The Top 7 Data Analytics Tools & Software in 2022

Discover a list of the top-rated best Data Analysis Software & Tools for data analysts

Data analysis software are analytic tools used by data analysts to carry out analysis, process and analyze data fast and easily.

As a data analyst, once you have data ready, you can easily analyze it using trusted data analytics software or top tools for data analysis.

As data analysts, if you want to be able to perform data analysis at the highest possible level, you certainly will need to make use of tools and software that will ensure the best results while decreasing costs and increasing profits. For this reason, the best data analysis tools and software becomes essential.

Choosing the right analytics tool can sometimes be challenging, as no tool is a one size fit all.

You should first understand the types of data your business or enterprise wants to analyze, and, by extension, your data integration requirements.

In this analytics tool reviews and guide, you will know what the top tools for data analysis are, after careful research and testing.

So, if you do not have any time to waste, below is an overview and list of the top best data analysis software you can use to analyze data in 2022.

The Top 7 Tools for Data Analysis in 2022

Here are the most popular analytics tools for data analysts in 2022:

1. Microsoft Power Business Intelligence (BI) (Best business intelligence tool)
2. Tableau Software.
3. Python (Best for general programming language)
4. R-Programming (Best statistical tool in research)
5. Microsoft Excel (Best spreadsheet software app)
6. Apache Spark (Best tool for big data analysis)
7. RapidMiner.

Expert Insight: Big Data Analytics software is widely used to provide meaningful analysis of a large set of data.

These analytical software tools help data analysts and data driven businesses find current market trends, market research reports, customer preferences, and other useful information required for meaningful business decision making.

What is Data Analysis?

Data analysis is the process of interpreting, evaluating & organizing data for the sole purpose of making it presentable.

You can learn more about data analysis, types and methods in our introductory guide to data analysis for beginners, and if you want to improve your analytics skills or go from beginner to expert, it might help to see some great data analysis courses and training you can start with.

Best Data Analytics Software and Tools for Data Analysis

Here then is a reviewed list of the top best data analysis software & tools for data analysts in 2022:

Best business intelligence tool: Microsoft Power Business Intelligence (BI)

Microsoft power business intelligence software is a powerful business analytics tool provided by Microsoft and has since grown to become the one of the most popular analytics programs available today on the market.

It is a top business intelligence platform which supports tons of data sources, thereby allowing you to create and share reports, dashboards and visualizations. Power BI also allows you to build automated machine learning models and integrates with Azure Machine Learning.

Features

• Comes in six versions: Desktop, Pro, Premium, Mobile, Report and Embedded. The desktop version is free but can almost be used for personal use while the other versions like Pro and Premium version come with a licensed fee.
• Allows you build your data to life with life dashboards and reports using a drag-and-drop technique (no coding mastery is required).
• Data storage – cloud, on-premises, and hybrid.
• Enables both batch and streaming analytics (including real-time analytics).
• Offers high security controls.
• Provides interactive visualizations with self-service business intelligence capabilities like forecasting, what-if-analysis, or natural queries language.
• Regarded as Magic Quadrant Leader among analytics and business intelligence platforms according to Gartner.

Some of the companies using Power BI include:

• Nestle.
• Ecolab.
• Adobe.
• GE Healthcare.
• Tenneco.
• Worldsmart.

Overall, Microsoft Power BI enables robust analytics and insightful reporting to streamline your business decision-making.

Best for data visualization: Tableau Software

Tableau is a data visualization and analytics platform that allows you to create reports and share them across desktop and mobile platforms, within a browser, or embedded in an application.

This platform allows you to create transparent filters, parameters, and highlighters. Tableau is a BI (Business Intelligence) tool developed for data analysts where one can visualize, analyze, and understand their data.

Key features of Tableau

• Tableau is easy to learn and deploy.
• It has a powerful drag and drop features that anyone with an intuitive mind can handle, which makes it easy to use.
• It is not free software, and the pricing varies as per different data needs.
• Allows you to work on live data-set and spend more time on Data Analysis rather than Data Wrangling
• Allows you join datasets, based on location.
• Tableau provides six products, which include Tableau Desktop, Tableau Server, Tableau Online, Tableau Prep, Tableau Public, and Tableau Reader.
• The Tableau Online solution offers a wide array of deployment options for a multi-cloud environment.
• Tableau Prep provides features like immediate results, which will allow you to directly select and edit the values.

Companies using Tableau

• Amazon.
• Audi.
• Deloitte.
• Citibank.
• LinkedIn.
• Barclays.
• Skype.

