Analytic Solver vs. IBM SPSS Modeler

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Analytic Solver
Score 7.0 out of 10
N/A
Frontline Systems Analytic Solver is an Excel add-on for performing data mining, and predictive analytics from within Microsoft Excel.N/A
IBM SPSS Modeler
Score 7.1 out of 10
N/A
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 SolverIBM 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 SolverIBM SPSS Modeler
Free Trial
NoYes
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeOptional
Additional DetailsIBM 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 SolverIBM SPSS Modeler
Features
Analytic SolverIBM SPSS Modeler
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Analytic Solver
-
Ratings
IBM SPSS Modeler
7.0
Ratings
18% below category average
Connect to Multiple Data Sources00 Ratings7.00 Ratings
Extend Existing Data Sources00 Ratings7.00 Ratings
Best Alternatives
Analytic SolverIBM SPSS Modeler
Small Businesses
IBM SPSS Statistics
IBM SPSS Statistics
Score 7.8 out of 10
Jupyter Notebook
Jupyter Notebook
Score 9.4 out of 10
Medium-sized Companies
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
Enterprises
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Analytic SolverIBM SPSS Modeler
Likelihood to Recommend
9.0
(0 ratings)
7.0
(0 ratings)
Usability
-
(0 ratings)
8.0
(0 ratings)
Support Rating
-
(0 ratings)
10.0
(0 ratings)
User Testimonials
Analytic SolverIBM SPSS Modeler
Likelihood to Recommend
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.
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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.
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Pros
  • Easy to use and learn.
  • Strong optimization platform for business and supply chain problems which can be modeled in Excel
  • It can handle multiple objectives and more than 8000 decision variables in a problem.
  • Lots of tutorial and examples are available to solve the problem
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  • A very nice and easy to use interface.
  • A great variety of analytics, from statistical calculation to data validation and predictive statistics.
  • Has a steep learning curve.
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Cons
  • 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.
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  • Some Analyses aren't there out of the box but can be added through open languages like R and Python.
  • Graphs could be better.
  • Unable to read data stored in OLAP databases
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Usability
No answers on this topic
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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Support Rating
No answers on this topic
The online support board is helpful and the free add ons are incredibly appreciated.
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Alternatives Considered
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.
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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.
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Return on Investment
  • - 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.
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  • I am able to study and work from home sustainably
  • I can help others have a high quality university education experience to graduate confident and competent to meet gaps in the wider community
  • Market research for my business
  • Help other small businesses to create viable and high quality products and services
  • Contribute to research projects: ethical, high quality data analyses and interpretation
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ScreenShots

IBM SPSS Modeler Screenshots

Screenshot of Use a single run to test multiple modeling methods, compare results and select which model to deploy. Quickly choose the best performing algorithm based on model performance.Screenshot of Explore geographic data, such as latitude and longitude, postal codes and addresses. Combine it with current and historical data for better insights and predictive accuracy.Screenshot of Capture key concepts, themes, sentiments and trends by analyzing unstructured text data. Uncover insights in web activity, blog content, customer feedback, emails and social media comments.Screenshot of Use R, Python, Spark, Hadoop and other open source technologies to amplify the power of your analytics. Extend and complement these technologies for more advanced analytics while you keep control.