9 of the Best AI Data Analytics Tools You’ll Need 

Austin Chia, contributor to the CareerFoundry Blog.

Have you tried out any artificial intelligence tools lately and thought—this could work for data analysis too?

Well, you’re on the right track! AI tools for data analytics have become invaluable among many data professionals .

In fact, AI tools for data analysis have been around for a while now, but have only recently seen more interest due to huge advances in generative AI.

Many new generative AI tools have appeared, and several existing data analysis tools have adopted AI into their products as well.

In this article, we’ll be taking a closer look at:

  1. AI in data analytics: An overview
  2. The 9 best AI data analytics tools out there
  3. Things to be mindful of when using AI in data analytics
  4. AI data analytics tools FAQ
  5. Key takeaways

If you’re curious to know how these AI tools can impact and speed up your data analysis workflow, keep reading!

1. AI in data analytics: An overview

To help you have a better picture of this new and exciting space of AI tools for data analysis, let’s start with an overview.

So, what are AI data analytics tools?

AI-powered data analytics tools are essentially programs that utilize artificial intelligence algorithms to help you achieve your objectives with more accuracy and efficiency.

These can be used for a range of tasks, such as:

To put things in simple terms, AI-powered data analysis tools are designed to automate repetitive data analysis tasks, leaving you, the human analyst, with more time and resources to focus on the bigger picture!

2. The 9 best AI data analytics tools out there

Let’s now have a look at some of the best AI tools used in data analytics today.

1. ChatGPT

ChatGPT (short for Generative Pre-trained Transformer) is a powerful AI large language model (LLM) developed by OpenAI which started gaining popularity in late 2022.

It’s designed to understand and generate human-like responses in a conversational setting. By leveraging deep learning techniques and pre-trained models, GPT can produce coherent, contextually relevant, and creative text based on the input it receives.

However, it’s essential to note that GPT is a text-based model and cannot perform physical tasks or execute actions outside of generating text.

If you’re looking to see what you can do with it, we’ve created a full guide to data analysis prompts.


ChatGPT comes with several pricing options.

The basic plan is free, while the ChatGPT Plus plan offers additional features at $20 per month.

The ChatGPT Plus plan offers more uptime during peak hours, quick response outputs, and priority access to new features.

What this tool is good for

ChatGPT is great for a huge range of applications in data analytics for its versatility and ability to write code.

For example, it can write simple data transformation scripts in Python or R, using just simple natural language prompts.

2. Tableau

Another common tool used among data analysts, Tableau, has joined in on the generative AI trend and has started to build some into its existing products.

Through the use of Tableau GPT and Tableau Pulse, you can even automate some common processes such as data analysis, preparation, and governance. Tableau GPT is based on Salesforce Einstein GPT, which is a combination of OpenAI’s enterprise-grade ChatGPT technology and Salesforce’s private AI models.

Tableau Pulse is also another feature to be included and launched across Tableau Desktop products. This provides you with personalized metrics and insights based on data. They’re also driven by the same AI models. All of this adds up to making Tableau a popular tool for augmented analytics which will fit well in your toolkit.


Pricing for Tableau GPT and Tableau Pulse is still unconfirmed until its launch in Spring 2024.

What this tool is good for

Tableau GPT and Pulse are great for personalizing data analysis tasks, allowing you to save time by automating certain processes. Additionally, these tools are well-suited for small teams with lower manpower since tasks can be automated.

It’s also good for data analysts who want some support in data governance and improved data visibility.

3. Power BI

Power BI is a business intelligence platform from Microsoft. It offers data analytics tools and services for visualizing data, creating dashboards, and sharing insights.

They’ve also launched an AI-powered feature called AI Insights for their Power BI Desktop tool, which is now available in all Power BI Desktop applications.

Currently, AI Insights on Power BI support several applications for AI:

These functions work by automating some processes in Power Query, which is built into your Power BI Desktop app.

As you can see, these applications are mostly related to text analytics and computer vision AI models. This is great if you’re a data scientist who works with machine learning models and would like to handle them all within Power BI.

Another amazing feature that would help many data analytics users is the Power BI Q&A function. The Power BI Q&A function allows you to use natural language to query and transform your data.

For example, by simply asking Power BI a business question you want answered by your data, a backend AI model would analyze your data and provide a suitable answer for you.

Such an interactive way to handle your data would make data more accessible to everyone and help bridge the gap between data professionals and those who are not as familiar with analytics.

This Power BI Q&A function also makes it a quick way for data analysts to search and explore data quickly before diving in deep for further analysis.


Power BI offers a free trial and several paid plans depending on your needs.

