Big data is not just another buzzword, write Alexandros Gazis, PhD candidate at the Democritus University of Thrace, School of Engineering and Theodora Gazi, PhD candidate at Athens University Law School. So, which industry fields can benefit from its use?

It is a well-known fact that almost all human activity can be digitally traced. This is due to the so-called Internet of Things (IoT), i.e., the large network of interconnected devices that use smart sensors to rapidly fuse data streams in real time.

More specifically, a vast amount of data is generated daily through the widespread use of electronic devices, not only from smartphones and computers, but also from everyday activities, such as browsing web pages.

Consequently, the main task for IoT technology is not data collection, but data processing. In other words, there is an enormous quantity of information transmitted from multiple sources that must be processed simultaneously to provide users with accurate outputs.

Big data processing

To process the large amount of information generated through IoT, big data serves as an analytic platform. The term ‘big data’ describes the methods used to extract, transform and load information from complex datasets to analytics application software.

Big data is also interconnected with other scientific fields, such as advanced analytics, artificial intelligence (AI) and deep learning. Moreover, big data can assist decision-makers by providing valuable insight on who is where, when and doing what (the four Ws). To answer these questions, big data analytics is categorised into the descriptive, the diagnostic, the predictive and the prescriptive.

Based on the above, this article explores how society can benefit from big data analytics. Specifically, it presents big data applications in different fields, for example in business industries, marketing, humanitarian aid, healthcare and engineering; fields that can take advantage of technological innovations to improve the efficiency of their services.

Business Intelligence

Big data plays a key role in the field of business intelligence (BI), which refers to the data-driven, infrastructural framework for business decisions. BI employs business analytics, data mining and data visualisation tools to assist companies’ divisions in efficiently organising their workloads and to enhance the quality of services offered. For instance, big data is useful in logistics to monitor inventories and propose optimal methods for distribution of assets.

Furthermore, human resources (HR) departments may incorporate big data analysis to enhance learning from within the company itself.

Both external partners and employees should be asked to provide feedback regarding business activities, including managerial decisions, health and safety operations, staff satisfaction and corporate responsibility.

After anonymising this information, businesses can develop automated systems using big data and AI to evaluate the company’s performance and provide insightful and constructive input on its operation. AI-generated forecasts can serve first-stage candidate screenings, propose internal rotations and monitor divisions’ overtime, job satisfaction and burnout rates.


Among the fields where big data can most powerfully impact the quality of services is marketing. Specifically, advertisers use big data to understand the key target audiences for their products or services to maximise their marketing outreach and business profits.

Data collection is typically performed through questionnaires and satisfaction surveys, which include tailor-made questions to measure customer satisfaction, brand engagement and loyalty. Subsequently, big data can significantly contribute to the processing of this information and facilitate the study of consumer behaviour and psychology.

The added value of deploying big data analytics in marketing is the ability to combine survey data with open data and unstructured information from different datasets to identify trends and reach out to diverse target groups.

Big data allows marketers to process data generated from web sources, including search engines, social media networks (e.g., Facebook, Twitter and LinkedIn) and applications (e.g., WeChat, Instagram and TikTok). In other words, the web serves as a global dataset from which information can be extracted using sentiment analysis, deep learning, natural language processing, clustering analysis and artificial neural networks to recognise, detect and categorise patterns.

By better understanding consumers and their behaviours, advertisers and companies can identify the needs of each target group according to factors such as age, sex and region, to create and share targeted product recommendations.

Humanitarian Aid

Big data’s contribution to humanitarian aid is equally substantial. On the one hand, it acts as a tool to assist aid actors in programme and asset coordination. On the other hand, big data can identify the needs of vulnerable populations, enabled by applications developed to facilitate the reporting of incidents of violent oppression and human rights violations, as well as requests for medical aid and supplies.

Big data supports effective data management in times of crisis, including pandemics, natural disasters and civil conflicts. In cases when access to information regarding the populations affected by humanitarian crises is limited, big data serves as a crisis-response tool.

