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Big data analytics is the complex process of examining large and diverse datasets to uncover hidden patterns, correlations, market trends, and customer preferences. It is a crucial tool for organizations to make informed business decisions and tackle complex problems. In this article, we will explore the significance of big data analytics, its applications, benefits, challenges, and its history and growth.

Just as you would want a trained physician to diagnose your health problems, you need experts in big data analytics to help solve complex business problems. Subject Matter Experts (SMEs) or Known Opinion Leaders (KOLs) who have proven success in your industry can apply AI and analytics methods to develop a roadmap and lead your organization to success.

Big data analytics is a form of advanced analytics, which involves complex applications with elements such as predictive models, statistical algorithms, and what-if analyses powered by analytics systems. It differs from traditional business intelligence (BI) queries, which answer basic questions about business operations and performance.

The big data analytics process consists of four main steps:

Many different types of tools and technologies are used to support big data analytics processes. Some common technologies and tools include:

Big data analytics applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information service providers. Streaming analytics applications are also becoming common in big data environments, as users perform real-time analytics on data fed into Hadoop systems through stream processing engines like Spark, Flink, and Storm.

Big data analytics has been embraced by a diverse range of industries as a key technology driving digital transformation. Users include retailers, financial services firms, insurers, healthcare organizations, manufacturers, energy companies, and other enterprises. Some examples of how big data analytics can be applied in these industries include:

The benefits of using big data analytics services include:

Despite the many benefits that come with using big data analytics, its use also presents challenges:

The term big data was first used to refer to increasing data volumes in the mid-1990s. In 2001, Doug Laney expanded the definition of big data by describing the increasing volume, variety, and velocity of generated and used data. These three factors became known as the 3Vs of big data. As per recent study most of the routine and daily based task will be automated in 2030.

The launch of the Hadoop distributed processing framework in 2006 was another significant development in the history of big data. Hadoop, an Apache open-source project, laid the foundation for a clustered platform built on top of commodity hardware that could run big data applications.

By 2011, big data analytics began to take a firm hold in organizations and the public eye, along with Hadoop and various related big data technologies. Initially, big data applications were primarily used by large internet and e-commerce companies such as Yahoo, Google, and Facebook, as well as analytics and marketing services providers. More recently, a broader variety of users have embraced big data analytics as a key technology driving digital transformation.

Big data analytics plays a crucial role in addressing complex business problems and helping organizations make informed decisions. Its applications, benefits, and growth have made it an indispensable tool in various industries. By understanding the challenges and choosing the right technologies and tools, organizations can harness the power of big data analytics to drive success and remain competitive in the marketplace.

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Big Data Analytics: The Key to Resolving Complex Business ... - ReadWrite