IBM SPSS Modeler vs. SAP Analytics Cloud

Fast NLP analytics are very easy in SPSS Modeler because there is a built-in interface for classifying concepts and themes and several pre-built models to match the incoming text source. The visualizations all match and help present NLP information without substantial coding, typically required for word clouds and such. SPSS Modeler is good at attaining results faster in general, and the visual nature of the code makes a good tool to have in the data science team's repository. For younger data scientists, and those just interested, it is a good tool to allow for exploring data science techniques.

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>> Using SAC predictive analytics capabilities for inventory management in a Production line setup has helped generate Purchase Requisitions and Purchase Orders for raw or semi-finished goods without much head-banging into Demand management rules. It does it beautifully with seamless integration with HANA core MM and PP modules, along with BI integration. It has resulted in 30% greater warehouse storage capacity, thereby saving revenue from piled-up inventory and associated manpower costs. >> SAC sometimes shows latency in working out a large data set, thus giving a poor user experience compared to its competition. Also, it may occasionally show misinterpretations when embedding data from 3rd-party systems into the HANA core dataset.

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• Combine text and data
• Provide facilities for all phases of the data mining process.
• Use a node and stream paradigm to easily and quickly create models.

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• It makes it easier yo analyse order and related records easily.
• We can easily maintain and track the performance of employees in organisation.
• Can easily track various aspects for the growth of an organisation thus allowing real time analysis and tracking of organisation's growth and performance.

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• Has very old style graphs, with lots of limitations.
• Some advanced statistical functions cannot be done through the menu.
• The data connectivity is not that extensive.
• It's an expensive tool.

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• Complexity in Data Modeling
• SAC supports various data sources, but improvements in the ease of connecting to and integrating with certain data repositories, especially non-SAP databases, would enhance the platform's versatility and integration capabilities.
• An offline mode for SAC could be valuable for users who need to access and analyze data without an internet connection. Additionally, optimizing performance for large datasets and complex visualizations would contribute to a smoother user experience.

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We are planning to review the licensing as we have issues with SAC dealing with huge datasets. Analytics area is good for import models but when we have live connections in place that's when we have issue with SAC dealing with huge datasets in live be it BW or be it HANA models in the backend.

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The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources

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I landed on a 7 out of 10 for a few key reasons. The tool is flexible in the sense that we are using very very very (very) little off the shelf solutions and customizing to our needs. Users enjoy having the customized data actions, key variance calculations, and easy to use headcount/salary planning in the same tool. One downside is that users are hard to please when they are used to excel. SAP Analytics Cloud simply is not excel, and the flexibility of excel in a crunch is not there.

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I would rate SAP Analytics Cloud an 8 out of 10 for scalability. It offers a flexible, cloud-based architecture that supports expansion across departments and geographies. The platform adapts well to growing data volumes and user needs, making it a strong choice for organizations looking to scale analytics capabilities efficiently.

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I would rate SAP Analytics Cloud’s performance an 8 out of 10. Pages generally load quickly, and reports run within a reasonable time frame, even with complex datasets. Integration with other systems is smooth and doesn’t noticeably affect performance. Overall, it’s a responsive and efficient tool for business analytics. But

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The online support board is helpful and the free add ons are incredibly appreciated.

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Since the implementation stage, the support team has been very helpful and assisting. Even in the later stages, the tech team had quite a rapid response. In general, SAP has provided us with great customer support, let it be for a specific product of SAP or for integration of different modules.

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Good videos and reference material available in SAP Portal.

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In hindsight, it would have been easier to have someone there in person. Questions were answered, but with 11 participants, it got a bit chaotic online

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SAC is a simple solution ad it works fine when connecting it to other SAP tools. On the other hand, connecting it to third party solutions brings difficulties when there's no previous design and the objetives are not clear. It is really important to integrate Business users from the start to provide with valuable business insights

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When it comes to investigation and descriptive we have found SPSS Statistics to be the tool of choice, but when it comes to projects with large and several datasets SPSS Modeler has been picked from our customers.

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SAP Analytics Cloud and Power BI are both tools that help businesses understand their data, but they have some differences. SAC, made by SAP, works well if your company already uses other SAP products. It's in the cloud, easy to use, and has features for analyzing data, getting insights, and planning for the future. Power BI, made by Microsoft, can be used in the cloud or on your own computers. It fits well with Microsoft tools, is easy to use, and can do advanced data analysis. SAC has built-in planning tools, while Power BI needs extra tools for detailed planning

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unit pricing

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Is good for use across multiple locations. It allows users to access data and reports from anywhere, regardless of their location. Can consolidate data from various sources, including different SAP systems and external sources, which facilitates cross-location analysis. SAC enables access to data and models from SAP Datasphere to create new stories. Detailed permissions can be defined for cross-departmental use.

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very simple

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• Positive - Ease of decision making and reduction in product life cycle time.
• Positive - Gives entirely new perspective with the help of right team. Helps expanding the portfolio.
• Negative - Needs to have good understanding about mathematical modelling, of which talent is rare and expensive. Hence, increase the costs for R&D and manpower.

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• Many manual data manipulations and exports in Excel have been replaced by the tool, providing management with improved insight into the amount of time spent at each stage of an invoice's lifetime, allowing bottlenecks to be discovered.
• We now have more insight into the data, and people with little technical experience can easily build stories.

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