Big Data Analytics is a vital tool that enables organizations to make informed business decisions and overcome complex challenges. In this article, we will take an in-depth look at the importance, applications, returns, challenges, history, and growth of big data analytics.

History and Growth of Big Data Analytics

The term "big data" was first used in the mid-1990s to refer to the increasing volume of data. In 2001, Doug Laney expanded the definition of big data to describe the increasing volume, variety, and velocity of data being generated and used. These three factors have become known as the 3V’s of big data. According to a recent study, most routine and daily tasks will be automated by 2030. 

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

By 2011, big data analytics, along with Hadoop and various related big data technologies, had gained a firm foothold in the enterprise and in the public domain. Initially, big data applications were used primarily by large internet and e-commerce companies such as Yahoo, Google, and Facebook, as well as analytics and marketing service providers. More recently, a wider range of users have embraced big data analytics as a key technology for digital transformation.

Understanding the Value of Big Data Analytics

The Power of Expertise

Just as you turn to a trained doctor to diagnose your health problems, you need to turn to big data analytics experts to solve complex business problems. Subject Matter Experts (SMEs) or recognized Thought Leaders (KOLs) who are known for their success in their industry can create a roadmap and apply AI and analytics methods to set your organization up for success.

Advanced Analytical Techniques

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

Big data analytics helps organizations identify opportunities for improvement and optimisation using their data. Big data analytics offers significant benefits to businesses in areas such as customer acquisition and retention, focused and targeted promotions, risk identification and innovation.

Key Big Data Analytics Technologies and Tools

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

• Hadoop: An an open-source large-scale data storage and processing framework capable of handling large volumes of structured and unstructured data.

• Predictive analytics:  Hardware and software that processes large amounts of complex data and uses machine learning and statistical algorithms to make predictions.

• Streaming analytics: Tools used to filter, aggregate, and analyze large amounts of data stored in different formats or platforms.

• Distributed storage: Data that is replicated in a non-relational database, providing protection against node failures and low-latency access.

• NoSQL Databases: Non-relational data management systems that work well with large, distributed data sets and do not require a fixed schema, making them ideal for raw and unstructured data.

• Data lake: A large storage repository that holds locally formatted raw data until it is needed.

• Data warehouse: A repository that stores large amounts of data collected from multiple sources using pre-defined schemas.

• Knowledge discovery/Big data mining: Tools that enable organizations to mine large amounts of structured and unstructured data.

• In-memory data structure: Provides low data access and processing latency by distributing large amounts of data across system memory resources.

• Data virtualisation: Provides data access without technical limitations.

• Data integration software:  organizes large volumes of data across multiple platforms, including Apache, Hadoop, MongoDB, and Amazon EMR.

• Data quality software:  Cleans and enriches large data sets.

• Data pre-processing software: Prepares data for further analysis, including formatting and cleaning unstructured data.

• Spark: An open-source cluster computing framework for batch and streaming data processing.

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Big data analytics applications often include data from both internal systems and external sources, such as weather data or consumer demographics 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 via stream processing engines such as Spark, Flink, and Storm.

It seems that the need for effective data management and analysis will continue to grow. Big data analytics is not just a trend, but an essential tool for businesses to remain competitive in today's data-driven world. Organisations are accumulating more and more data, driving the need for effective data management and analysis. Big data analytics will continue to grow and play an increasingly important role in business success.

Sources KnowledgeHut ↩

TechTarget ↩

Forbes ↩

Simplilearn ↩

Tutorial: Big Data Analytics: Concepts, Technologies, and Applications