What this tool is good for

Power BI’s AI-powered features are great if you’re looking to make text analysis tasks easier, quicker, and more efficient. The Power BI Q&A feature is also great for users who aren’t very familiar with data analytics and want to explore the data quickly. Additionally, it’s a great tool if you’re looking for an interactive way to handle your data.

4. Microsoft Excel

Microsoft Excel, the spreadsheet software that needs no introduction, has AI features in its system as well.

Launched way back in 2018, Analyze Data in Excel is a feature that leverages AI to suggest tables, charts, graphs, and other visuals which could help you better understand your data.

If you’re an Excel user who wants to get more insights into their data while keeping things simple, then this feature can be highly helpful.

This feature allows you to ask your data which fields you’d like to analyze further, and it would generate the relevant charts and Pivot Tables for you.

In more recent news, Microsoft 365 Copilot was launched as a large language model (LLM) assistant in March 2023 to help automate workflows and search for answers across your Microsoft applications. And, of course, this includes Excel.

Through a chat interface known as Business Chat, you’ll be able to query your data within Excel using natural language. Copilot will then search the data and present you with relevant information in an interactive manner, making it easier for users to explore their data and to conduct data analysis in Excel.

For more information, there’s a video showcasing Microsoft 365 Copilot.


Microsoft Excel is available with Microsoft 365 subscriptions, which start from $99 per year.

What this tool is good for

The Analyze Data in Excel feature is great for Excel users who want to quickly and easily gain insights from their data.

Additionally, Microsoft 365 Copilot is great for both avid Excel users and beginners that are not very familiar with data analytics since they can use natural language to query their data.

5. Jupyter AI

If you’re currently learning or using Python in your projects or work, you’re also probably familiar with Jupyter Notebooks. Well, you’re in luck because Jupyter has also launched an AI-powered extension called Jupyter AI.

Jupyter AI leverages generative AI models to create a conversational assistant to help you in coding out your data analysis tasks.

This is great news for Python enthusiasts since this extension is built to make it easier for users to work with and understand their data.

It also makes building machine learning models easier using the existing Jupyter architecture.

Here are some common Integrated Development Environments (IDEs) it supports:

  • JupyterLab
  • Jupyter Notebook
  • Google Colab
  • VSCode


Jupyter AI is a free, open-source extension and can be found in their GitHub repository.

What this tool is good for

Jupyter AI is great for Python users who want an easier way to work with and understand their data.

Additionally, it’s suitable for machine learning engineers since it helps make building models easier. Finally, since it supports a variety of IDEs and is an open-source tool, it can be used in a wide range of projects.

Overall, Jupyter AI is an amazing tool to have if you’re looking for an AI-powered assistant that could help you with coding out data analysis tasks in Python.

6. Polymer

Polymer is an AI platform that provides features for data visualization and business intelligence. Essentially, this AI data analytics tool makes it easy to create simple dashboards using just a few clicks.

Polymer makes data exploration easier by automatically generating charts, tables, and maps from your data set. This helps make the process of understanding data much quicker and simpler.

Unfortunately, the platform isn’t available for open-source and is only available through their paid plans.


Polymer offers three different plans for different use cases: Starter, Pro, and Enterprise, at $10, $20, and $500 per month respectively.

What this tool is good for

Polymer is great if you’re looking for an easy, quick way to visualize your data and gain insights into it. The simple dashboards generated can help you quickly understand your data analysis results instead of combing through multiple charts and tables.

7. MonkeyLearn

MonkeyLearn is an AI-powered text and data analysis platform that helps you extract insights from unstructured text.

The platform allows you to easily build and deploy custom models for sentiment analysis, intent classification, keyword extraction, and parts-of-speech tagging.

This is great if you’re looking to gain insights from unstructured text, like customer reviews or survey responses.


MonkeyLearn offers a free plan as well as several paid plans depending on your needs, including a Team plan at $299.

What this tool is good for

MonkeyLearn is great if you’re looking to gain insights from unstructured data like customer reviews and surveys. The platform makes it easy to build and deploy custom models for sentiment analysis, intent classification, keyword extraction, and parts-of-speech tagging.

8. Qlik Sense

Qlik Sense is a powerful business intelligence (BI) and analytics platform with AI capabilities.

The platform helps you to quickly uncover insights from your data sets, whether it’s structured or unstructured. For example, Qlik Sense provides automated insights for natural language processing (NLP), helping you identify trends in text-based data faster than ever before.

Qlik Sense also offers guided data exploration, allowing you to quickly explore your data and uncover patterns without writing any code.

It also offers conversational analytics through Insight Advisor Chat, which lets you ask about your data.


