**2. The role of digital innovation in transforming the society**

The challenges the economy and society have to face are several: the term "Great Challenges" refers to "specific critical barriers which, if removed, would help solve a major social problem with a high probability of global impact through widespread implementation" [1]. The United Nations has 17 Sustainable Development Goals including no poverty, zero hunger, good health and well-being, quality education, and gender equality [2]. Digital innovation can help to address the problems and

criticalities connected to them, to the extent that new technological and scientific organizational approaches are adopted [3]. Digital innovation can, for example, support social action in favor of active aging: data from smart devices and home monitoring technologies can support individuals and families in tackling the challenges of aging at home [4, 5]. Data have become the central resource for the creation of value and for decision-making processes at every level (company, family, institution), therefore also for policies: this brings out several new questions on how data should and can be governed to achieve social beneficial innovations by avoiding or mitigating unwanted outcomes [6–8].

Data governance within and across organizations must be based on new approaches to manage and control data that is today distributed across organizations and ecosystems [9]. A huge mass of data can be interpreted in a biased way to serve the interests of one or a few parts of the social actors involved, therefore it is necessary to understand how the different parts collaborate or compete to manage data governance [10] in order to align diverse interests to address grand challenges through data-enabled innovations [11]. The risks of using big data go beyond cybersecurity or data interoperability [12, 13]. In fact, most of the data is located outside the proprietary boundaries of a company, therefore out of its control: they are located within infrastructures shared by collective actors, on digital platforms [13, 14]. It is very interesting to understand how companies manage data and seek their value at aggregate levels such as platforms, ecosystems, or inter-organizational networks [12].

The social challenges are therefore multiple, digital innovation can help to address the problems and criticalities connected to them, but how? What new organizational, technological, and scientific approaches need to be adopted to fully grasp its impact? Data is certainly the central resource for value-creation processes. Technological progress in the age of data, algorithms, and digital networks is based on 3 key characteristics: it is exponential, digital, and combinatorial [15]. From the economics of resource scarcity to the economics of critical resource abundance: data [15]: how to collect them at the aggregate level of digital platforms? How to use them safely and fairly?

The aim of the paragraph is to analyze the scientific debate on the potential for change of digital innovation for people, businesses, and institutions and to identify the new questions raised by the literature.

### **2.1 The digitization of society**

Digital technologies are an integral part of the lives of people, organizations, and institutions [16] being applied in different fields such as food production ('precision farming'), electricity ('smart grids'), housing ('smart homes'), healthcare ('health apps'), mobility ('smart mobility'), peer-to-peer services ('sharing economy') and banking ('online payments') [16]. Against this background, European politicians have recently embraced the notion of a 'green digital transformation' based on the widespread use of digital technologies [17].

Data is the raw material of the information age, like land in the agricultural age and iron in the industrial age [18]. The digitization of cognitive processes, commercial exchange, of communication, has exponentially increased the potential and ability to collect data. Almost any good can be digitized, or converted into many bits that can be archived on a computer and sent on the net: this makes the goods transformed into data, very special since they develop an economy based on abundance rather than scarcity, to which the human being has always been used to [19]. The advancement of AI generates huge amounts of data. This phenomenon, widely called big data, refers

### *Digital Innovation and Sustainable Development: Two Sides of the Same Coin DOI: http://dx.doi.org/10.5772/intechopen.112294*

to the integration of huge amounts of different digitized sources of complex data structures [20]. The term Big Data refers to this enormous mass of data that circulates on the internet infrastructure, which can be used to understand, analyze and predict phenomena and procedural developments in real time [21]. The real meaning of the concept of Big Data lies in the ability to process a huge amount of information in near real time so that it can be put to some use [19].

Big data drives value creation through the data generated by trading partners in upstream, downstream, and horizontal collaboration to investigate opportunities [22]. Big data and AI interplay everywhere: big data is the product of AI, and the advancement of big data also promotes AI's development [23]. Big data can alleviate decision-making cognitive biases, improving the adaptive response to problem occurrence. Despite a rapid increase in the number of studies on big data over the last 10 years, much research remains practice-driven, and academic-related research remains in its early stages [23].

