Chapter And Authors Information
The human society is permanently changing in all aspects. It has experienced many challenges like wars, political conflicts, and financial systems collapse. Today it is hurt by an old well-known enemy – the pandemic. In order to make the things better and even to survive current society is investing in market, production, communications and technological progress. The common base of all these fields is the digitalization on the Communication and Information Technology (IT – Hardware, Software, Algorithms, and Methods). Since the 50-ies of the 20th Century the IT is in permanent development but this has not impacted all layers of the society. In the last decade it is observed a new trend. The IT impacts almost all layers and the technologies of the society – communications, productions, management, analysis, forecasts, education, health, social and medical security). This chapter is concentrated on the basics and tools of digital transformation of business and trends in the business and production in the frame of this new paradigm.
Keywords: Organizational Change Management, Digital Technology, Data Analytics, Cloud Computing, Mobile Technology.
The Digital Age is a challenge for companies. It has transformed the way companies interact with their customers, changing the processes and business models.
The digital disruption can change businesses according to digital transformations. With the development of digital technologies, the classical system of doing business has been disrupted and many companies have to react to digitalization. The digital transformation brings innovations not only into the delivering of product. The companies are trying to rebuild a business approach to agile by using a new innovative business model. “Digital transformation radically changes the way we live, work and treat one another. By its scale, scope and complexity, the digital revolution has no analogue in our history” (Schwab, 2016).
Digital Transformation is the application of technology to build new business models, processes, software and systems that results in more profitable revenue, greater competitive advantage, and higher efficiency. Businesses achieve this by transforming processes and business models, empowering workforce efficiency and innovation, and personalizing customer experiences (Schwertner, 2017).
Digital transformation as part of Industry 4.0 is characterized by the following principles (Schwab, 2016):
- Flexibility: Dynamic business processes in networks;
- Reduction of execution time: large data, context, improved decision-making capabilities;
- Customization – customization for customers in terms of planning, configuration, order, design and production;
- More efficient processes and services as a result of large data evaluation;
- Flexible and adaptable organization, instead of a formally divided work organization.
Digital transformation uses the following technologies: cloud, mobile, social, big data analysis, internet of thing, (and industrial internet of things), cognitive science. It requires a strategic view and staged approach. Technology Integration of digital transformation elements uses the synergy effect of interactions between them.
The technologies big data, cloud, mobile, and social are critical parts of the digital firm infrastructure. These companies are more efficiency, have higher revenues, and a bigger market valuation than the competitors. According to Bloomberg agency highly digitalized companies Amazon.com Inc. and Apple Inc. are expected to report revenues of more than $ 100 billion in the fourth quarter of 2020 amid global pandemic.
John Chambers, the former executive chairman and CEO of Cisco Systems, predicts: more than one-third of businesses today will not survive the next 10 years. The only ones that will survive will turn their companies into digital, techie versions of themselves, and many of will fail trying (Bradley et al., 2015). The good business answer is to manage digital change in an incremental manner, adopting new strategies one at a time and integrating them with the staff slowly, but productively.
The chapter researches an organization’s digital transformation of business processes and the impact of digital transformation on the business.
2.Industrial Revolution – Development and Evolution
Industrial Revolution, in modern history, is the process of change of production technologies/processes (from an agrarian and handicraft economy to industry and machine manufacturing, digitization and automation, and digital transformation). The main features involved in the Industrial Revolution are technological, socioeconomic, and cultural.
Industrial Revolution was a set of macro inventions that allowed the accelerated development of micro inventions. The consequences of this are irreversible and change the face of business and society globally.
2.1. First World Industrial Revolution: Power Generation
It is in the field of mechanization in the 18th century. The first industrial revolution, which began in Britain around 1760 and lasted until about 1840, was based on the James Watt steam engine. In 1769 James Watt took out the famous patent for “A New Invented Method of Lessening the Consumption of Steam and Fuel in Fire Engines.” Watt improves the steam engine by creating an important element of it – condenser, separated from the working cylinder and this way completes the Watt engine. By the steam engine mechanized labor is introduced in the textile industry. There is a transition from hand tools to machine tools. The steam engine significantly supported the growth of factories, the centralization of production and the use of interchangeable parts. This period is characterized by mass production of steel, chemicals and petroleum products.
