11 Best Data Analysis Tools to Work With in 2025

Data analysis is crucial in today's businesses and organizations. With the increasing amount of data being created at 328.77 million terabytes of data per day, and them being readily available to most businesses, having efficient tools that can help analyze and interpret this data effectively is essential.

Plus, the spike in Gen AI adoption in 2024 begs the question: what will AI do for data analysis? Which tools will emerge most popular?

In this article, we will discuss the top data analysis tools to use in 2025.

What are data analysis tools?

Data analysis tools are software or programs that help in collecting, cleaning, organizing, analyzing and interpreting large sets of data to derive meaningful insights. To extract valuable information from the data, these tools use techniques such as:

• Statistical analysis
• Machine learning algorithms
• Data visualization

(Related reading: most common data types & big data analytics.)

What AI is doing to the field of data analytics

Artificial intelligence (AI) is revolutionizing the field of data analytics by automating and streamlining various analytical processes. With AI, businesses can now analyze vast amounts of data in real time, identify patterns and trends, and make data-driven decisions faster than ever before.

Here are several examples of how AI is being used in data analytics in 2025:

• Data analysis with ChatGPT: An AI-powered assistant that brings data analysis to non-technical users.
• Anaconda AI Navigator: A platform to experiment with open-source LLMs locally.
• Jupyter AI: A platform to simplify the process of creating machine learning and deep learning models.
• Tableau AI: AI can automatically generate insights based on trends in data sets, reducing the time and effort required for manual data analysis.

Overall, AI is enhancing data analytics by providing more accurate insights, bringing automated business insight, and making it accessible to a wider range of users.

Today’s top data analysis tools

With that background and state of data analytics today, let’s now turn to the list of most popular data analysis tools.

Python

Python is a versatile and often favored programming language for data analysis and scientific computing. With a rich ecosystem of libraries like pandas, NumPy, and Matplotlib, Python facilitates complex data manipulation, analysis, and visualization with relative ease.

Python's readability and simplicity make it a go-to for newcomers and experts alike, bridging the gap between data exploration and actionable insights. Some common uses of Python in data analysis include:

• ETL (extract, transform, load) processes: Extracting data from various sources, cleaning and transforming it for analysis.
• Data visualization: Creating informative and visually appealing graphs, charts, and plots to communicate findings.
• Machine learning: Building predictive models using algorithms such as linear regression, decision trees, or neural networks.
• Web scraping: Extracting data from websites for analysis and insights.

Python's popularity in data analysis is likely to continue growing, with its increasing adoption by large companies and open-source community support.

Of course, Python is not only a data analytics tool. It’s also known for its versatility across many other fields outside of data analytics, such as software and web development.

Microsoft Excel

Yes, despite new tools emerging, Microsoft Excel remains a robust staple for data analysts. Microsoft Excel is a spreadsheet program that allows for extensive data manipulation, analysis, and visualization.

Its user-friendly interface and familiarity make it a popular choice for data analysis. With features like pivot tables, advanced functions, and macros, Excel offers powerful capabilities to handle large datasets with ease.

Some common uses of Microsoft Excel in data analysis include:

• Data cleaning and preparation: Using built-in functions or macros to clean and format data for analysis.
• Data manipulation: Sorting, filtering, and reorganizing data to identify patterns or trends.
• Statistical analysis: Performing basic statistical calculations such as mean, median, mode, standard deviation, etc. on large datasets.
• Formulae and functions: An extensive library of built-in functions for calculations.
• Pivot tables: For summarizing and analyzing data efficiently.
• Pivot charts: To create a wide range of visualizations.
• VBA programming: For custom macros and advanced data manipulation.
• Data modeling: With features like Power Pivot for handling large datasets.
• Conditional formatting: To highlight data trends and patterns easily.
• Add-ins and integrations: Extensibility through third-party add-ins.

Microsoft Excel's widespread use in businesses and organizations makes it a reliable tool for data analysis. Excel's integrations with Microsoft products like Power Query and Power BI make it great for smooth downstream analysis, too.

SQL

Structured Query Language (SQL) is the standard language for relational database management systems (RDBMS). It is used to manage, manipulate and query data stored in databases efficiently.

SQL is also one of the many query languages. Its popular variations include MySQL, PostgreSQL, and Oracle SQL. Despite some differences, most SQL variants follow similar syntax and principles. Some common uses of SQL in data analysis include:

• Data querying: Retrieving specific sets of data from a database based on given criteria.
• Data manipulation: Inserting, updating, or deleting data in a relational database.
• Data aggregation: Aggregate data using GROUP BY and aggregate functions like SUM and AVG.
• Database management: Create and manage database schemas and indexes to optimize performance.
• Transactional control: Manage database transactions with ACID (Atomicity, Consistency, Isolation, Durability) properties.
• Security and permissions: Control access to data with a granular permissions system.

Its syntax and paradigms form the backbone of most relational database systems. SQL skill sets are vital for data professionals to extract, transform, and load (ETL) data effectively and are used by data analysts, data scientists, and data engineers.

