In this report, we define and compare data analysis software and statistical analysis software so you can decide which tool is better suited for your needs.
Both data analysis and statistical analysis software solutions have common business applications, i.e., solving business queries by deriving insights from data. However, to do so, the former establishes a relationship between data points, and the latter uses mathematical models.
Despite having a common goal, the tools’ different methods of dealing with data set them apart. In this report, we’ll identify the similarities and differences between the two software categories and help you select the right one for your business requirements.
A data analysis tool enables users to analyze and visualize data. This tool cleans and processes raw data sets, establishes a relationship between data points, and identifies trends in order to present actionable insights and support data-driven business decision-making.
Data analysis software can perform various types of data analytics, such as descriptive analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Since this software category is capable of providing real-time results, it can be used for on-the-spot decision-making.
Statistical analysis software allows organizations to take full advantage of the data they possess to uncover business opportunities and increase revenue. It includes specialized solutions designed to work with particular statistical languages, such as Python and R, as well as more general applications that automate various data-manipulating operations, such as power analyses and data visualization.
The two major branches of statistical analysis are descriptive statistics and inferential statistics. The former describes the properties of sample data such as what has happened over a given period while the latter uses properties drawn from descriptive statistics to test hypotheses and make conclusions.
Both data analysis and statistical analysis software solutions are used to explore and analyze data sets. They both offer functionality for data collection and cleaning. Since both of them are a component of one another, a data analyst or a data scientist understands statistical tools, and a statistician has an understanding of programming languages such as R as well.
Data analysis software solutions are right for you if you have a vast amount of data. These solutions inspect, clean, model, and present that data in a comprehensible manner for easy understanding of non-technical users. They use machine learning (ML), artificial intelligence (AI), and automation to translate complex data into readable information.
Go for statistical analysis solutions if you have a limited amount of data or a small sample size. To process this data, the tools in this category use theories based on mathematics such as differential and integral calculus, linear algebra, etc.
The question now is: Which specific tool is right for your business? To start your search, head over to our data analysis software or statistical analysis software pages. There you’ll find a sortable list of products, software reviews from verified users, and comprehensive buyers guides.
These three products, listed in alphabetical order, are among the top-rated in both software categories.
To be included in the top-rated list, the products must:
- Be listed under Capterra’s 2022 Shortlist reports for data analysis and statistical analysis software. Learn more about the Capterra Shortlist methodology here.
- Meet our software market definition for data analysis and statistical analysis software:
- Data analysis: “Data analysis tools enable users to analyze and visualize data in order to identify trends and correlations with the goal of supporting decision making.”
- Statistical analysis: “Statistical analysis software allows organizations to take full advantage of the data they possess to uncover business opportunities and increase revenue. Statistical analysis software includes specialized solutions designed to work with particular statistical languages as well as more general applications that automate various data-manipulating operations, such as power analyses and data visualization.”