What Big Data is and what big data is for

Big Data is large volumes of varied information that require special methods of processing and analysis. They are characterized by volume, speed of arrival and variety of structure.

Traditional ways of processing such information may not be enough. That is why companies use Big Data technologies to extract useful insights from complex data sets and make decisions based on them.

Big data technologies include software tools for collecting, processing and analyzing information that is hard to handle with traditional means.

Why big data matters for business

Big Data analysis helps companies obtain information that can be used to improve processes and increase revenue.

The main areas of use:

Cost optimization. Big data analytics helps improve business strategies, raise productivity and react to potential problems in advance. This can help reduce operating costs.

Developing products and services. By analyzing information about customer preferences, companies can adjust marketing strategies and take the audience’s needs into account when creating new products.

Decision-making. Big data processing tools let analysts get the information needed to assess a situation and choose next steps faster.

Types of big data technologies

In the source classification, Big Data technologies are divided into two categories: operational and analytical.

Operational big data technologies

Operational data arises in the course of everyday activity: online transactions, social media interactions and company operations.

This information becomes the raw material for further analysis.

Examples of sources of operational data:

• online booking systems for train, plane and bus tickets and for cinemas;
• online stores and marketplaces, including Flipkart, Amazon and Myntra;
• social networks and messengers such as Instagram, Facebook, Messenger and WhatsApp;
• information about employees and the activity of large international companies.

Analytical big data technologies

Analytical technologies are used to process information, evaluate performance indicators and support business decisions.

They make it possible to build reports from collected data and use the results of analysis to solve practical tasks.

Examples:

• stock market data;
• information for weather forecasting;
• medical records that help doctors monitor patients’ condition;
• databases of space missions containing detailed information about projects.

Main technologies for working with Big Data

The article singles out four areas of working with big data: storage, collection, analytics and visualization.

1. Data storage

Storage technologies include infrastructure designed to host, retrieve and manage large volumes of information.

Such systems let programs access the collected data, use it and process it.

2. Data collection

In the source material this section is described through data mining — the process of extracting useful information from raw data sets.

The raw data may be large in volume, change constantly and arrive at high speed. That is why special technical tools are used to work with it.

3. Data analytics

Big Data analytics involves cleaning, transforming and modelling information to obtain the results needed for decision-making.

Analysis methods help discover hidden patterns, relationships between indicators, customer preferences and statistical characteristics of the market.

The resulting insights can be used to assess a situation and plan further actions.

4. Data visualization

Visualization is the presentation of information in a graphical format.

The following are used for this:

• charts;
• diagrams;
• maps;
• other graphical ways of displaying data.

Visual tools help people take in large volumes of information and notice trends, patterns and deviations.

Conclusion

Big Data combines technologies for storing, processing, analyzing and visualizing large volumes of information.

With their help, companies can study data about their own activity, find patterns and use the results to improve business processes.