Trending and Top Data Analytics Tools: We are all aware that data analytics helps firms and organizations in correcting their data, examining their operations and identifying opportunities for improvement. Data analytics is becoming increasingly necessary.

As a result, the need for data analytics tools has changed as well. The usage of data analytics technologies continues to confound many individuals and organisations. In order to assist you, we have put up a list of the finest and top data analytics tools for future. Check them out!

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Data Analytics Tools: Why are they important

Data analytics tools are software, applications and programs that gather and analyse data about a company, its clients, and its competitors in order to enhance business operations and help decipher patterns so that decisions may be made using the data. Tools for data analytics are crucial because they aid in the interpretation of vast amounts of data on broad subjects like market trends at the moment or the preferences of different client segments.

Top Data Analytics Tools

As previously noted, we have compiled a list of the best data analytics tools to aid businesses in operating more effectively. These tools are increasingly widely used and crucial for data and information gathering.

1. Python Programming Language

One of the most potent data analytics tools at the user’s disposal is Python. It includes a large number of packages and libraries. Python is a free, open-source program or software that includes libraries like Matplotlib and Seaborn that can be used for sophisticated visualization. One of the popular data analytics libraries included with Python is called Pandas.

Python is the first programming language that the majority of programmers choose to learn since it is simple and flexible. It is an object-oriented, high-level and free open-source programming language. It is very easy to write, which has been making it popular especially among new programmers and graduates, post-graduates.

Python is helpful for web development since it takes less time and has an easy-to-understand syntax. Numerous frameworks, including Flask, Django, Falcon, Web2Py, Sanic, and Pyramid, are supported by Python.

• Type of tool: Programming language.
• Availability: Open-source, with thousands of free libraries.
• Used for: Everything from data scraping to analysis and reporting.
• Pros: Easy to learn, highly versatile, and widely used.
• Cons: Memory intensive—doesn’t execute as fast as some other languages.

Numerous businesses are using and using this programming language. The following list of businesses uses this terminology:

Python is a computer language that is used by a few large international corporations, including Netflix, YouTube, and Facebook.

2. R Programming Language

The two founders, whose names both begin with the letter R, inspired the name “R” for the programming language. It is one of the most widely used languages for data analysis, visualisation, and statistical modelling. It is a programming language that is open-source.

Data manipulation is simple with the aid of R and packages like plyr, dplyr, and tidy. It excels at data analysis and visualisation with tools like ggplot2, lattice, plotly, and others. Additionally, a sizable development community is available for support. R can be downloaded for free from its official website.

• Type of tool: Programming language.
• Availability: Open-source.
• Used for: Mostly used for Statistical analysis and data mining.
• Pros: Platform independent, highly compatible, lots of packages.
• Cons: Slower, less secure, and more complex to learn than Python.

Companies using R –

Companies such as Google, ANZ, and Firefox use R as their programming language.

3. Microsoft Excel

The greatest spreadsheet program, including Excel, make it simple to store data, visualize data, do calculations using data, clean up data, and present data in a way that is easy to understand. Microsoft Excel, one of the most widely used tools for data analytics, offers users options like sharing workbooks, working on the most recent version for real-time collaboration, and uploading data directly from a photo to Excel, among others.

It provides goods for three main categories, including:

• For Home
• For Business
• For Enterprises
• Type of tool: Spreadsheet software.
• Availability: Commercial.
• Mostly used for Data wrangling and reporting.
• Pros: Widely used, with lots of useful functions and plug-ins.
• Cons: Cost, calculation errors, poor at handling big data.

Companies using Excel –

Excel is being utilised by IKEA, Marriott, and McDonald’s are a few businesses that use Excel.

4. Tableau

If you want to develop interactive visualisations and dashboards without having a strong background in coding, your search must end here. Data analysts may easily display, analyse, and comprehend their data with Tableau, a business intelligence tool.

Tableau is one of the top data analytics tools and best data visualization tools (top BI tools) which offers quick analytics and can investigate a variety of data sources, including databases, spreadsheets, Hadoop data, and cloud services. Due to its robust GUI, it is simple to use. It requires less work to create excellent interactive dashboards. With Tableau, a market leader, you can deal with real data without expending too much time on data manipulation.

• Type of tool: Data visualization tool.
• Availability: Commercial.
• Mostly used for: Creating data dashboards and worksheets.
• Pros: Great visualizations, speed, interactivity, and mobile support.
• Cons: Poor version control, no data pre-processing.

Companies using Tableau –

Some businesses that use Tableau for their data analytics needs include Citibank, Skype, Deloitte, and Audi.

5. QlikView (Qlik Cloud Analytics)

A self-service solution for business intelligence, data visualisation, and data analytics is called QlikView. It offers capabilities like Data Integration, Data Literacy, and Data Analytics in an effort to expedite the business value that can be obtained from data. Fast decision-making is made easier, and there are several capabilities for ad hoc queries.

