Data Analysis Tools: Definition and How to Choose One

By Indeed Editorial Team

Updated 6 February 2023

Published 2 May 2022

The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.

A data analysis tool is a software that helps an organisation collect and analyse data. Many analysis tools provide several functions and features for different business operations. By reviewing the various tools available, you can determine which one might be appropriate for your workplace. In this article, we define data analysis tools, list some analysis tools to consider, provide some helpful tips for choosing the right tool and detail the benefits of implementing one.

What are data analysis tools?

Data analysis tools are software platforms that provide a variety of features and functions for collecting, analysing and visualising data. Some tools might specialise in collecting and analysing specific sets of data. For example, some tools might focus on market statistics, while others focus on business performances and securities. Some tools might only focus on analysis, while others offer features for monitoring data quality, visualising statistics and securing data access and permissions. The data analysis tool that benefits a business can depend on the category of data they collect and the general business operation.

Related: What Does a Data Analyst Do? (With Skills and Career Steps)

Examples of data analytics tools

Below, you can find several data analysis tools that you can consider for your workplace:

Power BI

This data analysis tool offers excellent scalability and is usually suitable for individuals and businesses of all sizes. One of the most notable features might be its integration capabilities with Microsoft Excel. Power BI and Excel can provide almost all the features and functions that the business might require. Power BI is a Microsoft product, so it's usually compatible with most other Microsoft products. This data analysis tool offers over 100 data visualisation models, so there is likely one that suits the business' operation requirements.

SAP BusinessObjects

This software is essentially a data integration platform. This means it provides features for collecting raw data and transforming it into logical data sets for visualisation. SAP BusinessObjects is also compatible with Microsoft products such as Excel. Data scientists can effectively integrate data from the SAP software into Excel to perform complex calculations. SAP BusinessObjects can be an excellent tool for business operations that require real-time data analysis, such as an investment firm or bank. SAP BusinessObjects provides services for collecting and integrating real-time data, which means there is a minimal delay between data collection and data viewing.

Related: SAP Consultant: Definition, Duties, Requirements and Skills


Sisense is a highly flexible data analytics tool that can easily scale with business operations and data ingestion requirements. This tool focuses on providing its users with vast customisable features, including visualisations, data sources and data pipelines. Sisense provides features for integrating automated AI into workflows, applications, product analysis and data processing. This can be excellent for saving time and resources as a majority of the features can operate automatically.


This data analytics platform focuses on visualisations, customisable dashboards and structured query language (SQL) editors. Redash incorporates a simple user interface with editing functions, such as a drag-and-drop feature for editing and resizing visualisation models. One of the notable features might be its online SQL editor. SQL is the coding language that many database tools utilise for their foundational programming. An SQL editor can allow users to edit almost any function of a database or data stream. Using the SQL editor might require coding knowledge, but it can provide many customisable features to suit any business operation.

Jupyter Notebook

This data tool usually requires knowledge of program languages, such as Python, Java and Ruby. Jupyter Notebook is compatible with over 40 programming languages, so if the business has existing IT infrastructure, it can integrate effectively. This analytics tool has a share feature that allows users to share their tool dashboards with others via several file-sharing software. This can be useful for business operations that involve multiple departments that collaborate on projects. Jupyter Notebook also has an impressive capacity for storing and processing large quantities of data.


This data analysis tool is an open-source platform, which means the foundational coding is available to the community for modification. An open-source data tool can provide a variety of community-built functions and applications. These additional features can often relate to many business operations. KNIME incorporates a relatively simple user interface, so training requirements are often minimal for new users. This data analytics tool can suit most business operations of varying sizes.


This data analysation tool can be an excellent choice for business operations that involve unique data integration requirements. Domo provides its users with the ability to customise over 1000 data integration methods. An excellent feature of the Domo tool is that it incorporates a data warehouse and extract, transform and load (ETL) software. This means this platform can provide businesses with applications and software to suit most operations.

Tips for choosing the right data analysis tool

Below, you can find several tips to consider when choosing a data analysis tool for your workplace.

Determine the type of data

The type of data the business analyses can determine an effective analysis software for you to implement. Many tools might offer features for collecting and analysing a variety of data, while others focus on specific data. For example, if the business is a marketing firm, then you might choose an analysis tool that specialises in marketing data. These tools usually have a select range of trusted sources that collect data for marketing. They may also have features that provide data visualisation models unique to marketing services.

Identify the business goals

Depending on the business's goals, there may be some tools that offer features specific to those goals. For example, if the business is a financial investment firm, its goal might be to identify a certain number of investment opportunities within a given time. You might choose a data analysis tool that offers features for achieving this goal. There may also be an analysis tool that provides unique visualisation models for investment opportunities and share projections.

Related: Understanding Objectives vs. Goals (Including Examples)

Consider the IT infrastructure

Before choosing a data analysis tool, you can consider the existing information technology (IT) infrastructure of the business. If there is an existing infrastructure, you might choose a tool that has efficient software integration capabilities. This can reduce the necessity for upgrading IT infrastructure or changing IT systems. If the analysis tool has integration capabilities, you can also integrate it with other data tools. A data analysis tool might only offer features for analysing data, while other tools might specialise in data extraction and visualisation.

Related: What Is a Data Architect? (With a Step Guide on How to Become One)

Examine the complexity of data

Some data may be more complex than others. For example, in marketing, some data may be relatively simple, such as the number of existing customers, number of sales and number of conversions. Financial data might incorporate more elaborate data sets that require extensive formulas to interpret. If the business analyses and collects complex data, you might consider a more advanced data analysis tool. These advanced tools might involve a complicated user interface, but they usually provide more customisable features for interpreting complex data.

Related: What Does a Data Analytics Consultant Do? (Guide and Skills)

Consider the pricing

Depending on the business's budget for IT infrastructure, you may have limited options for a data analysis tool. Some tools are free and others can require a recurring fee. The difference in analysis tool pricing doesn't necessarily reflect the difference in features or functionality. Many free tools can provide an abundance of customisable visualisations, collection methods and integration capabilities. Free analysis tools are usually suitable for small business operations or businesses that don't focus on data analysis.

Benefits of implementing a data analysis tool

Here, you can find the typical benefits of implementing a data analysis tool:

  • Visualise complex data: Depending on the business's operation, it may incorporate complex data, such as algorithms or raw statistics. A data analysis tool can provide you with visualisation models that allow you to interpret and translate extensive data sets.

  • Collect data efficiently: Most analytical tools offer features for automating data extraction and collection. Compared to manual data extraction, a data analysis tool can save a considerable amount of time and resources.

  • Scale data with the business: If the business's data intake is increasing, then a data analysis tool can help you scale the data ingestion with the business. Analysis tools can collect and analyse extensive volumes of data, which can be necessary when the business grows.

  • Manage data effectively: If the business collects large amounts of data, it can become challenging to organise and manage it without an automated system. A data analysis tool can automatically categorise data sets and determine logical destinations for data exports.

  • Make informed decisions: A major benefit of using a data analysis tool is that it can provide you with an abundance of statistical information. This data can help you analyse risk and make informed business decisions.

Please note that none of the companies, institutions or organisations mentioned in this article are affiliated with Indeed.

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