What is big data and what is it used for?
Big data: main uses and applications
Nowadays, almost 18.8 billion connected devices share information over the Internet. It is forecast that revenues from IoT AEP platforms will grow at a compound annual growth rate of close to 31% between 2024 and 2029, reaching almost $250 billion in 2029. Big data analyses this "sea of data" to convert it into the information that is transforming our world.
The digital revolution is changing the economy, society and the way people interact with technology. At the centre of this change are the data generated by billions of users and connected devices in real time. According to PwC's Global Telecom Outlook 2025–2029 report, the Internet of Things (IoT) market is evolving towards a greater role for application enablement platforms (AEP) and sectors such as automotive and smart cities. It is forecast that revenues from IoT AEP platforms will grow at a compound annual growth rate of close to 31% between 2024 and 2029, reaching almost $250 billion in 2029. This growth in IoT will enable the development of new applications for both companies and consumers, with uses that are increasingly present in areas such as industry, healthcare, mobility and smart homes.
What is big data
Big data refers to the set of technologies designed to store, analyse and manage these massive amounts of data – a powerful tool aimed at identifying patterns amidst the chaos of the information explosion in order to design intelligent solutions. Today it is used in areas as diverse as medicine, agriculture, gambling and environmental protection.
Almost an endless number of applications: GPS systems can detect traffic jams in the area checked by a user and suggest alternatives; a subscription streaming TV channel has created the characters and plot of its most successful series by analysing the contents its viewers consume and prefer to watch; smart watches monitor the heart rate of millions of users and identify patterns that can anticipate to and prevent cardiovascular diseases; humidity sensors in crop fields plan the irrigation frequency, combining their data with the weather forecasts, and a long etcetera. Their applications have even reached the world of politics: Juan Verde, member of Joe Biden's President's Export Council and Spanish advisor to two U.S. Democratic Party campaigns stated that: "These are not the TV elections anymore; they are the elections of big data".
Although people sometimes use them as related concepts, big data, machine learning and artificial intelligence (AI) have different functions:
- Big data: enables the collection, storage and management of large volumes of data from multiple sources.
- Machine learning uses algorithms to identify patterns in that data and learn from them to make predictions.
- Artificial intelligence: utilises these models to automate decisions, generate recommendations or solve complex problems.
How does big data work?
The process of big data is based on different stages that enable data to be transformed into useful information:
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Data capture
Data is obtained via sensors, IoT devices, networks, digital platforms or business systems.
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Storage and processing
The information is processed using cloud infrastructure, distributed systems and platforms capable of handling large volumes of data.
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Advanced analysis
Algorithms, predictive models, machine learning and artificial intelligence enable the identification of patterns, trends and possible future scenarios.
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Practical application
Organisations use the results to improve processes, increase operational efficiency, personalise services or anticipate risks.
SEE INFOGRAPHIC: 8 key facts about big data and its future [PDF]
The eight key facts about big data and its future begin with the growth in professional roles: roles related to big data, data and artificial intelligence are forecast to grow by more than 30% by 2030. The second fact shows how these technologies will transform business models: 86% of companies expect AI and information processing technologies to have a significant impact on their organisations by 2030.
The third fact refers to the economic value of big data: the global market is estimated to reach $103 billion by 2027. The fourth fact focuses on the evolution of professional skills: 39% of current skills will change or become obsolete between 2025 and 2030 due to technological transformation.
The fifth figure highlights one of the main challenges facing businesses: 63% regard the lack of skills as a barrier to progress in their digital transformation. The sixth figure highlights the growth in the volume of information generated worldwide: analysts forecast it will reach 394 zettabytes of data by 2028.
The seventh statistic analyses the impact of big data and other technologies on employment: up to 78 million net new jobs could be created globally by 2030. Finally, the eighth statistic highlights the importance of continuous training: 77% of companies plan to train or retrain their staff to adapt to technological transformation.
Applications of big data (examples)
Key applications include:
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Smart grids
Real-time data analysis makes it possible to anticipate incidents, improve the quality of supply and manage energy resources distributed via smart grids more efficiently.