(Know the differences: SQL vs NoSQL.)

Data analysis with ChatGPT

With the rise of AI, data analysts can now leverage tools like Data analysis with ChatGPT to perform natural language-based data analysis. ChatGPT is a chatbot by OpenAI and is capable of understanding and responding to user queries in plain English.

The chatbot allows users to input datasets and have the AI generate code that can perform data analysis, transformation, and visualization tasks using Python, according to the prompts given.

Some advantages of using ChatGPT for data analysis include:

• Ease of use:  No need to learn complex coding languages, simply communicate with the chatbot in plain English.
• Timesaving: ChatGPT can generate code quickly for various data analysis tasks, saving time and effort.
• Flexibility: Can handle multiple datasets and perform a wide range of data manipulation and visualization tasks.
• Dynamic responses: The AI-based chatbot is continuously learning and improving its responses, making it more efficient over time.

The use of AI technology like ChatGPT is revolutionizing the world of data analysis by simplifying complex processes and making it more accessible to non-technical users.

Microsoft Power BI

Microsoft Power BI is a premier analytics platform offering interactive visualizations and business intelligence capabilities. With an intuitive interface that allows non-technical users can use with ease, Power BI plays a significant role in enabling organizations to visualize data and share insights across the enterprise or embed them in an app or website.

Furthermore, its ability to connect to a vast array of data sources means that Power BI can serve as a central hub for an organization's analytics workflows. This range of connectivity bridges the gap between data silos, facilitating a more unified data strategy.

As an analytics tool, Power BI provides diverse functionalities ranging from basic data manipulation to complex machine learning applications. Its integration with Microsoft Fabric enhances its capability to handle large datasets and provides advanced analytics solutions.

Power BI’s real power lies in its sharing and collaboration features. Dashboard reports can be shared with team members, creating a dynamic environment for real-time data-driven discussions.

(Source: Power BI Dashboard Templates, AnyInstructor)

It’s clear that Microsoft Power BI is more than just a tool — it's a comprehensive ecosystem designed to democratize data analytics.

(Related reading: the democratization of data.)

dbt

dbt is an open-source analytics engineering tool designed by dbt Labs for data analysts and engineers. It's a command-line tool that allows you to write modular, SQL-based transformations on top of your data warehouse.

dbt follows the ELT (extract, load, transform) approach to data modeling. This means that instead of transforming your data before loading it into a warehouse (as in ETL), dbt transforms the data inside the warehouse itself. With this approach, you can avoid manual coding for every transformation and maintain consistent models in your warehouse.

Some common uses of dbt in data analysis include:

• Data transformations: Writing SQL code to transform raw data into meaningful information for downstream analysis.
• Data modeling: Building a logical representation of your data using models, schema files, and custom configurations.
• Data testing: Automated tests ensure that new code changes do not break existing models or schema rules.
• Documentation generation: Generate documentation for your data models, making it easier to understand how the data is structured and transformed.

dbt also has a strong community that contributes to its development and maintenance. With plugins and integrations available, dbt can be easily customized to fit into an organization's specific needs.

Apache Spark

Apache Spark is a unified analytics engine renowned for large-scale data processing, streaming, and machine learning capabilities. Its resilience and distributed nature offer robust solutions for complex data workflows.

Given its speed and versatility, Spark has become a staple in the data science community, enabling scalable processing of big data workloads. Plus, Spark supports multiple languages, including Java, Scala, Python, and R, providing a versatile platform for a wide variety of data professionals.

Its API simplicity, coupled with multi-language support, makes it accessible to a broader range of analytical talent. The integration of Spark also allows for seamless querying of data in a manner akin to SQL-based systems. This familiar interface reduces the learning curve for professionals with SQL expertise.

Some common uses of Apache Spark in data analysis include:

• Data engineering: Efficiently process large datasets with distributed computing capabilities.
• Data analytics & machine learning: Utilize machine learning libraries like MLlib for predictive analytics.
• Real-time analytics: Process and analyze data streams in real-time with Spark Streaming.
• Graph Processing:  Use GraphX to perform graph analytics on large datasets.
• Data Integration: Spark’s connectors allow seamless integration with various data sources, such as Hadoop and Amazon S3.

Each component is fine-tuned to handle specific types of data workloads and analytical tasks.

So, we can say that Spark is a powerful tool for handling massive amounts of data and enabling advanced data analysis techniques. Its versatility and scalability make it the go-to solution for big data processing and analytics.

Tableau

Tableau is a data visualization tool that allows users to create interactive and visually appealing dashboards. It offers drag-and-drop functionality, making it accessible for non-technical users to create data visualizations with ease.

Tableau's main strength lies in its ability to handle complex and large datasets while providing real-time analytics capabilities. Additionally, unlike its competitor Power BI, Tableau has stood the test of time, evolving to meet the expanding needs of the data community through innovations like Tableau AI. Here are some key features of Tableau:

• Intuitive drag-and-drop interface: Create data visualizations with ease, without the need for coding.
• Real-time analytics: Connect to live data sources and get real-time insights.
• Advanced analytics capabilities: Utilize advanced statistical models like regression analysis and forecasting.
• Collaboration tools: Share dashboards and collaborate with team members in real-time using Tableau Online.
• Numerous integrations: Connect to various data sources and platforms, including cloud-based services like Google BigQuery and Amazon Web Services.

