7 Best Data Analytic Tools You Should Try for Free

Data analytic tools do not have to be expensive to be effective. These free tools can help companies gain new insights from different data sources and make valuable predictions.

The importance of data analytics is growing for companies looking for insights hidden beneath an ocean of data. Companies can better understand their customers and business objectives with tools designed to organize and classify different data sources and generate statistical insights.

Businesses have many questions and options when it comes to assessing data analytics tools. However, it can be crucial to determine the appropriate application and leverage its functionality.

We reviewed dozens of providers in our search for the best free data analytics software available today. We selected the following seven tools after considering the pros and cons of each tool and reviewing leading analytics studies and rankings from several research organizations. Take a look.

#1. Orange

Developed by the University of Ljubljana, Orange is an open-source data analysis software. It allows users to extract data, explore statistics, cell graphs, and scatter plots, and dig deeper into data using decision trees, hierarchical clustering, heatmaps, and line projections using visual programming and Python scripts in a terminal window.

The graphic interface in Orange allows users to concentrate on insightful data analysis instead of coding. The tool also includes machine learning components and plug-ins to extend data mining from external sources such as natural language processing, text mining, bioinformatics, network analysis, and associative rule mining.

#2. Tableau Public

One of the most popular data analysis tools, Tableau Public, is a data analysis and visualization tool allowing users to publish their data online interactively. Tableau's limited version is free with up to 1 GB of data space and 1 million data points.

Tableau Public can export data from Google Sheets, Microsoft Excel, CSV files, JSON files, statistical files, spatial files, web data links, and OData. It also allows the creation and sharing of interactive charts and maps on social media and public websites. Tableau is easy to understand, but companies need to invest to succeed.

#3. Trifacta

As an open-source data processing tool, Trifacta quickly prepares data for analysis. It allows you to explore, analyze and transform data from raw form into a structured format. Machine learning techniques are used to help users analyze and explore data. Data Wrangler is another name for Trifacta, which is more suitable for data cleansing.

It was created in 2012 by Joe Hellerstein, Geoffrey Hare, and Sean Kandel. It is designed to operate in the cloud and integrate with AWS. It has won recognition for its implementation of machine learning on AWS. Unlike Excel, Trifacta helps you work with large data sets. Trifacta's word-processing guide is also excellent.

#4. DataMelt

DataMelt (also known as DMelt) is a software development framework for statistical analysis and scientific visualization of big data. It is primarily used for modeling and analysis in life sciences, engineering, and financial markets. It can be used to analyze data in a variety of fields, such as Java, Ruby, Python, and more.

It offers companies access to a wide range of Dynamic Script libraries, covering thousands of Java classes and hundreds of Python modules for computation and visualization. A commercial or developer license is required to access advanced features, but the free version of DataMelt includes many essential functions for extracting and analyzing data.

#5. Microsoft Power BI

Power BI has been around for less than a decade and is a relatively new player in the data analytics tools market. Initially a companion to Excel, it became a standalone commercial data analytics tool suite in early 2010. With Power BI, you can generate visual reports and interactive dashboards with minimal preparation. Among the key benefits is its excellent data compatibility: it works with Excel and plain text files, database SQL Server, and cloud sources such as Google Analytics.

Data visualization is also good, but there is room for improvement in other areas. For example, its user interface could be more precise, it has rigid formulas, and its language could be easier to use. However, several subscriptions are available, including free ones. This is ideal if you want to familiarize yourself with the tool.

#6. KNIME Analytics Platform

The aim of the KNIME Analytics Platform is to provide visual programming to enable companies to process, analyze and simulate data. Featuring more than a thousand modules, hundreds of ready-to-use examples, and an integrated toolkit, it helps users discover the underlying meaning in data and then use machine learning to anticipate the future.

KNIME enables companies to drag and drop the dots that connect operations instead of writing code. It also enables data analytics to combine text files, databases, documents, images, web data, and Hadoop data into a unified data visualization dashboard.

#7. OpenRefine

OpenRefine was designed to support businesses that work with dirty data; in 2012, Google stopped supporting the project, but it remains available and volunteers regularly update it. The tool can also retrieve data from external websites and compare and combine data from different sources.

While OpenRefine is not the ideal solution for large data sets, analysts spend a lot of time cleaning data for predictive modeling, making it an essential and respected option for many companies.

Conclusion

Many different data analytic tools exist to extract potential business information. For this reason, it is important to consult a data analyst to help you determine your business needs before choosing the best solution. Since data backup is significant for any business, you must choose the right storage option. We hope this article has helped you understand the best top data analytic tools for 2023.