Big Data

Contents

• Definition of Big Data
• Characteristics of Big Data
• Importance of Big Data
• How Big Data Technology Works
• Data Analysis
• Big Data in Marketing
• Big Data and Personal Data

Definition of Big Data

Big Data refers to vast amounts of diverse information stored on digital media. This includes both general market statistics and personal user data: information about transactions, payments, purchases, movements, and audience preferences. The volume of Big Data can reach terabytes and includes text documents, images, and machine code. Analyzing such a massive dataset is impossible without specialized tools, as traditional methods and technologies cannot handle such a complex task.

Characteristics of Big Data

Big Data can be distinguished from regular data by several key characteristics known as "3Vs":

• Volume — the physical size of the data and the amount of storage space it occupies. The inflow of such data can reach 100 GB per day.
• Velocity — the speed at which the volume of information increases requires rapid processing and analysis.
• Variety — data comes in various formats: text, images, audio. They can be structured, semi-structured, or unstructured.

Some IT companies add a few more aspects to these characteristics:

• Variability — the flow of information can be unstable, complicating its processing.
• Value — the importance and complexity of processing the data, especially for businesses.
• Visualization — the ability to visually present the results of the analysis.
• Veracity — the accuracy of the data and the correctness of the methods used to obtain it.

Importance of Big Data

Big Data is applied across various industries: banking, insurance, retail, healthcare, logistics, science, and marketing. Wherever it is possible to collect and analyze large volumes of information, Big Data brings significant advantages.

Three global tasks that Big Data addresses:

• Building models — systematizing data and identifying causal relationships, making complex systems more transparent.
• Optimizing processes — automating routine operations, improving calculation accuracy, and saving resources.
• Making predictions — forecasting customer behavior and planning sales using analytics.

How Big Data Technology Works

Working with Big Data goes through several stages:

1. Data collection — data is sourced from various channels, including social networks, search engines, and devices.
2. Data storage — large volumes of information require significant storage capabilities, which can occur on proprietary servers, in cloud storage, or on public servers.
3. Processing and analysis — data is processed and analyzed using various methods and technologies.

Sources of Big Data can be divided into three main categories: social, transactional, and machine data. Social data is created by users, transactional data arises during operations, and machine data comes from sensors and devices.

Data Analysis

Data analysis can be divided into four main types:

• Descriptive analytics — describes the current situation using simple arithmetic operations.
• Diagnostic analytics — identifies patterns and causes of events.
• Predictive analytics — forecasts events based on historical data and algorithms.
• Prescriptive analytics — recommends optimal actions based on the analysis of various scenarios.

A variety of methods and techniques are used to work with Big Data, such as crowdsourcing, visualization, machine learning, and simulation modeling.

Big Data in Marketing

In marketing, Big Data is particularly significant as it allows:

• Segmenting the market and accurately dividing consumers into groups.
• Creating a profile of the target audience based on collected data.
• Personalizing advertising to increase click-through rates and the effectiveness of marketing campaigns.
• Predicting consumer behavior and their reactions to offers.
• Optimizing costs and managing inventory.

Big Data and Personal Data

Personal data constitutes a significant part of Big Data. This is information that can be attributed to a specific user. Companies must obtain user consent for the collection and processing of such data, which is often implemented through notifications and consents on websites. It is important to note that the security of personal data is a critically important issue, especially considering the frequency of data breaches.