What are data analysis tools?

Data analysis tools are software solutions, applications, and platforms that simplify and accelerate the process of analyzing large amounts of data. They enable business intelligence (BI), analytics, data visualization, and reporting for businesses so they can make important decisions timely.

The concept of data analysis is as old as the data itself. Today, it is deeply integrated into every aspect of business and decision-making, driving insights and strategies across industries. Big data and the need for quickly analyzing large amounts of data have led to the development of various tools and platforms with a long list of features. However, with the abundance of different types of data analysis tools in the market, what was supposed to be a simple task has become a complex undertaking.

This article aims to simplify the process of finding the data analytics platform that meets your organization’s specific needs. In addition to providing a list of some of the best data analytics tools available in the market today, it also mentions the important factors that you should consider before making a purchase decision. For the purpose of this article, the terms “data analysis tools” and “data analytics tools” will be used interchangeably to avoid any confusion.

The best data analysis tools to consider in 2024

Here’s our list of the best tools for data analysis, visualization, reporting, and BI with pros and cons so that you can make an informed decision:

Microsoft Power BI

Microsoft Power BI is one of the best business intelligence platforms available in the market today. Offered by Microsoft, it features a visual user interface (UI) that makes it easier to visualize data and share insights across the organization. BI and data analysts use Power BI to connect to data sources, transform raw data into meaningful insights, and build interactive reports and dashboards. Being Microsoft’s own offering, Power BI provides the most seamless integration with other Microsoft tools and the broader Azure ecosystem.

Pros

• Robust integration with other Microsoft applications and services
• Support for advanced analytics techniques like automated machine learning (AutoML) and predictive modeling
• Microsoft offers a free version with basic features and scalable pricing options to suit organizational needs.

Cons

• Users on popular review websites, like Gartner, report that the dashboards can easily get cluttered.
• While it offers a graphical UI, data modeling is still complex for non-technical users.
• Offers a limited experience with Mac OS.

Ideal for: creating data visualizations and reports for businesses of all sizes, with users ranging from technical beginners to analysts.

 

Tableau

Tableau (acquired by Salesforce in 2019) is another top business intelligence and visualization platform. It uses artificial intelligence (AI) enabled features to democratize data analytics and accelerate insights discovery. With Tableau, users can quickly connect to different data sources, clean and prepare data, create interactive visualizations, and build comprehensive dashboards. It excels at exploring data, discovering trends, and uncovering hidden patterns.

Pros

• One of the easiest analysis tools to use, even for new users.
• Tableau can be integrated with R and other tools to leverage advanced analytics capabilities.
• Ad-hoc analysis capabilities empower users to ask questions about their data and get answers quickly.

Cons

• One of the most expensive tools for analysis, particularly for organizations with many users.
• Users on review sites report sluggish performance with large data sets.
• Data modeling capabilities are not as robust as those offered by Power BI.

Ideal for: advanced data visualization and exploration, ideal for medium to large enterprises having data teams with some experience with data analysis tools.

 

Qlik Sense

Qlik Sense is an AI and ML-powered BI and analytics platform that enables users to create data visualizations, reports, and dashboards using its drag-and-drop based interface. Qlik’s Associative Engine allows users to freely establish relationship within data rather than relying on predefined queries.

Pros

• Offers seamless collaboration as users can quickly share reports and dashboards.
• Facilitates exploratory data analysis and enables rapid discovery of insights with its associative data model.
• The ability to automate the process of creating machine learning (ML) models with AutoML.

Cons

• The initial learning curve for non-technical users is a challenge.
• Amongst one of the most expensive data analysis tools.
• UI customization is not on par with other tools.

Ideal for: user-friendly data exploration and self-service analytics, well-suited for businesses of all sizes with a focus on intuitive data discovery.

 

SAS Viya

SAS Viya is an AI-powered, in-memory analytics engine that offers data visualization, reporting, and analytics for businesses. Users get simplified data access and integration from various sources with data quality tools and data lineage tracking built into the platform.

Pros

• Automated model selection makes it easier to uncover hidden insights and make predictions.
• Offers granular access control to maintain data integrity and regulatory compliance.
• Users can easily integrate R and Python.

Cons

• SAS Viya is one of the most expensive data analysis tools.
• Users find SAS documentation to be lacking, which complicates troubleshooting.
• Migrating from SAS 9.4 to SAS Viya is not straightforward and has a learning curve.

