SAP Business Data Cloud Reviews

My clients, like Bin Quraya and Sadaras, use it. At present, it is used in paper mills for tissues and bundles. Others are construction companies and chemical companies.

I'm given different tasks with deadlines. They want to finish on time. Even if they're given a task two days before, they have to complete onboarding within 24 hours. I'm the implementor. 

In my opinion, it is good for analytics. Everything is in the cloud. You can also use it for non-SAP sources. You can bring in visualizations for the data, for anything. It's a good feature for Analytics Cloud.

Predictive analytics is used for data sources. Sometimes, they are custom; you can bring your custom data source directly from predictive analytics, or we can use custom tables for forecasting. Forecasting and planning for sales and financial balance sheets are done here.

AI has already started this year. AI for ABAP has started, and AI for Analytics processes is there. Joule is one main feature; you can use it within SAP Analytics Cloud. It is used for planning.

All the features are there, maybe except for the one-gauge visualization, which is missing. I've already requested it from SAP. They're planning to put it in SAP. In the future, they're going to bring back a system for one gauge.

To improve the gauge and the dashboards for using the gauge. This visualization is one thing missing in the Analytics process. In Power BI, they're using these tools. Or there is a possibility in Eclipse; we can use RxScript. RxScript is rendered from the router, but on a mobile base, it's not using RxScript. It's not working.

I have been using it for almost seven years now. 

I would rate the stability an eight out of ten. 

It's scalable, and it is on average; this one is good.

I would rate the scalability an eight out of ten. Our clients are medium-sized businesses.

Customer support is good because they're relying on the HANA Cloud database. HANA is a good, fast database compared to other databases. That's why the customers are happy to use it.

The setup is okay. SAP takes care of everything with the cloud system notes. They change things from time to time, either by themselves or automatically. 

If there's any setup, it's only for your connector. You have to key in the connectors for this. Suppose SuccessFactors is there and HANA Cloud is there; there are different activities for each.

I am an implementer who does SAP implementation. Setup is easy because these days, SAP takes care of everything. They provide a URL for the SAP Cloud, and everything is maintained within SAP since it's cloud-based. If it were on-premise, we would have to set up everything in our company.

The pricing is reasonable. Compared to Power BI, we are a little bit higher. Not much, just a little bit.

I recommend it to other clients as well. The advantage is predictive analytics. We can use it directly without any customization.

Overall, I would rate it an eight out of ten. 

We use SAP Analytics Cloud to report and generate all our financial reports. Its robust capabilities allow us to create analytical reports using data from SAP and non-SAP sources.

There is a real-time reporting mechanism in place. SAP Analytics Cloud operates on the SAP S/4HANA database, ensuring real-time reporting is always available. This capability allows the business to monitor cash inflows and outflows at any given moment easily.

When using cloud analytics, you often need to build a staging environment and an aggregation layer if you have different applications. However, since everything is running in the SAP landscape on the HANA database, data extraction and aggregation have become much easier and more efficient at any given time.

There are definitely several areas that need improvement, but articulating them as specific priorities right now is challenging since we just implemented the project and it went live. It's difficult to pinpoint exactly what needs to be addressed.

I have been using SAP Analytics Cloud for five years.

The foundation needs to be stabilized after going live. If the requirements change, such as needing more reports or increased usage of Cloud Analytics, we must address those adjustments.

The system isn't experiencing frequent failures or timeouts, but there are instances when we deal with large datasets. We sometimes encounter high memory usage issues since it's running on our database. This can lead to errors stating that there isn't enough memory to extract data, especially if the hardware isn't adequate. If the hardware can't support the load, it becomes challenging for Cloud Analytics to function effectively.

I rate the solution’s stability a seven out of ten.

Different organizations are utilizing SAP Analytics Cloud; if I consider the end users, nearly 125 users are actively engaged. The usage numbers are relatively high because we're in the banking sector.

It is used quite frequently. Initially, it was mainly utilized for month-end closings, but now it’s used regularly. The reporting mechanism is integrated with SAP Fiori, allowing users to access data and billing reports on various devices, including tablets and smartphones.

