Why big data analysis is important ?

Big data is becoming a more popular concept day by day and is interpreted as the beginning of a new era. While a great transformation is taking place with the creation of the big data concept, the perspectives gained by the institutions and organizations from the data are changing and coming to different points.

Information and communication technology collects data from web pages, blogs, social media sites and more to improve the technology of its customers. With this data collected, researches are carried out in many areas such as marketing, public relations and security. However, there are many different techniques in the stages such as understanding the value of this data pile, collecting this data, processing it, presenting it to the users, storing it, and analyzing it. Today it is expected to see a huge increase in speed, variety, capacity and support for the increase in technology and the production of new systems.

Big data is a field that deals with methods of analyzing, systematically extracting information, or dealing with datasets that are too large or complex to be handled by traditional data processing application software. Data with many states provides greater statistical power, while data with higher complexity can result in a higher rate of false discovery. Big data challenges include data capture, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data sourcing.

Current usage of the term big data tends to use predictive analysis, user behavior analytics, or some other advanced data analysis method that extracts value from data and rarely reaches a certain dataset size.

Big data analysis helps organizations leverage their data and use it to identify new opportunities. This, in turn, leads to smarter business actions, more efficient operations, higher profits and happier customers. By interviewing 50 different companies, the following data were found on why big data analysis is important:

1. Cost reduction: Big data technologies such as Hadoop (the library that allows us to process on multiple machines in parallel) and cloud-based analysis provide significant cost advantages when it comes to storing large amounts of data. They can also identify more efficient ways of doing business.

2. Faster and better decision making: Combined with the speed of Hadoop and in-memory analytics, combined with the ability to analyze new data sources, businesses can immediately analyze information and make decisions based on what they learn.

3. New products and services: With analysis, the ability to measure customer needs and satisfaction gives customers the power to give what they want. With big data analytics, more companies are creating new products to meet customers' needs.

How is Big Data Used?

Big data; It means we can use photos, text, audio, video and leftover data in ways that were not possible even a few years ago. This is the beginning of a revolution in business, in almost every industry. Companies now know when customers want to buy certain things. Big data also helps companies run their operations much more efficiently.

Even outside of business, big data projects help change our world in several ways:

1. Improving healthcare: Data-driven medicine includes the analysis of large numbers of medical records and images that can aid in the early detection and development of new drugs.

2. Predicting and responding to natural and man-made disasters: Earthquake data can be analyzed to predict when earthquakes may occur at a later stage, helping organizations learn about what they can do for survivors. In addition, Big Data technology is used to monitor and protect the flow of refugees on war zones around the world.

3. Crime prevention: Police forces are increasingly adopting data-driven strategies based on their own intelligence and public datasets to more efficiently allocate resources and deter them when necessary.

Different techniques used for analysis include statistical analysis, machine learning, data mining, intelligent analysis, cloud generation, quantum computing, and data flow processing. We believe that in the future, researchers will pay more attention to these techniques to solve big data problems effectively and efficiently.

References:

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