Ideal for: advanced analytics and data science, catering to large enterprises with a strong focus on statistics and a team of data scientists.

 

Sisense

Sisense is a BI and data analytics solution that caters to both technical and business users. Like other tools, it allows users to connect to different data sources, both on-premises and cloud-based, combine data, and build dashboards and reports to communicate findings. Sisense integrates AI capabilities for automated insights generation and predictive analytics.

Pros

• It can be integrated into apps and software to enable data exploration within existing workflows.
• Sisense Mobile BI lets users access and interact with dashboards and reports from anywhere.
• Elasticube, Sisense’s unique data structure, optimizes data processing and analysis even with large data sets.

Cons

• Customization (e.g., for common charts) requires coding that the business users aren’t skilled at.
• A lot of useful features need to be bought separately as they don’t come built-in.
• Its feature set isn’t as expansive as some of the established players.

Ideal for: businesses of all sizes looking to empower their teams with analytics regardless of their technical expertise.

SAP BI Suite

SAP offers multiple analytics and BI solutions. SAP’s BusinessObjects BI Suite is for on-premises data reporting, visualization, and sharing. SAP Analytics Cloud takes it a step further with features like augmented analytics and integration with other cloud-based SAP solutions. Businesses looking to fully migrate to the cloud and leverage a more modern analytics platform should consider SAP Analytics Cloud.

Pros

• Ability to access both cloud and on-premises data through a single platform.
• Robust integration with other SAP tools (products and services).
• Tailored dashboards cater to specific user needs, delivering targeted insights for different departments.

Cons

• Compared to other analysis tools, implementing SAP is challenging.
• To get the desired level of depth in the insights summary, users must submit a very refined piece of information.
• The breadth of features leads to a steep learning curve.

Ideal for: large enterprises with a complex data landscape and a mix of technical and non-technical users.

Looker

Looker is Google’s enterprise-grade BI and analytics platform that enables users to unearth and share insights in real time. Users can leverage Looker to create interactive dashboards and reports and gain insights into various aspects of their business. This means that they can connect to diverse data sources, build data models, and perform ad-hoc analysis to answer specific questions.

Pros

• Gemini in Looker speeds up analytical workflows.
• Ability to create custom data products and monetize data.
• One of the most user-friendly platforms for analytics and BI.

Cons

• Looker is more costly compared to other analysis tools.
• Users on review channels report having performance issues in specific situations.
• Data modeling via LookML is not the most intuitive or user-friendly.

Ideal for: embedding analytics directly into applications for developers and data analysts working in medium to large enterprises.

 

R

Unlike commercial data analysis tools with intuitive drag-and-drop UIs, R is a free, open-source programming language that statisticians, data scientists, and researchers use for complex statistical analysis, data visualization, and developing statistical models. It offers a vast library of packages to perform specialized tasks like machine learning and time series analysis.

Pros

• Users can write their own functions and manipulate data in intricate ways.
• R’s active community means that support is readily available.
• Capable of performing an immensely wide range of analytical and modeling tasks.

Cons

• R has a very steep learning curve and caters to people adept at coding.
• It’s considerably slower with large data sets compared to other compiled languages (e.g., Python).
• Offers very limited built-in support for BI and requires integration with other tools.

Ideal for: statistical computing and advanced analytics by data scientists and statisticians.

 

Python

Python is another programming language extensively used for data analysis, machine learning, and scientific computing. Its libraries, like NumPy and Pandas, provide efficient tools for data manipulation, cleaning, and exploration. It’s relative ease-of-use, data science libraries, and visualization capabilities make it a top data analysis tool for experienced data scientists.

Pros

• Python integrates well with popular BI tools.
• Users can easily automate repetitive tasks.
• Its machine learning (ML) capabilities make it an excellent choice for data scientists.

Cons

• Mastering advanced data analysis and visualization techniques requires time and effort.
• Python is not the best option for use with very large data sets.
• Custom solutions with Python demand ongoing maintenance and support.

Ideal for: data analysis, data science, and machine learning by data scientists and programmers across businesses of all sizes.

 

Microsoft Excel

Excel is a spreadsheet software known for its data organization, analysis, and visualization capabilities. Users can perform basic calculations as well as complex analyses.

