Big Data Analytics: What it is, Importance, Tools and Real-Life Applications

Simply put, big data analytics is the use of advanced analytic techniques to examine big amounts of data or large data sets, to get meaningful information and uncover trends and patterns to make an informed business decision.

Creative image of group of analysts analyzing big data to explain and answer what is big data analytics and why it's important

In this guide, we explain what big data analysis, or, big data analytics (as it’s professionally called) is, in the easiest to understand way.

Data and its use in today’s business and technology world, is indispensable and hence cannot be overemphasized.

Big Data, just as the name says, is a term used to describe a large data set. This collection of data is huge in volume and still keeps increasing exponentially with time.

Big data analysis is performed when data has grown beyond the ability of regular data management tools to capture, manage or process.

What is Big Data Analysis?

Big data analysis (also known as “big data analytics”) describes the process of uncovering market trends, hidden patterns, and correlations in large amounts of raw data to help make data-informed decisions. These processes are often complex, and use familiar statistical analysis techniques—like clustering and regression—and apply them to much more extensive datasets with the help of newer tools.

In layman’s terms

Big Data Analytics uses efficient and advanced analytics techniques to collect, organize and arrange a huge amount of data sets (known as “big data”). These data can either be structured, semi-structured or unstructured from various sources, and in varied sizes.

Performing big data analysis will reveal information that would have been otherwise hidden. Information’s such as hidden patterns, correlations, market trends, customer preferences and other insights, that can help organizations make informed and better business decisions.

Big Data Analysis allows researchers, business owners and data analysts to make better, faster and more informed business decisions.

These days, most businesses and analysts now use advanced analytics techniques.

These techniques include, text analytics, machine learning, predictive analytics, data mining, statistics and natural language processing in order to gain new insights from previously untapped sources independently or together with existing enterprise data.

Big data analytics also helps organizations harness their data and use it to identify new market opportunities which in the long run leads to more efficient operations, smarter business moves, higher profits and satisfied customers.

Big data visualization image showing unorganized large data sets before analysis vs after big data analysis
Image visualization of big data unorganized and how it all gets organized after big data analytics

A Brief History of How the Term “Big Data” Came About

The term big data was first used to refer to increasing data volumes in the mid-1990s. Big data analytics which started in the 90s came into existence as a result of the need to respond to the increase in big data.

The concept of “big data” has been around for a very long time, even long before the term “big data”, was coined in the 1950s. Businesses were using basic analytics (i.e. examining major numbers in a spreadsheet manually).

Today, most companies now understand that if they capture all the data that streams into their businesses, they can apply analytics and get important value from it, hence, the need for big data analytics.

Early Use

At first, as the Hadoop ecosystem took shape and started to mature, big data applications were majorly the house of large internet and e-commerce companies like Google and Facebook which took advantage of big data analysis.

But between 2009 and 2010, healthcare companies, retailers, manufacturers and banks began to see the value of also being big data analytics companies.

How it’s being used today

Today’s advances in analyzing big data allow researchers to decode human DNA in minutes, predict where terrorists plan to attack, determine which gene is mostly likely to be responsible for certain diseases and, of course, which ads you are most likely to respond to on Facebook.

Data Analysis vs. Big Data Analytics: What’s the Difference?

The main difference between data analysis and big data analytics is the amount of data, speed and efficiency that big data analytics offers to most organizations and businesses, which is essential for today’s business operation. Big data analytics processes data faster than regular data analysis.

companies today process a large amount of data and conduct market research regularly to keep up with the latest market trends. Big data analytics makes the whole process of analyzing data faster and more efficient.

The ability to work faster, stay agile, and discover insights faster and efficiently gives organizations today a competitive edge they didn’t have before and also translates into immediate business decisions that can determine a win.

Importance of Big Data Analytics in Today’s Business Operation

Big data Analytics is indeed a revolution in the field of Information Technology as its use by most companies and organizations keeps increasing and improving year-in-year-out.

Big data has the properties of high volume, variety, and velocity as with the help of big data analytics, multiple operations can be performed efficiently within a single platform.

Below are a couple of reasons why Big Data Analytics is very important:

  • Big data analytics helps organizations to work with their data efficiently and use that data to identify new opportunities.
  • With the speed of Hadoop and in-memory analytics, combined with the ability to analyze new sources of data, businesses are able to analyze information immediately and make decisions based on what they’ve learned.
  • Big data tools such as machine learning and cloud computing help you stay ahead of the competitive curve through identification of opportunities and flaws in your organization’s practices.
  • Big data analytics gives you a full overview of all you have learned so far during the process of developing your company.
  • Big data analytics helps you make better decisions with confidence, based on an in-depth knowledge of what you know about your industry, marketplace and customers.
  • It helps various firms and organizations in developing cutting edge technologies that enable them to provide better customer service.
  • With big data analytics tools, multiple business strategies can be applied for the future success of your company which leads to smarter business moves, more efficient operations and higher profits.
  • Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data.
  • Big data analytics fills the growing demand for understanding unstructured data in real time
  • It is also important in handling the flood of unstructured data that defines today’s world which traditional data warehouses and relational databases cannot handle.
  • Big data analytics helps data scientists and statisticians to dig deeper into vast amounts of data to find new and meaningful insights.

