Frontline Systems Analytic Solver is an Excel add-on for performing data mining, and predictive analytics from within Microsoft Excel.
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IBM SPSS Modeler
Score 7.1 out of 10
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IBM SPSS Modeler is a visual data science and machine learning (ML) solution designed to help enterprises accelerate time to value by speeding up operational tasks for data scientists. Organizations can use it for data preparation and discovery, predictive analytics, model management and deployment, and ML to monetize data assets.
$4,670
per year
Pricing
Analytic Solver
IBM SPSS Modeler
Editions & Modules
No answers on this topic
IBM SPSS Modeler Personal
4,670
per year
IBM SPSS Modeler Professional
7,000
per year
IBM SPSS Modeler Premium
11,600
per year
IBM SPSS Modeler Gold
contact IBM
per year
Offerings
Pricing Offerings
Analytic Solver
IBM SPSS Modeler
Free Trial
No
Yes
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
Optional
Additional Details
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IBM SPSS Modeler Personal enables users to design and build predictive models right from the desktop.
IBM SPSS Modeler Professional extends SPSS Modeler Personal with enterprise-scale in-database mining, SQL pushback, collaboration and deployment, champion/challenger, A/B testing, and more.
IBM SPSS Modeler Premium extends SPSS Modeler Professional by including unstructured data analysis with integrated, natural language text and entity and social network analytics.
IBM SPSS Modeler Gold extends SPSS Modeler Premium with the ability to build and deploy predictive models directly into the business process to aid in decision making. This is achieved with Decision Management which combines predictive analytics with rules, scoring, and optimization to deliver recommended actions at the point of impact.
More Pricing Information
Community Pulse
Analytic Solver
IBM SPSS Modeler
Features
Analytic Solver
IBM SPSS Modeler
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
1. It is good tool for a mathematical model which is a single period and deterministic model 2. It is good for the users who are comfortable in handling the Excel Solver and needs to upgrade the Excel Solver for more than 200 variables 3. It works well for the multiple objective problems. 4. Difficult to manage the big model as 100 constraints and 2000 variables can limit the use of the tool's efficiency. 5. Its limitation is that a model designer can not make a big and complex model.
Modeler is well suited for understanding consumer data. The ability to create a prediction and then to understand what is driving that prediction is strong in Modeler. Modeler is closely aligned with the CRISP-DM data mining approach meaning it is not just the 'doing' but also the theoretical background behind the development of data mining models.
On the few occasions when I have used it to deal with problems of optimization of relatively large parameters (with a large number of restrictions and decision variables), the program has been slower, not substantially but slower, than programs such as the WinQsb, even when the latter runs on 32-bit machines and not 64. That has caught my attention, even though it is not a real problem for the uses I give to the program.
Given my partial function as a university professor, it has been much more effective and practical to use other software, due to the limited options that the educational license associated with the software has.
The ability to do predictive modeling, text analytics for both structured & unstructured data, decision management, optimization, and support for various data sources
The use of this software does not necessarily follow that it is "globally better" than others. In the department we have used this and others with similar characteristics, given that, as previously indicated, all the software has advantages and weaknesses with respect to other software with similar characteristics. Obtaining better results lies in the user's ability to detect those "benefits and weaknesses" and maximize their usefulness within the specific field of work in which they operate. In our case, one of the reasons that led us to try and use it, was related to trying to "tie" more processes to the same environment, which in this case is the one associated with the Excel database, in such a way as to reduce the initial manipulation and accommodation that should be made to the data if they come from different sources such as MATLAB, or WinQsb. This facilitates the use of software for the type of user who does not necessarily have deep knowledge of linear algebra or operations research, for example.
On the contrary, the most analytical and knowledgeable user manifested in a high percentage, preferring to use MATLAB as a tool, claiming that they have a greater and easier access to the calculation functions, which even in specific cases, could be modified.
We additionally use SAS Data Miner as a toolkit. Compared to SAS Data Miner, the SPSS Modeler is a good competitor. SAS probably is more integrated in the market for a visual-based code for data science activities. However, I don't think it offers anything better than SPSS, and I really like several of the helpful components for usability for SPSS like peaks into nodes.
- It has allowed finding ways to optimize (minimizing costs or times) the field processes involved in various projects.
It has even allowed, in specific cases where it was used for that purpose, to optimize the allocation of resources (people) to work in different jobs that present weekly variations of the activity that these people must perform.
It has allowed the sensitivity analysis of projects to changes in the decision variables related to them, which, and in very dynamic and changing environments, resulted in substantial decreases in money losses.