Sisense For Cloud Data Teams vs SPSS Statistics

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Our analysts compared Sisense For Cloud Data Teams 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.

Sisense For Cloud Data Teams Software Tool
SPSS Statistics Software Tool

Product Basics

Formerly known as Periscope Data, Sisense for Cloud Data Teams is a data analytics software tool that integrates seamlessly with the Sisense platform, offering advanced analytics that delivers actionable insights to teams that work with data in the cloud. It provides a single, cohesive interface for users to store, organize, analyze and visualize all their data for better decision-making. It empowers users of all kinds to produce, consume and share insights intuitively together, with or without coding knowledge.

Originally founded in 2012 in San Francisco, California, it was acquired by Sisense in May 2019 and rebranded in January 2020.
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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

  • Transforms Data Fast: Perform timely data querying and large-scale data ingestion for any volume of workload, through the analytics warehouse integration. Plug the data engine directly into cloud databases and optimize raw data by bypassing steps of the ETL process. Enable hassle-free data import via proprietary data caching technology
  • Centralized Data Warehouse: Creates a single source of truth by ingesting and storing data where it’s analyzed, speeding up analytics processes.
  • Ease of Use: Explore data and visualize trends through simple search query language, rather than via coding or modeling, making it an accessible solution for users of all technical skill levels.
  • Stats at a Glance: Understand and parse the results of data queries through Summary Statistics, without needing to write more SQL while exploring data and building models.
  • Reusable Analysis: Save time by storing frequently used in-house codes for swift collaboration without needing to start new queries from scratch.
  • Collaborative Insights: Enable collaboration between analysts and decision-makers through programming and self-service analytics combined. Hand off data analysis between teams, then publish and share insights with others via direct linking, password-protected links, email or Slack.
  • Self-Service BI: Pinpoint important data points in minutes, with reusable formulas and ad hoc analysis modeling that query data and return answers in real time.
  • R, Python and SQL On One Platform: Develop more advanced analytics processes with any programming language, with support for SQL, Python and R all in the same environment. Integrate open-source programming and formulas from other packages or libraries.
  • Scalability: Incorporate more complex datasets, higher volumes of data, more users and more, as the solution grows with the company.
  • Security: Have confidence in data security, with a cloud security infrastructure that upholds industry wide best practices and standards. Encrypt all traffic between users’ web browsers and Sisense’s servers, and for additional user-level security, manage data permissions, TFA and single sign-on functionality.
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  • 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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  • Native Data Connectors: Blend data together into a single accessible database via an ecosystem of native data connectors and ETL partners.
  • Cloud Data Pipelines: Control when and how often data is refreshed and what the flow of information looks like. Gain visibility and control over data pipelines with a flexible, low maintenance solution.
  • Data Discovery: Directly access CSVs and data sets curated by analysts and interact with these insights through a drag-and-drop interface that does not require fluency in SQL. 
  • Git Integration: Grant developers complete control over their analytics environment through Git integration, sophisticated version control, release management workflows as well as file-level access to all user-generated content, like reports. Create, edit, sync and delete all settings through Git. 
  • Model-as-You-Go: Perform ad-hoc analysis to answer crucial questions at the click of a button by generating custom, on-the-fly data models.
  • Data Visualization: Create and share advanced, highly customizable data visualizations, including scatter plots, bar charts, bubble charts, bullet charts, funnel charts, waterfall charts, control charts, Gantt charts, radial bar charts and more.
  • SQL Editor Tools: Shorten the amount of time necessary to go from query to answer with powerful SQL writing tools, such as query revision history, views, filters, autocomplete suggestions, formatting and more. 
  • Learn SQL: Experiment with SQL programming language and data querying via drag-and-drop fields and see how the results change. Encourage users who aren’t fluent in SQL to become more familiar with it.
  • Code Library: Reduce repetitive data entry tasks and save time for subsequent report generation. Store frequently used code in a common library via Snippets for easy access later. 
  • Spaces: Manage data-level permissions to gate and restrict access to sensitive data and customize dashboards on a per-organization basis.
  • Share and Embed: Share dashboards via password-protection enabled public URLs or embedding inside other web applications or web portals. Download dashboards as static PDF images for uses such as email distribution.
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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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#86

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User Sentiment Summary

Great User Sentiment 140 reviews
Great User Sentiment 1881 reviews
87%
of users recommend this product

Sisense For Cloud Data Teams has a 'great' User Satisfaction Rating of 87% when considering 140 user reviews from 4 recognized software review sites.

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.

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4.3 (18)
4.5 (72)
4.2 (712)
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4.51 (528)
4.3 (33)
4.5 (425)
4.8 (4)
4.6 (40)
4.1 (31)
4.2 (158)

Synopsis of User Ratings and Reviews

Data Querying: Almost 77% of users who mentioned data querying and data modeling said that this solution allows them to perform data queries of almost any kind.
Data Visualization: Of the users who mentioned data visualization, about 75% said that this tool excels at creating easily understandable graphics and visuals.
Sharing and Collaboration: Approximately 88% of users who mentioned this feature said that this solution facilitates strong collaboration on data analysis internally, while allowing for easy external sharing.
Implementation: In reference to setting up the platform, about 90% of users said that the process was smooth and quick.
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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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Reliance on SQL: While it excels in data querying, around 78% of users said that SQL knowledge is necessary to use this platform.
Self-Service Analytics: About 80% of users reviewing this feature said that data analysis is not truly self-service, requiring the involvement of a hands-on IT or analyst team.
Cost: Of the users who reviewed the cost of this tool, almost 86% of them found it too expensive, especially for smaller companies.
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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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Sisense for Cloud Data Teams is a powerful BI tool that excels in the hands of data analysts with coding knowledge. These analysts will be able to model, manage, prepare, manipulate and analyze data with ease. As the platform supports SQL, Python and R programming languages, the sky's the limit when it comes to extensibility, development and coding support. The platform allows for strong collaboration within an organization, designed to alleviate the time-consuming burden of reporting and data visualization on IT teams while allowing business users to access insights on their own time. Most of the features are built around making the analytics team’s job easier, with reusable code, built-in collaboration and more. The support team earned universal praise from users who contacted them, citing their swift, in-depth and informative answers as immensely helpful in resolving any issues they had with the solution. In addition to hands-on support, a speedy implementation process ensures that customers can get the platform up and running in no time. We found that users’ opinions of the platform’s functionality and ease of use differed significantly based on their own roles; analysts generally praised its robust data visualization and data querying features, while decision-makers often found it hard to use, due to the SQL language barrier. SQL is necessary for most functions, and even though there is a visual query editor that attempts to make SQL more accessible, some users said that the platform doesn’t do enough in this regard. Overall, users did agree that the tool makes it easy to collaborate with each other to bridge that gap, with most of the analytics being done on the technical end, letting business users simply access those results. Still, it’s difficult to accurately call Sisense for Cloud Data Teams a self-service analytics tool, as it requires an IT or analyst team’s involvement to truly shine. Additionally, the cost can be prohibitive to smaller businesses. Overall, Sisense for Cloud Data Teams can be extremely powerful in the right hands - for those who know SQL and how to use it, it’s a dream for querying data and delivering analytics, but for those who don’t have the necessary IT resources, it could prove difficult to fully maximize the value of this data solution.

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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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