Big data

A picture of a female presenting person wearing glasses standing in front of several computer screens.

Source: Artem - stock.adobe.com

Big data refers to large and complex data sets, particularly those created from sources of new data arising rapidly, in large volumes and with a high level of diversity [1]. These data volumes are so large that they can no longer be processed by conventional hardware and software. The problems posed by the handling of big data are solved by means of special distributed hardware and software, i.e. it operates in a network (cluster) comprising a large number of computers [2]. More recently, big data has been characterized by the "six Vs": "volume" (of the data), "velocity" (speed at which volumes of data are generated and transferred), "variety" (range of data types and sources), "veracity" (authenticity of the data), "value" (of the data to a business) and "validity" (consistency and quality of the data) [3].

To enable new observations to be made from the data, it must be processed by powerful analysis methods. Big data is analysed by the use of tools and technologies such as statistical analyses, data mining, artificial intelligence (AI), predictive analytics and machine learning (a subcategory of AI) [4]. Machine learning tools use data-driven algorithms and statistical models to analyse data sets in order to identify patterns, based on which conclusions are then drawn or predictions made. The algorithms learn from the data as they process it [5]. Depending on the nature of the data and the desired result, one of four learning models can be used: supervised, unsupervised, partially supervised or reinforcing [6].

Big data and cybersecurity are closely linked. Companies collecting large volumes of data are exposed to a high risk of cyberattack. Conversely, the combined use of big data and machine learning (AI) is an important tool in the fight against cybercrime, as it allows unusual activities to be detected swiftly and appropriate countermeasures to be taken [7].

Big data has an impact on almost all aspects of everyday life, business, science and society, and is regarded as one of the major drivers of innovation: in the world of work, significant changes will come about in numerous professions in coming decades, and new job profiles will emerge [8].


  • What is accelerating the trend, and what is slowing it down?

    The data volumes are growing rapidly: between 2012 and 2022, the volume of data generated globally increased tenfold [9]. In 2022, the annual volume of digital data generated or replicated worldwide amounted to around 104 zettabytes, with 284 zettabytes forecast for 2027 [10].

    Digitalization is responsible for this tremendous growth. Driving forces include the Internet and social media. However, the integration of digital measurement, control and regulation systems into a growing number of everyday objects is also a factor [11]. Smart homes, networked vehicles, wearables, smartwatches, smartphones, customer loyalty cards and numerous other interconnected devices and platforms constitute sources of data [12]. New technologies such as edge computing (fast and efficient data processing at or close to the data source), modern telecommunications infrastructures (5G/6G), the Internet of Things (IoT), augmented and virtual reality and cryptocurrencies also play an important role in the generation of data [13].

    For industrial companies, the availability of data of high quality is a key competitive factor for growth and innovation. Data-based business models and platforms are becoming increasingly important. This is driving an increase in the use of such applications in industry, particularly in the use of AI [9]. The information gathered with big data is also becoming increasingly important as a means for companies or public IT infrastructures to enhance their cybersecurity, by arming themselves against cybercriminals who themselves are leveraging large volumes of data.

    The research community also expects to benefit from analysis of big data in environmental and climate protection [14], medical research and diagnostics [15], the prediction of earthquakes and epidemics and the analysis of migration flows and traffic congestion [12]. Big data technologies can help public authorities to fulfil numerous tasks in the public sector, such as the provision of public services, environmental monitoring, urban development, smart city concepts and economic development. Applications for legislative impact assessment or analysis of public opinion are also possible. Further areas of application are public safety, the safety of critical infrastructure [16] and criminology [12].

    A study has shown that in 2022, 95% of companies worldwide had problems drawing insights from their (complex) data. Many of these companies still rely on time-consuming spreadsheet calculations for data processing, and report a lack of automation and shortages of skills, particularly advanced skills, such as in machine learning [17].

    In 2024, almost all German companies (98%) consider data analysis highly important for competitiveness, but only 37% of companies are already using big data. 48% are at least discussing or planning to use it. The obstacles to digitalization are attributable to a shortage of skilled workers, a lack of time, considerable demands on technical security and high outlay, at least at the implementation stage [18; 19].

    However, "big data as a service" (BDaaS) platforms can reduce the high initial investments by interested companies in the necessary technology on site by providing all the required resources in the cloud. BDaaS providers also take care of platform maintenance and updates, and the observance of provisions governing compliance and data protection. The resulting technical benefits and cost savings, particularly those of usage-based billing models, mean that smaller companies can also begin analysing large volumes of data [20]. BDaaS is a rapidly growing model: an average annual growth rate of 25.3% is anticipated, rising to almost 80 billion US dollars by 2032 [21].