Specifically, big data can be used for multiple purposes, e.g., to support aid programming, forecast migration flows, process crowdsourced data and produce crisis maps to monitor incidents and needs. Its usefulness has been highlighted by its deployment in the COVID-19 pandemic; it has mapped the population’s needs and monitored the effectiveness of services offered.


Big data serves as a useful tool to support personalised medical care. This term describes a personalised delivery of healthcare based on predictions, tailored to each individual patient, using targeted therapies and medicines for both prevention and treatment. To achieve this, a combination of data from multiple sources is needed, such as health records, genetic testing, laboratory results, or radiology exams.

It should be stated that existing solutions, such as the computer-based patient record (CPR) are not a panacea for all issues. CPR systems solely perform descriptive big data analysis, meaning they are neither predictive nor prescriptive.

Moreover, wearable biosensors enable users to monitor their health at any time via the analysis of biological data, such as biofluids (e.g., saliva, sweat and blood). And, data fusion allows doctors to forecast patients’ health and remedies via medical imaging, real-time alerts, monitoring, optimisation of ER admission and preventative medicine analysis.

Lastly, technological advancements in healthcare provide a unique opportunity to develop complex big data applications using AI automation, such as medical room and equipment sanitation procedures, monitoring of vaccination efficiency and side effects, automated machine-handling methods for operating remote surgeries and early diagnosis of clinical manifestations and diseases.


Big data is typically applied in engineering to monitor a system’s status for generic faults and to provide optimal, sophisticated, yet easily deployable solutions. In civil engineering, big data applications are used extensively by construction and maintenance professionals, by placing sensors in key locations such as pedestrian crossings, bridge foundations, roads and bridges themselves. These sensors provide engineers with the necessary data to monitor the ‘health’ of the constructions.

Additionally, in electrical engineering, smart grids can analyse electricity consumption via processing consumers’ and distributors’ data via big data, as well as monitoring machine parts (e.g., turbines or inverter modules) of power stations, such as wind farms and solar
power plants.

Moreover, since mechanical and production engineers are involved in the design and development of manufacturing and production, they are responsible for the development of new products that assist designer managers and factory workers. Management automation tools can fuse data to evaluate employees’ working hours and provide suggestions in order to reduce operational and process costs.

Finally, robotics and systems professionals rely heavily on big data to develop self-driving cars and guarantee safe driving, accurate navigation and high-quality user assistance. Data can be fused from both sensor devices and software tools that detect mechanical errors, trace obstacles and road lines to avoid collisions via cameras and manage energy to reduce gas emissions.


Big data is not just another buzzword; the increasing power of IoT and the industrial revolution 4.0 support the development of large, interconnected networks that constitute a valuable source for big data analysis.

Irrespective of whether big data is applied in business intelligence, marketing, humanitarian aid, healthcare, or engineering, the examples presented demonstrate that its applications are of high importance for multiple industries.

The question is not whether, or how, big data is important in our everyday lives, but rather what we can achieve and when, if we utilise its principles to their full potential.

Recommended reading

  1. Taylor P. (2020) ‘The Challenges of Big Data’, ITNOW, vol.62 (1), pp.56-57, BCS-Oxford University Press.
  2. Fan J., Han F., Liu H. (2014) ‘Challenges of Big Data Analysis’, National Science Review, vol.1 (2), pp.293-314, Oxford University Press.
  3. Bello-Orgaz G., Jung J.J., Camacho D. (2019) ‘Social Big Data: Recent achievements and new challenges’, Information Fusion, vol.28, pp.45-59, Elsevier.
  4. Gazi T., Gazis A. (2021) ‘Humanitarian aid in the age of COVID-19: A review of big data crisis analytics and the General Data Protection Regulation’, International Review of the Red Cross, vol.102 (913), pp.75-94, Cambridge University Press.
  5. Galetsi P., Katsaliaki K., Kumar S. (2020) ‘Big Data analytics in health sector: Theoretical framework, techniques and prospects’, International Journal of Information Management, vol. 50, pp. 206-216, Elsevier.