Data analysis has historically been a basis for data-driven operations. It assists businesses in getting insights and making well-informed strategic decisions. However, as the volumes of data increase dramatically, there are significant constraints on traditional analytical approaches.

Traditional techniques involve manual data analysis that is ineffective and error-prone when it comes to handling big databases. These tools facilitate real-time decisions using advanced technologies like NLP, machine learning, and deep learning to identify complex patterns.

The function of AI in data analysis, common tools and methods, upcoming developments, and other topics will all be covered in this article. A thorough understanding of how artificial intelligence approaches can be used in Big Data contexts can be gained from AI for Big Data Analytics” course offered by Oxford Training Center. Professionals who complete this one-week course will have the practical skills and understanding necessary to use AI tools and approaches to glean insightful information from large, complicated data sets.

What is data analysis?

Analyzing raw data in specific ways to extract information is known as data analytics. Numerous data analytics methods and procedures have been automated into mechanical procedures and algorithms that process unprocessed data for human use. Data analytics can be used to help a decision-maker make the best choice based on underlying information or to maximize a company’s performance.

Why is AI important in data analysis?

AI uses consumer data analysis to tailor services, increasing engagement and satisfaction. By automating data analysis, operational expenses, errors, and manual labor are reduced. It also guarantees effective access to the relevant information through the automation of data organizing, labeling, and retrieval. Businesses can respond quickly to shifting market conditions due to AI’s rapid insights. AI is perfect for Big Data situations because it can process data from many sources efficiently.

Businesses can sort through vast volumes of data more quickly and find insights more naturally and intuitively by using AI solutions for data analysis. AI technologies are used by even the most sophisticated data intelligence platforms, such as Domo, Tableau, or Power BI, to improve user experiences, provide more insights, and guarantee that users discover the answers to their business problems.

10 best AI tools for data analysis

Now that we’re talking about the ways to use AI tools in data analysis, let’s explore the most effective tools in 2025 and how they might help ‌build your enterprise. Here is a rundown of tools one should consider, along with their advantages and disadvantages, while picking the one that will be most beneficial to you.

1. IBM Cognos Analytics

For data scientists, analysts, and developers alike, IBM Cognos Analytics offers a one-stop shop for everything related to data analysis and AI development. From data exploration and model development to smooth deployment, this powerful platform encompasses the whole data science lifecycle. It gives you a thorough, accurate, and dependable picture of your company, allowing you to predict future trends, outcomes, and the causes of them.

IBM Cognos Analytics has a freemium business model, offering limited data capacity and basic functions without charge. More data storage, access to sophisticated capabilities like social media data analysis, and the capacity to connect to a wider variety of data sources are all provided by upgraded plans. Premium pricing starts at USD 42.40 * per month per user, while standard pricing starts at USD 10.60 per month per user.

2. Tableau

It takes a little more experience to use Tableau’s AI tools because they were created with data scientists in mind. Nonetheless, data scientists can still use AI for a variety of data science methods, including predictive analytics and what-if scenario modeling. Easy data field arrangement by simply dragging and dropping visual images allows the analysts to build visuals without necessarily having to be technical users. 

This is due to the user-friendly interface of Tableau. You will always be working from the latest data, as Tableau is capable of connecting with live data, which makes it possible to analyze and visualize information in real time. While on the road, you can analyze and make decisions via mobile devices, accessing and engaging with your dashboards and visualizations. Easily combine Tableau with R programming and statistical tools for in-depth, thorough analysis and potent data presentation.

3. Microsoft Power BI

Microsoft’s Power BI is a user-friendly data visualization tool specifically made for corporate analytics. Because of its smooth integration with other Microsoft applications, it is a sensible option for companies that are currently using the Microsoft ecosystem. To have smooth tracking and analytics, you will also be able to connect Power BI to Xero, Google Analytics, and HubSpot, among other business apps, through the API.

Power BI’s user interface is fairly intuitive for those who are used to Excel, and the flexibility of the wide connectivity with MS support gives it the flexibility to be a minimal training, self-service tool. Users are also able to download native mobile apps for Windows, Android, and iOS devices, along with Power BI Desktop, which is for Windows 10 or Windows 11. For businesses that have to store their data and reports on-site, there is also a Power BI Report Server. A particular desktop application, appropriately named Power BI Desktop for Power BI Report Server, should be used for that version of Power BI.

4. KNIME

Konstanz Information Miner, or KNIME for short, is a free and open-source data analytics tool for data processing, integration, reporting, and visualization. It requires little to no code and plugs in machine learning and data mining libraries. For data scientists without a strong background in programming who need to combine and process data for machine learning and other statistical models, KNIME is an excellent tool. Point-and-click modeling and analysis are made possible by the graphical user interface.

5. Excel

The most popular program for working with spreadsheets and creating analyses is Microsoft Excel. Excel, which has been developed over decades, is extensible using Visual Basic, its native programming language and can accommodate nearly any common analytics workflow. Excel is good for basic analysis, but it is not good for massive data analysis because it has a limit of about 1 million rows and lacks good versioning and collaborative features. For extensive and cooperative analyses, businesses should take into account more recent cloud-based analytics tools.

6. Python

Python is a high-level, open-source language that data scientists and technical analysts use often. It now boasts over 200,000 available packages and more developers in the world than Java does. Python can interoperate with third-party tools for data visualization and machine learning and can do a variety of studies on its own as well. Seaborn Plotly and Matplotlib are common programs for data visualization. Other analytics systems are also amenable to design that allows them to use Python as an interface.

7. Apache Spark

One of the greatest data analytics tools is Apache Spark, which offers fault tolerance & a programming interface for managing the entire cluster. This open-source distributed computation technology enables specialists in data science and analysis to handle big data streams with ease. Well-known businesses like Oracle, Hortonworks, Verizon, and Visa use Apache Spark to compute data in real-time quickly and easily. 

8. Qlik Sense

With the help of the cutting-edge analytics platform Qlik Sense, users of all experience levels can produce AI-powered insights and make wise decisions. It offers two choices: Qlik Sense® Client Managed for on-premises implementation in highly regulated businesses and Qlik Cloud® Analytics for cloud-based SaaS deployment.

You can create and incorporate Qlik’s real-time insights and visualizations into your business apps using JavaScript and robust APIs and libraries. Qlik Sense’s embedding toolkit makes it simple to embed analytics, even if you don’t know how to code.

9. Jupyter Notebook

An open-source web application called Jupyter Notebook is used to create and distribute documents that include narrative prose, mathematics, live code, and visualizations. Projects involving machine learning and data analysis frequently employ it.

10. RapidMiner

RapidMiner gives users all the tools they need to combine, purify, and convert data before running statistical and predictive analytics models. Almost all of this may be done by users using a straightforward graphical user interface. R and Python scripts can also be used to enhance RapidMiner, and the company’s marketplace offers a large number of third-party plugins. Nonetheless, the program is highly optimized for its graphical user interface, allowing analysts to independently prepare data and run models.

Future challenges of using AI tools for data analysis

AI-powered data analysis appears to have a promising future, but there are still obstacles to overcome. Naturally, ethical issues are brought to the fore. The effects AI technology has on society and an organization’s cultural values are among the ethical issues raised by this technology.

A business that depends too much on AI-driven data analysis at the expense of human analysts, for example, runs the risk of constraining its desire for innovation and causing social disruption. Despite its ability to effectively mimic creativity, AI is not creative. It can only “create” depending on the data that is supplied to it and is predictive. Speaking of data, another significant drawback is that AI’s effectiveness depends entirely on the quality of the data it is given.