I rate the solution’s scalability a seven out of ten.

Support is still improving, and getting support from SAP is a bit exorbitant. 

Sometimes, the queue will be on the queue, and yours may be the P1 ticket. You will be in the queue and not get the best support.

Neutral

We previously used Hyperion, and then Power BI came into the picture. Earlier, many different BW systems were also in use.

Setting up the production server takes about seven to eight working days. However, we also have a sandbox installation where we can monitor the required time, assess downtime, and determine what integrations or standard interfaces need to be enabled and how data flows. From this, we can build a comprehensive inventory of the necessary implementations, integrations, and ports that need to be enabled, including database connectivity and whether we should use the SAP Cloud Integrator or different APIs.

If you have reliable systems, setting up the SAP Analytics Cloud production system typically takes six to seven working days.

We need a basic administrator, a database administrator, an SAP S/4HANA specialist, and a Cloud Analytics expert. Four to five resources are required, but this can vary depending on the project's size, the database's complexity, and the number of reports being developed.

The implementation of SAP Analytics Cloud at British Petroleum was done with a different organization, and currently, I’m working with Standard Chartered Bank. I don't have much visibility into what improvements were made at British Petroleum, as I'm not associated with them anymore. For Standard Chartered, if there are any improvements or business needs, we will evaluate those as we move forward. 

Reflecting on the three projects I've executed, I see that the earlier ones were challenging, particularly regarding server installation and cloud integration with SAP S/4HANA. Over time, the process has become easier with the introduction of different APIs and standard interfaces by SAP. 

I rate the initial setup a seven out of ten, where one is difficult and ten is easy.

It is very expensive.

Business decisions depend on how data is utilized and structured. For example, the finance department needs to monitor revenue management, assess the impact of costs, and evaluate operational expenses. Predictive analytics is invaluable for analyzing revenue, costs, and financial performance. It provides insights on the demand and supply side, making it easier for the business to make informed decisions.

The current project where we've implemented SAP Analytics Cloud went live three months ago at Standard Chartered Bank. We plan to decommission all other reporting mechanisms, such as Power BI, Tableau, and Crystal Reports. We are currently evaluating the output from SAP Analytics Cloud regarding reporting and its collaboration with other applications. It’s too early to say whether Power BI is better than SAP Analytics Cloud or vice versa, as both tools have their strengths and address different needs.

Overall, I rate the solution a seven out of ten.

Our primary use cases involve data insights. We gather data from various sources, including SAP and non-SAP systems, and use SAP Analytics Cloud to generate reports. 

We also leverage DataSphere* to connect to systems that don't have direct connectors within SAP Analytics Cloud, bringing that data into the platform.

It helps with planning and forecasting capabilities. 

So planning and forecasting are specifically useful for companies that need to see their potential load for next month's production and what procurement they should be planning for. We consider both factors in this process.

So, in the first case, you're planning for plant utilization. Maybe your plant is equipped to manufacture 1000 pieces per month, let's say bikes, but your sales are roughly 800. Then there's no need to run at full load.

However, on the other hand, if you're manufacturing a thousand 1000 pieces or bikes per month but have 1200 or 1100 orders, you can think of how best to utilize your capacity to increase production from 1000 to at least 1010. 

Forecasting is actually dependent on that. Without forecasting, effective planning isn't possible.

Some beneficial features include data adaptation or massaging capabilities – the platform makes that simple. 

The user interface (UI) is also quite intuitive and straightforward. 

Visualization is quite seamless and awesome. Once the data messaging is set up, it's straightforward and simple to select how it should be displayed. So, that's another advantage of SAP.

Predictive analytics capabilities are also good. It has positively impacted decision-making. Recently, we did predictive analysis for a Middle East client. We worked with their OT systems.

So, for OT systems, the client needed to plan for predictive maintenance. They have a heavy manufacturing focus and wanted to accumulate data and get strong insights. 

They had various data sources – your furnace has some data, your chiller has different data, and it's all local to those systems. The challenge is getting everything centralized for insights. We leveraged SAP for this, and it was very beneficial because they're primarily an SAP client, meaning they use SAP ERP. 