The increases in data collection and the growth of processing power are two complementary elements: the more data is available, the more one invests in powerful computers and in plenty of memory to process them and process usable information. The more powerful computers are, the easier it is to collect large volumes of data and produce larger and more in-depth data sets. Big Data is evolving the process of collecting, managing, and processing data, thus generating new ways of accessing corporate information and new interpretative models [18].

The growing use of digital technology in the food, energy, health, and mobility sectors will increase the consumption of electricity and rare materials: it is not clear whether sustainable behaviors promoted by digital innovations will compensate for these extra costs. Digitalization, therefore, could be at odds with a transition toward sustainability unless the digital regime is refocused toward inclusive practices, democratic governance, and environmental regulation [16].

Over the past 10 years, AI has made enormous strides, thus making available a growing number of practical applications that are transforming the world [24]. In the academic literature, there is no unique and consensual definition of AI. AI-based solutions can be defined as systems with the ability to act intelligently, correctly interpreting external data, and using these objectives to execute particular tasks by a flexible configuration, even to the extent of reproducing human behaviors with cognitive, social, and emotional intelligence [25]. AI refers to machines performing cognitive functions usually associated with human minds, such as learning, interacting, and problem-solving [26]. Beyond the centrality of data for operating and competing in the age of AI [9, 27], the accessibility and ownership of organizational and customer data have been recognized as a fundamental advantage for firms to learn faster [27], and create innovation using AI [28]. Russell and Norvig [29] summarize the several definitions of AI systems into four categories along two dimensions: reasoning–behavior dimension and human performance–rationality dimension, that is:


AI systems should have the following capabilities: natural language processing to communicate in a natural language, knowledge representation to store information, auto-mated reasoning - the use of the stored information to answer questions and to draw new conclusions, and machine learning to adapt to new circumstances and to detect and extrapolate patterns [29, 30].

The fundamental accelerator of this progress is "deep learning", that is, an AI technique based on multi-layered artificial neural networks: the basic principles of deep neural networks have been known for decades but the latest striking achievements have been made possible by the confluence of 2 long-lasting trends in information technology [24]:


The blockchain is a technology based on the logic of the distributed database, i.e. a database in which the data is not stored on a single computer but on multiple devices connected to each other and connected to the network, which works through a communication protocol. The distributed network or Distributed Ledger (DLT) refers to "a set of systems conceptually characterized by the fact that they refer to a distributed ledger, governed in such a way as to allow access and the possibility of making changes by multiple nodes of a net" [31]. BCT has been described as a promising and disruptive technology, which, through its mechanisms, is able to change how value is extracted and delivered [32]. BCT is one of the technologies underpinned by the Industry 4.0 paradigm [33], defined as "a digital, decentralized and distributed ledger in which transactions are logged and added in chronological order with the goal of creating permanent and tamperproof records" [34] and refers "to a fully distributed system for cryptographically capturing and storing a consistent, immutable, linear event log of transactions between networked actors" [35].

Blockchain technology is based on decentralization and the uncontrollability of the system [36]. However, it has been realized how essential the need to authorize transactions is [37]: nodes need acceptance to become part of the network. Blockchain technology continues to operate in a decentralized way, supporting the system under authorization and authentication, eliminating the privacy issue [38]. BCT records information concerning the nature, quality, quantity, location, and ownership in ledgers [39]. Given the characteristics of this technology, BCT represents a useful tool to facilitate data sharing, enhancing its transparency, accountability, efficiency, safety, and traceability [40, 41] and protecting it from tampering, deletion, and revision [42]. Just as HTML has become the standard language for the web, the blockchain may have the technological ingenuity that will make it the protocol for trusted transactions. The web was essentially made from HTML. The great innovation consisted in making the web something visible, accessible, and easily navigable, thus allowing other innovations to stratify on this platform. The blockchain makes trusted transactions the protocol upon which much more can be built [18].

The introduction of blockchain would allow the sharing of information in a reliable and secure environment while ensuring its immutability. Every actor in a supply chain would no longer need to use paper documents or rely on central entities or third parties to certify the various information and documents produced during the process [43].