2.2. Second World Industrial Revolution: Industrialization
It is a period of the intensive use of electrical energy and mass production techniques in the beginning of 20th century. The period is based on two innovations: introduction of the assembly line in slaughterhouses in 1870 and electrification which drives mass production in a variety of industries. The development of the automotive industry in the early 20th century marks the beginning of another historical moment. Henry Ford introduced the assembly line for the auto conveyor model.
The rapid pace of development at that time brought to the fore the problem of personnel management. It is becoming increasingly difficult for one person to coordinate and control the activities of workers. The beginning of this period is also characterized by the lack of work staff of managers to distribute tasks and responsibilities, to manage workers and to solve tasks related to the work process.
The development of electricity and telecommunication channels – telephone, radio and later television, become the main engines that drive and connect the individual units.
2.3. Third World Industrial Revolution: Electronic Automation
It is a period of the widespread digitization and automation through electronic and IT systems.
Digitization is a converting data to a digital (computer-readable) format. Digitization describes the pure analog-to-digital conversion of existing data and documents. The data itself is not changed; it’s simply encoded in a digital format. Digitization can be efficiency benefit when the digitized data is used to automate processes and enable better accessibility.
“Digitization is of crucial importance to data processing, storage and transmission, because it allows information of all kinds in all formats to be carried with the same efficiency and also intermingled” (Wikipedia, 2017).
The third industrial revolution was associated with advanced telecommunications technology. Another focus is on renewables, which are combined with a number of other socio-economic and political forces. Economist and energy visionary Jeremy Rifkin said in an interview with Forbes magazine in 2011 “We need to bring the best entrepreneurial talent and scientific and technological know-how together, and work with local, regional, and national governments and their respective business communities and civil society organizations to transform the infrastructure of the global economy and prepare the world for the next great economic era” (Forbes, 2011).
2.4. Fourth World Industrial Revolution: Smart Automation
It is in the world of production, digitalization of the production processes – merging the real world and the virtual technologies through the use of cyber-physical systems; smart factories; artificial intelligence, robotics and other. If digitization is a conversion of data and processes, digitalization is a transforming organizational business processes for digital. As Gartner defines it, digitalization is “the use of digital technologies to change a business model and provide new revenue and value-producing opportunities; it is the process of moving to a digital business.” Digitalization moves beyond digitization, leveraging digital information technology to entirely transform a business processes: evaluating, reengineering and reimagining the way doing business.
Digitalization is the ‘organizational process’ or ‘business process’ of the technologically-induced change within industries, organizations, markets and branches. Digitalization of manufacturing industries has enabled new production processes and much of the phenomena today known as the Internet of Things, Industrial Internet, Industry 4.0, machine to machine communication, artificial intelligence and machine vision. Digitalization of business and organizations has induced new business models, new eGovernment services, electronic payment, office automation and paperless office processes, using technologies such as smart phones, web applications, cloud services, electronic identification, blockchain, smart contracts and cryptocurrencies, and also business intelligence using Big Data. Digitalization of education has induced e-learning (Khan, 2016).
The rise of new digital industrial technology, known as Industry 4.0, is a transformation that makes possible to gather and analyze data across machines, enabling faster, more flexible, and more efficient processes to produce higher-quality goods at reduced costs. This manufacturing revolution will increase productivity, shift economics, foster industrial growth, and modify the profile of the workforce ultimately changing the competitiveness of companies and regions.
The aim of Industry 4.0 is to increase flexibility and productivity. As such, manufacturers will be able to produce customer-specific components fast, cost-effectively and in small quantities, while automated processes will simultaneously ensure that individual component parts are reordered and that the order remains fully transparent within the company.
Advanced digital technology is already used in manufacturing, but with Industry 4.0, it will transform production. It will lead to greater efficiencies and change traditional production relationships among suppliers, producers, and customers – as well as between human and machine (Figure 1). According to “Boston Consulting Group”, nine technology trends form the building blocks of Industry 4.0: big data and analytics, autonomous robots, simulation, horizontal and vertical system integration, the industrial internet of things, cybersecurity, the cloud, additive manufacturing, augmented reality (BCG, 2016).
