IBM SPSS Modeler vs. SAS Enterprise Guide

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
IBM SPSS Modeler
Score 9.4 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
SAS Enterprise Guide
Score 9.3 out of 10
N/A
SAS Enterprise Guide is a menu-driven, Windows GUI tool for SAS.N/A
Pricing
IBM SPSS ModelerSAS Enterprise Guide
Editions & Modules
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
No answers on this topic
Offerings
Pricing Offerings
IBM SPSS ModelerSAS Enterprise Guide
Free Trial
YesNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeOptionalNo setup fee
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
IBM SPSS ModelerSAS Enterprise Guide
Considered Both Products
IBM SPSS Modeler
Chose IBM SPSS Modeler
Python requires knowledge of programming, higher learning curve vs IBM SPSS Modeler
Chose IBM SPSS Modeler
IBM SPSS Modeler is considerably easier to use. It allows for very rapid development and the ability to get to a goal quickly. There is no need to learn a new programming language so the analyst has the ability to focus on the problem rather than the pedantics of managing …
Chose IBM SPSS Modeler
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 …
Chose IBM SPSS Modeler
SPSS has a great set of analytical models, but SPSS is especially strong (compared to other tools) in complex statistical modeling and predictive analytics/statistics. However, the data connectivity features of SPSS are not the best, as the data sources SPSS can work with are …
Chose IBM SPSS Modeler
The field of data analytics has important value for each organization. IBM SPSS Modeler is one of the leaders in this highly competitive vertical. IBM SPSS is very intuitive compared with others, and has reduced the complexity. This software has various good functionalities …
Chose IBM SPSS Modeler
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.
SAS Enterprise Guide
Chose SAS Enterprise Guide
Python-based platforms like Pandas or Spark are very good too at displaying data and do exploratory analysis. I definitely prefer them to SAS EG. It's just too slow, and doesn't let you peek into the data very easily. Lots of clicking, and I'd rather just write some code, …
Chose SAS Enterprise Guide
This was used by the unit before I joined. It was compared to SPSS but I was not included in that discussion.
Chose SAS Enterprise Guide
Although not used in the enterprise, I have used Anaconda Python to shape and cleanse data from Excel reports that was too difficult for SAS to complete. The object oriented nature and the Pandas package made ingestion of the data and reshaping more useful in this use case. …
Chose SAS Enterprise Guide
SAS EG has better Graphical User Interface to build project trees and help users to create data queries/calculations. SAS EG can handle bigger data sets compared to other programs. You can easily clean the data sets and manipulate the data. It is easier to send the project tree …
Chose SAS Enterprise Guide
Why I prefer SAS EG: Data processing speed is much faster than that R Studio. It can load any amount of data and any type of data like structured or unstructured or semi-structured. Its output delivery system by which we have the output in PDF file makes it very comfortable to …
Chose SAS Enterprise Guide
It gives more flexibility in terms of writing codes, and you're able too see expected output and then you go on to modify
Chose SAS Enterprise Guide
Tableau : A good tool for visualisations but SAS is better for running production scripts & using adhoc analysis
Chose SAS Enterprise Guide
I haven't used SPSS myself but from what I was told, integration of data was much more limited and not easy to used.
Also, the number of people with SPSS knowledge is less than the number of SAS users so finding workforce can be an issue.