Tableau's intuitive interface and advanced features make it a popular tool for businesses of all sizes.

R

R, a programming language dedicated to statistics and data visualization, is acclaimed for its versatility and power in data analysis.

For those who aim to dive deep into data analysis, R offers a comprehensive suite of statistical and graphical tools. It has become the go-to tool for many statisticians and data scientists, where it provides an open-source environment to perform sophisticated data manipulation and complex analyses.

Compatibility with various data formats and extensive plotting capabilities also make it highly recommended for academics and industry professionals alike.

Among its strengths is an active community who contributes a wealth of packages that extend the core functionalities, tackling everything from econometrics to bioinformatics. Here are some common R packages used:

• ggplot2: A powerful data visualization package for creating aesthetically pleasing graphs.
• dplyr: Efficiently manipulate large datasets using intuitive functions and syntax.
• tidyr: Clean and tidy messy data into organized formats, ready for analysis.
• caret: Provides a comprehensive set of tools for training and evaluating predictive models.
• Bioconductor: A collection of packages for bioinformatics and biological data analysis.

R's vast array of packages makes it a versatile tool for various analytical tasks, including statistical modeling, machine learning, and text mining.

Looker

Looker is a cloud-based data analytics platform that provides end-to-end solutions for data exploration, analysis, and visualization.

It offers an intuitive interface and powerful features like LookML (Looker Modeling Language), which allows users to define data models and perform complex SQL queries without needing to code.

Some key features of Looker include:

• Data exploration and discovery: Users can explore data and create visualizations without any SQL knowledge.
• Collaboration tools: Share findings with colleagues and collaborate in real-time to make data-driven decisions.
• Centralized data repository: Looker connects to various data sources, providing a central location for all business data.
• Real-time analytics: Get real-time insights from live data sources.
• Customizable dashboards and reports: Create customized dashboards and reports in Looker Studio that can be easily shared with team members.

Looker's user-friendly interface, collaboration capabilities, and powerful features make it a popular choice for businesses looking to make data-driven decisions.

KNIME Analytics Platform

KNIME Analytics Platform is an open-source data analytics platform that integrates various components for building data workflows, from data ingestion to predictive modeling and deployment.

It offers a visual interface, making it accessible even for non-technical users. Some key features of KNIME include:

• Drag-and-drop interface: Easily build data pipelines or workflows without any coding knowledge.
• Integrations with various tools: Connect to various data sources, databases, and APIs.
• Advanced analytics capabilities: Perform complex data analysis tasks using built-in tools like machine learning algorithms and statistical models.
• Collaboration tools: Share workflows and collaborate with team members in real time.
• Extensive community contributions: Access a vast library of nodes contributed by the KNIME community for extended functionalities.

Its modular data pipelining concept promotes a plug-and-play environment where users can effortlessly adjust and scale their analytics workflows. Moreover, its open-source nature allows for continuous development and improvement of the platform.

Overall, the KNIME Analytics Platform is ideal for businesses looking for a flexible, cost-effective, and customizable data analytics tool.

Honorable mentions

Apart from the above-mentioned tools, there are several other data analytics platforms that deserve mention:

• QlikView: A self-service business intelligence and data visualization tool with a focus on a user-friendly interface and powerful features.
• Sisense: Boasts advanced analytics capabilities and AI-driven insights for businesses looking to gain deep insights into their data.
• Snowflake: A cloud-based data warehouse with a scalable and secure environment, making it suitable for businesses of all sizes.
• Google Analytics: A popular web analytics tool that helps track website traffic and provides valuable insights into user behavior.

Although these might not be the most popular tools, they are worth considering based on specific business needs and use cases.

With the constantly evolving field of data analytics, there will always be new tools and technologies to manage and make better use of their data.

Which data analysis tool is for you?

One of the primary considerations in selecting a data analysis tool is understanding the specific needs of your organization. Each platform offers unique features and functionalities, so you'll need to assess the level of technical expertise required as well as your business requirements.

Tableau may be the ideal choice for those looking for a user-friendly interface with powerful visualization capabilities. R's flexibility and expansive analytical abilities make it a popular choice for statisticians and data scientists.

Looker's cloud-based infrastructure and emphasis on collaboration make it an attractive option for businesses needing a scalable, user-friendly solution. KNIME's open-source nature and modular approach make it a versatile choice for both novice users and experienced data scientists.

(Want to skill up? Explore these data analytics & data science certifications.)

Wrapping up

These are just some of the popular data analysis tools out there, and each organization tends to have a specific data stack that combines several of these tools to bring about business insight. 

As the data landscape evolves under the influence of new technology like AI, some of these tools may not remain popular. Still, these are very well-known tools at the moment and could be an asset to any organization that wants to leverage data for decision-making.