    Strict data protection requirements [18], in particular, are an obstacle to the rapid spread of big data. The "Datenwirtschaft in Deutschland" (Data Economy in Germany) study, produced by the German Economic Institute (IW), shows that the General Data Protection Regulation (GDPR) is still causing legal uncertainty, even after several years’ application in practice. 85% of the companies surveyed cited grey areas of data protection legislation as an obstacle to greater commercial use of data. 73% of the companies cite the lack of legal certainty in the anonymizing of data as a specific example [22].

    A dearth of uniform and legally robust standards for the anonymization of personal data is one of the challenges facing companies. The GDPR lacks a positive definition of anonymization [9]. Where groups are formed following anonymization and the case numbers they contain are too small, it may be possible to associate the data with real persons. In this case, in the sense of the applicable legal requirements, the data may at best be considered pseudonymized, but not anonymized [16].

    Limiting of purpose as described in the German Federal Data Protection Act may also inhibit the use of data: where personal data are concerned, consent for the data to be used at a later stage for purposes other than those expressly stated at the time of collection is often still lacking. As a result, existing bodies of data can often be analysed only following anonymization, or with the fresh granting of consent. Both require considerable effort and are often almost impossible to achieve in practice [16].

    On the one hand, AI for the use of big data can help to reduce energy consumption, improve energy efficiency and counteract climate change. On the other, AI technology itself has a considerable CO₂ footprint. Training AI models with large volumes of data is particularly energy-intensive. Data centres and data transmission networks have each been found to account for 1% to 1.5% of total global electricity consumption. The associated greenhouse gas emissions amount to 1% of energy-related global emissions [23]. At 500 to 650 TWh in 2021, the combined electricity consumption of all the world’s data centres was approximately equal to the electricity requirements of the whole of Germany [24], and the trend continues to rise sharply. Legal requirements concerning the energy consumption of AI systems are enshrined in the EU’s AI Act, which will come into force in 2026 [25]. As yet, it is unclear what specific impact the resource efficiency requirements will have on the growth of big data.

  • Who is affected?

    The automotive industry, insurance companies, the chemical and pharmaceutical industries and the power generation and distribution sector were pioneers in the use of big data analyses [26]. Tech start-ups also make particularly intensive use of big data and data analytics [27]. However, the significance of big data is growing steadily, even for small and medium-sized enterprises [28].

  • Examples (in German only)
  • What do these developments mean for workers' safety and health?

    Big data is able to optimize processes and facilitate workflows in a range of industries, during analysis for example of machine data and production quantities, or of energy costs, running times, cycle times and inspections. In logistics, it can improve processes and control personnel deployment and capacity planning. In personnel management, big data can be used to determine whether employees are being deployed in accordance with their skills, and where potential exists to optimize working hours and make them more flexible [29], in order to relieve employees without adversely affecting operational processes. In the oil and gas industry, accidents are often caused by fires or explosions or exposure to harmful substances. In this context, the use of big data and smart software for intelligent gas detection by means of sensors can be crucial for early warning of hazardous situations [30].

    Big data and learning systems can support occupational safety and health institutions in their supervisory activities, for example by swiftly identifying companies exhibiting particularly high risks. The increased efficiency frees up time for consulting and monitoring tasks. The wider information base can also be used to organize these tasks more purposefully. Big data can be used to identify patterns in accident statistics, thereby improving risk assessment. This enables companies to target their investments in prevention better and reduce accident rates and costs [31].

    A global study (2023) of employees, half of whom were managers, the other half employees without leadership responsibility, found that huge volumes of data can place excessive demands on the employees. For example, 91% of managers say that the flood of data is adversely affecting the success of their businesses; 72% even stated that they no longer make any decisions on the basis of data [32]. Smaller companies in particular are more likely to find big data a burden: according to one study of the opportunities provided by big data for small and medium-sized enterprises, involving a survey of such companies and institutions concerned with data, many SMEs lack the expertise, the time and the right technologies to exploit the potential of their data [29].

    Extremely large and unstructured volumes of data (dark data) also pose a resource problem: expertise is often not available for the drawing of insights from data that is difficult to process. Although dark data is of no value without further processing, it occupies storage capacity and consumes energy resources. Such data is estimated to account for up to 90% of company data [33]. Much unstructured data presents particular challenges for industry (43%), as it is worth analysing only if the data is relevant and of high quality; otherwise it may be misinterpreted, with possible consequences for the company's success and its employees' job security [29].

