The digital world has transformed society at economic, personal, and professional levels. At the centre of this transformation are data, generated every second by millions of interconnected devices.
According to Gartner, by 2025 there will be around 20 billion connected devices worldwide. All the information produced by these systems is stored, processed, analysed, and managed through Big Data technologies.
If you want to specialise in this field, the Master in Big Data Analytics at Universidad Europea provides advanced training in data management, analytics, and emerging technologies such as Data Science and Machine Learning, preparing you for one of the most in-demand areas of today’s digital economy.
Definition of big data
Big Data refers to a set of technologies, processes, and methodologies designed to store, manage, and analyse extremely large volumes of data. Its purpose is to transform raw data into valuable information by identifying patterns, trends, and correlations that support smarter decision-making.
Big Data is widely used across sectors such as healthcare, education, finance, marketing, environmental studies, and sport, where traditional data-processing tools are no longer sufficient.
Types of big data
To fully understand what Big Data is, it is important to recognise the three main types of data it works with:
Structured data
Structured data has a fixed format and is usually numerical. It is typically stored in relational databases or spreadsheets, such as SQL databases or data warehouses.
Unstructured data
Unstructured data has no predefined format and is often text-based or multimedia. Examples include social media posts, images, videos, audio files, and emails.
Semi-structured data
Semi-structured data combines elements of both structured and unstructured data. Some parts are organised, while others are not. Common examples include web server logs, XML files, and sensor data.
Key characteristics of Big Data
Big Data is commonly described through the 5Vs, although additional characteristics are often included:
- Volume: massive amounts of data generated continuously.
- Velocity: the speed at which data is created, collected, and processed.
- Variety: multiple data types and sources.
- Veracity: data quality, accuracy, and reliability.
- Value: the ability to extract useful insights from data.
- Visualisation: representing data in a clear and understandable way.
- Variability: managing data that changes in meaning or structure over time.
These characteristics explain why Big Data requires specialised tools, platforms, and professional skills.
What is Big Data used for? Uses by industry
Big Data helps organisations optimise processes, increase efficiency, and make strategic decisions based on evidence rather than intuition.
Advertising and marketing
Companies analyse customer behaviour, preferences, purchases, and interactions to:
- Target advertising more precisely
- Predict consumer needs
- Increase customer retention
- Develop new products and services
Education
In education, Big Data improves learning experiences by analysing student performance, personalising content, and reducing dropout rates.
Healthcare
Big Data enables personalised medicine and predictive analytics, supporting:
- Early detection of diseases
- Real-time medical alerts
- Electronic health records and telemedicine
Cybersecurity
By analysing large volumes of data in real time, Big Data helps detect unusual patterns and prevent cyberattacks using machine learning models.
Banking and finance
Financial institutions use Big Data to identify fraud, manage risk, detect suspicious behaviour, and improve customer profiling.
Transport and logistics
Through GPS data, sensors, and cameras, Big Data supports real-time traffic monitoring, route optimisation, and improved transport safety.
Meteorology
Big Data is essential for weather forecasting, climate change analysis, and early warnings for natural disasters.
Sports
In sport, Big Data is used to analyse athlete performance, optimise training, and design match strategies based on data insights.
Advantages of Big Data
Some of the main benefits of Big Data include:
- Better and faster decision-making
- Anticipation of market trends
- Cost reduction and operational efficiency
- Accurate data segmentation
- Identification of competitive advantages
- Greater accessibility to information
- Improved data security and control
Big Data: real-world examples
Many global companies use Big Data as a strategic asset:
- Amazon uses recommendation algorithms to suggest products based on user behaviour.
- Uber applies Big Data to calculate dynamic pricing in real time.
- Spotify analyses listening habits to create personalised playlists.
- Netflix uses viewing data to personalise content recommendations and improve user retention.
Conclusion: Essential to understand big data today
Understanding what is Big Data is essential in a world driven by data, automation, and digital transformation. Big Data is no longer exclusive to technology companies; it is a core component of decision-making across all industries.
In summary:
- Big Data transforms large datasets into valuable insights.
- It plays a key role in sectors such as healthcare, finance, marketing, and education.
- It offers strong career opportunities in analytics, technology, and data strategy.
If you want to build a career in big data in this field, the Master in Big Data Analytics at the Universidad Europea, along with other programmes within our masters in busness and technology, will equip you with the technical, analytical, and strategic skills needed to become a data professional ready for the challenges of the digital economy.