Big Data Analytics came into picture at the commencement of 21st century. According to IBM, “we generate 2.5 quintillion bytes of data per day, so much that 90 percent of the data in the present world has been created in the last 24 months. These data are generated through sensors that are used to obtain climate information, posts on social networking websites, digital pictures and videos uploaded online, transaction records of online sales, and cell phone GPS signals, to name just a few.” Big Data plays an important role in the businesses in the present times. The need to maintain massive amounts of structured and unstructured data has become a persistent issue for large organizations.

History of Big Data

Towards the end of the 19th century, information overload was noticed. There was continuous information overload as population continued to increase in the US as 20th century approached. Around 1940, libraries adopted new storage tools that could address the rising demand of updated study material. In the consecutive year, brainy personalities named this overwhelming information overflow as “information explosion”. In the year 1944, the expansion of knowledge was predicted to be a huge storage and retrieval problem.

Four years later, Claude Shannon that was known as “A Mathematical Theory of Communication” published an article. It suggested that there should be a proper framework to figure out the precise data requirements for information transmission on noisy channels.

A physicist from Germany, Fritz-Rudolf Guntsch conceptualized virtual memory in 1956. At the end of 20th century, there was an unbelievable expansion of computing power and the World Wide Web. In the year 1997, NASA scientists Michael Cox and David Ellsworth used the terminology “big data” for the very first time and computer systems were finding it increasingly difficult to deal with the booming data.

Future of Big Data

According to CSC report, 4300% increase in yearly data is forecasted by the year 2020.

Big Data Analytics importance

Big Data Analytics enables the businesses to utilize the data effectively. As a result, the business can be more successful and profitable. Customers will also satisfy with the services. Big Data Analytics is important in the following ways.

1. Economical: Hadoop and Cloud based analytics used in Big Data analytics are very cost effective as far as storage of enormous information is concerned.
2. Time saving: Customers can easily interact with the business owners or the representatives in real time with the help of analytics. This goes a long way in reducing time by quick.
3. Facilitates good decision making: Information can be analyzed faster and that is the main reason why decisions can be made more quickly and in a better way.
4. Better products: In order to give the customers the maximum satisfaction, Big Data Analytics can help to develop new products and services that can cater the needs of customers successfully.

Trends in big data analytics

Big Data Analytics is a constantly evolving technology. The top trends when it comes to Big Data Analytics are discussed below.

1. Big Data Analytics and Cloud Computing: Hadoop was previously used to process the enormous information. Nonetheless, in the present times, many advanced tools can do the task of data processing in the cloud. Amazon Redshift is the latest tool that is used to manage structured data. It is user friendly and quite economical for expansion on virtual machines instead of manual machineries.
2. Data operating system for enterprises- Hadoop: Hadoop is being evolved as a data operating system that can be used for many purposes. By the help of this, it would be possible to operate different data and carry out analytics operations simply by plugging those systems into Hadoop after making it the file storage system.
3. Big Data lakes: It is suggested that you have to enter the data after designing the data set. Data Lake is also known as enterprise data lake or enterprise data hub. A Hadoop repository would be used to save those data sources and a data model would not be designed in advance. Rather than that, people would be able to use tools to analyze data by using the high-level definition of the existing data in the lake.
4. DevOps and DevOps tools: Cloud will ceaselessly expand with the constant increase in the competitiveness of the business by using latest tools. With the help of DevOps, the integration and deployment methods would be significantly enhanced. This would become possible by the adoption of machine data analytics services harnessing simple algorithms.
5. CISO (Chief Information Security Officer) and security operations: Analytics are generating newer intelligence around system because of which security operations would improve largely. In addition, DevOps teams would join hands with CISO and security teams to give rise to safer application systems.
6. Log Management: Managing log data is not a cakewalk even for technology professionals. Use of analytics in order to regulate the insights obtained from customers, applications and infrastructure logs is the sole way by which the complicated structure of cloud and hybrid-cloud can be dealt with.
7. Business intelligence value: In upcoming years, it is assumed that business intelligence would shift from rear view to continuous real-time intelligence.
8. IoT, cloud and Big Data amalgamation: Internet of Things will prove to be a deadly application for the cloud and it would drive the data explosion even more. As a result, cloud and data companies like Google and Amazon would develop IOT services in order to transmit data easily to the cloud based analytics engines.

Conclusion

The enormous volume of the data set is just a snippet of the Big Data equation. Data managers should make tactful use of Big Data Analytics to make the most of this amazing technology. Once an individual is well versed with the basics about Big Data Analytics, there is a whole world of Big Data Analytics and commercial solutions related to it are still waiting to be explored.