Data analytics tools are tools and software used by data analysts to carry out analysis and process small or large data easily. Once you have data ready, you can try analyzing it using different tools or software.
As a data analyst, 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.
Choosing the right data analytics tool can sometimes be challenging, as no tool fits every need.
In this guide, I have compiled a list of the top best data analytics tools amongst the varieties of software available in the market today, that will help you improve your business both now and later in the future.
Top 7 data analytics tools
These are the top 7 data analytics tools and software for data analysis:
- Microsoft Power Business Intelligence (BI).
- Tableau Software.
- Microsoft Excel.
- Apache Spark.
Big Data Analytics software is widely used to provide meaningful analysis of a large set of data. These software analytical tools helps 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.
1. Microsoft Power Business Intelligence (BI)
The Microsoft power business intelligence software is a powerful business analytics tools provided by Microsoft and has since grew to become the one of the most popular analytics program 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 build automated machine learning models and integrates with Azure Machine Learning.
- 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 comes 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.
Top Companies using Power BI include;
- GE Healthcare.
Overall, Microsoft Power BI enables robust analytics and insightful reporting to streamline your business decision-making.
2. 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.
- 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 multicloud environment.
- Tableau Prep provides features like immediate results, which will allow you to directly select and edit the values.
Top companies using Tableau include;
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.
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 look 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.
- 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.
- 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 analyses processes.
Top companies using Python include;
Overall, Python is the fastest growing, object-oriented, high-level programming languages today which is easy to read, write, maintain and is a free open source software and can also handle text data as well.
R-programming 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.
- 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 include;
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 includes multiple calculations with vectors, running code without a compiler, data science application functions, and statistical language.
5. 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 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.
Top companies using Microsoft Excel include;
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.
6. 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
- 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 batch and streaming data.
- An integrated analytics engine which is popular for data pipelines and machine learning models development.
Top Companies using Apache Spark include;
Apache Spark is a free open-source software that supports large-scale data processing with the help of an extensive ecosystem.
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.
- 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;
- Hewlett Packard Enterprise.
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.
What is Data Analysis?
In simple words, 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 here, and if you are looking to brush up your analytics skills or go from beginner to expert, here are some of the best data analysis courses and training you can start with.
How do you choose the right data analysis tool?
The first thing you need to do when selecting a data analytics tools 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.
- Consider if it will it 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.
- 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 a tool’s data modeling capabilities. Many data analytics tools today supports 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.
- Finally, consider price and licensing of each tools. 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.
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 trillions 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 amount 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.