Big Data Analytics

Big data analytics uses advanced analytics on large structured and unstructured data collections to produce valuable business insights. It is used widely across industries as varied as health care, education, insurance, artificial intelligence, retail, and manufacturing to understand what’s working and what’s not to improve processes, systems, and profitability.

It comprises vast amounts of structured and unstructured data, which can offer important insights when analytics are applied. Big data analytics does this quickly and efficiently so that health care providers can use the information to make informed, life-saving diagnoses.

In this guide, you'll learn more about big data analytics, why it's important, and its benefits for many different industries today. You'll also learn about types of analysis used in big data analytics, find a list of common tools used to perform it and find suggested courses that can help you get started on your data analytics professional journey.

The importance of big data analytics

Big data analytics is important because it helps companies leverage their data to identify opportunities for improvement and optimisation. Across different business segments, increasing efficiency leads to overall more intelligent operations, higher profits, and satisfied customers. Big data analytics helps companies reduce costs and develop better, customer-centric products and services.

Data analytics helps provide insights that improve the way our society functions. In health care, big data analytics not only keeps track of and analyses individual records but it plays a critical role in measuring outcomes on a global scale. During the COVID-19 pandemic, big data-informed health ministries within each nation’s government on how to proceed with vaccinations and devised solutions for mitigating pandemic outbreaks in the future.

Big data analytics tools

Harnessing all of that data requires tools. Thankfully, technology has advanced so that many intuitive software systems are available for data analysts to use.

• Hadoop: An open-source framework that stores and processes big data sets. Hadoop can handle and analyse structured and unstructured data.

• Spark: An open-source cluster computing framework for real-time processing and data analysis.

• Data integration software: Programs that allow big data to be streamlined across different platforms, such as MongoDB, Apache, Hadoop, and Amazon EMR.

• Stream analytics tools: Systems that filter, aggregate, and analyse data that might be stored in different platforms and formats, such as Kafka.