Ethics and Trust to Big Data in Management: Balancing Risk and Innovation

Grzegorz Polok, Grzegorz Filipczyk, and Anna Losa-Jonczyk

University of Economics in Katowice


Management based on large data sets nowadays seems to be increasingly more common. Creating Big Data sets and striving for using them to extract the sought knowledge is a challenge and opportunity in the era of the digital economy. The users of digital technologies, both as consumers of digital content and services as well as employees and associates of the organizations, leave many digital traces while using common digital technologies. These collections are created from digital data streams (often already collected in the cloud) out of which — using analytical systems — knowledge can be extracted to be incorporated in business processes and made available in the organizations’ communication processes using digital media.

The purpose of this chapter is to depict the correlation between the use of Big Data in management, and the aspects of their ethical use in the context of risk and innovation in the organization. This chapter begins by discussing the place of Big Data in the concept of digital risk. Next, the problem of trust in Big Data and the building of reasonable trust in Big Data were characterized. This chapter ends with an analysis of the impact of solutions in the field of Big Data ethics on organizational innovation and the balancing of the security of the organizations and the digital risk and innovation in organizations that utilize the concept of evidence-based management. The conclusion indicates the importance of the considerations made for management practice and the possibilities of resolving the conflict between risk and confidence in data, and innovative management of the organization.

The Digital Risk Associated with Big Data

Digital technologies that are undoubtedly developing dynamically offer huge benefits for the companies. The list of benefits is long and includes, among others, higher business intelligence, faster product launch, better customer relationships, greater work productivity, and the greater profitability of the entire organization. However, as the pace of digital transformation increases, threats and, consequently, the risks of conducting business by organizations increase (Mayer-Schonberger, Cukier, 2013).

The business has always been and still is associated with risks. A new type of risk has emerged since organizations began using computers to process data, information, and knowledge: the digital risk (Davis, Patterson, 2012). In the past, the concept of digital risk was largely limited to the area of cybersecurity threats. The digital risk was related to the problems of hacker attacks and computer fraud. These threats remain ubiquitous. However, today, in the era of Big Data and Artificial Intelligence, new threats appear, e.g., the security of digital footprints extends beyond ensuring the right infrastructure (e.g., network, data center, cloud). Digital footprints leave data that is shared with customers, suppliers and partners, official and fake social media accounts, websites, and applications representing employees and the company. However, threats and risks also result from the effects of data centralization due to a change in consumer preferences and the widespread use of mobile devices. Uploading data into the so-called cloud means that several huge financial databases will be created in the near future. Yet, the process of creating Big Data does not only affect the area of finance (Zwitter, 2014; Herschel, Miori, 2017).

Hie digitization of business and the emergence of Big Data introduce completely new digital risk classes for individuals and organizations. They include, among others, threats such as:

■ Making incorrect, inaccurate assumptions not only in the data modeling process but throughout the data lifecycle

■ Data theft for unauthorized use

■ Creating and distributing fake news not only by people but also by software agents

■ Making decisions by the organization and its stakeholders based on the analysis of false data and fake news

■ Reinforcement of prejudice, discrimination, and exclusion (through algorithms) that reinforce social and economic injustice, preferring specific groups and discriminating against others

■ Unethical or even unlawful use of data insight

■ Use of the data for purposes that the individuals that originally disclosed them would not have agreed to, without their consent

These digital threats cannot be remedied solely by ensuring strong security through technological solutions. New technologies, including blockchain, aim to minimize fraud and increase data security. However, especially when using the Internet of Tilings to attract investors and customers to achieve organizational goals, organizations must use completely transparent, auditable, and rather unchanging solutions. Solutions covering the entire data lifecycle “trusted from the very beginning” are necessary, which contribute to building trust in Big Data and the organization.

Trust in Big Data

Young people who make use of the benefits of the Internet and mobile phones, with confidence and even carefree, leave a lot of digital traces. On their basis, among others, Big Data is created. Research shows that trust in Big Data collected in organizations is also high, although no analysis of their creation is carried out. Many users of digital technologies (the Internet) are not aware of how the data they create is being used or of the threats they generate. This, of course, also applies to the (security of) employees of organizations and the organizations themselves.

