StackShareStackShare
Follow on
StackShare

Discover and share technology stacks from companies around the world.

Follow on

© 2025 StackShare. All rights reserved.

Product

  • Stacks
  • Tools
  • Feed

Company

  • About
  • Contact

Legal

  • Privacy Policy
  • Terms of Service
  1. Stackups
  2. Utilities
  3. Business Intelligence
  4. Business Intelligence
  5. IBM Cognos Analytics vs Shiny

IBM Cognos Analytics vs Shiny

OverviewComparisonAlternatives

Overview

Shiny
Shiny
Stacks208
Followers228
Votes13
IBM Cognos Analytics
IBM Cognos Analytics
Stacks19
Followers17
Votes0

IBM Cognos Analytics vs Shiny: What are the differences?

<Write Introduction here>
  1. Cost: IBM Cognos Analytics is a paid solution, which may require a significant investment depending on the organization's needs and scale. In contrast, Shiny, as an open-source tool, is free to use, making it a more cost-effective option for businesses with budget constraints.

  2. Programming Language: IBM Cognos Analytics mainly utilizes drag-and-drop functionality and minimal coding, making it more user-friendly for business users without extensive programming knowledge. On the other hand, Shiny relies heavily on R programming language, requiring users to have a solid understanding of R to create interactive web applications.

  3. Customization: IBM Cognos Analytics provides predefined templates and dashboards for quick report generation, limiting the extent of customization available to users. Shiny offers greater flexibility in customization, allowing users to design and develop highly tailored and personalized data visualization applications.

  4. Deployment Options: IBM Cognos Analytics typically requires installation on a server or cloud platform, which can be a more complex deployment process. Shiny apps can be easily deployed on the web, making them more accessible to users across different devices without the need for extensive setup.

  5. Integration: IBM Cognos Analytics seamlessly integrates with other IBM products and services, providing a more cohesive analytics ecosystem for users. In contrast, Shiny is more versatile in terms of integration, allowing users to combine it with a wide range of libraries and tools within the R environment.

  6. Community Support: Shiny benefits from a robust community of R developers and data scientists, providing a wealth of resources, tutorials, and support for users. IBM Cognos Analytics, while supported by IBM, may have a more limited community base available for troubleshooting and assistance.

In Summary, IBM Cognos Analytics and Shiny differ in cost, programming language, customization, deployment options, integration capabilities, and community support.

Share your Stack

Help developers discover the tools you use. Get visibility for your team's tech choices and contribute to the community's knowledge.

View Docs
CLI (Node.js)
or
Manual

Detailed Comparison

Shiny
Shiny
IBM Cognos Analytics
IBM Cognos Analytics

It is an open source R package that provides an elegant and powerful web framework for building web applications using R. It helps you turn your analyses into interactive web applications without requiring HTML, CSS, or JavaScript knowledge.

It is a business intelligence solution that empowers users with AI-infused self-service capabilities that accelerate data preparation, analysis, and report creation. It makes it easier than ever to visualize data and share actionable insights across your organization to foster more data-driven decisions.

-
Protect your data; Visualize your business performance; Share critical insights easily
Statistics
Stacks
208
Stacks
19
Followers
228
Followers
17
Votes
13
Votes
0
Pros & Cons
Pros
  • 8
    R Compatibility
  • 3
    Free
  • 2
    Highly customizable and extensible
No community feedback yet

What are some alternatives to Shiny, IBM Cognos Analytics?

Metabase

Metabase

It is an easy way to generate charts and dashboards, ask simple ad hoc queries without using SQL, and see detailed information about rows in your Database. You can set it up in under 5 minutes, and then give yourself and others a place to ask simple questions and understand the data your application is generating.

Superset

Superset

Superset's main goal is to make it easy to slice, dice and visualize data. It empowers users to perform analytics at the speed of thought.

Cube

Cube

Cube: the universal semantic layer that makes it easy to connect BI silos, embed analytics, and power your data apps and AI with context.

Power BI

Power BI

It aims to provide interactive visualizations and business intelligence capabilities with an interface simple enough for end users to create their own reports and dashboards.

Mode

Mode

Created by analysts, for analysts, Mode is a SQL-based analytics tool that connects directly to your database. Mode is designed to alleviate the bottlenecks in today's analytical workflow and drive collaboration around data projects.

Google Datastudio

Google Datastudio

It lets you create reports and data visualizations. Data Sources are reusable components that connect a report to your data, such as Google Analytics, Google Sheets, Google AdWords and so forth. You can unlock the power of your data with interactive dashboards and engaging reports that inspire smarter business decisions.

AskNed

AskNed

AskNed is an analytics platform where enterprise users can get answers from their data by simply typing questions in plain English.

Redash

Redash

Redash helps you make sense of your data. Connect and query your data sources, build dashboards to visualize data and share them with your company.

Azure Synapse

Azure Synapse

It is an analytics service that brings together enterprise data warehousing and Big Data analytics. It gives you the freedom to query data on your terms, using either serverless on-demand or provisioned resources—at scale. It brings these two worlds together with a unified experience to ingest, prepare, manage, and serve data for immediate BI and machine learning needs.

Periscope

Periscope

Periscope is a data analysis tool that uses pre-emptive in-memory caching and statistical sampling to run data analyses really, really fast.

Related Comparisons

Postman
Swagger UI

Postman vs Swagger UI

Mapbox
Google Maps

Google Maps vs Mapbox

Mapbox
Leaflet

Leaflet vs Mapbox vs OpenLayers

Twilio SendGrid
Mailgun

Mailgun vs Mandrill vs SendGrid

Runscope
Postman

Paw vs Postman vs Runscope