Figure 1. A history of industrial revolutions: Industry evolution with key developments, Deloitte University Press
Sources. Germany Trade & Invest, “INDUSTRIE 4.0—Smart manufacturing for the future,” July 1, 2014; National Academy of Science and Engineering, “Securing the future of German manufacturing industry: Recommendations for implementing the strategic initiative Industry 4.0,” (Deloitte analysis, 2016).
“INDUSTRIE 4.0 connects embedded system production technologies and smart production processes to pave the way to a new technological age which will radically transform industry and production value chains and business models.” (MacDougall, 2014).
The fourth industrial revolution, says Schwab, is more significant, and its ramifications more profound, than in any prior period of human history. He outlines the key technologies driving this revolution and discusses the major impacts expected on government, business, civil society and individuals (Schwab, 2016).
Over the past two decades, a new digital transformation unfolds. Increasing the capacities and joining the data transfer, computation and storage capacities and the extent to which digital technologies penetrate the economy led to transformation phase, based on Internet of Things (IoT) (Schwertner et al., 2018). According to a report by the International Telecommunication Union (ITU) and Cisco Systems, the current IoT technology is rooted in their potential to solve some of the world’s most pressing problems. Recently, Information and Communication Technologies (ICT) such as Mobile Units, Internet and Big Data have greatly increased their contribution to global development projects by improving end-to-end performance and service delivery (ITU, 2016).
The drivers of technological change (digital transformation) are:
- Cloud and mobile technologies;
- Big Data analysis and processing power;
- New energy supplies and technologies;
- Internet of Things, Industrial Internet of Things;
- Sharing economy, crowd-sourcing;
- Robotics, autonomous transport;
- Autonomous database management systems;
- Artificial Intelligence;
- Advanced manufacturing, 3D printing;
- Advanced materials, biotechnology (World Economic Forum, 2016).
Digital transformation impacts on the company work organization and design, the role of employees, business models, business processes and the environment/infrastructure. They will change significantly.
3.Organizational Change Management and Strategy Management
Digital technologies ‐ social, mobile, analytics and cloud ‐ are impacting organizations and most areas of human activity. Organizations need to integrate these digital technologies and their capabilities to transform processes, engage talent and drive new business models to compete and strive in the digital world (Schwertner, 2017).
Organizational change occurs when a company makes a transition from its current state to some desired future state. Managing organizational change is the process of planning and implementing change in organizations in such a way as to minimize employee resistance and cost to the organization while simultaneously maximizing the effectiveness of the change effort.
Digital business transformation is the integration of new digital technologies into all business areas, leading to fundamental changes to how businesses operate and how they deliver value to customers. It’s also a cultural change that requires organizations to continually challenge the status quo, experiment, and get comfortable with failure. A business may take on digital transformation for several reasons. In the pandemic, an organization’s ability to adapt quickly to supply chain disruptions, time to market pressures, and rapidly changing customer expectations has become critical. In these times of uncertainty during the pandemic, it’s more important for business to be flexible and to be prepared for an unexpected business circumstance. Companies are completely rethinking and changing their business models. Many of the leading companies are doubling the digital transformation of their business processes. The flexibility of business models, management and employees developed during the pandemic is crucial for stabilizing the economic performance of organizations. Building a stable business model that is not affected by such crisis situations is an approach to maintaining the business and its expansion.
Projects are being developed for digitilization and integration of entire value chains – from product design, parts delivery and production to logistics and after-sales service. Such an approach allows sellers in an online trading platform to execute personalized orders within days, using surplus available products. The period from production to delivery has been reduced from a few months to two weeks during the crisis.
In the past, huge and well-capitalized companies started business automation. The development of technology and business models now allows small or start-up companies to reap the same benefits of digital transformation and be successful.
According to International Data Corporation (IDC) spending on the digital transformation of business practices, products, and organizations continues “at a solid pace despite the challenges presented by the pandemic” (IDC, 2020). IDC forecasts that global spending on digital transformation technologies and services will grow 10.4 percent in 2020 to $1.3 trillion. That compares to 17.9 percent growth in 2019, “but remains one of the few bright spots in a year characterized by dramatic reductions in overall technology spending” (IDC, 2020).
