Artem Oppermann | Nov 14, 2022

The world is swimming in data. According to some estimates, about 2.5 quintillion bytes of data is produced every day (that’s 2.5 followed by 18 zeros). And by 2025, humans are predicted to produce 463 exabytes of data each day — an extraordinarily large number when you consider that all the words ever spoken by mankind is estimated to equal a measly five exabytes.

Impressive numbers aside, the immense amount of data swirling around these days is only as valuable as the information you can extract from it. Without the ability to analyze and derive insight, every click, search, purchase and tweet is lost in the ether, along with all the valuable information it carries. As such, data analysis — the technique by which raw data is transformed into useful statistics, insights and even predictions — has become an incredibly important area in tech.

Data Analysis Tools to Know

  • Airtable
  • Google Data Studio
  • IBM Cognos
  • Looker
  • Microsoft Power BI
  • Oracle Analytics Cloud
  • R
  • RapidMiner
  • Splunk
  • Tableau

Of course, there isn’t enough time, resources or intelligence in the world for humans to be able to process all of this data on their own. That’s why data analysts and other professionals rely on the many data analysis tools out there to help them.

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How Are Data Analysis Tools Used?

Data analysis has become the bedrock of modern business operations. Companies across every industry, from retail to healthcare, dedicate a lot of time and money to not only gathering and organizing data, but also finding patterns and trends in the data so they can streamline their business operations and even make predictions.

To be able to perform data analysis quickly and at the highest level possible, analysts and data professionals must rely on tools and software — and there seems to be a tool for everything.

Business intelligence platforms use SQL databases, cloud platforms and machine learning to transform a company’s internal data into strategic insights about what’s currently working and what could work in the future, helping to make more self-aware and evidence-based decisions.

Statistical analysis tools are used to manipulate, explore and generate insights from data — often with the help of data science programming languages.

And data visualization tools are exactly what they sound like: tools that develop visual representations in order to tell a story about the data.

Meanwhile, predictive analytics tools use current data sets to forecast the likelihood of certain events, providing companies with a blueprint to follow. These tools can be used to anticipate the future success of products, reduce customer churn, nip cybercrime in the bud, and much more.

Here are some popular data analysis tools on the market today.


Data Analysis Tools to Know


As a cloud-based spreadsheet database, Airtable is designed to help users collect, organize, visualize and analyze their data in a collaborative way. Much like a Microsoft Excel or Google Sheets spreadsheet, an Airtable spreadsheet is organized into rows and columns. And the platform is no-code, meaning users can manage and analyze the data they’ve gathered without having to understand SQL, or structured query language.


Apache Spark

Apache Spark is an open-source analytics tool used for processing large-scale data. The software provides scalable and unified processing that is capable of executing data engineering, data science and machine learning tasks in Java, Python, R, Scala or SQL. Apache Spark was developed by the Apache Software Foundation, which also created tools like Apache Mahout, Apache Storm and more.


Google Data Studio

Google Data Studio is a free data analytics and visualization tool that automatically integrates with other applications in the Google universe, including Google Analytics, Google Ads and Google BigQuery. Because it can easily integrate with other Google services, Data Studio is good at analyzing data stored on Google specifically.


IBM Cognos

As the business intelligence component of IBM’s Watson, Cognos offers built-in artificial intelligence tools that analyze data and then explain insights in plain terms. It also has automated data preparation software that can clean and aggregate data sources, allowing for quick integration and analysis of data sources.



Short for Konstanz Information Miner, KNIME is a free, open-source analytics platform that supports the integration, processing, visualization and reporting of data, allowing it to make interpretations and predictions. It also plugs in machine learning and data mining libraries with little to no programming requirements, making it especially useful to data scientists who want to integrate and process their data for machine learning and other statistical models but don’t have strong programming skills.

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Looker is a cloud-based business intelligence and data analytics platform, featuring automatic data model generation that scans data schemas and then infers relationships between tables and their data sources. Data engineers can also modify the generated models thanks to a built-in code editor. Offering reports and visualizations that are clear and shareable, Looker is designed to be accessible for both data analysts and business teams.



Metabase is yet another free and open-source analytics and business intelligence platform. But what sets it apart is its ability to allow users to ask questions, providing a more approachable way for less-than-techie users to analyze their data with simple filtering and aggregations. More technically advanced users can go straight to raw SQL for more complex analysis. 


Microsoft Excel

We would be remiss if we didn’t include this data analysis classic in our roundup. Launched in the 1980s, Microsoft Excel is among the oldest analytics tools on the market today, but it is still widely used thanks to its versatility and simple design. Once data is filled into the rows and columns of an Excel spreadsheet, users can export and send their data, as well as analyze it. And because it has decades of development supporting it, the platform can support nearly any standard analytics workflow. There are a few limitations to Excel, though. It doesn’t have an automation feature, and it is not suitable for analyzing large data sets because it has a limit of about a million rows. 


