Big Data Technologies: Characteristics, Features and Advantages Big Data Technologies: Characteristics, Features and Advantages

According to IDC, the global data volume will be growing approximately by 61% annually, reaching 175 Zb by 2025. In our daily time, when information is being generated very rapidly, an important role is given to data processing and analysis. Big Data technologies are used in order to solve these problems. Let’s find out what Big Data is, its peculiarities and where it is used nowadays.

What is Big Data?

Big Data is a number of varied methods of processing structured and unstructured large data volumes used to solve various issues.

Big Data as a term appeared by virtue of Clifford Lynch, the editor of Nature journal, in 2008. The term was applied in special edition dedicated to active growth of the global data volume.

Despite the recent term implementation, big data existed before. Therefore, it didn’t use to be valuable, since it required the significant computational power, much time and high financial costs to study the information. With the advent of technologies for processing multi-gigabit data (Hadoop platforms), the situation has changed, introducing Big Data to various fields.

Big Data Characteristics

In accordance with Meta Group, Big Data can be described by so-called 3V characteristics: Volume, Velocity and Variety.

  • Volume – the big data size;
  • Velocity – the regular data updating and the constant data processing;
  • Variety – possibility for the simultaneous processing of different types of information: text, images, video, etc.

Based on the constant development of the Big Data technology, IBM eventually offered to supplement this list with a fourth V - veracity (plausibility). IDC added viability and value to the list.

Sources of Big Data and Why it Matters?

The main data generator is a man. Users leave an information trail, visiting various websites, making inquiries in search engines, placing orders in online stores, using IoT devices, etc.

Top key sources of Big Data:

  • The Internet: social networks, blogs, mass media, forums, websites;
  • Sensors, controllers: IoT sensors, DARs and DVRs, smart gadgets, smartphones, etc.;
  • Corporate ERP modules: archives, internal information of the company or organization, etc.

Due to Big Data Analytics, interpretation of different information, recognition of patterns and making a forecast became easier and much more descent. For example, Big Data helps to identify the area of the city requiring some products or services, shows what goods can be interested to the potential customers, and can predict disease outbreaks and even places of possible crime commitments. The more details are learned, the more accurate final result will be.

For instance, meteorologists take weather data over the past 100 years and analyze it. As a result, they reveal patterns, identifying the warm or cold period, as well as the monsoon season during the year/month. Based on this information, they can forecast the weather for the immediate future.

Methods of Big Data Analysis

Large amount of information has itself no value for a person, thus the data should be analyzed for its successful applying. In order to process the information, various tools are used, a list of which is constantly updated. Thefollowingtechniquesandmethodscanbeseen below:

  • classification – prediction of the segment-specific consumer behavior;
  • сluster analysis – the classification of objects into groups due to their common features;
  • crowdsourcing – data collection from multiple sources;
  • data mining – detection of previously unknown and useful information that will help to make decisions in different spheres;
  • machine learning – creation of self-learning neural networks that process the information quickly and descent;
  • signal processing – identification of signals against the background noise and their further analysis;
  • data fusion and data integration – the data converting into a single format (for example, transcription of audio and video to text);
  • unsupervised learning – identification of hidden functional connections in data;
  • visualization – the performance of analysis results in the form of diagrams and animations.

Scope of Big Data Application

Nowadays, Big Data is helping to solve various problems in different industries, such as retail, healthcare, finances, manufacture, energy, tourism, ecology, entertainment. Owing to the processing and analysis of the massive data, authorities, businesspeople, scientists, developers and other persons concerned are improving the quality of goods and services, and develop business as well.

Mobile operators use the massive data in Ukraine, monitoring the movement of subscribers. It helps them to cope with different challenges. Big Data technologies, for example, allow to outline the busiest Ukrainian roads. On the basis of received information, a number of highways are selected to be fixed primarily.

Big Data is also used extensively in retail in order to help companies to find areas with the target audience, determining the location of new stores. Such approach is widely used by Vodafone (Ukraine), ATB Market chains, Silpo, etc.

Big Data was applied in Kiev to select areas with overcrowded kindergartens, monitor the traffic flow and optimize the public transport lines.


With the development of technologies in the world, the amount of data has increased significantly. Big Data technologies allow to benefit from massive information efficiently and quickly. They allow state structures and business representatives to optimize various processes, and end users to receive better services.

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