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25 Must-Have Data Analytics Tools To Ace Data Management

The selection of available data analytics tools expands as the discipline of data analytics develops. Which tools for data analysis do I need to master if I want to pursue a profession in this area? We’ll discuss some of the most important data analytics tools in this article, along with their benefits. You’ll receive a brief description of each, outlining its uses, benefits, and drawbacks, for both open-source tools and for-profit software. 

Must-Have Data Analytics Tools To Ace Data Management

Data analysis is crucial to how modern businesses operate. Choosing the finest data analytics tool may be challenging because no one solution can satisfy all demands. To assist you in selecting the best data analytics tools for your company, let’s first take a closer look at a few of the most popular solutions now available and then compare the essential factors for choosing between them.

Before assessing the instruments at hand, there are a couple of issues to take care of. It is important to first comprehend the types of data that your business desires to analyze and, as a result, its data integration requirements.

Additionally, in order to establish a single point of truth for analytics, you must choose data sources as well as the tables and columns contained within them and clone them to a database system before you can start analyzing data. Additionally, you want to assess data governance and data security. For instance, access control and authorization mechanisms should be in place if data is transferred between departments to safeguard sensitive data.

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How to Select Data Analytics Tools

Once the data is prepared, you may experiment with various tools for analysis. How can you locate one that is ideal for your business? Learn who will be utilizing your analytics solution and start by taking into account the business demands of your firm.

Will it be utilized by data scientists and skilled analysts, by non-technical consumers who require an easy-to-use interface, or would it be appropriate for both types of users? While some platforms emphasize point-and-click analysis for less technical users, others offer an interactive element for iterating on code development, generally utilizing SQL. Additionally, the tool needs to enable visualizations that are pertinent to your business.

Think about a tool’s capacity for data modeling. Others can handle data modeling on their own or provide a semantic layer. You must use SQL to represent your data before analysis if you wish to utilize one that doesn’t.

Finally, think about licensing and cost. While some services are free, others require a license or a membership. Users shouldn’t overlook the many reliable free solutions that are readily available because the most costly tools aren’t always the most feature-complete.

Now that you know what factors to look for in data analytics tools, let’s jump into the list.

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List of Top 25 Must-have Data Analytics Tools

We’ll begin our list with the absolute necessities—the data analytics tools you need to have.  By the time you read this post’s conclusion, you’ll know how to go forward, whether you’re getting ready for an interview or picking which tool to study next. The data analytics tools we’ll discuss are as follows:

1. R 

Like Python, R is a popular open-source language for programming. It is commonly used to create statistics and data analysis techniques. Although R has a more difficult learning curve than Python, R has simpler grammar. However, it is widely used for data visualization and was created expressly to handle complex statistical computing tasks. Similar to Python, R has a network of open-source system software CRAN (the Comprehensive R Archive Network), which contains more than 10,000 packages.

It can make use of code written in languages like C, C++, and FORTRAN and interacts well with other systems and languages (including big data software). The software’s drawbacks include inadequate memory management and the absence of dedicated support staff, despite a helpful user base that may be tapped for assistance. However, RStudio is a fantastic IDE that is tailored specifically for R, which is always a plus!

2. Python

Since its introduction, Python has become one of the most popular programming languages. Its popularity is mostly a result of the fact that it is a quick and simple language to learn. However, with the emergence of analytical and statistic frameworks like NumPy, SciPy, etc., it evolved into one of the most potent data analytics tools. It now provides thorough coverage of mathematical and statistical operations.

Programmers and other computer professionals are increasingly shifting to analytics. Because the majority of these individuals are already acquainted with Python, many data scientists now favor it as their go-to data analytics tool.

3. Power BI

Microsoft Power BI, one of today’s greatest business intelligence tools, offers a variety of data sources. Users may make and share dashboards, visualizations, and reports using this tool. Users may create a Power BI app by combining many dashboards and reports for easy deployment. Users of Power BI may set up automated models for machine learning and the software connects using Azure Machine Learning.

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4. Tableau 

Users may build reports using Tableau, a portal for data visualization and analytics, and distribute them via browsers or applications embedded on desktop and mobile devices. It may operate locally or on the cloud. A substantial amount of the Tableau service is powered by VizQL, the primary query language for it. This reduces the requirement for end-user performance improvements by converting drag-and-drop dashboards and visualization components into effective back-end queries. However, complicated SQL queries are not supported by Tableau.

5. ClicData 

A complete business intelligence platform, ClicData has several capabilities for data integration, data transformation, automation, and visualization. ClicData is completely cloud-based and compatible with all platforms and devices.

With its drag-and-drop interface, you can quickly connect, combine data from numerous sources, and create dashboards. They provide both comprehensive BI with in-app support and professional services, as well as self-service BI including online materials.

6. Google Data Studio

A freemium dashboarding and data visualization tool called Google Data Studio seamlessly connects with the majority of other Google products, including Google Analytics, Google Adwords, and Google BigQuery. The fact that Data Studio interfaces with other Google services make it a fantastic tool for anyone who needs to explore their Google data.