Microsoft Power BI

As Microsoft’s more technologically advanced data analytics tool, Power BI offers support for dozens of data sources and allows users to create and share reports, displays and dashboards. It is composed of several individual products, including Power BI Desktop, Power BI Pro and Power BI Mobile — all of which can be integrated with each other and other Microsoft products. Plus, Power BI lets users create and implement automatic models by applying machine learning with Azure Machine Learning.



Mode is focused on providing data scientists with an easy, iterative environment to work in. It offers everything from an interactive SQL editor and notebook for analysis, to visualization and collaboration tools for the less technically inclined users. Mode also has a Helix data engine that streams and stores data from external databases, allowing for swift and interactive analysis.


Oracle Analytics Cloud

The Oracle Analytics Cloud is a suite of cloud-based business intelligence and data analytics tools, focused on helping big companies transform their legacy systems into modern cloud platforms. It offers a wide variety of analytical features, from basic visualizations to machine learning algorithms.



Qlik is an analytics and data integration tool that includes visualization features like Qlik Sense, which allows users to create interactive dashboards, as well as share maps, graphs and other visual elements with their teams. The platform also uses artificial intelligence to automatically generate analyses and insights, and natural language processing to better understand user intent. People who prefer conversation to visualization can chat with Qlik’s AI-enabled Insight Bot.



R is an interpreted programming language designed for use in statistical computing and graphics. Featuring numerous graphical tools and more than 15,000 open-source packages (including many for loading, manipulating, modeling and visualizing data), R is considered to be one of the most comprehensive statistical programming languages available — capable of handling everything from data manipulation to statistical analysis. That said, R requires quite a bit of programming skill, so it is mostly used by professionals working in data science, such as statisticians and data miners.

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Developed in partnership with software company Altair, RapidMiner is a predictive analytics platform that does everything from fraud detection to churn prevention. Nearly all of this can be done through RapidMiners’ simple interface, allowing analysts to prepare data and run models on their own. However, the platform can also be expanded using R, Python and other third-party plug-ins on the company’s marketplace.



Redash is used for querying data sources and making visualizations. Its code is open source, and it has a query editor that provides a simple interface for writing queries, exploring schemas and managing integrations. Then, query results are cached within Redash, and users can schedule updates to run automatically.


SAS Advanced Analytics

SAS Advanced Analytics is a module of the larger SAS data analytics platform, including SAS Business Intelligence and SAS Forecasting. The advanced analytics module is focused specifically on providing predictive analytics with the help of AI technology. It also offers text mining and data visualization tools to help companies take full advantage of their data, as well as built-in security features to help protect sensitive data.



Sisense is a data analytics tool that is focused mainly on business intelligence, allowing data engineers, analysts and developers to connect and prepare data securely, as well as manage, embed and explore insights across various devices. The platform is kitted out with a collection of drag-and-drop tools and interactive dashboards for a more collaborative and easy user experience. And it is all built on an API-driven, cloud-native architecture to allow for flexible and scalable deployment.



Developed by big data company Talend, Stitch allows companies to quickly load data from hundreds of sources into a data warehouse, where it is then structured and ready for analysis. Talend also has another tool called Data Fabric, which combines data integration with data governance and integrity, as well as offers application and API integration.

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Splunk focuses mainly on helping the IT, cybersecurity, Internet of Things and data analytics sectors. The platform features a comprehensive data hub that is able to manage data insights, security and full-stack scalability. For navigating big data, Splunk can bring together hundreds of terabytes of data across a variety of servers into one interface, allowing businesses to visualize and interpret their data by seeing it in context.



Of all the data analytics and visualization tools, Tableau is among the most well-known. It is owned by software giant Salesforce, and is designed so that everyone at a given company can see essential patterns and correlations in institutional data, without having to write a single line of code. Rather than technical data mining, Tableau relies on a person’s innate ability to spot patterns, so its visualizations range from color-coded maps to live graphics that update in real time. Much of Tableau runs on top of its core query language, VizQL, which translates drag-and-drop components into back-end queries — thus minimizing the need for the end user to optimize its performance.



ThoughtSpot allows people to explore and glean important information from their company’s data, regardless of their tech abilities. SpotIQ, its AI-powered system, finds insights to help users find patterns and trends in the data. It allows them to automatically link tables from different data sources in order to break down data silos, which is beneficial for many industries. In marketing, for example, everyone from marketing managers to customer care representatives can pull the data they need to understand customer usage, which allows for better marketing decisions and (ideally) an improved customer experience and loyalty.

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