The definition of Big data is usually quite simple to decipher – it is a huge amount of information, often haphazard, which is stored on some digital medium. However, the array of data with the prefix “Big” is so large that the usual means of structuring and analytics can not “digest” it. Therefore, the term “big data” is understood to mean also technologies of search, processing and application of unstructured information in large volumes.
In order to designate an array of information as “big” it must have the following features:
- Volume – data is measured by the physical size and space occupied on the digital medium. Big” refers to arrays over 150 GB per day.
- Speed, update (Velocity) – information is regularly updated and big data intelligent technologies are needed for real time processing.
- Variety – information in arrays can have heterogeneous formats, be structured partially, completely and accumulate haphazardly.
Two additional factors are considered in modern systems:
- Variability – data flows can have peaks and drops, seasonality, periodicity. Outbursts of unstructured information are difficult to manage and require powerful processing technologies.
- Data Value – information can be difficult to perceive and process, making it difficult for intelligent systems to operate. For example, an array of messages from social networks is one level of data, while transactional operations are another.
The principle of big data technology is to inform the user as much as possible about an object or phenomenon. The purpose of this data familiarization is to help weigh the pros and cons in order to make the right decision. In intellectual cars on the basis of an array of the information the model of the future is under construction, and further various variants are simulated and results are traced.
The sources of this data include:
- Internet blogs, social networks, websites, media and various forums;
- corporate information – archives, transactions, databases;
- reader readings – meteorological devices, cellular sensors and others.
The principles of working with data sets include three main factors:
- System extensibility. It is usually understood as horizontal scalability of media. That is, the volume of incoming data has increased – increased capacity and the number of servers to store them.
- Failover resistance. You can increase the number of digital media, intelligent machines in proportion to the volume of data infinitely. But this does not mean that some machines will not fail or become obsolete.
- Localization. Individual arrays of information are stored and processed within a single dedicated server to save time, resources and data transfer costs.
Here are some examples outside the business and marketing sphere of how big data technologies are used:
- Health care. More knowledge about diseases, more treatment options, more information about medications – all this makes it possible to combat diseases that 40-50 years ago were considered incurable.
- Prevention of natural and man-made disasters. The most accurate prediction in this area saves thousands of lives. The task of intelligent machines is to collect and process a lot of sensor readings and based on them to help people determine the date and place of a possible cataclysm.
- Law enforcement agencies. Big data is used to predict a crime spike in different countries and to take deterrent measures where the situation requires it.
Methods of analysis and processing
The main methods of analyzing large amounts of information include the following:
- In-depth analysis, data classification. These methods came from technologies of working with ordinary structured information in small arrays. However, the new conditions use improved mathematical algorithms based on advances in the digital sphere.
- Crowdsourcing. At the heart of this technology is the ability to receive and process billions of bytes of streams from multiple sources.
- Split testing. Several elements are selected from an array and compared to each other in turn “before” and “after” the change. А\В tests help determine which factors have the greatest impact on the elements.
- Prediction. Analysts try to set some or other parameters to the system in advance and further check the behavior of the object on the basis of large amounts of information.
- Machine learning. Artificial intelligence in the future is able to absorb and process large volumes of unsystematized data, later using them for self-learning.
- Network activity analysis. Methodologies of big data are used to investigate social networks, relationships between account holders, groups, communities.
Big data in business and marketing
Business development strategies, marketing activities, advertising are based on analysis and work with available data. Large arrays allow to “turn over” huge volumes of data and, accordingly, to adjust the direction of brand, product, and service development as accurately as possible.
What are the business benefits:
- Creation of projects that are highly likely to be in demand among users, buyers.
- Study and analysis of customer requirements with the existing service of the company. The work of service personnel is corrected on the basis of the calculation.
- Identification of customer loyalty and dissatisfaction through the analysis of various information from blogs, social networks and other sources.
- Attracting and retaining the target audience through analytical work with large amounts of information.
Prospects for development
In 2019, the importance of understanding and working with volumes of information increased 4-5 times compared to the beginning of the decade.
The integration of big data into small and medium businesses and startups came in mass:
- Cloud storage. Technology for storing and working with data in the online space allows you to solve a lot of problems of small and medium businesses: cheaper to buy a cloud than to maintain a data center, staff can work remotely, do not need an office.
- Deep learning, artificial intelligence. Analytical machines imitate the human brain, that is, artificial neural networks are used.
- Dark Data – collection and storage of not digitized data about the company, which have no significant role for business development, but they are needed in technical and legislative plans.
- Blockchain. Simplification of Internet transactions, reducing the cost of these operations.
- Self-service systems – special platforms for small and medium-sized businesses are being introduced, where data can be stored and systematized independently.