Big Data and Banks: What You Need to Know About Big Data

Big Data and Banks: What You Need to Know About Big Data

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Information technology and technology are constantly evolving and improving. Together with them, the volume of electronic information is increasing, which requires not only processing and storage but also the so-called analysis. What seemed big 5-10 years ago is now minuscule.

A huge amount of information is called a special term. Namely, Big Data. This is a whole area in IT that requires special attention. Every large and rapidly growing company needs a Data Specialist. But not only firms and retail outlets use big data. They are involved in government structures and banking activities.

This article will tell you all about big data, as well as how it is used in real life. Also, learn about new data management solutions.


Big Data is a huge amount of information that steps over Tetrabytes.

Materials in the electronic form of such volumes cannot be processed by conventional computers. For this, special machines are used. And for analysis, various technologies for processing large “dates” are used.

BigData can be:

  • structured;

This is completely different information in electronic format. Includes text and media files, tables, as well as charts, and databases. It is impossible to determine them in advance.

Big data – approaches, methods, and tools for processing huge volumes of structured and unstructured information, as well as significant transformation in order to obtain results. Used in the economy, banking, and not only.


It is worth paying attention to the fact that special technologies are used to work with big data, as well as people trained for this. They are called BigDat analysts or BigData-Engineer.

But the corresponding “object” has a number of properties. If they (or at least one of them) are missing, the relevant information cannot be attributed to BigDate:

  1. Speed. Data is growing exponentially and constantly. Their spawn rate is incredibly fast.
  2. Heterogeneity. These “objects” in the basis do not have homogeneity. These are completely different electronic materials that are collected from various sources.
  3. Volume. The size of the corresponding component does not allow storing materials on conventional media. The volume of information is constantly increasing.

These are the original properties. Big data used to be defined by the respective three dimensions. With the further development of technology and business, completely new features of these have appeared. Namely:

  1. Utility. Processed materials must necessarily be useful not only to the economy but also to the enterprises using them.
  2. Variability. Information flows have their own ups and downs. May be periodic and seasonal.

The analysis of electronic materials in huge volumes ultimately helps to filter out unnecessary, unnecessary information in a particular area. What remains is the data that helps you run your business efficiently.

Main Sources

Big data is collected from all over the world. But they have the main, “key” sources. It is customary to include:

  • Internet (social networks, media publications, blogs, and so on);
  • information received from readable devices;
  • corporate materials (archives, databases, transactions).

But relevant information can also be obtained from other sources. Big Data is collected constantly from all over the world. Even the search queries that users enter are related to the corresponding element.

Work Principles

There are various methods and principles of working with “date”. These include:

  1. Expandability. Horizontal scalability of electronic media is provided. This means that with an increase in incoming information flows, the capacity and servers for storage increase.
  2. Fault tolerance. Big Data technologies are methods and machines that can fail from time to time. But servers and devices designed to store the corresponding streams must be stable. Access to information is provided on an ongoing basis, regardless of the situation.
  3. Localization. Arrays are stored and processed within one specific server. This helps to save not only time but also resources for the exchange of information.

It is worth paying attention to the fact that a variety of methods are used to process data. Each of them provides its own nuances, features, and scope. This helps analysts quickly find the best solution for the further conduct of the company or government structure.

Working Methods

Not a single person can cope with Big Data without additional devices and computers. Special repositories cope with the constant flow of information in electronic format. First, big data enters the storage location, after which it is passed through special algorithms. They help to structure, as well as to get the most useful information for a particular area.

As a result of the performed manipulations, relationships are found that are used to predict and predict the future. The so-called artificial intelligence helps to cope with such operations.

In the process of “studying” data sets, analysts and specialists in the relevant field follow certain methods of work. These include:

  • machine-learning;
  • analyzes of social networks/sentiments/classification tree;
  • associations of learning rules;
  • mining;
  • genetic algorithmization;
  • regression analyses;
  • split testing;
  • crowdsourcing;
  • analysis of network activity.

All these techniques contribute to solving various complex problems for business, government needs, and entrepreneurship.

Machine Learning

This technique allows artificial intelligence to work without obvious signs of programming. Machines collect information that is entered by users and then offer similar content.

Machine learning helps:

  • determine what is spam and what is useful data;
  • make recommendations based on user preferences;
  • find out which products are considered the most useful and popular;
  • establish so-called yurtarifs.

This is only a small part of what the corresponding technique is used for. But there are other scenarios as well.


There are various methods for managing and working with big data. All sorts of tests are quite common.

In the case of the study of sentiment, you can:

  • based on customer feedback, improve the activities of the enterprise;
  • to come up with a variety of incentives and services that will begin to meet the needs of the public to the maximum;
  • based on information from social networks to understand what potential customers think.

The study of social networks also refers to the methods of working with “dates”. This schedule helps:

  • understand how people of certain segments of the population build relationships;
  • find out the popularity and significance of a particular person in society;
  • look for the shortest ways to connect several people;
  • understand the social structure of customer bases.

Statistical classification helps with:

  • automatic classification of documentation into categories;
  • division of organisms into groups;
  • development of various Personal Accounts and profiles on the Web.

With the use of regression analysis, it is possible to:

  • make predictions about how certain conditions affect the behavior of the public;
  • assess the level of customer satisfaction;
  • monitor the dependence of the cost of housing depending on the area of ​​​​space and the area in which it is located.

In economics and entrepreneurship, such techniques help to solve various problems and look for optimal solutions in production. But there are other scenarios as well. Some of them have distinguished themselves in particular demand in banking and the state security system.

Genetic Algorithms

Big data can be processed using genetic algorithms. This model is used for:

  • scheduling;
  • calculation of the optimal amount of materials in production in order to reduce costs without compromising quality;
  • creation of artificially creative software.

It is based on the principles of evolution, as well as inheritance, natural selection, and mutations.

Association Rules

Association rule research is a kind of method for discovering interesting relationships between different variables in large databases. Helps:

  • effective merchandising;
  • extracting visitor data from logs;
  • analyze bio information;
  • tracking system logs of real and potential intruders;
  • determine how purchasing needs and abilities are changing.

For the first time, such a technique was used by large retail chains. Big data was obtained through POS terminals.

Deep Analysis

Deep analysis is a derivative of the technology of working with ordinary structured information within small arrays. In the new conditions, more advanced mathematical algorithms are used. They are based on digitalization. This technique is better known as mining.


Big data can come from multiple sources at the same time. To successfully process these, crowdsourcing is used.

In the process of implementation, a huge number of people are involved. They help to look for ways out and solutions for certain circumstances.

Split Testing

This time it is necessary to select several components from the general array of information. They are compared with each other in the states “before” and “after” adjustments. This alignment is called A / B testing.

He helps:

  • determine the relationship between the two components;
  • find out the factors that have a greater influence on certain objects;
  • carry out a huge number of integrations with maximum accuracy.

Split tests contribute to the determination of parameter fluctuations. Finding such dependencies is extremely important for a business.

Network Activity

The analysis of a large amount of data can be carried out by the network method. It is found in the study of social networks, as well as the relationship between the owners of profiles, communities, public.

Through network activity, services are able to generate recommendations on age, interests, and other socially significant parameters.


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