What Is Big Data? A Brief Guide to Processing Large Data Sets

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Never before in history have we had access to as much data as we do today. These data represent a vast knowledge base that proves invaluable when making business decisions, provided, of course, that the owner of the data knows how to use them. This is where Big Data technology comes into play, allowing for the analysis of large data sets and the extraction of meaningful insights.

History and Development of Big Data

The origins of Big Data can be traced back to the 1960s and 1970s when the first data centers were established, and relational database technology was created. However, it was not until the early 21st century that the magnitude of data generated by internet users, such as through Facebook posts or YouTube videos, became apparent. It was during this time that the first open-source platforms for storing and analyzing Big Data sets emerged, including technologies like Hadoop and NoSQL databases, which facilitated data management and reduced storage costs.

Effective utilization of Big Data is still ahead of us. This is thanks to cloud computing and graphical databases, which enable rapid and comprehensive analysis of vast amounts of data.

What Is Big Data? Values and Information Offered by Big Data

Big Data refers to complex data sets that reach businesses and are used to solve business problems that were previously challenging to address. The key characteristics of Big Data are:

  1. Volume: Big Data involves processing enormous amounts of unstructured data with low density. In some cases, this can be tens of terabytes, and sometimes even hundreds of petabytes of data for certain businesses.
  2. Velocity: Big Data is characterized by the rapid acquisition, processing, and utilization of data. Some products can operate in real-time or near-real-time.
  3. Variety: Big Data encompasses different types of data, including sound, video, or text. Furthermore, these data can originate from various sources such as the internet, mobile devices, email, social media, or smart networked devices.

Equally important in the case of Big Data are the value and accuracy of the data obtained. Uncovering the intrinsic value within data sets is a complex process that requires asking the right questions, recognizing patterns, making conscious assumptions, and predicting behaviors. The accuracy of data collected by a business is crucial for reliance on that data.

Big Data Analysis: Data Management Technologies and Methods

As mentioned earlier, Big Data involves enormous amounts of data from various sources and at high speeds. Traditional software solutions are inadequate for managing such data sets. This is why specially designed systems, tools, and applications are used for Big Data analysis.

Effective Big Data analysis comprises several stages, including:

  1. Collection: Unstructured, structured, and partially structured data are first collected from different sources and then stored in a repository, such as a data lake or data warehouse.
  2. Processing: At this stage, data is validated, sorted, and filtered.
  3. Cleansing: Data cleansing involves rectifying excessive, incorrect, incomplete, conflicting, or improperly formatted data.
  4. Analysis: Analysis is carried out using tools and technologies such as data exploration, artificial intelligence, predictive analysis, machine learning, and statistical analysis.

The goal of Big Data analysis is to identify and predict patterns and behaviors within the data.

Application of Big Data: When and for Whom?

Big Data technologies are primarily used by sectors that require rapid processing of massive data sets. As a result, these solutions are popular in industries such as telecommunications, finance and insurance, commerce, and manufacturing.

What can Big Data be used for? Analyzing large data sets is beneficial for:

  • Risk assessment.
  • Monitoring processes and activities.
  • Predicting trends.
  • Forecasting events and business outcomes.
  • Personalizing marketing and sales communications.

How is Big Data applied in practice?

  • Companies increasingly use it to predict demand for new products or services among customers.
  • Big Data helps in predicting potential mechanical failures, enabling more efficient machine maintenance and maximizing their operational lifespan.
  • Large data sets are used to train models in machine learning.

Challenges of Using Big Data

Let’s now take a look at the challenges associated with the practical application of Big Data.

First, there’s the issue of the sheer size of data sets subject to analysis. The problem is that data volumes continue to grow; we are talking about data doubling roughly every two years. Such rapid data growth makes it challenging for businesses to efficiently store this data.

Similarly, preparing data for analysis, including data selection and cleansing, is extremely time-consuming and can take up to 50% to 80% of the time before the data can be used at all.

Another challenge with Big Data is keeping up with the evolving technology, which changes rapidly.

Benefits of Big Data

While utilizing Big Data technology comes with several challenges, it offers long-term benefits, including:

  1. Comprehensive Answers: Big Data technology provides more comprehensive answers to questions, instilling greater confidence in the acquired data.
  2. Rapid Information Retrieval: Big Data enables faster access to detailed information, facilitating informed business decision-making.
  3. Cost Efficiency: Scalable storage systems reduce the costs associated with maintaining large data volumes, while simultaneously enhancing operational efficiency.
  4. Predictive Insights: Big Data analysis not only reveals customer trends but also predicts their behaviors. This allows for the creation of personalized products and services tailored to customer needs.


Thanks to Big Data, we gain a better understanding of potential customers and the environment in which we operate. Is this technology exclusively for the largest players in the market? Not at all. The right programs for analyzing large data sets can bring benefits even to smaller companies. It’s worth using them to design products and services even better suited to potential customers.