In digital transformation, it is not enough to use as many technologies as possible. The strategy is moving, it must have a clear vision for the company’s development, and then be supported by the unlimited possibilities of these technologies that are related to the chosen strategy (Schwertner, 2017).
Successful digital transformation goes hand in hand with reengineering and optimization of business processes in the most appropriate way for the strategy. The digital transformation of the business seems different for different companies and it is difficult to give a strategy that is valid for everyone.
Only one platform is needed to connect all business units in the organization. Without a single platform, digital transformation can’t be achieved. The goal should be a seamless interaction with the customer at all touch points of business (Schwertner, 2017).
According to a study by the Massachusetts Institute of Technology, digital-transformed businesses are 26 percent more profitable than norms (MIT, 2013).
The terms digitalization and digital transformation are often used interchangeably. Many companies are still not striving for a real transformation of their business model, but only for the digitalization of existing processes that do not correspond to the current business situation and do not adapt flexibly enough. The real digital transformation occurs when there is a desire for better efficiency and changes in the processes are initiated at the structural level in the company.
Strategic management provides overall direction to an enterprise and involves specifying the organization’s objectives, developing policies and plans to achieve those objectives, and then allocating resources to implement the plans. Without the necessary strategy, too many companies are focused on technology rather than on the customer. It is necessary that organizational change, technology and data integration are addressed equally to achieve successful digital transformation of business (Schwertner, 2017).
Successful organizations must leverage strategy, culture and leadership to harness the potential of digital transformation of business. Organization’s digital strategy goal are: improve customer experience, increase efficiency, improve innovation, improve decision making, transforming the business. The organization is innovative compared to their competitors. Management has sufficient skills, experience to lead digital strategy (Schwertner, 2017).
Digital business transformation can only be successful if there is a well-founded strategy and leadership. Transformational changes are required to implement the digital transformation, which is related to strategy, leadership and organizational culture. Business practice research has shown that businesses with a successful digital-based business have a clear strategy, commitment to senior management with change, motivated employee involvement in the process, and focusing on changes to customer needs and interests (Schwertner, 2017).
One of the approaches to exploring digital transformation is the grouping of changes in three areas: consumer behavior, business processes and business models (Kane et al., 2015).
The transformation of consumer experiences in the use of products and services of the organization is expressed in the in-depth study of market segments and their behavior in the marketing space, consumer behavior and loyalty, interactive communication with customers in the sales process and many digital contact points between the organization and the customers.
Transformation of the business processes of the organization covers the automation of R&D (Research & Development), production and distribution processes. Digital technologies also enable the capacity of people to work at different levels in different functional areas. Increasing distance work at the employees’ home, decision-making on the basis of real customer relationship data helps to speed up decision-making on the availability of production in different production units.
Transformation of the business model is done through digital business modification, a new digital business and digital globalization. These processes take place by adding digital content to existing products and services and introducing new digital solutions.
Characteristic of modern man is his digital connectivity, mobility, existence as part of a larger digital community. These features, related to the business segment element of a business model, mean the digital business segmentation and customer relationship solutions (Schwertner, 2017).
Digital business transformation is seen as a set of 7 elements: business model, organizational structure, digital skills of employees, digitization of business processes, IT infrastructure, digitization of products / services, digital channels for interaction with clients (Kane et al., 2015).
Rapid changes in the digital environment entail shortening strategic planning times and shrinking to annual planning, careful handling of extrapolation of existing data, perception of the need for continuous change, decentralized management, etc.
“A business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural or other contexts. The process of business model construction and modification is also called business model innovation and forms a part of business strategy. Business model innovation is an iterative and potentially circular process” (Geissdoerfer et al., 2017).
The term business model is used to represent core aspects of a business, including purpose, business process, target customers, offerings, strategies, infrastructure, organizational structures, sourcing, trading practices, and operational processes and policies including business culture.
“A business process is a collection of related, structured activities or tasks by people or equipment in which a specific sequence produces a service or product (serves a particular business goal) for a particular customer or customers. A business process may often be visualized as a flowchart of a sequence of activities with interleaving decision points or as a process matrix of a sequence of activities with relevance rules based on data in the process. The benefits of using business processes include improved customer satisfaction and improved agility for reacting to rapid market change” (Weske, 2012).