    Misinterpretation of data may conceivably even give rise to physical hazards or accidents if it impairs the functionality of systems or machines. Data-driven production optimizes manufacturing processes; access to real-time data allows potential problems and failures to be identified in good time. It also enables maintenance and servicing work to be carried out before failures occur (predictive maintenance) [34; 35]. Conversely, data on safety-critical functions can harbour risks if it inadvertently prompts an intervention in running processes. This is possible in chemical production, for example, if the pressure, temperature and quantity of coolant are selected incorrectly and control over reactions is lost [36].

    Access to big data often permits specific individuals to be inferred, thereby raising issues of data and personal privacy. Big data analyses present a risk of anonymized or pseudonymized data being deanonymized. Where data is extensive and detailed, in particular, the probability is high that comparison of the characteristic data will make re-identification possible [37].

    Indeed, big data technologies may influence employers' decisions and thus affect employees’ employment and promotion prospects. Extensive databases can be used to train algorithms to analyse employee behaviour and make predictions. Data on employees can be collected from a range of sources inside and outside the workplace, such as the number of keyboard clicks, information from social media, the number and content of telephone calls, websites visited, physical presence, movements in the office, content of emails, and even tone of voice and body movements [38].

    However, it is difficult to reconcile analyses of behavioural data with the basic principles of data reduction and data economy laid down in the German Federal Data Protection Act (BDSG). In addition, valid consent to data being used for big data analyses cannot usually be obtained in practice. In most cases, even the permissions granted under the BDSG are not justified, owing to the employees' personal rights and an absence of proportionality [39]. Altogether, the situation also appears difficult for companies as well: a study conducted by the Fraunhofer Institute for Secure Information Technology SIT (2023) criticizes the fact that the current legal framework is insufficient for the safe processing of big data and anonymization of personal data, and creates legal uncertainty for companies [40].

    Big data not only requires appropriate data protection where employees are affected, but also places strict requirements on the handling of large volumes of data by the employees responsible. For example, data is to be protected over its entire life cycle in the sense of "data life-cycle management" (DLCM). This naturally applies in the first instance to sensitive data, which must be protected from the moment it is created [41]. Time pressure and insufficient personnel resources may lead to intensification of work and excessive burdens on employees.

    Additional workload may arise when significant volumes of data in companies are still stored in outdated systems or in analogue formats such as paper or film. To enable all the data to be analysed automatically, this information must be digitized and access to it centralized. This requires a digital strategy that takes account of document and information security, including the secure disposal of paper documents [42]. Where authorities and public institutions are affected, data must also remain usable for decades, irrespective of the form of storage used. However, the European Commission seeks to promote interoperable archiving and data management, and supports organizations in the long-term retention of information through its "eArchiving Initiative" [43].

  • What observations have been made for occupational safety and health, and what is the outlook?
    • The volumes of data in the digital society are growing rapidly. Proper handling of big data is therefore highly relevant across all industries.
    • Big data is inextricably linked to AI. Together, the technologies can create economic and scientific progress and innovation, and can modify processes and activities and make them more efficient.
    • The use of big data, for example to control production or safeguard hazardous processes, has the potential to protect employees and reduce their workload. The occupational safety and health community has the task of ensuring that these opportunities are taken advantage of, and are not thwarted by fears in the workforce of rationalization, surveillance and excessive demands. Finally, the way big data is used in the workplace raises questions regarding data protection and the safeguarding of employees’ personal rights.
    • For AI and big data to be used humanely in the interests of occupational safety and health, the fields of computer science, statistics, law and the social sciences must be more closely interlinked.
    • Big data also presents the German Social Accident Insurance with new means of optimizing internal processes and enhancing prevention services. A coordinated utilization strategy and cooperation can help to identify common fields of action, and to exploit synergies.
    • The individual German Social Accident Insurance Institutions and their labour inspectors, who are the direct point of contact for companies, must be equipped to provide expert advice for companies on the use of big data and AI, in compliance with occupational safety and health regulations. Small and medium-sized companies require particular support, as the handling of big data usually constitutes a major challenge for them owing to a shortage of personnel resources.
    • The Competence Centre for Artificial Intelligence and Big Data at the Institute for Occupational Safety and Health of the German Social Accident Insurance (IFA) supports the accident insurance institutions in the planning and implementation of specific projects. It is also a point of contact for policymakers, the research community and wider society.
  • Sources (in German only)

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