region hierarchy from the parent.

Then, we worked on the calculated numbers, for example, the gross margin, which was the total revenue minus COGS. We then divided that number by total revenue times one hundred. From there, we could pull the data from the file and select source actuality to validate it. 

We performed all of this using SAP Analytics Cloud.

In the most recent release, the schedule publication was officially updated and it's been added on MS Office 365. I like how we had an EPM for B to C perspective; it's a great enhancement. Also, I believe the B to B connectivity has been improved. Android support for mobile is really good also. Power BI has always had android support in place, so manual support for SAP Analytics Cloud is also a very good feature.

Smart Insights have also been enhanced, especially in regards to the Stories.

Apart from that, I would like to see more data-handling enhancement. Smart planning can give us immediate results.  When we come to analytics, If we could get more enhancement, we could probably completely skip the data warehousing concepts. This would be a big improvement.

With all of the new updates, now we can combine different types of data together and give our customers a full picture. 

I am happy with the new release that is coming out. It's comparatively a little slower than the version we had from 2019-2020; however, the enhancements they have added are much better than previous versions.

The most valuable feature is that we can allocate, spread, and distribute the actual figures.

The cost is an area that could be improved. I also think that Ofice 365 should be incorporated. People would find it to be more user-friendly with Office 365 integrated. Flat file integration can be a distinguishing factor.

Custom visuals is another area of improvement. There are also some mobile features that are missing. There are features that aren't available on the mobile. 

There should also be better connectivity tools for HANA and Success Factors. They have minimal visualization relations and they have the third-party add-ins.

I have been using this solution for the past six months.

We have 300-plus Stories that we already had in the system, that we had to do performance tuning. I brought it down to 267, to be exact. We had over 302 dashboards and then we brought it down to 111 stories on 267 visualizations. When we have too many pages, the performance gets impacted a little on the initialization part. When we open the story, it takes a little bit of time because it's not on-premise. Because our clients are based in the Netherlands and we are based in Bangalore, we had to manage the different time zones, and the business hours are different. We had to find out during the peak business hour how the SSE behaves.

We found that in the peak business hours it does slow down a little bit, considering the internet speed and everything. We did take everything into account. Then we checked the performance and the data also. In order to be competitive with the other software in the market, a little enhancement in this part also would give the upper hand compared to other BI tools.

We used to go in a cognitive window wherein we don't have any caches in place, so that we could get an accurate picture of the performance. But we also want to reduce the number of filters, and then we want to do more slicing and dicing across the data and try to see how it improves. In the near future, we would try to test the data more.

It performs quite well. Custom visuals would definitely help us to improve a little more. 

There are more viewer licenses. We have got close to 130 users for Analytics Cloud. And for developer access, for read and write, at our location we have around six plus two for planning, altogether 11 to 12 licenses. 

We have BW HANA. So we have only four developers as of now. We used it more extensively before the pandemic but not as much now. We do have plans to increase usage in the near future. 

The technical support is quite good. Any time there is a new release and we have any issues, we connect with the technical support team and then go ahead and write about it in a blog so that the other users can also get help for similar issues. 

We previously used Cognos Analytics and Tableau. I've done a couple of POCs on Tableau and worked on micro-strategy. We switched to SAP Analytics Cloud primarily for the planning, development, and the user-friendliness. When SAP comes up with a solution, they definitely try to be up to market. The features were not in a premature stage. We had faith that SAP had enhanced features compared to other BI tools.

The initial setup was pretty easy. It was also easy for my team. They thought it was easy to install. There's no download, there was no complexity.

We do the maintenance for it because we know the ins and outs of the development. We also do the admin.

If you are new to SSE and are trying to implement SSE in your project, don't think that SSE is an SAP product. Look into the features because I am sure you'll be satisfied with the features. They come out with the releases. I go into the release notes to understand what are the enhancements are. This will make it easier to understand. 

If you have somebody who isn't as experienced with BI, even that person can still learn SSE and can cope up with the BI requirements. In India, if you want to be a BI compatible, you have to have at least three years' experience so that you can understand all of the data warehousing concepts, and EDL. With SSE, learning is very smooth and easy.

I would rate it a nine out of ten. 

The missing point would be because of the performance. 

We integrate with the SAP and it encompasses the entire effective practice for various industries. If you're activating SAP Analytics Cloud, it includes dashboards, analytics, and safety statements. It features a comprehensive library ready for use in month-end planning, development, product management, analytics, and dashboards, which is very beneficial. There is a component related to planning. If you already have a planning or budgeting process in place, you can use SAP Analytics Cloud for planning features. The tenant is available for collaboration, allowing different parties to participate in planning and budgeting for new and adaptable cycles.

SAP Analytics Cloud has several valuable features. Firstly, it provides dashboards that display the number of candidates who have applied for specific positions, categorized by region, and the duration required to fill each position. Additionally, it shows the number of vacancies awaiting approval or already open based on relevant information. For example, it can illustrate accounts by email or location. It also offers insights into various metrics such as average gains within specified ranges. It can be customized for different domains and scenarios.

It can address specific needs. For instance, it was used by a client to validate national ID numbers for candidates who had previously applied for specific exams, preventing duplicate applications. This functionality allows users to upload relevant data and apply validation checks before proceeding with recruitment processes. It also aids in consolidation. While the product itself lacks native consolidation features, users can create consolidated statements by combining data from various legal entities. This enables users to drill down into specific values and analyze data at a granular level.

