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QuickSight vs SPSS Statistics

Last Updated: November 18th, 2024

Our analysts compared QuickSight vs SPSS Statistics 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.

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Product Basics

Amazon QuickSight is a business intelligence tool that enables organizations to visualize and analyze data, derive insights, and make informed decisions. It empowers users with interactive dashboards, data exploration capabilities, and machine learning-driven insights, making it an ideal solution for businesses seeking to leverage data for better outcomes.

The benefits of Amazon QuickSight include its ease of use, fast performance, and scalability to handle large datasets. Its popular features include natural language querying, automated insights generation, and integration with other AWS services. Compared to similar products, Amazon QuickSight stands out for its affordability, ease of deployment, and robust feature set.

Pricing for Amazon QuickSight is based on a per-user, per-month subscription model. It offers flexible pricing options to accommodate varying business needs, ensuring cost-effectiveness and scalability as organizations grow.

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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Product Insights

Self-Service BI: Get answers to questions and share insights independently through self-service analysis of business data. Connect to data sources, manipulate values to create visualizations, invite others to collaborate and share dashboards and reports — all without the need to install any application.
Multi-Tenancy: Its multi-tenant architecture with namespaces allows vendors to provide data exploration and dashboarding capabilities for multiple applications at the same time. Host multiple clients and add templates created and shared by dashboard authors to the proprietary visualizations library.
Embedded Analytics: Embed dashboard sessions into proprietary software to enhance business with console analytics. Personalize the appearance of visualizations and reports by adding custom fonts, colors and styles. Set defaults and error handling to match the host application for a cohesive feel.
Report Sharing: Stay informed as to how key metrics are performing by scheduling automatic email updates. Ensure secure access to dashboards and email reports by showing only relevant data through targeted reports.
Flexible Pricing: Save on upfront costs and capacity planning through its p ay-as-you-go pricing model for report consumers — people who view dashboards, but don’t create them.
Mobility: Get data insight and forecasting summaries on-the-go through its mobile version, available on both iOS and Android. Drill down into mobile-optimized dashboards and reports for in-depth analysis and filter to view specific metrics, even when away from the office. Share results with others and get email alerts for changes in business data as they happen.
Automate Deployments: Deploy applications at scale, keeping them online for continued access while minimizing downtime. AWS CodeDeploy provides automated app deployments to multiple cloud compute services, such as Amazon EC2, AWS Fargate, AWS Lambda, and on-premise servers. Seamlessly integrates with continuous delivery process pipelines, such as AWS CodePipeline, GitHub and Jenkins.
Promote Data Literacy: Identify key metrics, perform what-if analysis through simple point-and-click actions, discover hidden trends and generate forecasts — without needing any technical skills. Generates dashboard summaries in easy-to-understand language to enable shared understanding of data insights.
Scalable: Its serverless architecture makes it easily scalable, ensuring fast loading of dashboards even during peak traffic. Scales as the organization grows to take on larger loads — no need to set up dedicated servers.
Data Security: Its Enterprise version provides additional security features through permissions-based access to AWS data, row level security, SSO and data encryption. It is FedRamp, HIPAA, PCI PSS, ISO and SOC compliant, keeping with industry best practices.

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.

