Big Data

Big Data or Big Data. What it is?

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Big Data is a modern term that refers to a large amount of structured and unstructured information that floods the business sphere every day. But the volume of this information is not the most important thing, how organizations interact with it is much more important. This data is analyzed and used to make decisions, as well as to build strategies for the development and strengthening of companies.


The term “big data” refers to data that is so abundant and complex that its rapid growth is difficult or impossible to handle using traditional approaches. Acquiring and storing large amounts of information has long been a stumbling block for analysts, so the concept of big data gained momentum in the early 2000s. Then Douglas B. Laney formulated the ” rule of three V” , which is now used everywhere, and then it was the basis of the concept of big data:

  • Volume (Volume) : Organizations receive information from a variety of sources, including the exchange, smart devices (“Internet of Things” – IoT), industrial equipment, video, social networks and a number of other resources. In the past, storage of this data has been a problem, but low cost storage on platforms such as  Hadoop  and so-called “data lakes” have eased this burden.
  • Velocity : With the development of the Internet of Things, the flow of information has flooded the business field at an unprecedented rate, and they must be processed in a timely manner. RFID tags, sensors and smart meters make it possible to deal with data flows in near real time.
  • Variety : Data comes in all possible formats, from structured, numeric data from traditional databases, to text documents, emails, video, audio files, and stock data.

We consider two more features inherent in big data to be relevant:

  • Variability : In addition to growth rate and diversity, the flow of data is unpredictable—it changes frequently and significantly. It’s not easy, but business owners need to know what’s trending on social media and how to curb seasonal and thematic data peaks.
  • Veracity : Reliability is the quality of the data. Due to the variability of sources, the process of linking, selecting, cleaning and transforming data in the system is difficult. Businesses need to build relationships and correlate a hierarchy of multiple data links into a single system. Otherwise, their data will quickly get out of control.


What matters is not the amount of data you have, but what you do with it. You can take information from any source and analyze it to find answers to the following questions:

  1. How to reduce prices?
  2. How to save time?
  3. How to optimize offers and develop your product?
  4. How to make wise decisions?

By combining powerful analytical approaches and big data, you can achieve business goals such as:

  • Determining the causes of failures, identifying problems and production defects in near real time.
  • Generation of sale coupons in accordance with the habits and characteristics of the buyer.
  • Recalculate the total portfolio of risks in minutes.
  • Fraud warning.


Big data is of great interest to manufacturers. The onslaught of the Internet of Things and its associated devices has created a powerful surge of information that organizations are collecting, structuring and analyzing. Big data is always an opportunity to make big discoveries – for any organization, big or small.

Deep learning requires big data because it allows you to separate hidden patterns from the answers to your questions without “fitting” the data. The deeper you study, the higher the quality of the data, the better the results.


Today, exabytes of big data open up countless opportunities to improve manufacturing. From more accurate forecasts to improved operational efficiency and a better customer experience, anything is possible when big data is used wisely. Analytics is the engine of change that affects the whole world. It is the key to improving living conditions, curing disease, protecting vulnerable populations and conserving resources.


Before big data can start working for a business, it is necessary to understand the path – sources, systems, owners and users – that big data goes through. Below are five key steps to becoming the Big Data Boss – structured, unstructured and semi-structured.


Ideally, a big data strategy is a plan designed to ensure that you can see all the available ways to ingest, store, process, distribute and use data within and outside the company. A big data strategy sets the bar for business success in the face of an abundance of information. When developing a strategy, it is important to consider the existence – and future development – of the business, its technologies, goals and initiatives. This calls for big data to be treated like any other valuable asset, and not like a second-rate application.


  • Data streams come from the Internet of Things and its associated devices, flowing into information systems from smart clothes, cars, medical devices, industrial equipment and more. This information can be analyzed right at the moment of receipt, deciding what should be kept from it, what should be discarded, and what should be further analyzed.
  • Social media data comes from sources such as Facebook, YouTube, Instagram, etc. This category includes a huge amount of image, video, voice, text and audio data suitable for marketing, sales and supporting functions. This data is often unstructured or semi-structured, so analyzing and processing it is a unique challenge.
  • Publicly available data comes from open source arrays such as, run by the US government, or the CIA World Factbook and the EU Open Data Portal.
  • Other sources of big data are lakes, supplier and customer clouds.


Modern computer systems are able to provide the speed, power and flexibility necessary for processing data arrays. In addition to reliable access, companies need methods to collect data, verify its quality and ensure data management, as well as data storage and preparation for analytics. Some data may be stored locally in traditional storage, but there are also affordable, low-cost ways to store data in clouds, lakes, and Hadoop.


With the help of high-performance technologies such as grid computing or in-memory analytics, organizations can use all their big data for analysis. Another approach is to pre-determine the relevance of the data. In both cases, big data analytics is a valuable experience for any company. Large amounts of data are increasingly used in modern analytical developments such as artificial intelligence.


Well-processed data that can be trusted will allow you to conduct high-quality analysis, on the basis of which you can make reliable decisions. Every business needs to harness big data and act on the information it provides to stay competitive. Make decisions based on analytical results, not on intuition. The advantages of such solutions are obvious. Data-driven organizations perform better, are more mature and more profitable.


Big data requires sensitive management and the support of advanced analytical technologies. To prepare big data that changes every second for analytics, you need to access, profile, clean and transform the data. With a large number of sources, volumes and growth rates, data preparation can take a huge amount of time, and professional help is indispensable.

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