Top Data Analytics Tools To Use in 2022

The technique of transferring raw data into valuable insights, useful statistics, and detailed explanations for making data-driven decisions is called data analysis. It is safe to say that data analysis is slowly becoming the cornerstone of most modern business operations. 

There is an overwhelming number of data analytics tools available out there. As a result, choosing the right data analytics tool has become a daunting task for most business analysts. Fortunately, you will be able to make a better and informed decision by researching the most widely used data analytics tools. 

A Beginner’s Guide To Data Analytics Tools 

Data analytics tools is an umbrella term that is used for classifying applications and software systems, which are used by Data Analysts for creating and executing analytic processes. The use of these data analytics tools allows businesses to make informed and smarter decisions, which increases productivity and profits. 

It can be quite difficult to identify the right data analytics tools for your business in some cases. However, considering the requirements of your business should help you to narrow down the list of options. Once you have done that, you will need to consider who will be using the data analytics tools. 

If the tools are used by seasoned data scientists or data analysts, then you will need to select an advanced data analytics tool that offers an immersive experience while creating codes. On the other hand, freshers will need an easy-to-use tool with some essential features. Now, let us take a look at some of the most commonly used data analytics tools of 2022. 


The object-oriented programming language, Python was originally introduced to help developers with web and software development. However, it was enhanced later for data science. Python features a fine selection of friendly libraries for different aspects of scientific computing. 

Python is currently one of the fast-growing and most popular programming languages in the world. One of the major reasons for the increasing popularity of Python is that it is very easy to learn.

The development of statistical and analytical libraries such as SciPy and NumPy has made Python a powerful data analytics tool. Python is capable of offering comprehensive coverage of mathematical and statistical functions. Python is used by companies like Google, CERN, NASA, Netflix, and Spotify for data analysis. 

R is another popular and widely used data analytics tool that is used by Google, Facebook, Uber, Twitter, and several other internationally renowned companies. It becomes the leading programming language for data analysis, visualization, and statistical modeling. 

This software comes with a number of innovative and advanced packages like tidy, dplyr, and plyr, which makes data manipulation easy. R also has several other packages such as ggvis, lattice, and ggplot, which will help you with data analysis and visualization. The number of packages in R is estimated to over 8,000 at the moment. 

However, one of the major downfalls of R is that the programming language has a steep learning curve. You will need to have basic knowledge of coding to learn R.


The statistical software suite, SAS is the most loved choice of data analysts when it comes to predictive analysis, data management, and Business Intelligence. SAS is very easy to learn thanks to its simple GUI. However, having a basic understanding or knowledge of SAS programming language will give you an added advantage when using SAS. 

SAS is equipped with Visual Analytics Software, which will serve as an excellent tool for smart visualizations, Text analytics, self-service analytics, and interactive dashboards. SAS is also used in weather forecasting, BI, and pharmaceutical industries. 

Apart from that, companies like Deloitte, Apple, and Citibank are using SAS for clinical research reporting. Some other companies that use SAS are Netflix, Twitter, Google, and Facebook. 


Most of us are familiar with Microsoft Excel and we would have probably used it at least once in our lives. Excel is a powerful but simple tool, which can be used for data analysis and data collection. Microsoft Excel is easy to learn, readily available, and widely used by millions of people from all over the globe. In fact, it is currently used by over 250 million people. 

Excel comes with a Data Analysis Toolpak feature, which offers a wide range of options to users when performing statistical analysis. The graphs and charts in Microsoft Excel will provide a clear visual representation of data, which will help businesses to make informed decisions. 

However, users will need to enable and configure the Analysis Toolpak feature in Microsoft Excel to use it. If you are a beginner who is stepping into the world of data analysis, then Excel is an excellent tool to get started. 

Power BI 

Power BI is another excellent business analytics solution launched by Microsoft. Three separate versions of Power BI are available to users and they are Premium, Pro, and Desktop. Users who are looking for a free business analytics solution should check out the desktop version of Power BI. The Premium and Pro versions of Power BI are not free. 

Power BI makes it possible for analysts to visualize data and share the results across the organization with ease. You will also be able to display business data in live reports and dashboards when using Power BI. 

Another fascinating aspect of Power BI is that it can be easily integrated with a wide range of tools, which includes Microsoft Excel. Some of the top companies that are using Power BI are Ecolab, Tenneco, and Nestle. 

Final Thoughts 

The vast amount of data generated by businesses can be used to draw valuable insights. Data analysis is the best option to make it possible. Data analysis will not just help businesses to make better decisions.

Instead, it will also assist them to streamline their business operations, handle setbacks, mitigate risks, and enhance security. It is critical to find and use the ideal data analytics tool that suits the requirements of your business to get the best results from data analysis. 

Scroll to Top