For example, marketers may create dashboards to analyze data from Google Adwords and Analytics to learn more about client conversion and retention. If the data is copied to BigQuery beforehand using insights from data like Stitch, Data Studio can also deal with the information collected from a variety of different sources.

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7. Sisense

The data analytics tool Sisense is designed to assist technical developers in the business analytics procedure and in displaying all of their company data. It provides a huge selection of drag-and-drop applications and dashboards for teamwork. The distinguishing characteristic of the Sisense platform is its proprietary in-chip technology, which optimizes computation to use CPU caching rather than slower RAM. For some procedures, this can result in computing that is 10–100 times quicker.

8. Redash

Redash is a tool for quickly and affordably obtaining data from various sources and making infographics. A reasonably priced hosted edition of the code is offered for companies who need to start operating right immediately. The core of Redash is its query editor, which provides a simple user interface for building queries, exploring schemas, and managing integrations. Redash has a cache for query results, and users may schedule automated updates to execute.

9.  Apache Spark

With the use of the software architecture Apache Spark, data scientists and analysts can swiftly analyze enormous data volumes. A decentralized analytics platform called Spark was developed to analyze massive, unstructured data collections. While there are other frameworks that are similar (like Apache Hadoop), Spark is incredibly quick. It is approximately 100 times quicker than Hadoop since it uses RAM instead of local memory. As a result, it is commonly used in the development of machine learning models that demand a large amount of data.

It also features a collection of machine learning methods called MLlib that includes, to mention just a few, clustering, regression, and classification algorithms. The drawback of Spark’s high memory use is its high computational cost. Additionally, it misses a document management system, necessitating connection with other programs like Hadoop.

The open-source processing system created with an analytics focus is Spark, particularly for unstructured data or massive amounts of data. In the past several years, Spark has become a very popular data analytics tool. This is due to a number of factors, one of which is the straightforward interaction with the Hadoop environment. Spark is perfect for analytics since it comes with its own machine-learning toolkit.

10. QlikView

The two biggest names in data visualization are effectively competing for first place: Qlikview and Tableau. It is said that Qlikview is a little bit quicker than Tableau and offers seasoned users a little bit more versatility. Tableau is simpler to understand and has a more user-friendly GUI.

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11. Talend

A variety of cloud apps are available from Talend for integrating information. It’s intended to assist companies in consolidating all of their data into a single interface so that teams may access the appropriate info as needed.

Users of the platform may examine data without writing any code thanks to a number of built-in machine-learning components. It makes use of algorithms for classification, grouping, recommendations, and regression. In addition to several paid alternatives, Talend provides a free open-source version.

12. Splunk

Compared to some of the more well-known data analytics solutions, such as Cloudera and Hortonworks, Splunk is more widely used. It began as a “Google for log files,” which means that processing data from machine log files was its main function. In the present, it has developed into much more. Splunk is simple to use and has excellent visualization possibilities.

13. QuickSight 

The BI and analytics program used by Amazon is called QuickSight. AWS, SaaS, spreadsheets, and other cloud data sources may all be connected to using this cloud service. The purpose of QuickSight is to enable decision-makers to easily and visually analyze and grasp Data. It can, however, be utilized for machine learning because it has sophisticated capabilities. It supports collaborative analysis and the sharing of analyses and reports, just as Power BI.

14. RapidMiner 

A strongly integrated data science platform is RapidMiner. It was created by the same business that uses no programming to accomplish predictive analysis as well as other sophisticated analytics such as data gathering, sentiment analysis, machine learning, and visual analytics.

Any form of data source, such as Access, Spreadsheet, MSQL, Oracle, Sybase, Ingres, MySQL, Dbase, etc., may be included in RapidMiner. The tool is quite strong and can provide analytics based on settings for real-world data transformation, allowing you to choose the format and data points for predictive analysis.

Before doing predictive analytics and creating statistical models, RapidMiner is a data analytics tool that meet all the technical requirements of its users, including integration, cleansing, and data transformation. The majority of work is completed by users using a straightforward graphical interface. Utilizing R, Python, and different third-party plugins found on the company’s marketplace, RapidMiner may also be enhanced.

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15. KNIME 

The Konstanz Information Miner, often known as KNIME, is a free and open-sourced data analytics tool that provides data integration, processing, visualization, and reporting. With little to no code required, it incorporates frameworks for data analysis and machine learning. KNIME is fantastic for Data Scientists who need to include & analyze Data for creating Machine Learning and other simulation approaches but do not naturally possess good programming abilities. Its graphical user interface makes analysis and modeling simple point-and-click processes.

16. Airtable

An easy-to-use cloud collaboration application called Airtable is described as a “bit spreadsheet, portion database.” Like other conventional spreadsheet applications, it offers data analysis and data visualization features, but it also has a strong database at the back end. You can quickly organize, track, and discover data in a database by utilizing “views.” Additionally, using an API, developers may integrate Airtable with other programs.

