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Since using this tool, I can now make decisions faster, even when there is just a small change in the data.
We see a return on investment with IBM Planning Analytics as we are able to forecast the budget for the entire year with significant time saving.
I have seen a return on investment such as a thirty percent increase in efficiency.
The enterprise subscription offers more benefits, ensuring valuable outcomes.
I have seen a return on investment with SAS Visual Analytics, as it includes AI capabilities and automatic functions with scheduling, allowing reports to be made from live data, which contributes to a new vertical for revenue generation.
We have a multi-level support system, with the initial level handled by the company we bought the license from and subsequent support from IBM.
Instead, we rely on third-party partners recognized by IBM, who provide cost-effective support.
The customer support for IBM Planning Analytics is very responsive and supportive 24/7.
They provide callbacks to ensure clarity and resolution of any queries.
They assist us when we encounter issues and provide help while we complete tasks.
It handles literally every single data we give to it without lagging or crashing.
Scalability is quite hard to implement in TM1, largely since the on-premise installation chosen back in 2014.
Scalability is straightforward but it is pricey since it's a SaaS model priced per user.
SAS Visual Analytics scalability is very good and positive.
The performance of IBM Planning Analytics has always been fast and reliable for my needs, even when dealing with huge volumes of data.
This stability is really important as we use it for budget calculation, which is time-consuming.
SAS Visual Analytics is stable and manages data effectively without crashing.
The abundance of features results in complexity, requiring strict guidelines for developers to ensure simplistic approaches are adhered to.
IBM's visualization needs significant improvement.
IBM Planning Analytics does not work offline, which makes it difficult for me and my team to work or gather required data when we do not have an internet connection.
Training on SAS Visual Analytics is required to help overcome these issues.
In terms of configuration, I would like to see AI capabilities since many applications are now integrating AI.
TM1 is quite expensive, and I'd rate the pricing as an eight out of ten.
While IBM's solutions were costly before, the introduction of SaaS models has reduced prices significantly.
Regarding pricing, the cost is high compared to the competitors of IBM Planning Analytics.
Its stability helps controllers win time in their planning processes.
It also integrates machine learning and AI engines, enabling us to use algorithms for inventory forecasting which optimizes our inventory and replenishment rates.
The scenario modeling feature in IBM Planning Analytics is probably the most robust planning and forecasting solution we use, especially for what-if scenarios.
After implementing SAS Visual Analytics, we have generated a new way to generate revenue by providing live data visuals to our clients and making our team aware of data in real time, which has had a significant positive impact.
The ability to query information from our Excel data into SAS to view specific data is invaluable.
| Product | Mindshare (%) |
|---|---|
| IBM Planning Analytics | 4.0% |
| Anaplan | 5.8% |
| Workday Adaptive Planning | 5.6% |
| Other | 84.6% |
| Product | Mindshare (%) |
|---|---|
| SAS Visual Analytics | 1.6% |
| Tableau Enterprise | 9.3% |
| Qlik Sense | 4.7% |
| Other | 84.4% |


| Company Size | Count |
|---|---|
| Small Business | 18 |
| Midsize Enterprise | 4 |
| Large Enterprise | 18 |
| Company Size | Count |
|---|---|
| Small Business | 13 |
| Midsize Enterprise | 10 |
| Large Enterprise | 19 |
IBM Planning Analytics offers a robust planning, budgeting, and forecasting platform powered by TM1 technologies, integrating with Excel while offering real-time calculations, data governance, and security.
Supporting flexible scenario modeling and forecasting, IBM Planning Analytics enhances planning processes via machine learning, real-time data calculations, and meaningful collaboration. In-memory processing and data slicing boost automation, reduces errors, and increase performance, while the centralized database facilitates secure and governed data management. Sandbox environments assist users in testing scenarios, and integration with Excel is crucial for financial planning and resource allocation.
What are the key features of IBM Planning Analytics?In multiple industries, IBM Planning Analytics is key in supporting budgeting, planning, and forecasting efforts. Financial services use it for cash flow modeling and resource allocation, while manufacturing sectors benefit from its dashboard-driven data visualization capabilities. Businesses utilize its robust reporting and real-time analysis functionalities to manage resources and assess future risks effectively.
SAS Visual Analytics offers rapid data processing and advanced forecasting with interactive reporting and visualization. It integrates with diverse data sources, enhancing scalability and automation, enabling data-driven decisions and extensive insight generation.
SAS Visual Analytics provides comprehensive data handling through its advanced reporting and visualization features. Businesses benefit from its ability to process data quickly and deliver insights via interactive dashboards and well-structured reports. Although it faces performance challenges with large datasets and has a complex installation process, it supports both cloud and on-premises deployments. Users can leverage its capabilities in data extraction, transformation, and loading, making it a valuable tool for finance, statistical analysis, and enterprise reporting. Despite some gaps in machine learning and integration with newer data stores, its scalability and flexibility in data management remain key advantages.
What are the most significant features of SAS Visual Analytics?SAS Visual Analytics is implemented across sectors such as insurance and education for tasks like building dashboards and performing business intelligence. It is extensively used in finance and statistical analysis, turning complex data sets into actionable insights, supporting both cloud and on-premises environments.
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