Data Connectivity: Connect to a multitude of cloud-based and on-premise data sources such as Salesforce, JIRA, Github, Teradata, MySQL, SQL Server, Postgres and many more, or upload data from Excel, JSON, CSV and Presto files.
Integrations: Perform predictive data analysis by integrating Amazon Sagemaker ML-based data models with its supported data sources, such as S3, Athena, Redshift, as well as third party sources that include Salesforce and JIRA, among others. Easily integrates with BI tools such as Domo and Tableau for data querying through an intuitively similar SQL interface.
Data Visualization: Create interactive visualizations and reports by choosing from a rich library of graphs, charts and tables. Build dynamic dashboards by interacting with data in real time through drag-and-drop, drill-down and filtering options.
Reporting: Its Enterprise edition provides ad-hoc as well as scheduled reporting to share targeted reports on a daily, weekly or monthly basis. Subscribe to receive reports via email. Creates a custom snapshot of the report based on individual data permissions.
In-Memory Engine: Perform calculations and analyses at the speed of thought by querying through its super-fast parallel in-memory calculation engine, SPICE. Built from the ground up, it prepares data faster by replicating it in memory and updates in real-time as the underlying data changes.
Dashboards: Create data dashboards with 28 supported charts, such as filled maps, histograms, funnel charts, stacked area charts, waterfall charts and boxplots, as well as tables and pivot tables. View information based on business context by sorting on fields not shown in the visualization. Provide report readers with on-sheet controls to slice and dice data by filtering on fields. Offers multiple report layout customization options that include adding drop-downs with single-select or multi-select sorting.
ML-Based Insight: Get in-depth data insights through its out-of-the-box ML and NLP capabilities. Perform queries on data in seconds in simple language through its natural language attribute, Q. It learns from data patterns to provide accurate predictions based on historical trends. Detect anomalies and variations in data aggregates through scheduled anomaly detection.
Localization: Available as a global solution with support for 10 major languages: English, German, Spanish, French, Italian, Portuguese, Japanese, Korean, Simplified Chinese, and Traditional Chinese.

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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Analyst Rating Summary

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User Sentiment Summary

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

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Synopsis of User Ratings and Reviews

Self-Service Analytics: QuickSight empowers business users to create and customize visualizations, dashboards, and reports without relying on IT or data scientists.

Cloud-Based Platform: QuickSight's cloud-based architecture eliminates the need for costly on-premise infrastructure and provides seamless access from anywhere with an internet connection.

Scalability and Performance: QuickSight handles large datasets efficiently, allowing users to analyze vast amounts of data in real-time.

Easy Integration: QuickSight seamlessly integrates with popular data sources, including Amazon Redshift, Amazon Aurora, and Salesforce, enabling users to access and analyze data from various systems.

Machine Learning Capabilities: QuickSight leverages machine learning algorithms to provide predictive analytics, anomaly detection, and automated insights, empowering users to uncover hidden patterns and make data-driven decisions.

Collaboration and Sharing: QuickSight facilitates collaboration among teams by allowing users to share dashboards, reports, and insights securely.

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.

Pricing Structure: Complicated pricing model that can lead to unexpected costs for advanced features and usage.

Limited Customization: Pre-built dashboards and visualizations may not fully meet specific business requirements, limiting customization options.

Data Connectivity: May not seamlessly integrate with all data sources, requiring additional effort for data preparation and ingestion.

Limited Advanced Analytics: Lacks certain advanced analytical capabilities, such as predictive modeling and machine learning algorithms, which may be essential for deeper insights.

User Interface: Some users find the user interface to be cluttered and not as intuitive as other BI tools, impacting the ease of use.

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.

Amazon QuickSight has received positive user reviews for its ease of use, intuitive interface, and quick data visualization capabilities. Users find it simple to connect to data sources, create dashboards, and generate insights. The drag-and-drop functionality and pre-built templates make it accessible to users of all technical skill levels. QuickSight's strengths include its integration with other Amazon Web Services (AWS) products, such as Amazon Redshift and Amazon S3, allowing for seamless data analysis. It also offers advanced features like machine learning-powered insights and natural language querying, providing users with deeper analytical capabilities. Compared to similar products, QuickSight is praised for its cost-effectiveness and scalability. Users appreciate its pay-as-you-go pricing model and the ability to handle large datasets without performance issues. However, some users have expressed concerns about the limited customization options and the lack of certain advanced features found in competing products. Overall, Amazon QuickSight is well-suited for businesses and organizations looking for an easy-to-use and affordable business intelligence solution. Its intuitive interface, integration with AWS products, and scalability make it a valuable tool for data visualization, analysis, and decision-making.

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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