During the pandemic, the good strategy of the digital company would be to continue to expand its activities in new areas and to continue to hire highly qualified specialists without reducing its staff.
The digitalized company can adopt a new strategy related to the changing IT market, the increased requirements of IT professionals and the development of new business sectors. The growth of the company requires optimization and improvement of internal processes, imposition of communication and transparency, as well as focusing on new strategies for staff development. Feedback from employees are very important and have great weight in the implementation of innovations and changes in business models.
The strategic tools of business to meet all the challenges and economic consequences of a pandemic around the world are related to strategic business thinking in times of crisis. Business strategy in time the pandemic includes agility and resilience.
The impact due to the world pandemic on private businesses in Europe forces the businesses to make performance under pressure, but agility is improving the performance.
Focusing on resilience is an important approach. The current operational and financial challenges that many companies are facing make this pandemic an agility stress test for their business models. Leaders whose companies have been able to combine flexibility with a focus on people, technology and their customers, can achieve better profitability and expect stronger performance in the future. Flexibility is the main advantage of companies that are successfully going through the current situation, and again it will provide them with the best possible starting positions after its end.
Companies are changing the way they manage the growth of their business, relying much more on freelancers and subcontractors and suppliers than on full-time employees. Managing the combination of remote and freelance workers requires new skills from the managers responsible for human resources.
Analysts describe as flexible companies that “have the balance and ability to shift their focus, priorities and resources to respond to changing circumstances”. These are customer-oriented, people-oriented organizations. They are technological and changing factors that reinforce each other. Strong leadership and a strong corporate culture are also particularly important. “More agility translates into greater resiliency for the future. The most agile private businesses are not only better able to navigate through the current pandemic by adapting to market environments. They are also transforming their business by focusing on sustainability through technology, and actively integrating innovation. That’s helping them emerge even stronger from the pandemic” (PricewaterhouseCoopers, 2020).
PricewaterhouseCoopers (PwC) found that successful business strategies during a pandemic include:
- Strategic decision-making based on customer focus – 97% of successful organizations in the study;
- Ability to make quick decisions – in 96% of successful organizations;
- Support for continuing education – in 94% of successful organizations;
- Focus on talent development and retention – in 93% of successful organizations;
- Use of technologies to increase efficiency and cooperation – in 90% of successful organizations;
- Strategic priorities such as the formation of flexible teams to work on specific projects, the use of real-time data for key decisions and the creation of multidisciplinary groups.
Very often the difference between successful organizations and others is the vision for the future. In the PwC survey, 58% of successful organizations plan to enter new markets and create strategic alliances; 39% of successful organizations plan acquisitions; 29% – cooperation with the startup ecosystem.
From a purely practical point of view, 75% of business leaders see great opportunities in increasing the use of new technologies, and 57% in presenting new products and services. Changing business models is also a priority for 57% of successful businesses, and 49% plan to rely on the development of research and development (PricewaterhouseCoopers, 2020).
The analysis of all the data leads to the definite conclusion that sustainable development, backed by flexible strategies, is the right path that can lead industries out of the pandemic crisis. That is why 81% of successful businesses point to sustainable business practices as the main pillar for market recovery after the pandemic.
According to the 2018 Harvey Nash/KPMG CIO Survey of more than 4,600 CIOs, the CIO’s top operational priority is “improving business process.” But among CIOs at digital companies the CIO’s top operational priority is “developing innovative new products.” Rather than focusing on cost savings, IT has become the primary driver of business innovation. Embracing this shift requires everyone in the company to rethink the role and impact of IT in their day-to-day experience (KPMG, 2018). IT department staff are driving digital transformation strategy, but everyone in the company work of implementing and adapting to the massive changes that go along with digital transformation.
4.The Impact of Digital Transformation on Employees and Customers
Digitization transforms the business and the way of work, changes the boundaries of production, distribution and consumption. These trends are opportunities such as the emergence of new products, processes and techniques, as well as threats to employers and employees who need to adapt the organization of work, the distribution of work and skills. Now, the overall effects on the labor market are dominated by the global pandemic, which requires telecommuting for employees and managers in digital companies.
