SPSS Statistics vs KNIME

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Our analysts compared SPSS Statistics vs KNIME based on data from our 400+ point analysis of Business Intelligence Tools, user reviews and our own crowdsourced data from our free software selection platform.

SPSS Statistics Software Tool
KNIME Software Tool

Product Basics

IBM SPSS, or Statistical Product and Service Solutions, is a data analysis platform that provides advanced statistical insights to propel business decision-making and research by crunching large datasets. With a user-friendly interface, it empowers everyone from basic users to experienced data scientists to perform statistical analysis. Scalable and agile, it is suitable for companies of all sizes.

Users can purchase it through an on-premises license or a subscription plan to a hybrid SaaS. Users who choose either monthly or annual subscription receive access to the base version and can choose which of three optional add-ons to include, if any, between Custom Tables and Advanced Statistics, Complex Sampling and Testing, and Forecasting and Decision Trees.

Perpetual and term licensees can choose between four editions that offer different levels of functionality: Base, standard, professional and premium. It was first launched as Statistical Package for the Social Sciences by three Stanford students in 1968 and later acquired by IBM in 2009.
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KNIME is an open-source end-to-end data analytics solution. It utilizes visual workflows with drag-and-drop functionality and thousands of nodes to lessen the data analytics learning curve data, with more than 1,800 prebuilt default workflows for streamlined setup.

It allows for data ingestion, preparing, cleansing, analyzing and visualizing. It can be scaled for deeper analytics through integrations with sophisticated data modeling capabilities. It can be hosted on-premise or in the cloud through Microsoft Azure.
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$99 Monthly
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$0 Open-Source
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Product Insights

  • Increase Reliability of Analysis: Data analysts and scientists can reach more dependable conclusions and ensure high accuracy, backed by numbers and models made from their data.
  • Assists in Decision Making: Organize and analyze data sets in order to draw conclusions, glean insights and make data-driven business decisions.
  • Statistics with Speed: Maximize productivity by quickly crunching data sets. Handle complicated tasks in a third of the time of many non-statistical programs.
  • Data Management: Remove manual work, as the system does all the legwork in preparing and organizing data sets for analysis. It estimates and uncovers missing values, improving the accuracy of reports.
  • Ease of Use: According to the IBM website, 81% of reviewers rank SPSS as easy to use. A point-and-click interface and natural language processing allow analytics capabilities to be accessed by those without coding skills or advanced statistics knowledge.
  • Scalability: Built to scale and work with large volumes of data, supporting anything from basic descriptive analytics to advanced statistics simulations. Purchase as many licenses as needed, ensuring cost-efficiency for small businesses as well as robustness for large scale enterprises.
  • Customized Predictive Analytics: Tailor predictive analytics to unique needs and perform ad hoc analysis to find the information needed, making better predictions over time.
  • Free Trial: Access all capabilities with a free 14-day trial period.
  • Academic Versions: An academic version is optimized for higher education and research. Program availability varies based on user roles, including students, teachers, researchers and campus-wide administrators.
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  • Open-Source: Join a network of thousands of users, enabling collaboration and support. The source code is free to download and access.  
  • Free To Use: Save money by getting access to all of the platform’s features for free. Licensed productivity and collaboration extensions are available at a cost. 
  • Increased Business Intelligence: Get digestible, actionable data to make informed business decisions. Aggregating large datasets into predictive and prescriptive models via comprehensive visualizations and summary statistics gives users projections for the best course of action.  
  • Scalable: Obtain access to big data by scaling up the project in-platform. Integrations to distributed and multi-threaded data processing allow projects to grow. 
  • End-To-End Analytics:  It is capable of handling some tasks from start to finish without integrations. Additional integrations may be required for increasing scale and completing more sophisticated analytics. 
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  • Data Source Connectivity:  Enables reading and writing data from a wide spectrum of file formats and sources. These include ASCII text files, spreadsheets and databases like Microsoft Excel and Microsoft Access, as well as those from other statistics packages. 
  • Data Preparation: Streamlines the data preparation process. Identify invalid values, view patterns of missing data and automate data preparation to analyze and clean up large data sets in a single step. Validate the accuracy of analysis with a thorough, efficient data conditioning workflow. 
  • Point-and-Click Interface: Allows users without coding knowledge to leverage point-and-click data analysis with drop-down menus and drag-and-drop functionality. 
  • Automated Analytics: Automate common tasks with syntax and create customized data analyses that run using algorithms. 
  • Comprehensive Statistical Analysis: Perform many kinds of statistics tests, including but not limited to linear and non-linear models, simulation modeling, bayesian statistics, custom tables, complex sampling, advanced and descriptive statistics, and regression.
  • Ad Hoc Analysis: “Slice and dice” data by creating customized tables to dig deeper and improve understanding.
  • Predictive Analytics:
    •  Uncovers complex relationships between variables with functions like time series analysis, forecasting, neural networks and temporal causal modeling. 
    •  Simulates values and accounts for the uncertainty of the future using probability distributions. 
    •  Improves predictive models with multilayer perception and radial basis function. 
  • Geospatial Analysis: Explore the relationship between data points that can be tied to specific locations.
  • Direct Marketing: Improve campaigns and target key customers. Conduct advanced statistics analysis of customers or contacts with RFM (recency, frequency, monetary) analysis.
  • Open-Source Integration: Enhance syntax with programming languages R and Python through a library of more than 100 free extensions on the IBM Extension Hub, or opt to build programs.
  • Export with Ease: Export data to a proprietary file format. Can be exported to a variety of widely accessible formats such as text, Microsoft Word, PDF, Excel, HTML, XML, XLS and more. Export to a variety of graphic image formats.
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  • Sharing and Collaboration: KNIME Hub is an online repository for existing workflows, nodes and extensions that can be easily installed into a user’s workflow. Upload workflows and search for the components needed for projects. 
  • In-database or Distributed Processing: Process data in-database or through a distributed cluster like Apache Spark for increasing scale. It has prebuilt workflows for in-database processing, like SQL Servers. 
  • Model Predictions and Validation: Using machine learning and AI, produce predictive and prescriptive models. Use performance metrics such as AUC and R2 to verify models.  
  • Visual Workflows: Using a drag-and-drop interface, compose a workflow with little to no coding. Prebuilt generic workflows and components can be downloaded from KNIME Hub. 
  • Data Management: Handles all steps of the extract, transform and load processes. It can ingest, blend, prepare, cleanse and store structured and unstructured data. It can combine data types, including PDF, JSON, CSV and unstructured types like documents and images. 
  • Data Visualizations: Compile analyses into reports with heat graphs, bar charts, scatter plots and more. Visualizations can be exported as PDFs, PowerPoints or other formats.  
  • Tool Blending: Tools with unique domains can be combined within a workflow via native nodes. These include Python or R scripting, processing connectors, machine learning and AI. 
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Product Ranking