Pros

• Excel offers a rich library of statistical, mathematical, financial, and text functions.
• Robust integration with other Microsoft applications.
• PivotTables allows users to summarize and analyze large data sets.

Cons

• Excel struggles with very large data sets.
• It lacks advanced statistical modeling capabilities.
• It offers limited data transformation capabilities.

Ideal for: basic data analysis and reporting, suitable for all business sizes and users with varying technical skills.

KNIME Analytics Platform

KNIME, an acronym for Konstanz Information Miner, is a popular open-source data analytics platform designed to streamline data-intensive processes. It offers data scientists, analysts, researchers, and anyone involved in data-driven decision-making a visual UI for exploring, cleaning, analyzing, and visualizing data.

Pros

• KNIME is free for commercial use.
• Its large and active community is a reliable source of support.
• Users can build custom workflows tailored to their business needs.

Cons

• Mastering KNIME’s UI requires considerable training.
• KNIME’s reporting and visualization options are limited compared to tools like Tableau and Power BI.
• Working with unstructured data is complex.

Ideal for: data analytics and workflow automation, aimed at users having moderate technical expertise.

 

RapidMiner

Up next on our list of data analysis tools is RapidMiner, a data science platform that aims to simplify the data mining process. Data scientists, analysts, and business users use RapidMiner for data preparation, exploration, modeling, evaluation, and deployment.

Pros

• It offers automated model building and optimization features.
• The tool integrates easily with big data sources.
• RapidMiner offers built-in features to tweak variables for higher accuracy.

Cons

• Compared to other analytics tools, RapidMiner is expensive.
• The learning curve is steeper, especially with advanced features.
• Managing data science workflows requires skilled professionals.

Ideal for: data scientists and analysts in businesses of all sizes for data wrangling, mining, modeling, exploration, and predictive analytics.

 

Orange

Orange is another open-source machine learning and data mining tool primarily targeted at data scientists, researchers, and students with a focus on data analysis and visualization. The analysis tool uses visual programming to simplify data mining.

Pros

• Users find it straightforward to install and use.
• Orange integrates with Python with ease.
• It provides a variety of plots and charts for data exploration and visualization.

Cons

• According to user reviews, the documentation lacks depth.
• A high degree of dependency on Python’s external libraries.
• Customer support is not one of Orange’s strong suits.

Ideal for: data visualization and machine learning, well-suited for data analysts and researchers of all experience levels.

 

Domo Data Experience Platform

Domo is a cloud-based data analytics tool that makes data accessible to everyone in an organization. Like other tools for data analysis, Domo features a visual UI that enables users to create visualizations, reports, and dashboards without requiring extensive technical skills.

Pros

• Users can explore data independently without relying on IT.
• Domo handles large data sets and delivers real-time insights.
• It offers an interactive mobile data analytics application.

Cons

• Being a cloud-based analysis tool, Domo can easily become expensive.
• The guides are not as effective, given the complexity of advanced features.
• Domo might not be the best option for organizations reliant on on-premises infrastructure.

Ideal for: unified data management, business intelligence, and collaboration, targeting mid-sized to large enterprises.

 

IBM Cognos Analytics

IBM Cognos Analytics is a business intelligence (BI) and analytics platform catering to a wide range of users, such as executives, business analysts, and data scientists.

Pros

• Offers the ability to explore data via conversational queries based on natural language processing (NLP).
• It is straightforward to build and customize dashboards.
• Uses AI to automatically provide deeper insights.

Cons

• The breadth of features leads to a steep learning curve.
• The UI of the mobile analytics app is not polished.
• Users on review channels report performance issues when dealing with very large data sets.

Ideal for: enterprise-grade AI-powered analytics and self-service reporting, ideal for large businesses with a mix of technical and non-technical users.

What makes a data analytics tool great?

Software packages for data analysis come with varying feature sets and capabilities. Whereas some offer a complete end-to-end analytics solution including data integration, cleaning, analysis, and visualization, others focus more narrowly on specific aspects such as advanced statistical modeling or real-time data processing.

As a rule of thumb, a great data analysis tool today combines data ingestion, preparation, exploration, analysis, visualization, and collaboration into a single, easy-to-use package.