Overall, with Tableau, you can analyze and visualize data better than any other data visualization software in the market and it is a data analytics tool that can be used by an organization of any size as it provides many solutions with different features for each solution.

Best for general purpose programming language: Python

Python is one of the most multi-purpose programming languages used by data analysts as it is not subject to the computer’s local processor and it is easy to learn as well as extremely accessible to code when compared to other popular languages such as Java, PHP or C++.

Python is a popular tool among users that are looking for an open-source solution and simple coding processes as its syntax is relatively easy to learn.

Python software for data analysis can handle various analysis on its own, and can integrate with third-party packages for machine learning and data visualization.

Features of Python

• Supports multiple file-formats
• Offers packages such as Matplotlib, seaborn, and vispy for data visualization and graphical analysis.
• It is a free, open-source tool (software) that is easy to learn.
• It has an extensive developer community for support and is the most widely used language.
• Handles many different analyses on its own, and also integrates with third-party programming languages such as C/C++, Java, PHP, C# for machine learning and data visualization.
• Has machine learning libraries such as Scikitlearn, Theano, Tensorflow and Kera, StatsModels,which are used for statistical modeling, mathematical algorithms, machine learning, and data mining.
• Used for advanced analysis processes.

Major brands using Python

• Spotify.
• NASA.
• Dropbox.
• Google.
• Netflix.
• CERN.
• Reddit.
• Facebook.
• YouTube.

Overall, Python is the fastest growing, object-oriented, high-level programming language today which is easy to read, write, maintain and is a free open-source software and can also handle text data as well.

Best tool for statistical analysis: R-Programming

“R” is a free open-source programming language and the leading programming language which is majorly used by statisticians for statistical analysis, data visualization, Big Data and machine learning.

R is the leading analytics tool in the industry and widely used for statistics and data modeling. It runs on a wide variety of platforms such as UNIX, Windows and MacOS.

Key Features of R-Programming for data analysis

• Offers packages such as plyr, dplyr, and tidy which makes data Visualization in R relatively easy.
• Provides tools to automatically install all packages as per user requirement, which can also be well assembled with big data.
• It has over 15,000 packages which allows you to browse the packages by categories.
• Exceeds SAS in various ways such as in terms of capacity of data, performance and outcome.
• Provides linear and non-linear modeling techniques.
• Most of the standard functions are written in R language.

Top companies using R-Programming

• Uber.
• Facebook.
• Firefox.
• Twitter.
• Google.
• ANZ.

Overall, R-Programming is a programming language that is used majorly for data science because it provides features that are useful for data science. Some of the features which this platform provides include multiple calculations with vectors, running code without a compiler, data science application functions, and statistical language.

Best for spreadsheet: Microsoft Excel

Almost everyone knows Microsoft excel, from basic functions such as creating spreadsheets to more advanced data processing.

Whether you are an expert in Tableau, R-Programming or SAS, you will still need to use Excel.

Excel is a basic, popular analytical tool that is widely used in almost all industries. It is the most common tool used for manipulating spreadsheets and building analyses.

Features of Excel for data analysis

• Suitable for simple analyses.
• Supports almost any standard analytics workflow and can be extended through its native programming language, Visual Basic.
• Excel is not a free open-source tool as it is a part of the Microsoft Office “suite” of programs.
• Does not need a UI to enter data as you can start right away.
• Readily available, widely used and easy to learn and start on data analysis.

Companies using Microsoft Excel include:

• McDonald’s.
• IKEA.
• Marriot.

Overall, if you are looking for the best open source and most common tool used for manipulating spreadsheets and building analyses, then Microsoft Excel is for you.

Want to learn excel for data analysis? We have a detailed guide and review of the best excel courses for beginners and advanced users, to help you get started, regardless of your skill level.

Best for big data analytics: Apache Spark

Apache Spark is a fast-growing large-scale data-processing engine which was developed by the University of California, Berkeley’s AMP Lab in 2009. It executes applications in Hadoop clusters 100 times faster in memory and 10 times faster on disk.

Spark is indeed a powerful engine for data analysts that need support in their big data environment.

Key Specs and features of Apache Spark

• It comes with libraries such as SparkSQL, MLlib, GraphX, and SparkStreaming.
• Spark is a free, open-source tool that is easy to learn.
• Can access diverse data sources as it can run on platforms like Hadoop, Apache Mesos or standalone.
• A high-performance tool that works well for batching and streaming data.
• An integrated analytics engine which is popular for data pipelines and machine learning models development.