Real Life Applications of Big Data Analytics in Various Industries

Big Data’s popularity has extended beyond the tech industry to include healthcare, education, governance, retail, manufacturing, BFSI, and supply chain management & logistics, to name a few.

Big Data Analytics can not only foster data-driven decision making, but they also empower your workforce in ways that add value to your business.

The use of Big Data analytics is very flexible to other fields as well.

With the use of big data analytics, there has been a large growth in multiple industries.

In what industries can big data analytics be used?

Big data analytics can be applied in the following industries:

In Banking

Big data search analytics helps banks make better financial decisions by providing insights to massive amounts of unstructured data. The information is available and analyzed when it’s most needed.

The process avoids reliance on overlapping systems. It also focuses on fraud detection using big data analytics.

In Technology

Big data visual analytics provides the insights researchers need to try more trials faster as clinical research trials commonly fail, even after using a lot of resources and time. With the use of big data visual analytics, automated solutions that affect speed and efficiency are provided.


Big data analytics lets hospitals get important insights out of what would have been an unmanageable amount of data as the volume of patient, clinical and insurance records in healthcare generates mountains of data.

When useful information is extracted out of structured and unstructured data, it results in better outcomes in patient treatment and organizational efficiency, hence, the need for big data analytics in the healthcare industry.

Manufacturing industry

Big data analytics is useful in preventative maintenance of equipment, such as detecting anomalies before a failure as the supply chains of manufacturing are complex and big data analytics allows manufacturers to better understand how they work thus facilitating advance problem solving in every area of the manufacturing industry.

Aviation industry

With big data analytics, airlines can enhance their operations with the meaningful insights which includes everything from flight paths to which aircraft to fly on what routes, since they are known for gathering a large volume of data that results from categories like customer flight preferences, traffic control, baggage handling and aircraft maintenance.

Government agencies

Government agencies face constant pressure to do more with less resources. Public safety agencies are expected to combat crime and budgets do not always rise in conjunction with crime rates.

With big data analytics, law enforcement agencies can work smarter and more efficiently. It also allows any government agency to streamline operations and better target resources for maximum results and helps in handling census data.

Retail trade

Retailers are able to understand customer behavior and preferences better than ever before with big data analytics.

Advanced analytics of customers gives retailers the ability to predict trends and create more profitable products; and also, transaction data based on buying habits allows retailers to cater to specific customer demands.

Big data analytics software and Tools

Analytics tools and software are widely used in providing meaningful analysis of a large set of data. This software analytical tools help in finding current market trends, customer preferences, and other information.

PS: If you would love a detailed list comparing top analytics tools, we have made a detailed guide and reviewed list of the best tools for data analysis here, after rigorous testing.

Some of the most popular tools for big data analysis are:

Power BI

Power BI, which is an acronym for Power Business Intelligence, is a Business Intelligence and analytics platform that serves to gather data from various sources, including big data sources, process, and convert it into actionable insights.


This is a cloud-based ETL solution and powerful on-platform transformation tool that allows you to clean, normalize, and transform data while also adhering to compliance best practices.


Talend is a big data analytics software that simplifies and automates big data integration in relation to allowing big data integration, master data management and checks its quality.


Tableau is a powerful big data analysis tool that can blend multiple data points. It offers customizable dashboards and real time data in visual methods for exploration and analysis.


Skytree is one of the best big data analytics tools which offers accurate predictive machine learning models that are easy to use and also empowers data scientists to build more accurate models faster.


This is a language for statistical computing and graphics. It is also used for big data analysis and also provides a wide variety of statistical tests.

Apache spark

This is one of the powerful open-source big data analytics tools which supports over 80 high-level operators that make it easy to build parallel apps and also provides built-in APIs in Java, Scala, or Python.

Microsoft HDinsight

This is a Spark and Hadoop service in the cloud. It provides big data cloud offerings in two categories, Standard and Premium. It provides an enterprise-scale cluster for the organization to run their big data workloads.

Splice machine

Splice machine is one of the best big data analytics tools that can dynamically scale from a few to thousands of nodes to enable applications at every scale.

Factors to consider when selecting a big data analytics tool

Some factors must be put into consideration when selecting a big data analytic tool and these factors include:

  • License Cost, if applicable.
  • The cost involved in training employees on the tool.
  • Quality of Customer support.
  • Support and Update policy of the big data tool vendor.
  • Hardware/Software requirements of the big data tool.
  • Reviews of the company.

References and resources

ITT Visual Information Solutions Team
ITT Visual Information Solutions Team

ITTVIS's team of researchers, writers, and editors dedicate hours to ensure every single piece of content on is accurate, accessible and actionable for both established and aspiring entrepreneurs. The team is made up of experts on a variety of topics from business management to everyday technology solutions to maximize productivity. They consistently optimize the website’s content to ensure that all of the information is accurate and up-to-date.