So it's easy to pull data from OT systems and SAP. Having the Cloud component is also helpful, as they can look at and change reports at runtime. They were able to get insights five levels deep.

Most often integration is there, but in cases where adapters or connectors are not available, we have to opt for DataSphere to bring data into the landscape, and then it allows connection. For the majority of cases, it works well, especially because the integration suite is helpful.

Challenge:

There are challenges, such as initial connection times when direct connectors aren't available.

For example, the initial connection can take a long time when direct connectors are not available. In these cases, you have to use a tool like DataSphere, and then you can connect. 

But that's beyond their control; it's not really their limitation. It's like if you haven't opened your door, and we want to enter your house, we have to find another way in.

Area of improvement:

One potential improvement is how we can transpose data. With SAP, if you have data from one system with a declared data type and a different system with another data type, the transfer is tricky. 

Currently, they allow us to fix the data type, and then we work with that. But sometimes, it would be easier if we could declare a data type dynamically. 

The way we do it now is to manipulate text into text, make some assumptions, and then get the data. This can sometimes lead to errors. So, data transportation could be handled better.

I've been working with SAP Analytics Cloud for three years.

SAP Analytics Cloud is mostly stable. However, customers have reported some issues. While the product itself is stable, if users can't easily leverage it, that's a problem. 

For example, the logs show the overall health of the cloud services, but it's difficult to get granular insights. If I want to see how much bandwidth a specific service is using, or how much data is routed through it, that information isn't readily available. 

So, technically stable, but some improvements are needed in terms of user experience.

It's quite scalable. SAP provides the ability to connect even when there isn't native connectivity, demonstrating their focus on scalability. 

If your data volume increases in the future, scaling up is easy and doesn't require excessive effort.

It seems like SAP's support team is still getting up to speed on this product. They probably have to reach out internally to find the right experts. While they respond with 'We'll look into it,' the follow-through can take a while. 

If you call SAP support with a question they don't immediately know the answer to, they'll take notes and connect you with the right person. That part is good. 

The first-level support person does the right thing. But, the expectation is a quicker follow-up within maybe 24 hours, and that's not always happening. They take some sweet time to give a response. 

Neutral

We often use alternatives. This can be due to customer preference or because they offer better flexibility and pricing. For example, Power BI on Azure is a common choice.

There are other differences, but primarily, it comes down to flexibility and pricing.

Complexity arises when you need to connect with systems that don't have standard connectors. Otherwise, it's straightforward and simple. 

It's those cases where DataSphere comes into play that introduce complexity.

For deployment, one person is needed to understand the landscape and translate it into the 'to be' system, and another person for actual migration. You also need someone from infrastructure. So, around three people on the technical side, or maybe five if you include project managers.

For maintenance, three people are required. One from infrastructure, another from technology, and one for coordination or from an architect's perspective. These people might be working at a maximum of 10% capacity on this project, as their responsibilities could be spread across multiple projects.

SAP Analytics Cloud is a bit higher [in price]. Because, if Microsoft can afford a better product and lower cost, SAP can afford at least the same, or even lower.

At least it should be competitive. If Azure or Google can provide an alternative, so can SAP.

If SAP is claiming a premium, it really needs to be seen as such. When comparing, say, burger A from one place to burger B from another, you have to show why you are charging a premium. For example, if burger A is $1.40, why should I pay $5 for burger B when it is available at a lower cost?"

Otherwise, people will compare the prices directly, wondering why they should pay more for something that's available at a lower cost elsewhere.

Either show the correct value proposition or else adjust to the same level.

Overall, I would rate the solution a seven out of ten. I recommend using this solution because we are SAP shop. 

But at the same time, on the ground, we are aware that clients nowadays are not dependent just on us. "Us" as in us being the consultants. So, they also do some prep work. They also reach out to some consultants, like us. They consult a minimum of three or up to eight to ten consultants each hour before they say yes to us. 

So, at that point, they are already aware of what the potential alternatives could be. Though we are recommending SAP Analytics Cloud, they already have insight about the possibilities in the market. So, it's not always easy for us to convince them to opt for SAP Analytics Cloud either.

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.

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 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 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.