10 Data Analysis Tools and When to Use Them

Data analysis is essential for businesses and professionals to find critical insights from their data to make informed decisions and predictions. In today’s data-driven world, professionals increasingly rely on data analysis to make competitive decisions, engage effectively with their target audience, and successfully lead projects. One way to meet these targets is through data analysis tools. 

By understanding which tool is right for your purpose, you can meet the needs of your project while boosting your overall productivity and efficiency. Explore 10 data analysis tools, how they differ, and how you can showcase related skills to potential employers. 

What are data analysis tools?

Data analysis tools are software programs, applications, and other aids that professionals use to analyse data sets in ways that characterise the big picture of the information and provide usable information for insights, predictions, and decision-making purposes.

You can describe them as a collection of maps, diagrams, and charts designed to gather, interpret, and visualise data across diverse applications. Choosing the right data analysis tool is essential to maximise your efforts and make it easier to meet your professional goals.

Who uses data analysis tools and why?

Professionals across several sectors use data analysis tools to help them convert raw data into valuable insights that drive decision-making and innovation. The following offers a quick snapshot of who uses these tools and why:

• Data analysts: Data analysts use data analysis tools to uncover trends and insights from complex data. Companies can use this data to guide better decision-making and increase profits.

• Business analysts: Business analysts leverage these tools to help businesses optimise operations and profitability based on data-driven decisions and strategies. 

• Project managers: Project managers use data analysis tools for diverse tasks, from budget management to problem-solving and enhancing team productivity.

• Digital marketers: Digital marketers use data analysis tools to craft effective marketing strategies and campaigns and ensure promotional activities hit the right target.

• Data scientists: These tools build new models and processes, enabling data analysts to perform their jobs effectively and helping businesses interpret complex data and resolve intricate problems.

10 data analysis tools and when to use them

With large amounts of data, it can become challenging to understand what the data is showing and gain valuable insights. Data analysis tools help you understand the big picture of your data and then represent it in an appropriate format for stakeholders and people within your professional network with broad backgrounds.

For data mining

Data mining helps users find the key characteristics of their data so they can apply this knowledge to real-world problems. Data mining software helps automate this process by looking for patterns and trends within the data. Three common data mining software you may benefit from include the following.

1. RapidMiner

RapidMiner is a comprehensive package for data mining and model development. This platform allows professionals to work with data at many stages, including preparation, visualisation, and review. It can benefit professionals with data that isn’t in raw format or that they have mined in the past.  

RapidMiner also offers an array of classification, regression, clustering, and association rule mining algorithms. While it has some limitations in feature engineering and selection, it compensates for them with a powerful graphical programming language.  

This software is suited for people with all types of backgrounds. It can be leveraged in various industries for diverse applications, such as manufacturing, life sciences, energy, and health care. Because it can work with previously mined data, this software can be particularly useful for researchers or data scientists working with historical data.

2. Orange

Orange is a package renowned for data visualisation and analysis. It is especially appreciated for its user-friendly, colour-coordinated interface. You can find a comprehensive selection of colour-coded widgets for functions like data input, cleaning, visualisation, regression, and clustering, making it a good choice for beginners or smaller projects. 

Despite offering fewer tools than other platforms, Orange is still an effective data analysis tool, hosting an array of mainstream algorithms like k-nearest neighbours, random forests, naive Bayes classification, and support vector machines. 

The platform holds particular value for certain types of professionals with its add-ons. For example, you can find tools for gene ranking and enrichment analysis if you work in bioinformatics and molecular biology. You can also find tools for natural language processing, text mining, and network analysis that may benefit you depending on your profession.

3. KNIME

KNIME, short for KoNstanz Information MinEr, is a free and open-source data cleaning and analysis tool that makes data mining accessible—even if you are a beginner. Along with data cleaning and analysis software, KNIME has specialised algorithms for sentiment and social network analysis. With KNIME, you can integrate data from various sources into a single analysis and use extensions to work with popular programming languages like R, Python, Java, and SQL. 

If you are new to data mining, KNIME might be a great choice. Resources on the KNIME platform can help new data professionals learn about data mining by guiding them through building, deploying, and maintaining large-scale data mining strategies. Because of this, many companies use KNIME to help their employees gain data processing and extraction experience. 

For data visualisation

Data visualisation is a powerful way to transform raw data into meaningful and comprehensive visual representations. In some cases, this step is the last step of the data mining process and is integral for transforming insights into an easy-to-share format for a general audience. 

It provides a way to understand complex data patterns, trends, and insights that people might miss in text-based data. Visualisation can also be used to check for errors within the data set or to help data analysts become familiar with the structure of the data. 

Data visualisation tools help professionals streamline the data visualisation process. You can use these tools to manipulate data and create basic to advanced graphical representations visually. Common ones you may encounter include the following.

4. Tableau

Tableau is a leading data visualisation software widely utilised in business analytics and intelligence.

Tableau is a popular data visualisation tool for its easy-to-use interface and powerful capabilities. Its software can connect with hundreds of data sources and manipulate the information in many different visualisation types. It uniquely appeals to business users, who appreciate its simplicity and centralised platform. Data analysts also appreciate more advanced big data tools for tasks such as clustering and regression.

