Data analysis is used by a billion people worldwide. Amid the AI revolution, businesses recognize the transformative potential of AI tools for data analysis. Data analysis, like spreadsheets, is fundamental in business operations, as they work together to improve efficiency. Even though used by a billion people worldwide, data analysis is often halted by a list of manual and time-consuming processes. In such cases, we can use AI tools to analyze data.

AI tools for data analysis can utilize machine learning and deep learning to find insights in the data and reveal patterns. These tools can address the complexities of data analysis more efficiently, eliminating the inevitable human errors. This article will explore the best AI tools for data analysis.

AI and Data Analysis

AI tools for data analysis serve as intelligent aids, using algorithms for predictive modeling and data cleaning. These tools efficiently analyze vast datasets, unveiling hidden trends for informed decision-making. AI tools for data analysis also streamline data organization, providing a clear overview and automating repetitive tasks, saving time and enabling focus on critical matters. Effective communication of insights through visualization techniques like charts is another example.

Top 10 AI Tools for Data Analysis

Using machine learning and deep learning models, AI tools for data analysis constantly evolve and learn from the new data, improving the analysis techniques for best insights. Here are some best AI tools to analyze data.

1. Polymer

Polymer’s insight automation empowers users to create impressive data visualizations and dashboards effortlessly, without the need for coding and one of the best AI tools to analyze data. It offers multiple integrations with tools like Facebook, Google Ads, Airtable, Shopify, etc.

Features:

• Data Connectivity: Polymer connects to diverse sources, including CSV and Google Ads.
• AI-driven Analysis: Polymer utilizes advanced AI tools for data analysis.
• Flexible Visualization: Users can customize analysis as per their requirements.

Pros:

• Its interface is user-friendly, making visualizations and dashboard accessible without coding.
• It connects to various data sources.

Cons:

• Pricing may be relatively high.
• Users may need time for familiarization with advanced features.

Pricing:

• Polymer offers monthly plans starting at $20 for Starter and $40 for Pro.
• Yearly plans provide a 50% discount. Enterprise pricing is customizable.

Link: Polymer Search

2. Tableau AI

Tableau, known for dynamic dashboards and user-friendly visualization, has introduced Tableau AI for accessible data analysis. In the real-time, it offers users to edit their professional dashboards and reports.

Features:

• Trust Layer: Tableau uses generative AI without compromising on data security.
• User-friendly: Tableau provides a convenient interface.
• Efficient data analysis: Tableau automates data analysis and preparation.

Pros:

• Einstein Trust Layer ensures data privacy.
• It efficiently automates data preparation.

Cons:

• Tableau is not suitable for large datasets.
• Higher pricing plans may be a limitation.

Pricing: The pricing plans start from $12 per month.

Link: Tableau AI

3. MonkeyLearn

MonkeyLearn serves as a flexible platform for analyzing text and data, focused on extracting valuable insights from unstructured text. It is considered as one of the no-coding platform which helps in visualizing and rearranging their data.

Features:

• Custom model: Users can create models for sentiment analysis.
• Unstructured text: This tool is ideal for gaining insights from unstructured data.
• No-coding platform: MonkeyLearn is designed for simplicity. It is a no-coding platform.

Pros:

• The tool excels in gaining valuable insights from text data.
• Being a no-coding platform makes it accessible to a broader audience.

Cons:

• It does not cover data analysis beyond text data as efficiently.
• Its effectiveness is dependent on the quality of input.

Pricing: MonkeyLearn offers various plans, including a free academic plan and MonkeyLearn API at $299 per month with 10k queries per month.

Link: MonkeyLearn

4. Jupyter AI

Jupyter AI is an AI-powered extension that utilizes generative AI models to serve as your coding assistant in data analysis (AI tools to analyze data). Currently, it is packed up with Python language-based analysis where users can provide prompts.

Requirements:

You need to have Python (Version 3.8-3.11) installed on your computer, JupyterLab 4 and access to atleast one model provider with necessary credentials like API keys (for OpenAI models).

Features:

• Conversational Assistant: Jupyter AI utilizes generative AI to create a conversational assistant.
• Integration with Jupyter Environments: It supports most popular Integrated Development Environments (IDEs).
• Versatility in Model Providers: JupyterAI supports various generative model providers.

Pros:

• It integrates with various IDEs, providing flexibility and is free to use.
• It is compatible with many generative models.