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Renewable energy
Predictive models help to forecast wind and solar power generation, optimise the operation of facilities and improve their maintenance.
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Predictive maintenance
Early detection of anomalies in assets and infrastructure enables action to be taken before potential failures occur and improves their availability.
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Customers and services
The analysis of consumption patterns facilitates the development of more personalised solutions tailored to users' needs.
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Cybersecurity and resilience
Identifying anomalous behaviour helps to protect critical systems and strengthen the ability to respond to potential threats.
One of the main applications of advanced data analysis is the study of consumer patterns. Social networks, such as Facebook, Twitter or Instagram, are a tool used by brands to learn more about their consumers and connect with them. Companies have also started to gather data from their consumers. A company specialising in big data and retail intelligence has installed 15,000 sensors in the shopping areas of 25 countries. Thanks to the data gathered with these sensors, they have detected that only 59% of customers entering a shop have bought something from the shop.
Digital transformation within businesses will continue to drive demand for professionals capable of managing and analysing large volumes of data. According to the World Economic Forum's Future of Jobs Report 2025, big data specialists are among the roles projected to see the strongest growth up to 2030. The report estimates a 30 to 35 per cent increase in demand for data-related professionals such as analysts, data scientists, big data specialists and data engineers, driven by the adoption of advanced technologies such as artificial intelligence.
Digital transformation companies leads to the generation of huge volumes of data that organisations do not know how to use and manage. And this is already being portrayed in the labour market. In 2024, big data specialists were the second most sought-after profile. Companies are now asking their candidates to have international experience, strategic vision, analytical capacity and adaptation to change as the main requirements.
Big data is also a working partner of another technological milestone: artificial intelligence (AI). They work together, as the millions of data processed by big data is necessary to train AI to make complex decisions that increasingly resemble the human cognitive process. AI then provides a set of tools and techniques to perform faster and more advanced analysis of that data.
Analysts forecast that by 2030 a total of 170 million new jobs will be created worldwide (leaving a net gain of 78 million, after taking into account the number of jobs displaced), and that among these, big data specialists will be among the fastest-growing roles in the technology sector. What’s more, 86% of companies state that AI and information-processing technologies will transform their businesses. Will we be ready to take that step?
Challenges of big data
The growth in data volume and the expansion of technologies such as artificial intelligence offer great opportunities, but they also present new challenges that we must address to ensure the reliable and sustainable use of information.
Among the main challenges are:
- Data quality and reliability: having accurate and up-to-date information is essential for obtaining appropriate results and making sound decisions.
- Privacy and data protection: managing large volumes of data requires ensuring data protection and compliance with regulatory frameworks.
- Cybersecurity: the rise in connected devices and digital systems makes it necessary to strengthen protection against potential threats.
- Algorithmic biases: data-driven models must be designed and monitored to avoid results that are unrepresentative or discriminatory.
- Specialised talent: Technological developments require professionals with skills in data analysis, artificial intelligence and digital management.
- Energy consumption of digital infrastructure: the growth of data centres and processing systems presents the challenge of moving towards more efficient and sustainable solutions.
- Governance and responsible use of AI: establishing clear criteria for control, transparency and oversight is key to harnessing the potential of these technologies.
Iberdrola: driving technological innovation
At Iberdrola, we integrate digital technologies throughout our value chain to accelerate electrification and move towards a smarter, more efficient and sustainable energy model. The advanced use of data and artificial intelligence enables us to optimise our processes, improve the management of our assets and strengthen the quality and resilience of the energy system.
Within the context of big data and our group, data becomes a key element in the operation of smart grids, renewable energy facilities and predictive maintenance, which helps us anticipate potential incidents and improves the availability of our assets. We also use these technologies to offer better services to our customers as well as to strengthen our cybersecurity capabilities by identifying potential risks and improving our ability to respond to threats.
We are also driving the responsible use of artificial intelligence and data, guided by criteria of security, control and operational continuity. We currently have more than 300 artificial intelligence projects underway, focusing on areas such as growth, productivity, operational efficiency, service quality and the resilience of the energy system.