Additionally, the tool should:

• be easy to navigate, even for those without extensive technical knowledge
• be flexible enough to enable users to tailor it to their specific needs and preferences
• offer a variety of visualization and reporting options
• be able to connect to various data sources (databases, spreadsheets, APIs, etc.)
• offer the ability to handle and manipulate large data sets
• have built-in features for handling missing data, outliers, and inconsistencies
• have features to conduct multiple types of analyses

How to choose a data analysis tool for effortless BI and analytics?

Every business wants to equip its team of data and business analysts with the best tools for data analysis in order to make decisions backed by reliable data. However, since no two businesses have identical data needs, it’s equally improbable for a single analytics solution to meet both requirements fully. And so, it is incumbent on the organization to evaluate its data needs and select a tool that caters to them most effectively.

Determine the type of data your organization handles

Different tools excel in handling different types of data formats. For example, some tools are adept at processing large volumes of unstructured text data for sentiment analysis, while others specialize in relational databases for specific data analysis. So, as the first step, consider the type of data your organization needs to analyze and the data sources it comes from. Is it structured data from databases and spreadsheets, semi-structured data like logs and JSON files, or unstructured data such as text documents and social media feeds?

Evaluate if you need a dedicated tool for data integration

This leads us to the fact that a tool designed strictly for analysis and reporting cannot handle all the nuances of data and almost always needs a data integration platform as a bridge. These tools specialize in extracting data from various sources, transforming it into a unified format, and loading it into a data warehouse or analytics environment, all the while ensuring data quality and accessibility, which are essential for accurate and reliable analytics.

Consider the requirements and expertise of the end-user

You also need to consider the technical expertise of the end-user—will a non-technical user (business analyst) or a more technically sound user (data analyst or data scientist) use it? While some platforms provide features that cater to the requirements of both user groups, other tools only cater to one or the other. Tools powered by a visual, drag-and-drop UI appeal to business users, while data scientists generally need more control and, therefore, prefer analytics solutions that offer a mix of coding (usually SQL) and point-and-click features.

Other factors that you should consider are:

• The tool’s pricing structure.
• The quality of customer support.
• The depth of documentation.
• Potential vendor lock-in.

What use cases can you cater to with a data analysis tool?

A comprehensive data analytics platform should be capable of delivering a wide variety of use cases.

Data visualization

This is when businesses need to translate data into visual representations (charts, graphs, maps) to understand patterns and trends. To visualize data, you need tools with a wide range of chart types, interactivity, customization options, and integration with other data analysis tools.

Business Intelligence (BI)

BI transforms data into actionable insights to support strategic decision-making. For BI, you need data analysis tools that simplify data modeling, reporting, and visualization, and offer collaboration features.

Reporting

Reporting involves creating predefined, formatted data summaries for regular consumption. It requires tools with report templates, scheduling, distribution, and data export options.

Advanced data analytics

Sometimes businesses need to apply complex statistical and machine learning techniques in order to extract hidden patterns and insights. For advanced data analytics, you need specialized data analysis tools with statistical functions, machine learning algorithms, predictive modeling capabilities, and data mining features.

Exploratory data analysis (EDA)

It involves investigating data to discover patterns and anomalies without predefined hypotheses. EDA can be done with tools offering data profiling and visualization features, statistical summaries, and interactive exploration capabilities.

Data mining

To discover patterns in hidden in large datasets, businesses need data analysis tools with data preprocessing, association rule mining, clustering, classification, and prediction capabilities.

Data engineering

Many businesses design and build data infrastructure to support data analysis. To simplify the process, they need specific tools for data extraction, transformation, loading (ETL), data warehousing, data lake management, and cloud integration.

Many modern data analysis tools offer a combination of these capabilities to provide a comprehensive solution. For example, a BI tool typically includes data visualization, reporting, and basic analytics features, while a data science platform focuses on advanced analytics, machine learning, and data engineering.

 Astera delivers analysis-ready data for your BI and analytics platform

The reliability of an analysis is only as good as the data it’s based on. Two requirements to make accurate and timely business decisions are: high-quality data and seamless access to it. Given the increasing complexity of data, tools focused on analytics and reporting often fall short when it comes to integrating the disparate data sources and preparing the data for analysis.

This is where Astera proves to be invaluable. It’s an AI-powered data management platform that enables you to combine data from multiple sources into the destination of your choice, whether on-premises or in the cloud. Try Astera for free or schedule a demo to see it in action.