Cons:

• Users need to have prior experience with Python.
• Performance depends on the generative model available.

Pricing: It is an open-source tool.

Link: Jupyter AI

5. Qlik

QlikView integrates AI for comprehensive data analysis with user-friendly features and easy to use functionalities. It is one of the most convenient BI based and data visualization solution for both technical and non-technical users.

Features:

• Data Exploration and Collaboration: This platform facilitates collaboration with versatile drag-and-drop editors.
• Automated Insights with NLP: It utilizes Natural Language Processing (NLP) for automated insights.
• Qlik has introduced conversational analytics through Insight Advisor Chat.

Pros:

• Insight Advisor Chat allows user-friendly inquiries about data.
• Utilizing NLP helps in analyzing text data.

Cons:

• It may not work as well for data other than text.
• Users may experience a learning curve adapting to these features.

Pricing: Various pans starting at $20 per user per month.

Link: Qlik

6. ChatGPT

ChatGPT’s Advanced Data Analysis allows direct file input, enhancing understanding and error-checking during code execution. Also, for the premium users, with the help of multiple prompts, data can be analyzed in a better way.

Features:

• File Format Support: It supports multiple file formats, including text, PDFs, and code.
• Python-Powered Execution: It executes operations using Python, using ChatGPT’s underlying model.
• Code Conversion: It can effectively converts code between programming languages.

Pros:

• It offers flexibility in file formats.
• Python is widely-used language, enhancing the platform’s versatility.

Cons:

• It may not be effective for niche programming languages.
• The high price may be a limitation.

Pricing: This is exclusively available only to GPT-4 users, pricing for which starts at $20/month.

Link: ChatGPT – Data Analyst

7. Akkio

Akkio is a no-coding platform that boasts lead-scoring features, enabling users to qualify and segment lead lists, helping them prioritize hot leads.

Features:

• Fast and User-Friendly: Users can enjoy a seamless experience with Akkio’s intuitive interface.
• Generative Intelligence: This platform can combine analytics and machine learning.
• Lead Scoring Capabilities: Akkio can qualify and segment lead lists, prioritizing hot leads.

Pros:

• It enables users to prioritize leads.
• Its ability to combine analytics and ML helps in generating insights.

Cons:

• Users new to the platform may find its extensive features complicated.
• Akkio’s integrations are limited.

Pricing: Akkio offers two plans, for basic and professional use, starting at $49/month and $99/month per user, respectively.

Link: Akkio

8. IBM Watson (Analytics)

IBM Watson analytics is a cloud-based service capable of thorough data mining and predictive analysis for business users. This data analytics processor uses advanced AI algorithms to analyze human speech too.

Features:

• Real-time Analytics: This platform can access real-time insights, forecasts, and BI tools.
• Predictive Analysis: IBM Watson uses AI algorithms for predictive modelling.
• Automation: It uses automated insights to identify patterns in your data.

Pros:

• It provides users with real-time analysis.
• Automation greatly enhances the platform’s efficiency.

Cons:

• This tool may be complex to beginners.
• Real-time analytics are dependent on a stable internet connection.

Pricing: Various customizable plans.

Link: IBM Analytics

9. KNIME

KNIME is an open-source platform with versatile tools for data analysis and easy integration with multiple platforms are possible for effective results.

Features:

• All-Inclusive Toolkit: KNIME provides a complete toolkit for data analysis.
• User-centric capabilities: This platform caters to a large variety of users.
• Versatile Integration: KNIME provides a smooth experience for integration.

Pros:

• KNIME offers an extensive toolkit for data analysis.
• It integrates with various data sources.

Cons:

• It requires significant computational resources.
• It may be complex to use.

Pricing: It is an open-source tool.

Link: KNIME

10. Microsoft Power BI

Microsoft Power BI is a well-known business intelligence platform, allowing users to navigate and visualize data efficiently for valuable insights.

Features:

• Advanced Analytics with AI: Power BI integrates machine learning models and AI-powered features.
• AI Insights: AI Insights for Power BI Desktop introduces advanced AI functionalities.
• Users can import data from many sources.

Pros:

• It is flexible with data sources.
• It integrates ML models, enhancing the depth of analysis.

Cons:

• The advanced features are available at a high price.
• It does not have an easy interface.

Pricing: Various pricing plans are offered by Microsoft Power BI based on your needs.

Link: Power BI

Comparing AI Data Analysis Tools – Features and Pricing