Big Data analytics is the process of discovering patterns, correlations, and other information in huge and complex data sets. It uses advanced analytical technologies, including machine learning algorithms, Artificial intelligence, and statistical methods, to transform data into valuable information. Big Data analytics is used in areas such as business, health, finance, and marketing as well as in research, supporting decision-making and process optimisation.
Big Data Analytics
Type of technology
Description of the technology
Basic elements
- Data sets: Data with high volume, variety, and processing speed (3V: Volume, Variety, Velocity).
- Machine learning algorithms: Methods used to automatically analyse data and draw conclusions from it.
- Data warehouses: Systems for collecting, storing, and processing large-scale data.
- Algorithms for statistical analysis: Methods that help identify patterns and correlations.
- Data visualisation: Tools for presenting analysis results in the form of charts and reports.
Industry usage
- Retail: Analysing customer behaviour to personalise offers and optimise inventory.
- Medicine: Predicting treatment outcomes based on medical data analysis.
- Finance: Fraud detection and credit risk management.
- Logistics: Supply chain optimisation based on operational data analysis.
- Marketing: Market segmentation and customer sentiment analysis on social media.
Importance for the economy
Big Data analytics is crucial for modern businesses, enabling them to better understand markets and customers and optimise operational processes. By analysing data, companies can predict trends, minimise risks, personalise offers, and make better business decisions. In the health, finance, manufacturing, and logistics sectors, Big Data supports the development of innovative solutions and increases operational efficiency.
Related technologies
Cloud Computing
Tools and platforms for managing and analysing large data sets, such as Hadoop or Spark.
Mechanism of action
- Big Data analytics involves collecting large data sets from a variety of sources, such as IoT devices, social media, transactional systems, and medical data. The data is then processed and analysed using advanced algorithms that identify hidden patterns, correlations, and trends. By using machine learning and statistical algorithms, analytical systems generate predictions and recommendations based on past data. The results can be visualised, facilitating business or operational decisions.
Advantages
- Better decision-making: Data enables more informed business and operational decisions.
- Cost optimisation: By identifying inefficiencies and optimising processes, costs are reduced.
- Personalisation: Customer data may be used to create personalised offers.
- Forecasting: Ability to predict trends and behaviour based on analysis of historical data.
- Increasing innovation: New business models and products can be created based on lessons learned from data analysis.
Disadvantages
- Privacy: Analysing large data sets can lead to violations of user privacy.
- Data security: Large-scale data processing involves the risk of data theft or loss.
- Incorrect data: If the data is incomplete or erroneous, analyses can lead to bad decisions.
- Complexity: Big Data processing and analysis can be complex and expensive.
- High infrastructure costs: Data storage and processing require advanced and expensive infrastructure.
Implementation of the technology
Required resources
- Data sets: High-volume and diverse data from a variety of sources.
- IT infrastructure: Computing power and data storage systems, such as Hadoop or Spark.
- Software: Tools for processing and analysing large data sets.
- Team of specialists: Data engineers, analysts, and Big Data specialists.
- Computing environment: Cloud platforms enabling distributed processing.
Required competences
- Machine learning: Knowledge of techniques used in analysing large data sets.
- Data analysis: Ability to interpret results and draw conclusions based on analysis.
- Programming: Knowledge of Big Data tools, such as Hadoop, Spark, and Python.
- Statistics: Ability to apply statistical methods in data analysis.
- Data management: Knowledge of techniques for collecting, organising, and processing large data sets.
Environmental aspects
- Energy consumption: The processing and analysis of large data sets require considerable energy resources.
- Emissions of pollutants: Data centre development can contribute to CO2 emissions.
- Raw material consumption: IT infrastructure requires raw materials for the production of servers and storage devices.
- Recycling: Upgrading infrastructure generates electronic waste, which must be properly managed.
- Water consumption: Data centres require significant amounts of water for cooling.
Legal conditions
- Legislation governing the implementation of solutions, such as AI Act (example: regulations on accountability for Big Data–based analytics).
- Environmental standards: Regulations for data centre energy efficiency (example: ISO/IEC standards for energy management).
- Intellectual property: Rules for protecting data processed in large collections (example: copyright related to data and analysis results).
- Data security: Regulations for the protection of personal data and sensitive information processed in Big Data (example: GDPR).
- Export regulations: Restrictions on the export of technology and data to sanctioned countries (example: international data transfer regulations).