17. Excel 

In practically all businesses, Excel is a fundamental, well-liked, and often-used analytical tool. Regardless of your level of Sas, R, or Tableau proficiency, Excel will still be required. When the need for studies on the client’s internal data arises, Excel becomes crucial. It examines the intricate work of presenting a data summary with a pivot table preview that aids in gathering and analyzing data in accordance with customer needs. Excel offers a feature for sophisticated business analytics that supports modeling abilities. It contains prebuilt features including time grouping, DAX measure generation, and automated relationship recognition.

18. Mode

Data analytics software called Mode offers data analysts a simple and flexible environment. It provides a collaborative toolkit for new users, an integrated SQL editor, and a notebook environment for research and visualization. To enable quick and interactive analysis, the mode contains a special Helix Data engine that feeds and saves Data from external databases. Ten GB of data may be stored in memory by the data analysis.

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19. SAS Business Intelligence

With the help of many tools from SAS Business Intelligence, self-service analytics is made available. It offers various built-in capabilities for collaboration, such as the capacity to push information to mobile devices. Despite being a thorough and adaptable platform, SAS Business Intelligence is possibly more expensive compared to some of its rivals. Due to its adaptability, larger businesses could consider the cost to be worthwhile.

20. Looker 

Looker connects with current technologies to bring in fresh, laser-focused data that can reveal data links that weren’t previously apparent, assisting teams in making better decisions.

Models have been created uniquely for each customer thanks to tools and software that may be modified. Additionally, a large number of their “embedded analytics solutions” are pre-built for sectors including e-commerce, healthcare, and more.

21. SQL Programming Language 

The standard language designed to connect with databases is called Structured Query Language (SQL), and it is very helpful when working with structured data. Structured data is simple to arrange because SQL, among other things, can be used to search, add, edit, and remove data.

The majority of structured data is stored in SQL, making it simple for programs created in the language to unlock data and produce effective outcomes.

22. Jupyter Notebook

An open-source online program called Jupyter Notebook enables you to create collaborative documents. These incorporate narrative text, mathematics, live programming, and visuals. Think of something that resembles a Microsoft Word page but is far more dynamic and tailored for data analytics! It’s an excellent tool for displaying work as a data analytics tool: Python and R are among the more than 40 languages supported by Jupyter Notebook, which runs in the browser. It also supports a variety of outputs, including HTML, photos, videos, and more, and connects with large data analytics tools like Apache Spark.

But it has its limitations, just like every instrument. Version control for Jupyter Notebook documents is inadequate, and it is difficult to trace changes. This implies that it isn’t the greatest location for developing and analytics work (you should utilize a specialized IDE for both), and it isn’t ideal for teamwork. This also implies that if you share the document with anyone, you’ll need to provide them with any other assets (like modules or runtime systems) because it isn’t self-contained. But it continues to be a crucial data analytics tool for presentation and educational purposes.

23. IBM Cognos

With the use of built-in AI technologies, IBM Cognos’ business intelligence platform can unearth insights buried in data and explain them in simple terms. Additionally, Cognos provides automated data preparation capabilities that automatically combine and clean up data sources, enabling speedy integration and testing of data sources for analysis.

24. Datapine 

Users of all skill levels can access Datapine’s straightforward yet advanced analytical tools. With a drag-and-drop user interface, advanced predictive analytic tools, and interactive dashboards and visualizations, such a well business intelligence program may be used. Furthermore, by employing the sophisticated SQL option, advanced users may design their own queries. Datapine stands out for its quickness and ease of use.

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25. Oracle Analytics Cloud

Another group of tools for data and business intelligence in the cloud is Oracle Analytics Cloud. It is concentrated on assisting large organizations in converting their antiquated mechanisms into digital cloud platforms. Users make use of a diverse range of analytical capabilities, including machine learning techniques and simple visualizations, to derive insights from data.



Q1. Which tool is most frequently used for data analysis?

Of course, Excel is the data analytics program that is utilized the most frequently worldwide. Excel will still be used for the tedious labor, regardless of whether you’re a specialist in R or Tableau. Typically, non-analytics professionals won’t have access to SAS or R on their computers. But Excel is available to anyone.

Q2. Why are tools for data analytics necessary?

Data analysts utilize software and programs known as data analytics tools to create and carry out analytical procedures that assist businesses in making better, more informed business choices while lowering costs and raising profits.

Q3. What tools are necessary for data analysts?

In order to provide data analysts more time for real analysis, technologies for data analysis can also be utilized to automate time-consuming operations. However, there are a few essential instruments that are employed in data analysis. Data management technologies like R, and SAS, are among them, along with SQL, Python, and Tableau.


We looked at some of the most common and essential data analytics tools available in this post. The main lesson to learn is that there isn’t a single tool that can perform all tasks. A proficient data analyst is well-versed in a variety of programming languages and applications. Utilizing data analytics tools, businesses may get insights from consumer data and identify patterns and trends to improve business choices.

Whether you need to undertake fundamental or complex data analysis, there are many internet resources available to you. Businesses may now profit from enormous volumes of unstructured data owing to no-code machine learning software, which makes complex data analysis simpler than ever.


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