Main menu

Pages

10 Advantages Of Big Data


Advantages of big data is a newly invented term that expresses the huge amount of structured, semi-structured, and unstructured data that can be extracted from the information and used in machine learning projects for artificial intelligence and other modern analytics applications.


10 Advantages Of Big Data That Make Everyone Love It



Big Data and Internet of Things Technologies:


Such big data can come from countless different sources, the most important of which is the data generated by smart machines in smart homes, and real-time data sensors in the Internet of Things (IoT) technologies. which have become in huge numbers. Leave data in its raw form or preprocess it using data mining tools or data preparation software before analyzing it. Such as business transaction systems, customer databases, medical records, internet browsing records, mobile applications, social networks, aggregated results of scientific experiments.


Advantages of big data:


Improving Decision-Making Process:


Recent research and studies indicated that 36.2% of respondents said that better decision-making processes are the most important goal of big data analysis challenges related to them. Likewise, 84.1% had taken the initiative to work towards achieving their goals, and 59% had achieved significant and remarkable success with an overall success rate of 69.0%.


Increase productivity:


Another survey from (Syncsort) indicated that 59.9% of respondents use special tools to deal with big data technologies such as (Hadoop) and (Spark) with the aim of increasing productivity for business users. Modern big data tools allow analysts to analyze more data, more quickly, increasing their personal productivity. Additionally, the insights gained from those analyzes often allow organizations to increase productivity more broadly across the company.


Reducing costs:


Surveys from both (Syncsort) and (NewVantage) indicated that big data processing makes many significant contributions to organizations in reducing private expenses. And in an effective manner, nearly six out of ten big data tools from Syncsort helped them increase operational efficiency and cut costs, and nearly two-thirds of NewVantage respondents said they had started using big data to cut costs. Interestingly, however, only 13.0 percent of respondents chose cost reduction as a primary goal for big data analytics, suggesting that for many this is just a welcome side benefit.


Improving customer service:


As the surveys indicated that customer service development was the second most important goal of big data analysis projects, and many companies succeeded in achieving many successes in obtaining higher rates of customer satisfaction, including social media, systems Customer relationship management (CRM), and other customer touchpoints today give businesses a wealth of information about their customers, and they naturally use this data to better serve those customers.


Fraud Detection:


There is an important and distinctive use of big data analytics, which is the ability to know fraud methods, especially in the financial services industry, and thus one of the most important of these many advantages of big data analytics systems, which rely on machine learning, so it is highly effective in detecting fraud and anomalies patterns. These capabilities can give banks and credit card companies the ability to spot stolen credit cards or fraudulent purchases, often with the cardholder knowing something is wrong.


Increase revenue with big data:


When organizations use big data to improve decision-making and improve customer service, increased revenue is often the corollary. In the Syncsort survey, more than half of respondents (54.7 percent) said they use big data tools to increase revenue and accelerate growth based on better insights.


Increasing the speed of work:


Important reports and research have shown that 41.7% of users have indicated the great benefit of big data is working to raise the speed of business and information technologies, and it is one of the most important benefits of many big data, as many organizations use their big data to harmonize Its IT and business efforts are better, and it uses its analytics to support faster and more frequent changes in its business strategies and tactics.


Innovate Big with Big Data:


Innovation is the most important feature of Big Data technology, with a NewVantage survey showing that nearly 11.6% of CEOs are investing in analytics for this big data as a key step to getting innovations to power their market. They also believe that if they can access their innovations that are unique and that their competitors do not have, they may be able to break out of the rest of the market with new products and services.


Achieving the fastest in the market:


The CEOs also indicated that they have used big data to get their tasks done more quickly in the labor market. Only 8.8% indicated that this is the most important goal for big data, but 53.6% have started working towards that goal, and of those 54.1% have achieved some success. It is also possible that this advantage of big data will lead to additional benefits, such as faster growth and increased profits.


What is the difference between data and information?


Big data refers to a large volume of data information, Before we start defining big data, we must first understand what is data? How does it differ from information? In fact, data is the primary form of any content we produce, and to clarify that, if you have ten people, for example, and you measure their heights and record them on a piece of paper, this paper holds the data.

As for information, it is the product of any processing of raw data, and it means if you take the tallest of these ten people in the same example and get an arithmetic average for it, this average is information because it gives a useful measure. Whereas data are just numbers recorded on a piece of paper.


Data Sections:


Raw data can be divided into three types:


  • Structured data: data that is organized into tables or databases.
  • Unstructured data: It constitutes the largest proportion of the data, which is the data that people generate on a daily basis from text writing, images, video, messages, and clicks on websites.
  • Semi-structured data: It is a type of structured data, but the data is not in the form of tables or databases.


When did the term big data start?


Big Data was defined in 2011 by the McKinsey Global Institute, which was defined as any sophisticated term that describes a large volume of data that comes from countless different sources. Thus, with a size that exceeds the capacity of traditional database tools, and the huge flow of making big data available, especially with the emergence of artificial intelligence and the Internet of Things. Advances in digital computing and data science allow from the collection and storage of big data to the ability to manage and analyze this data.


What does big data consist of?


Big data consists of all of the structured information, which is a small part that may reach only 10%, compared to the unstructured information, which makes up the bulk of the remaining, and thus, the unstructured information is what all human beings produce from information, to include all messages in e-mail Videos, tweets, Facebook posts, WhatsApp chat messages, website clicks, and more. And this (Big data) has become a reality we live in, so the Oxford Dictionary adopted the term and added it to the dictionary with other new terms such as the tweet.


How large is this data?


Big data is one of the most important modern technical trends, which works to create effective values in various sectors by scanning and analyzing data, and over time, the data produced by users has grown rapidly for several reasons, including purchase data in supermarkets and commercial markets, and shipping invoices. Banks, health, and social networking sites.

With the development of artificial intelligence techniques, the most important of which is facial recognition and people of all kinds, as well as the Internet of things (IoT) and smart homes, they will be able to find more details and information about anyone, and with the increasing number of devices connected to the Internet, devices that we are not used to connect to the global network such as Cars, refrigerators and washing machines all contribute to increasing the volume of data produced.