What’s the Big Deal with Big Data Analytics? - Exabeam

What’s the Big Deal with Big Data Analytics?

March 31, 2022


Reading time
8 mins

Big data is changing the way we communicate, conduct research, and operate businesses. The large variety and volume of data available today holds the key to many advances in technologies and services, allowing organizations to learn from past and present behavior, and even predict future patterns using tools like SIEMs.

Big data allows both public and private organizations to optimize their operations to best meet the needs of their customers. However, this large amount of raw data can be overwhelming and sorting through a mountain of information can be a massive task. Analytics can help to make the data usable.

In this page you will learn:

What is big data analytics?

Big data analytics is the process of analyzing large, complex, and fast-changing datasets to uncover hidden patterns, unknown correlations, and other insights. Organizations collect large amounts of data, which they process using big data analytics tools to help inform business decisions.

The term “big data” appeared in the mid-1990s to refer to increasing data volumes. At the beginning of the 21st century, it expanded to include the growing variety of data being generated, as well as the velocity at which this data is created, updated and stored. By 2011, organizations started to use the technology to extract value from big data.

The importance of big data analytics

Before big data analytics, it took a long time to process large amounts of data. Businesses would gather data and run analytics to extract useful information. Today, fast and scalable analytics tools help companies decide their future without exhausting time and labor.

Tools provide insights that help businesses better understand the needs of their customers. They can help guide the development of new products to satisfy these needs.

Business benefits of big data analytics technologies include:

  • Opportunities for new revenue
  • More effective marketing
  • Customer service improvement
  • Faster and more efficient operations
  • Competitive advantages over rivals

Who uses big data analytics?

Nearly every industry has begun adopting this technology. Banking, manufacturing, government, and healthcare are among the biggest adopters today.


Banks can apply the results of big data analysis in real time and make business decisions accordingly. They use data insights for the following activities:

  • Spending patterns of the customers
  • Identifying the main channels of transactions (ATM withdrawal, credit/debit card payments)
  • Splitting the customers into segments according to their profiles
  • Fraud management and prevention
  • Risk assessment, compliance and reporting
  • Customer feedback analysis and application
  • PCI security


Big data analytics can make sense of the health data of an individual or population. This can potentially help to prevent epidemics, cure disease, and cut costs. Researchers and healthcare professionals can integrate patient data from multiple sources, such as hospitals, clinics, and surgeries. This allows them to identify the warning signs of serious illnesses as early as possible, making treatment simpler and less expensive.


Manufacturers taking advantage of advanced analytics can reduce process flaws to improve efficiencies and their bottom lines. Given the complexity of production activities that influence yield, manufacturers need to diagnose and minimize process flaws. Big data analytics provides such an approach.

Operations managers can explore process data and optimize production costs. The manufacturing sector is most affected by big data trends, due to the nature and amount of data that it produces. Most manufacturers are starting to discover the potential of big data tools, but some of the biggest manufacturers are already taking advantage of them.

Life sciences research

Life sciences researchers are under pressure to innovate faster than ever. Big data analytics offers the promise of discovering new insights and accelerating breakthroughs.

Life science research organizations have made great efforts towards becoming analytics-driven through the collection of large amounts of data from previous research activities. This data is analyzed to benefit their current research.

Big data analysis can have a significant contribution to life sciences research. This includes medical literature, patents, genomics, and chemical and pharmacological data that researchers would typically use in their work. Big data analysis in the areas of drug target identification and drug repurposing shows that big data analysis can accelerate the identification of new drug candidates and new drug targets.

Benefits of big data analytics

Big data analytics forecasts market trends, identifies hidden correlations between data flows, and extracts customers’ preferences from the details. These analytical insights help teams make quick, informed decisions and build satisfying products.

With the speed of big data analysis and the ability to analyze new sources of data, businesses can analyze information immediately and make decisions based on what they’ve learned.

Big data analytics can:

  • Reduce costs — By identifying more efficient ways of doing business, it can help companies lower their expenses.
  • Increase revenue — By analyzing customer purchasing behaviors, companies can find out which products are sold the most and accelerate growth based on these insights.
  • Improve customer service — By providing insights into customer needs so companies can develop products accordingly. It can also analyze customer sentiment regarding existing products and services and show where improvements are needed.
  • Detect fraud — Big data analytics systems that rely on machine learning can detect patterns and anomalies. This enables banks and credit card companies to spot stolen credit cards or fraudulent purchases, often before the cardholder even knows that something is wrong. Security is an area that can benefit from analytics capabilities, such as user and entity behavior analytics (UEBA).

Big data analytics technologies

Big Data analytics involves the use of analytics techniques like machine learning, data mining, natural language processing, and statistics. Large enterprises and multinational organizations use these techniques in different ways.

Data management

Big data analysis requires an organization to collect and store large amounts of data sets. To analyze all these data sets organizations turn to data mining. Data mining is the process of finding anomalies, patterns and correlations within large data sets to predict outcomes. All these processes should be managed through data management.

Data management gives companies the ability to collect a variety of data from multiple sources and process it in real-time. Data management allows organizations to achieve a global understanding of their clients, products, and operations. With proper management of big data, organizations are able to be more productive and efficient.

Machine learning

Machine learning (ML) enables computers to make decisions without being explicitly programmed to do so. In big data analytics, ML technology allows systems to look at data sets, recognize patterns, build models and predict future outcomes. It is also closely associated with predictive analytics. Machine learning makes it possible to automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results. ML is especially useful for speeding up threat hunting processes.


Many commercial big data solutions are based on Hadoop. Hadoop is a set of open source programs and procedures for storing data across multiple computers, allowing them to work in parallel. Hadoop was developed by the open source software non-profit Apache Software Foundation in 2006 to allow teams to read and write data faster.

NoSQL databases

Traditional SQL databases are not suited to big data because they organize data in a relational table, which takes a long time to sort. Non-Server Query Language (NoSQL) databases are non-relational. They store files in their native format and retrieve them based on a system of tags, which makes them faster than SQL databases. You can use a NoSQL database to create a data lake ━ a repository for large amounts of raw data.

Predictive analytics

Of the different types of big data analytics tools, the most sophisticated is predictive analytics. While descriptive analytics tells you what happened, and diagnostic analytics explains why it happened, predictive analytics attempts to determine what will happen next. It helps businesses to avoid risks when making decisions. Predictive analytics hardware and software solutions process big data to discover, evaluate and deploy predictive scenarios. This type of technology is often used in threat intelligence, to help organizations recognize patterns of attacks and prepare for them.


The greater the data, the greater the opportunity to obtain business insights. But too much data can be almost impossible to manage without an effective means of extracting usable information. This article offered an introduction to big data analytics, who’s using it, what its benefits are, and which technologies enable it.

With the growing complexity and sophistication of our global economy, the importance of big data will only continue to grow. Big data analytics has become an essential tool for businesses to improve their current products and services, and to explore future trends.

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