7 Best Data Analytics Tools for the Big Data Analysis

data analytics tools

data analytics tools

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Are you aware of the different data generation sources for a modern corporate setting? Data analytics tools, Well, as per big data analytics companies, there is email, social media apps (Including Facebook, Instagram, Linkedin, Pinterest, etc.), inbound and outbound calls, website visitors, queries, feedback, mentions, shared information, and many more. 

All these said channels generate 2.5 quintillion bytes of data every single day, and this amount will only escalate and accelerate in the future. As per researches, in the last two years, more than 90% of data in the world and the global conundrum only fueled its prospects. 

Generally, this data wasn’t processed or analyzed for derivative conclusions, but since organizations realized its importance, big data analytics services became a phenomenon. 

But first, what is Big Data?

As per definitions, big data describes the large volume of structured and unstructured data received by businesses every day. As per the Big Data experts, it is not the amount of it that matters, but how an organization cleans and treats it does. Big data, when analyzed correctly, can help organizations make better business decisions, review strategic business moves, and derive meaningful insights regarding the market and customers. 

Concept of big data is not new to big data analytics companies, but its understanding and usability did bewilder them. Accumulating this data from several business resources, applying analytics, and deriving conclusions are just one way modern businesses render significant value. 

That said, when a company generates almost TBs of data every day or month, it becomes essential for them to analyze it and achieve desirable results. However, such prospects are not possible using ordinary tools and software. Since the volume of data is itself very large, it would take more than traditional software to receive accurate results in a short period. 

Why is big data analytics services necessary?

Now that you know what big data is- let’s reflect on its importance in the modern corporate setting. All organizations work on an adamant mantra; Change is the only constant! If organizations are not changing their services or products as per the variating customer demands, it becomes impossible to explore new opportunities, achieve leverage against the competition, and make smart business moves. 

This is where the big data comes into play! 

Big data analytics companies add efficiency, profits, and satisfaction to your corporate ladder. In the long run, big data analytics companies help you with:

  1. Cost reduction: Modern Technologies like Hadoop and cloud-based analytics address cost-related demands. They offer significant cost advantages in terms of storing large amounts of data, analyzing it, and identifying efficient ways to make it useful. 
  1. Make better business decisions, faster: Thanks to Hadoop and other in-memory based analytics, you can now analyze old and new data using similar tools. It allows businesses to make use of traditional data formats, in turn, speeding the decision-making process. 
  1. Work on existing products and launch new products/services: The idea of utilizing big data is to give customers what they want, addressing a wider audience group at the same time. Using big data, you can review the type of products and services that interest your customers, the reviews suggested in an existing lot and creating items that meet customer needs. 

Top data analytics tools for companies 

As mentioned before, data analytics is an extensive field that requires modern tools and a compendious approach. 

That said, there are several different big data analytics tools used by big data analytics companies to collect, clean, and analyze this data. 

The below list includes seven different tools that might come in handy for companies offering big data analytics services. 

Datapine for Business Intelligence:

BI or business intelligence tools fulfill data analytics requirements. The task sheet includes analyzing, monitoring, and reporting on meaningful insights post data to study and analytics. The top features of Business Intelligence include self-service, predictive analytics, and advanced SQL models. 

Datapine is one of the best business intelligence tools, perfect for beginners and advanced users that need a fast and reliable data analytics solution. Its user interface is easy to master. You can drag and drop desired values and create personalized charts and graphs. The top features include:

  • Easy to advance SQL mode 
  • Powerful predictive analysis  
  • Interactive charts and dashboards
  • Build your query book
  • Visual mode 
  • High speed and simple features 

Tableau:

Tableau Software is interactive data visualization tool used in the Business Intelligence industry. It is used for data perception analysis, which involves simplifying raw data into an easily understandable format. The visualizations are executed in the form of dashboards and worksheets. Moreover, Tableau fits into the understanding of data analytics of all levels.

Users can personalize the created dashboard to include features they deem to be necessary. It is one of the trendiest data analytics tools, running on both cloud or on-premises networks. 

The top features of this tool include data blending, real-time analysis, the collaboration of data, and customized dashboards. This tool is perfect for several industrial sectors, researchers, market reporters, etc. 

Also read: 4 Tips To Make Your B2B Sales More Successful In 2021

Microsoft Power BI

Power BI is a business analytics tool produced by Microsoft, used for providing interactive visualizations and business intelligence services. It comes with a simple interface allowing end-users to create their reports and dashboards. The features and visualizations of Microsoft Power BI go further than bar and pie charts, providing you all the necessary insights in specific areas. 

The tool is popular in the domains of data analytics companies, packed with a hundred content templates and integrations. You can use it on the cloud or set up an on-premise gateway for its usage as well. You can choose a data representation theme, study its history, and arrange its sequence. Best feature about this tool is it allows users to ask questions in real-time and pin it to their dashboards.

Google Data Studio 

A freely available tool from Google, it makes your data informative, easy to read, customizable, and well-segregated into dashboards and reports. Using this tool, you can:

  • customize data into visual reports
  • organize data for a structured look
  • generate static reports
  • use modern in-build tools to bring your data to life
  • work seamlessly along with other solutions
  • measure and analyze the performance of your websites and apps
  • shift from data related to one product to the other, thus saving time and improving efficiency
  • analyze data from a big data warehouse using BigQuery Data 
  • visualizing key business metrics
  • utilize space to explore your data in greater detail. 

Best feature about this tool is, it naturally coordinates with other Google applications like Google Analytics, Google Ads, Google BigQuery, and other Google administrations. 

If you’ve accumulated Google data regarding your product and service, and you need to analyze it- there is no better tool than Google Data Studio for such big data analytics services.  

SAS:

One of the oldest programming languages for data control, created in 1966, this data analytics tool helps analyze data from other sources in its efficiaous, open, and manageable portal. Developed by the SAS Institute for data management, advanced analytics, and predictive analysis, it allows users to examine raw data, profile possibilities, analyze online data, and improve correspondences. 

Several big data analytics companies employ SAS to perform efficient analysis on data of any size, manage, and conclude actionable results. 

Python:

This modern analytics tool has been there for quite some time and fundamentally resembles JavaScript, Ruby, and PHP. Python includes artificial intelligence libraries like Theano, TensorFlow, Keras, Scikitlearn, etc. Other libraries like Beautiful Soup and Scrapy scour data from the web, while Matplotlib is perfect for data visualization and reporting. This open-source programming language can deal with text data as well as a numerical one, which is one of best features of it. The object-oriented scripted language effectively serves the purpose of specialized experts and data researchers who want to analyze data and present it in meaningful form. 

Although Python is a versatile and reliable programming language, its drawback is that it takes a lot of memory and is slower than several other modern languages as well. However, its portability is one reason why users love its operating systems without making any changes to its existing code. Moreover, it can run on both macOS and Windows.

Microsoft Excel:

At some point in time, we all have used this evergreen tool for constructing spreadsheets and building reports. Moreover, its freely available nature is one reason why Excel has such prominence in the user market. That said, it supports all standard analytics, even the extendable ones like a local programming language, Visual Basic. 

Excel’s simplistic interface lets a user perform general analysis. Users can even perform data analytics for large sets of information, although it restricts to around 1 million times. Most modern enterprises prefer to use cloud-based analytics and collective examinations, but Excel provides invaluable built-in features like pivot tables for better analysis. 

Conclusion

Other than the mentioned tools, big data analytics companies employ several hundreds of resources depending on amount of data to be analyzed and its importance. 

Also read: How WordPress development can benefit your business?

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