Mitigating internal and external threats is crucial for any organization. The new threats mentioned above require the adoption of solutions at the level of organizational framework (including the introduction of a code of data ethics), at the strategic level (including the introduction of a catalog of best practices), and at the operational level (including the application of best practices at every stage of the lifecycle of projects, products, and services). Tire analysis of threats and risks arising from (associated with) Big Data should be included in (integrated with) each implemented project, every offer, and every new business venture based on Big Data.

Digital trust is, in fact, a widely expressed and accepted belief that an organization is trustworthy and that the promoted image of the organization or brand is credible, secure, transparent, and consistent with reality in terms of its digital practices. A simultaneous introduction of this, relatively new in organizations, the perspective of the business building will provide the organization with the opportunity to simultaneously manage risk and build trust by consistently assessing how ethics are taken into account in decisions based on Big Data.

Hie threats mentioned above can be identified, managed, and effectively controlled when organizations, not ignoring technical safeguards, prioritize ethical practices regarding data and algorithms for their processing throughout the decision-making process (in other words: throughout the entire data lifecycle or the entire chain of their supply). Big Data ethical aspects should the organizations aim for their activities are to be effective, cannot be limited to the last stages of the cycle. The data lifecycle includes:

■ Recording data (and their origin) from sensors, systems, or people and obtaining permission for their use wherever possible

■ Saving data in a trusted area that is both secure and easily accessible for further processing

■ Combining different data sets to create a larger data set that is larger than the sum of its parts

■ Applying the knowledge acquired through data analysis to make decisions, make changes, or provide a product or service

■ Providing owners with access to data sets or new consumers with insight into the data

■ Researching and transforming data to extract information and discover new knowledge

■ Deleting data from servers to prevent their sharing or future use

Tire development of sound ethical control throughout the entire supply chain of data, information, and knowledge applies to all types of digital risk throughout the whole lifecycle of data used in the organization. With the right solutions for data ethics and algorithms at the corporate governance level, at the strategic management level, and the operational level, organizations can build digital trust. An alternative approach, thus leaving data and algorithms unsupervised, can permanently damage consumers’ confidence in the brand or stakeholders’ trust in the organization.

Building a Sensible Trust in Big Data in Organization Management

The data is merely the starting point for decisions and actions. Similar to a surgeon performing an operation (before they perform an operation), the manager plans, organizes, and makes changes to the organization. Similar to a surgeon, they not only remove “sick” cells and organs but also implement new (similarly to the surgeon - implant) and motivate others to accept the plan, undergoing implementation or already implemented changes (therapies). By analyzing the data, one seeks to extract from it the knowledge necessary to make decisions based on it and subsequently take actions to achieve the organization’s goals.

Organizations, considering data ethics in their activities, are able to improve their clients’ trust in the organizations and strengthen the legitimacy of their business activities (Crane et al., 2008). This is particularly important for organizations that have undergone a digital transformation and become publishers or participants of digital platforms and ecosystems. In the digital market, where consumers differentiate (discriminate) suppliers based on their ability to build trust, achieving a high level of trust by an organization increases the attraction toward the brand and becomes a strong distinguishing feature for companies. This applies to all industries and sectors of the economy - nowadays especially to the financial sector (electronic banking services, payments, insurance), but increasingly more to other industries, including media and tourism.

Data ethics affect not only the organizations’ activities. Organizations rely on external data. Therefore, trust in this data is an important problem for organizations. Their main source is data created on social media. It is obvious that fake news, especially created by bots, should not constitute the basis for the organizations’ decisions and actions. Including them in big textual data is a serious threat, and their detection and elimination is a serious challenge for the organizations. Their creation is, to a large extent, beyond the control of the organization and its managers. However, the rest of the large data lifecycle can be controlled. This allows it to be considered trustworthy and consistent with the principles of its ethical use in the management of the organization. This is related not only to the application of codes of ethics but also to corporate social responsibility programs (of the organization).

Digital trust is hard to build but surprisingly easy to lose. This makes it an increasingly more significant factor in differentiating organizations in the digital economy. Trust in the brand facilitates the development of the organization through product development, cooperation with partners, expansion into new markets, and increasing the share in existing markets. Hence, it translates into measurable financial results. Therefore, devoting attention to ethics in data, information, knowledge (and thus evidence) management brings financial benefits - it is not just an attitude for naive philanthropists.

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