#86

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Business Intelligence Tools

#89

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Business Intelligence Tools

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User Sentiment Summary

Great User Sentiment 1881 reviews
Great User Sentiment 236 reviews
87%
of users recommend this product

SPSS Statistics has a 'great' User Satisfaction Rating of 87% when considering 1881 user reviews from 6 recognized software review sites.

89%
of users recommend this product

KNIME has a 'great' User Satisfaction Rating of 89% when considering 236 user reviews from 4 recognized software review sites.

4.3 (18)
n/a
4.2 (712)
4.3 (41)
4.51 (528)
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4.5 (425)
4.6 (18)
4.6 (40)
4.6 (139)
4.2 (158)
3.9 (38)

Synopsis of User Ratings and Reviews

Research Analysis: It’s easy to analyze and interpret large and complex datasets to generate insights, according to 96% of reviewers who mention this feature.
Ease of Use: The platform is user-friendly and easy to navigate as observed by almost 95% of reviewers mentioning ease of use.
Interface: The interface has a clean layout and visually appealing design according to 75% of users referencing it.
Syntax: It’s easy to use (copy and paste) and proof syntax, and save for later, as noted by 95% of reviewers who talk about this feature.
Resources: Of users mentioning support, 93% agreed that access to various support manuals and courses, as well as an extensive user community, was helpful.
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Functionality: It provides a comprehensive set of nodes and functions to process large quantities of data, as noted by 69% of users who referred to functionality.
User Friendly: It is intuitive and easy to use, as noted by 79% of reviewers who refer to ease of use.
Connectivity: Around 77% of users who talked about connectivity mentioned its ability to seamlessly connect and integrate with multiple sources.
Cost: All users were happy that the solution is available free of charge, with no data limits.
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Learning Curve: It needs extensive learning to understand advanced tools and different aspects, as observed by almost 75% of reviewers mentioning training.
Cost: More than 90% of users referencing the price remarked that the product is a bit expensive.
Slow Operation: The system runs slow at times while using huge or complex data sets, and needs to restart, as observed by 92% of reviews on this topic.
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Performance: Nearly 95% of reviewers who mentioned performance said that the solution runs slowly and uses too much CPU and memory.
Visualization: Approximately 67% of users who specified visualization talked about its lack of proper visualization options.
Support: About 67% of users who reviewed support mentioned how hard it is to get proper documentation or support.
Learning Curve: KNIME has a steep learning curve, according to about 64% of users who mentioned the learning curve.
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SPSS Statistics is a point-and-click data analysis software that allows non-technical users to leverage advanced statistical analysis. Users praised its user-friendly, visually appealing interface. Many reviews also appreciated how easy it is to use and proof syntax, along with the extensive help documentation available for better understanding. Despite its ease of use, a majority found that using advanced tools involved a steep learning curve. Additionally, its cost runs on the high side and processing large amounts of information slows down its performance, as observed by most reviewers. It’s a good fit for students, data scientists and companies that want to analyze data sets but don’t have the technical resources and expertise to use a more advanced tool.

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KNIME is a robust open-source solution with cross-platform interoperability. It integrates with a range of software, such as JS, R, Python and Spark. With a variety of nodes and functions, it can process large datasets with a decent level of control in each step. Workflows are displayed as connected nodes, making it easy to isolate and fix specific steps. It also contains built-in tools to create and test supervised and unsupervised machine learning models. Users found the UI very intuitive and flexible. On the flip side, they found the tool visually lacking and primitive. The system also has performance and stability issues. Processing big data is very time consuming since the platform isn’t cloud-based. Users reported excessive memory usage as well. It also lacks reporting or monitoring features. Decent technical knowledge is required to fully leverage its capabilities.

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