If you aim for a career in business analytics or intelligence, Tableau can be a valuable skill. It can help you ask questions and gain a deeper understanding of your organisation’s data. 

5. Google Charts

Google Charts is a free online tool that excels at producing an array of interactive and engaging data visualisations. Its design caters to user-friendliness, offering a comprehensive selection of pre-set chart types that can be embedded into web pages or applications. 

Google Charts' versatile nature allows it to be integrated with many web platforms, including iPhone, iPad, and Android, extending its accessibility. 

With its high customisation and user-friendly nature, this tool is ideal for creating compelling data visuals for web and mobile platforms. It’s also a great option if you need to publish your charts. The integration makes it straightforward to publish on most web platforms by sharing a link or embedding the link into a website’s HTML code.

6. Datawrapper

Datawrapper is a tool primarily designed for creating online visuals, such as charts and maps. Initially conceived for journalists reporting news stories, its versatility makes it suitable for any professional in charge of website management. The tool supports various outputs, including scatterplots, line charts, stacked bar charts, pie charts, range plots, and various maps and tables. 

Depending on the intended use, Datawrapper offers free and paid options, allowing flexibility according to your needs and budget.

Although straightforward to use, Datawrapper does exhibit some limitations, especially when compared with other tools in its category. The primary constraint is its inability to integrate directly with data sources. This means users must manually input data into the tool, which can be tedious and susceptible to mistakes if not performed precisely.

If you’re interested in crafting web graphics and don't mind the manual data input, learning to use Datawrapper could be a worthwhile addition to your skill set.

For businesses

Data analysis is a powerful tool for understanding the story data tells and using it to make informed decisions. In short, data is gold. Businesses can use their data-driven insights to increase their performance, improve customer satisfaction, gain a competitive advantage, and benefit the overall health of their company. 

Whether part of a small or large organisation, learning to utilise data analytics effectively can help you take advantage of the wide range of data-driven benefits. In addition to Tableau, a few data analytics tools that may help boost your business insights include the following.

7. Microsoft Excel and BI

Microsoft Excel, fundamentally spreadsheet software, also has noteworthy data analytics capabilities. Because of the wide enterprise-level adoption of Microsoft products, many businesses already have access to it.

Using spreadsheet data, you can use Excel to construct at least 20 distinct chart types. These range from standard options—such as bar charts and scatter plots—to more complex options like radar charts and treemaps. Excel also has many streamlined options for businesses to find insights into their data and use modern business analytics formulas.

However, Excel does have its boundaries. If your business needs more robust data visualisation tools within the Microsoft ecosystem, Power BI is a great option. Designed specifically for data analytics and visualisation, Power BI can import data from an array of sources and produce visualisations in various formats.

8. Qlik

Qlik is a global company designed to help businesses utilise data for decision-making and problem-solving. It provides comprehensive, real-time data integration and analytics solutions to turn data into valuable insights. Qlik’s tools help companies understand customer behaviour, revamp business processes, uncover new revenue opportunities, and manage risk and reward effectively. 

9. Google Analytics

Google Analytics is a tool that helps businesses understand how people interact with their websites and apps. You add a special Javascript code to your web pages to use it. This code collects information when someone visits your website, like which pages they see, what device they’re using, and how they found your site. It then sends this data to Google Analytics, which organises it into reports. These reports help you see patterns, like which products are most popular or which ads bring people to your site. 

10. Spotfire

TIBCO Spotfire is a user-friendly platform that transforms data into actionable insights. It allows you to analyse historical and real-time data, predict trends, and visualise results in a single, scalable platform. Features include custom analytics apps, interactive AI and data science tools, real-time streaming analytics, and powerful analytics for location-based data. 

If you are a decision-maker in your organisation, such as a marketing manager or data scientist, you might benefit from Spotfire’s scalable analytics platform when visually exploring your data. 

Promote your knowledge of data analysis tools.

Working with data analysis software is a highly valued skill for data analysts. Advocating for your data analysis skills can be a strategic move to highlight your capabilities, enrich your professional image, and open up new opportunities. 

A few ways you can promote your knowledge and expertise in data analysis tools include the following:

• Add your skills to your CV: Your CV is the perfect place to highlight your proficiency in specific data analysis tools. Under the skills section, list the tools you are familiar with and briefly describe the tasks you’ve accomplished using these tools.

• Create a professional portfolio: Showcasing your work on a professional portfolio can effectively demonstrate your skills. This could include successful projects you’ve completed or problems you’ve solved using data analysis tools.

• Utilise LinkedIn and other social profiles: Use platforms like LinkedIn to highlight your skills and knowledge in data analysis so recruiters and employers can find you. Endorsements and recommendations from colleagues or superiors can also add credibility to your skill set.

Get started with Coursera.

Data analysis tools can help streamline the process of taking raw data and turning it into a visual representation, making it easier to spot patterns and trends, gain insights, and for those less familiar with data analysis to understand its meaning. Available data analysis tools offer various features, making it essential to consider your needs when selecting one. 

The Google Data Analytics Professional Certificate on Coursera teaches you more fundamental data analytics tools. Not only will you have the opportunity to learn basic analytics skills that can prepare you for an entry-level job, but you will be able to learn these skills at your own pace in as little as six months.