Qlik Sense comes with a few different pricing plans depending on your needs. Qlik Sense Business costs $30/month billed annually.

What this tool is good for

Qlik Sense is great if you’re looking for an AI-powered business intelligence platform that can easily uncover insights from your data sets.

The automated insights for NLP help you quickly identify trends in text-based data, while the guided data exploration feature makes it easy to explore your data and uncover patterns without writing any code. Finally, the Insight Advisor Chat provides a convenient way to ask about your data.

9. Kanaries RATH

Kanaries RATH is an AI assistant that’s built for exploratory data analysis. This software is known for those who want to automatically generate data visualizations without coding.

It also features Data Painter, which allows you to easily explore your data and find meaningful ways to visualize it. Additionally, the platform also helps perform AI-enhanced data cleaning during your preparation phase.

Their free plan also includes PyGWalker—a no-code visual analysis interface in Jupyter Notebook.


Kanaries RATH offers both a free and paid plan at 10 per month, depending on your needs.

What this tool is good for

Kanaries RATH is great if you’re looking for a way to automate your data analysis workflow.

Additionally, the platform helps you with exploratory data analysis, as well as AI-enhanced data cleaning. If you’re looking for a tool to help you find meaningful ways to visualize your data quickly and easily, then it’s an excellent choice.

3. Things to be mindful of when using AI in data analytics

With every emerging and powerful tool, there are always some considerations to be aware of.

In AI-powered data analytics, not all outputs are 100% accurate, so you’ll need to be aware of the following before you use them in your projects or work.

1. Data quality and bias

When using AI in data analytics, you should be aware of the potential for bias to be a part of your results. That’s because AI algorithms are typically trained on existing data sets and can thus inherit any existing biases.

Therefore, it’s important to double-check your data sets if you want accurate results from your analysis. 

2. Data privacy and security

Data privacy and security are other things to be careful with when using AI in data analytics.

The more sensitive the data, the higher the risks associated with it, so you’ll need to ensure all your systems are secure and compliant with relevant regulations.

You should also consider encrypting your data sets to protect them from unauthorized access or use. 

If you’re planning to use company data sets, do check with your IT team to see if using any open-source tools is allowed.

In such situations, going for closed, proprietary AI tools for data analytics might be a better idea.

3. Ethical considerations of using AI tools

If your code or data visualizations are made by AI, would you be responsible for ensuring that data is accurate?

Since data is so sensitive and crucial to business decisions, you’ll also have to consider the ethical aspect of using AI tools when working with data.

For example, if you’re using an AI tool for the generation of SQL queries for your data analysis workflow, you’ll have to be answerable to the accuracy of output it gives.

This means you need to be mindful of the potential for misuse or abuse when using AI in your data analytics workflow. A good way to ensure this doesn’t happen is always to double-check the outputs to ensure your data is still accurate.

4. AI data analytics tools FAQ

To help give you a bigger picture of the fast-moving space of AI in data analytics, I’ve answered some common questions you may have.

How is AI changing data analytics?

AI is transforming data analytics by introducing fast and automated ways to make sense of complex data sets through natural language search. AI tools enable users to quickly identify patterns, draw insights, and create visuals that can be used for decision-making. AI has also made it easier for businesses to automate their processes related to data analytics, freeing up time and energy for more important tasks.

Will AI replace data analysts?

AI has the potential to reduce some tasks that data analysts normally do, but it’s not likely to replace them entirely. AI can streamline certain aspects of the job and automate mundane tasks, but there’ll be many areas where a human touch is still needed.

Data analysts are also needed to interpret results from AI-generated models, as they have the ability to think critically and understand the implications of their results. Therefore, AI will not replace data analysts, but will rather be used to complement their work.

Also, since data within businesses is sensitive in nature, the role of a data analyst is still crucial for ethical considerations in verifying information given by such generative AI models.

5. Key takeaways

To wrap things up, I’ve put together some key takeaways from this article.

  • AI is changing data analytics by introducing fast and automated ways to make sense of complex data sets through natural language search.
  • There are various AI tools available that help with data analysis, from both existing software and new ones, such as Microsoft Excel, Jupyter AI, and Tableau.
  • You should be mindful of potential bias in your data sets and ensure all your systems are secure and compliant with relevant regulations.
  • AI won’t replace data analysts entirely, but rather streamline certain aspects of the job and automate mundane tasks.
  • Lastly, it’s important to consider the ethical implications when using AI in data analytics.

I hope this article has provided you with a better understanding of how AI can be used in data analytics and which tools are available.

If you are curious about entering the field of data analytics, try out this free 5-day data analytics short course. For more related reading on other areas within data analytics, check out the following:

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