Big Data and information systems in corporate governance

Track 5: Big Data and information systems in corporate governance


When it first appeared, the term Big Data referred to a phenomenon which, with the advent of Web 2.0 and the development of mobile tools, tablets and smartphones, has become explosive and targets the collection and use of data for social, scientific and business purposes. The phenomenon is generated by the accumulation of large quantities of inhomogeneous data, but whose value is strategic in that the information that can be extracted from it is useful for the management of organisations and processes. This phenomenon consists of three main dimensions, known as the “3Vs”: Volume of data, Variety of sources and types, and Velocity of growth. These data are from heterogeneous sources: company databases, social networks, the Internet of Things (IoT) to name just a few; data represented in different, often unstructured, ways, and increasing at an unstoppable rate. The Internet of Things (IoT) plays a leading role because, thanks to the spread of sensors, microprocessors and low-cost actuators, it enables the creation of networks that energise all business functions, acting as artificial nervous systems receiving signals continuously or at a fixed frequency, transmitting them, accumulating them, processing them for Advanced Analytics systems that can run business processes or, at least, develop scenarios that are useful for management.

Big Data are used in three fundamental areas:

1) Organisations and human resources: two of the various management applications in which Big Data brings innovations concern the hard component of an organisation – design of the organisational structure – and the soft component, made up of the human resources that perform its activities. There are still few scientific contributions on this, yet there is a strong focus on the use of Big Data in organisational processes and for the analysis of human resources. The use of data on the processes, timing, effectiveness and efficiency of individual practices or complex organisational models significantly increases an organisation’s awareness of itself. The data represented can lead to the improvement and redesign of organisational structures, even through the use of statistical techniques based on the optimisation of systems or probability analyses, enabling full exploitation of the predictive value of the analysed data. In terms of the soft aspects of an organisation, People Analytics is an emerging area of innovation which, although drawing on traditional principles of human resource management, represents a fundamental change in the ability of organisations and their leaders to understand, shape and strategically optimise their workforces. This change stems from the application of statistical techniques to collect, analyse and display complex data concerning individual employees, teams, divisions and the workforce as a whole, to obtain useful information. Such approaches can provide greater transparency about an individual’s performance, skills, aptitudes, weaknesses, threats and future potential, and can be useful throughout the employee’s life cycle, from the acquisition of talent to retirement. They can also be used to profile team dynamics and communication networks, to understand how they affect resilience and organisational outcomes. In addition, People Analytics techniques are increasingly used beyond work metrics in new areas that were previously beyond the reach of departments or HR managers, including the monitoring of an employee’s personal emails, social media activity, biometric data, and interactions with digital devices and apps. These can be presented as a means of supporting the “employee experience” or improving the “well-being of the workplace”, whereas, the fact is that they provide 24-hour information about the whereabouts, activation, mood, future health risks and social life of employees. However, these innovations are linked to major ethical challenges, such as protecting employees’ right to privacy and autonomy, and they raise broader ethical issues concerning the future of human work and employment in a digitalised and algorithm-led society.

2) The decision-making process aimed at creating customer value: Big Data have emerged as a disruptive technology that is producing effects by remodelling business intelligence, a sector involving the analysis of data and their use to improve decision-making processes in marketing, in order to create, deliver and capture customer value, with positive effects on the return on investment. In recent years, the extensive use in business of applications (including instant messaging, Facebook messenger and WhatsApp) and online platforms has increased the availability of data, which has progressively been used to analyse consumer behaviour, develop marketing strategies, forecast marketing trends, and produce new, more detailed and faster statistics. The use of Big Data is not limited to large multinationals, however; even SMEs, particularly those in sectors with a customer-centric approach, can find make good use of it. The triangulation of small data is a particularly important technique used in marketing, and is specifically applied to the measurement of consumer perceptions in marketing research, through surveys on samples of a few hundred respondents. Big Data, on the other hand, measures actual consumer behaviour on large samples of millions of consumers. This triangulation bridges the well-known gap between stated behaviour and actual behaviour.

3) In production, where Big Data is a pillar of the so-called fourth industrial revolution: Industry 4.0. Its aim is the re-organisation of the entire value chain. After the first three industrial revolutions of mechanisation, electricity and information technology, the introduction of IoT and Cyber-Physical Systems (CPS) in the factory is a catalyst for the fourth. At the heart of Industry 4.0 is intercommunication between the actors and the related objects involved in the production process. Robots and systems access all data in real time to react as quickly as possible to events, incidents or non-conformities. The data collection and analysis methodologies that are typical of Big Data play a fundamental role in factory 4.0. Big Data can be used to optimise production processes through:

  • Warehouse management: optimisation of inventories to avoid costs and waste. A system that coordinates purchasing choices, production methods and procurement optimises relations with suppliers.
  • Supply chain: IoT networks for the collection and management of information facilitate dialogue between all actors in the supply chain, regardless of the size and location of a business. Therefore, logistics and transport functions can also benefit from smart data management to synchronise the action times of actors on a supply chain.
  • Operations analysis: within a production unit, the networks of sensors located on production lines and machinery generate large quantities of data which, once processed by smart and distributed algorithms, generate information that benefits stakeholders and management. Not only production operations, but also predictive maintenance activities are facilitated by the advanced processing of information relating to process parameters.
  • Quality, environment and safety management systems: the availability of consumer preference data is the input for product and service design processes and subsequently informs the choice of technologies and process variables. The implications for the environment and workplace health and safety are the logical consequence of a process of optimisation of production units to meet the needs of consumers. Management systems need data for the definition of objectives and to assess to what extent they have been achieved. The collection of data that is the basis of the monitoring required by international standards can be facilitated by IoT and Big Data.

Therefore, this track therefore welcomes and encourages theoretical and empirical contributions on the following themes:

  • The role of data analysis techniques and the creation of agile organisations
  • The role of data in organisational and cultural change
  • Big Data and talent management
  • Use of data for climate analysis
  • Data-based human resources management strategies
  • The use of talents and human resources in the strategic management of the company
  • Ethics and respect for privacy in the use of personal data
  • New Key Performance Indexes related to the use of Public Administrations
  • Innovative Business Intelligence and Big Data solutions for improving marketing-related decision-making processes

Use of Big Data emerging from online platforms as data sources

  • Triangulation of small data with Big Data
  • Big Data for analysing consumer behaviour
  • Big Data for developing marketing strategies
  • Big Data for improving the competitiveness and performance of Small and Medium Enterprises
  • Big Data and social networks
  • Development of a data-driven theory of knowledge
  • Big Data and production and logistics management
  • Big Data and supply chain
  • Big Data and quality management systems


Publication of contributions

The best contributions presented at the conference may be fast-tracked to the following journals, subject to the ordinary refereeing process:

  • Management Control (Editor in Chief: Luciano Marchi)
  • Sinergie – Italian Journal of Management(Editors in chief: Gaetano Golinelli, Claudio Baccarani)
  • Other journals pending confirmation