Top Companies using Apache Spark include;

• Netflix.
• Visa.
• Shopify.
• Hortonworks.
• eBay.
• Uber.
• Verizon.
• Yahoo.
• Slack.
• Oracle.

Overall,
Apache Spark is a free open-source software that supports large-scale data processing with the help of an extensive ecosystem.

Best for data processing: RapidMiner

RapidMiner is a data analytics tool used for data processing, building machine learning models, and deployment. It is named by Gartner as a visionary in 2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms.

The RapidMiner platform helps data scientists and analysts improve their productivity through automated machine learning.

Features

• Advanced analytics features for descriptive and prescriptive analytics.
• An open-source data analytics tool that is easy to use.
• Integrates with Python and R as well as support for database connections such as Access, Excel, Microsoft SQL, Tera data, Oracle, Sybase, IBM DB2, MySQL, IBM SPSS, Dbase.
• Can generate analytics based on real-life data transformation settings.
• Provides more than 1500 algorithms.
• Built-in security controls.
• Provides five products for data analysis, RapidMiner Studio, RapidMiner Auto Model, RapidMiner Turbo Prep, RapidMiner Server, and RapidMiner Radoop.
• Groups the requests and reuses Spark containers for smart optimization of processes.

Top companies using RapidMiner include;

• BMW.
• Hewlett Packard Enterprise.
• EZCater.
• Sanofi.

Overall,
RapidMiner is a data analytics software platform that can be used to carry out predictive analysis and other advanced analytics like data mining, text analytics, machine learning and visual analytics, model deployment and deep learning without any initial programming skills.

How to choose the best analytics tools for data analysis

The first thing you need to do when selecting analytical tools or a software tool for data analysis is to first understand the types of data your enterprise wants to analyze, and, by extension, your data integration requirements.

Ideally, start by considering your organization’s business needs and then know the skill level or preferences of your data analyst or the person who will be using your analytics tool.

Are you a beginner or professional data analyst?

Consider if it will be used by sophisticated data analysts and data scientists, or by non-technical users who need an intuitive interface, and in most cases, where you have both types of users, check if it suits both kinds of users.

Choose a tool you can use and understand

While some platforms provide an interactive experience for iterating on code development (typically using SQL), many others focus more on point-and-click analysis for beginners or less technical users since the world is going the easy way. The tool should also provide support for visualizations relevant to your enterprise.

Consider the software data modelling capabilities

Consider a tool’s data modeling capabilities. Many data analytics tools today support a semantic layer or can perform data modeling themselves. However, if you want to use one that doesn’t have these, you’ll have to use SQL or a tool like dbt to model your data prior to analysis.

Price and licensing

Finally, consider the price and licensing of each tool. While some data analytics tools are free, others charge licensing or subscription fees. Even though the most expensive tools are not necessarily the most feature-complete, it all comes down to simplicity and tailored support. In the same way, users should not ignore the many robust free analytics solutions available.

Types of Data Analysis Tool

• Business Intelligence Tools.
• Statistical Analysis Tools.
• Purpose Programming Language.
• SQL Consoles.
• Standalone Predictive Analytics Tools.
• Data Predictive Analytics Tools.
• Data Modelling Tools.
• ETL Tools.
• Automation Tools.
• Unified Data Analytics Engines.
• Spreadsheet Applications.
• Industry Specific Analytics Tools.
• Data Science Platforms.
• Data Cleansing Tools.
• Data Visualization Tools and Platforms.

Business Intelligence Tools

BI tools, short for Business Intelligence tools, are one of the most represented means of performing data analysis.

These tools specialize in business analytics, and will be of great benefit to you if you need to analyze, monitor, and report on important findings.

I particularly like BI tools because they are simple analytics solutions you can easily adjust to your level of knowledge, without the need for heavy IT involvement.

Some of the features that make BI tools unique include: ease of use, helpful features such as self-service, predictive analytics, and advanced SQL modes.

Analytics Tools Frequently Asked Questions

Conclusion

The word ‘Data’ has been in existence for a very long time as it plays a crucial role in decision making for business operations amidst the trillion gigabytes of data that is generated each day by each user with each user creating 17 megabytes of data each second.

So, in order to be able to deal with such large amounts of data, a Data Analyst is required not just to analyze data, but to also select and use the required tools for data analysis to gather insights from these data.

Whether you’re just starting out as an analyst or a business that wants to be data driven, the need to know the various tools used by data analysts for data analysis becomes very important.