SAP Analytics Cloud allows you to quickly perform various tasks in terms of virtualization. CyberArk itself requires some enhancements to better connect with other technologies. While it's easy to connect with SAP, using analytics tools may sometimes necessitate sourcing information from different platforms. You might need data from SAP or other technologies/platforms to access specific information not available within SAP. SAP is effective for managing storage and enhancing virtualization capabilities. However, when accessing data from diverse platforms, complexities may arise.

We are building connectors with other platforms as a service. We develop functions to enhance comprehensive analytics, including predictive analysis, which is required to be integrated into its cloud. 

I have been using SAP Analytics Cloud for four to five years.

The product is not stable.

The solution’s scalability is terrible. 50 users are using this solution.
Utilizing the server in the cloud doesn't require much effort; you simply use it, by the way. It requires the administrators to use this tool.

The initial setup is moderately complex, but not highly technical. It requires someone who understands the features. This individual should know how to use the features, select appropriate storage options, choose suitable graphs for display, and determine when to use specific measurements or build custom ones. 

Since SAP Analytics Cloud is already cloud-based, there are no deployment steps involved. The process mainly entails connecting and configuring the necessary settings. This involves adding your organization as a tenant, providing the username and password, and possibly specifying a company ID or other relevant information.

Deployment and maintenance may require around 21 individuals.

You get the full benefit to assess the affordability of the product over time. The SAP platform offers various products, each serving different needs. They need to have various products available for selection. SAP Analytics Cloud is highly advantageous. 

The product is expensive and has a yearly subscription.

If we are using a scale or another device, but there is no other way to integrate. You can use all of our services. So if you compare SAP Analytics Cloud with Power BI. Power BI has different connectors that make integrations easier. When it starts with SAP Analytics Cloud, it's easy to connect to specific products when possible.

It give compliance with very good visibility. They support the key users inside the organization for identifying specific areas where SAP Analytics Cloud will decline.

Building the dashboard, analytics, and testing some use cases involves integrating with other platforms based on customer requests. 

Overall, I rate the solution an eight out of ten.

We use SAP Analytics Cloud to create executive dashboards for our organizations. It is a comprehensive analytic solution that allows users to review functional data globally in a clear and organized format, and incorporate features for data visualization and analytics.

There is a seamless integration with SAP S4HANA which is really valuable to our organization and its functions. Since the data is stored in the same ecosystem, it creates a simple process of building dashboards, without making the process cumbersome The Analytics Cloud provides standardized templates and storage options, making it easy to plug into existing structures.

We're utilizing custom stories that have already been developed, streamlining the development process. It serves as a convenient starting point and contributes to the overall ease of development. Another advantage is that it doesn't provide the same level of calculation power as Power BI and Tableau. SAP Analytics Cloud emphasizes pushing analytics back into the database, recommending data manipulation and preparation outside of the platform. This approach is seen as an advantage, as it allows for more robust data handling.

However, there are areas of improvement, particularly in terms of visualization. The front-end interface of SAP Analytics Cloud is not as impressive as Tableau and Power BI. The charts in other solutions have a more visually appealing and customizable look, while in SAP Analytics Cloud, there's a lack of alignment between numbers and charts. This graphical aspect, especially concerning charts, is not as satisfactory in SAP Analytics Cloud compared to its counterparts.

I have been using SAP Analytics Cloud for the past six months.

In terms of scalability, SAP Analytics Cloud seems well-aligned with the concept of being an SAP product. It is designed to work seamlessly with SAP ERP, making it scalable when used in conjunction with SAP ERP, especially for large-scale operations. While there might be challenges when connecting to external sources, the current structure allows connectivity to various sources such as Excel sheets and Cloud-based databases like Xarel. Although there may be limitations in the current setup, there's potential for future scalability to include a broader range of databases for connections to SAP Analytics Cloud.

I haven’t claimed support services as of now, but the online resources are really useful for small queries.

SAP Analytics Cloud is a cloud-based solution, meaning there's minimal installation required on your end.You obtain login details, pay for access, and log in to your cloud account to develop or access data. As an end user, you simply log in through the web application using your credentials. Whether you're a developer or an end user, the platform is accessible through the web, and the storage is created accordingly.

The maintenance primarily involves administration tasks. As a developer, I don't handle the administration part; someone dedicated to administration takes care of tasks like managing access and overseeing the platform. Regarding updates and maintenance, SAP does this on a biweekly or quarterly basis, depending on your subscription or preference. So, maintenance involves ensuring the platform is regularly updated by SAP.

I wasn't directly involved in the pricing discussions, but when I joined the organization, they had already purchased the SAP Analytics Cloud solution. The organization faced challenges due to its perceived expense, leading to restrictions on granting access to many users.

As a recommendation, when deciding on using SAP Analytics Cloud, it primarily depends on whether your company is already utilizing SAP as its ERP. If SAP is the ERP, this solution makes sense due to seamless integration and optimization within the same organization. However, if you're considering Oracle or Postgres, alternatives like Tableau or Power BI might be more suitable. The learning curve, as SAC employs Core Data Services (CDS), which can be challenging for developers unfamiliar with this concept. Finding experienced SAC developers can be a challenge compared to the more prevalent expertise in Tableau or Power BI. Community support is another consideration. While Tableau and Power BI have strong and active communities for support and knowledge-sharing, SAC may have a smaller and less engaged community.

I would rate it 6 out of 10.