The whole SAS solution just made much …
Features
IBM SPSS ModelerSAS Enterprise Guide
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
IBM SPSS Modeler
8.8
Ratings
5% above category average
SAS Enterprise Guide
-
Ratings
Connect to Multiple Data Sources8.70 Ratings00 Ratings
Extend Existing Data Sources8.70 Ratings00 Ratings
Automatic Data Format Detection9.00 Ratings00 Ratings
MDM Integration9.00 Ratings00 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
IBM SPSS Modeler
9.0
Ratings
6% above category average
SAS Enterprise Guide
-
Ratings
Visualization9.00 Ratings00 Ratings
Interactive Data Analysis9.00 Ratings00 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
IBM SPSS Modeler
9.0
Ratings
10% above category average
SAS Enterprise Guide
-
Ratings
Interactive Data Cleaning and Enrichment9.00 Ratings00 Ratings
Data Transformations9.00 Ratings00 Ratings
Data Encryption9.00 Ratings00 Ratings
Built-in Processors9.00 Ratings00 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
IBM SPSS Modeler
9.0
Ratings
7% above category average
SAS Enterprise Guide
-
Ratings
Multiple Model Development Languages and Tools9.00 Ratings00 Ratings
Automated Machine Learning9.00 Ratings00 Ratings
Single platform for multiple model development9.00 Ratings00 Ratings
Self-Service Model Delivery9.00 Ratings00 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
IBM SPSS Modeler
9.0
Ratings
6% above category average
SAS Enterprise Guide
-
Ratings
Flexible Model Publishing Options9.00 Ratings00 Ratings
Security, Governance, and Cost Controls9.00 Ratings00 Ratings
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IBM SPSS ModelerSAS Enterprise Guide
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Score 8.4 out of 10
IBM SPSS Statistics
IBM SPSS Statistics
Score 8.3 out of 10
Medium-sized Companies
Posit
Posit
Score 10.0 out of 10
Posit
Posit
Score 10.0 out of 10
Enterprises
Posit
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Score 10.0 out of 10
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User Ratings
IBM SPSS ModelerSAS Enterprise Guide
Likelihood to Recommend
9.5
(0 ratings)
5.3
(0 ratings)
Likelihood to Renew
-
(0 ratings)
8.0
(0 ratings)
Usability
8.8
(0 ratings)
5.0
(0 ratings)
Support Rating
10.0
(0 ratings)
5.3
(0 ratings)
Implementation Rating
-
(0 ratings)
7.0
(0 ratings)
User Testimonials
IBM SPSS ModelerSAS Enterprise Guide
Likelihood to Recommend
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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For writing out longer code creation for shaping data on complicated reports, the clean UI is helpful. If exploring data though, SAS Studio would be better suited given its easier interface for GUI graph building.
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Pros
  • 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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  • It can load a huge amount of data as compared to R Studio and Excel.
  • Data processing speed is very fast, millions of records are loaded into this software very easily and data manipulation is also very easy.
  • Inbuilt Statistical functions and procedures make it very comfortable to use for non analytics professionals as well.
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Cons
  • 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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  • I would like to see advance interactions with external databases to be able to kill ongoing queries from SAS. As of now, you can stop pretty much any ongoing process besides the one running on a remote database (killing SAS/EG doesn't stop the remote process)
  • When creating prompts for programs, it would be nice to be able to have conditional prompts (based on the selection of other prompts). The prompts are clearly a recent feature and constantly under development but I wish it would be more powerful.
  • More of a SAS metadata issue but when loading SAS/EG (first connection to the server), it takes a few seconds which feels like a long time. I really don't understand why the initialization of the session can take so long. Don't get me wrong, this has no real impact on productivity but that 10s delay just feels really like eternity when you want to run some code in a new session.
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Likelihood to Renew
No answers on this topic
On account of current user experience and the organization-wide acceptance.
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Usability
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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It's not all bad, but I don't believe that an enterprise purchase of SAS is worth the expense considering the widely available set of tools in the data analytics space at the moment. In my company, it's a good tool because others use it. Otherwise, I wouldn't purchase a new set of it because it doesn't have some of the better analytical functions in it.
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Support Rating
The online support board is helpful and the free add ons are incredibly appreciated.
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Although I use SAS support for information on functions, these are SAS related and haven't really come across anything that is specifically for SAS EG.
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Implementation Rating
No answers on this topic
I've not worked hands-on with the implementation team, but there were no escalations barring a few hiccups in the deployment due to change in requirement & adoption to our company's remote servers.
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Alternatives Considered
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.
Read full review
Python-based platforms like Pandas or Spark are very good too at displaying data and do exploratory analysis. I definitely prefer them to SAS EG. It's just too slow, and doesn't let you peek into the data very easily. Lots of clicking, and I'd rather just write some code, rather do clicking.
Read full review
Return on Investment
  • 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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  • Faster decision making, through powerful big data handling functionalities.
  • Faster operations on daily basis, once the project tree is built, unskilled personnel can use it in their daily operation.
  • Don’t need to choose SAS EG if you are not going to be handling big data. (such as over 1 million rows and 50 columns)
  • You need skilled personnel to build the initial project tree.
Read full review
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.