There are no data volume restrictions and it responds instantly. Making the most efficient, cost-effective, and economical business decisions is made possible with the help of QlikView. Additionally, it provides a range of items for its consumers, some of which are accessible for a free 30-day trial. Many customers all across the world rely on QlikView.

Companies using QlikView –

A few of the companies that are using QlikView are NHS, CISCO, and SAMSUNG.

6. Microsoft Power BI

Microsoft’s Power BI is yet another potent corporate analytics tool. It was first developed as an Excel plug-in but was later updated as a standalone suite of corporate data analysis tools in the early 2010s. With a short learning curve, Power BI users can easily build interactive visual reports and dashboards.

• Type of tool: Business analytics suite.
• Availability: Commercial software (with a free version available).
• Mostly used for: Everything from data visualization to predictive analytics. 
• Pros: Great data connectivity, regular updates, good visualizations.
• Cons: Clunky user interface, rigid formulas, data limits (in the free version).

Some of the products under Power BI are as follows-

• Power BI Desktop
• Power BI Premium
• Power BI Pro
• Power BI Embedded
• Power BI Mobile
• Power BI Report Server

Companies using Power BI –

The well-known businesses implementing Power BI include GE Healthcare, Adobe, and Heathrow.

7. SAS (Statistical Analysis System)

A well-liked commercial package of business intelligence and data analysis tools is called SAS, widely known as the Statistical Analysis System. The SAS Institute created it in the 1960s, and it has changed ever since.

Today, client profiling, reporting, data mining, and predictive modelling are its key uses. Software designed for the enterprise market is typically more reliable, adaptable, and simple to use for big businesses. A basic GUI is present. Therefore, learning it is simple. To use the tool, nevertheless, a solid understanding of SAS programming is a distinct benefit.

• Type of tool: Statistical software suite.
• Availability: Commercial.
• Mostly used for: Business intelligence, multivariate, and predictive analysis.
• Pros: Easily accessible, business-focused, good user support.
• Cons: High cost, poor graphical representation.

Companies using SAS –

Companies that are using SAS are Google, Twitter, Accenture, and Genpact.

8. KNIME

An open-source, cost-free platform for data analytics, reporting, and integration is called KNIME, or Konstanz Information Miner. It is designed for analytics on a GUI workflow and aids in data collection as well as the creation of models required for production and deployment. KNIME offers two software options.

Specifically, the KNIME server and analytics software. With the first, you can create workflows, and reusable components, and clean up your data. The deployment of workflows, automation, and team collaboration is done using the knime server.

• Type of tool: Data integration platform.
• Availability: Open-source.
• Mostly used for Data mining and machine learning.
• Pros: Open-source platform that is great for visually-driven programming.
• Cons: Lacks scalability, and technical expertise is needed for some functions.

Companies using KNIME-

Various companies are using KNIME. Some of them are – Deutsche Telekom, Continental, Siemens, and Novartis.

9. Apache Spark

One of the most active Apache projects is this one. It has an interface that guarantees implicit data parallelism and is open-source. Spark is a framework that supports applications while preserving the fault tolerance and scalability of MapReduce. It was initially created in 2012 and then donated to the charitable Apache Software Foundation.

• Type of tool: Data processing framework.
• Availability: Open-source.
• Mostly used for Big data processing and machine learning.
• Pros: Fast, dynamic, and easy to use.
• Cons: No file management system, rigid user interface.

Companies using Apache Spark –

Companies including Verizon, Visa, Oracle, and Hortonworks, use Apache Spark are using Spark

10. Splunk

Splunk is a platform for searching, analysing, and visualising machine-generated data that has been collected from applications, websites, etc. Splunk has developed products in a variety of industries, including IT, Security, DevOps, and Analytics, and was recognised by Gartner as a Visionary in the 2020 Magic Quadrant for APM.

Splunk Products:

• Splunk Free
• Splunk Enterprise
• Splunk Cloud

Companies using Spunk –

Companies like Dominos, Otto Group, Intel, and Lenovo, which are trusted by 92 of the Fortune 100, use Splunk in their daily operations to identify processes and correlate data in real time.

How to choose a right Data Analytics Tool for you?

As we have already examined the best and most effective top data analytics tools and solutions, many renowned businesses have already chosen them. Let’s now discuss how to select the ideal analytical instrument for our companies.

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First, remember that no single data analytics solution can solve every data analytics problem you might encounter. You might choose one tool from this list to meet the majority of your demands, but lesser tasks may call for the usage of a different tool.

Second, determine precisely who will need to use the data analysis tools by taking into account the organisational demands from a business standpoint. Will they be predominantly used by other data scientists or analysts, non-technical consumers who need an interactive interface, or both? Many of the tools in this list can be used by either kind of user.

Third, take into account the tool’s capacity for data modelling. Does the tool include these features, or will you need to undertake data modelling before analysis using SQL or another tool?

Finally, take into account the practical implications of price and licencing. Some of the options are completely free or have some features that are free to use (but will require licencing for the full product).

With this, our blog has come to an end. We anticipate that all of our readers will find this blog to be helpful. Visit our website frequently to read more articles about data analytics.

Happy Learning :)