The impact of digitization on the labor market manifests itself as new requirements for new skills in the implementation of new engagements. The employees have to be trained and re-qualified or replaced by others who already have the needed abilities. Digitization creates both new jobs and makes some of the already existing redundant. Wider use of ICT (information and communication technology) and new digital technologies such as data analysis, artificial intelligence (AI), 3D printing, cloud and mobile technology, the Internet of Things (IoT) and robotics are changing labor markets. But technology innovations are not new to the world. Some examples can be found in the past when, for example, new technologies like steam engines and electricity also shake the labor market. Interaction between people in work processes has an important role in the distribution of work. Many people work within business organizations. In the digital transformation, this field of work is changing, with work platforms for mediating between individuals online without people or organizations intermediating outside of this platform.
Research has shown that digital transformation will contribute to the polarization of the labor market. This is most likely to be felt in low- and medium-skilled jobs where automated processes can replace labor. In the future, the demand for highly qualified professionals with digital skills, entrepreneurial skills and creative thinking is highly appreciated.
Digital transformation changes companies from within, influencing organizational structures and management strategies, as it also influences externally in their interaction with customers, partners, and competitors.
Developing new technologies and increasing dependence on new business models continually reduces transaction costs, redefines business processes in trade, and optimizes business organizational structures.
Reducing transaction costs in most cases leads to the entry of new competitors and market dynamics, and new entrants often have other business models. Technological changes can generate additional economic activity, either by creating new products and services, or by improving existing ones and redirecting them to new market segments. New technologies also aim to improve the efficiency and effectiveness of employees in the organization. (Kane et al., 2015).
Digital transformation and related business platforms dramatically alter the work organization, lead to significant changes in job retention and labor force specialization. Digital transformation seeks to maximize capital assets by increasing the intensity of labor in the mainstream. This substitute effect, however, is accompanied by a compensatory effect that leads to redistribution of tasks between people and robots/machines/software within the same companies and between sectors. Digital business transformation is a driver of organizational change and allows for more systematic monitoring and control of employees’ work in the office or in the home work.
Digital transformation provides new opportunities for internal and external cooperation. For example, virtualization technologies, such as cloud computing, allow businesses to centralize data storage while decentralizing access to data, thus allowing employees to access and share digital resources quickly and distantly. Cloud technologies allow smaller companies to reduce their hardware and software costs by implementing cloud computing solutions.
IT technologies motivate companies to specialize mainly in industry or product, and other additional activities are outsourced. Especially in the field of services and production, the organization of labor is very flexible in terms of time and space. Data analysis improves the control of production process managers, both qualitatively and quantitatively, by monitoring results, providing electronic transactions and reporting customer satisfaction (World Economic Forum. White Paper, 2016).
Information security and data protection are important responsibilities for the business IT team. Therefore, the company’s systems undergo annual audits and certifications, disaster recovery processes are implemented to ensure continuous service to businesses, employees and customers. The IT department takes care to provide the latest and best security solutions on the market. Some of these solutions include encryption of information, strictly limited access to networks based on solutions from leading providers and reserved connectivity to the main data centers that provide cloud services. “The growing practice of exported key components, remote access and storage, carries a number of new security risks associated with operating systems, communication protocols and applications” (World Economic Forum. The Future of Workplaces, 2016).
“Innovations in digitization, analytics, artificial intelligence, and automation are creating performance and productivity opportunities for business and the economy, even as they reshape employment and the future of work” (McKinsey, 2017).
“Approximately 12% of the global goods trade is conducted via international e-commerce, with much of it driven by platforms such as Alibaba, Amazon, eBay, Flipkart, and Rakuten. Beyond e-commerce, digital platforms for both traditional employment and freelance assignments are beginning to create a more global labor market. Some 50% of the world’s traded services are already digitized. These transformations enable small and medium-sized enterprises around the world to compete head-to-head with larger industry incumbents” (McKinsey, 2017).
Digital transformation and Artificial Intelligence (AI) will improve productivity and economic growth of the business organizations, but millions of people worldwide may need lost the work or switch occupations or upgrade skills.
Companies implement new modern tools to organize remote work for their employees. The skills of employees to work with specialized IT products remotely are a necessity to keep the job. Management focuses on solving problems in the organization of work, increasing the scope of customer support through tools, including chatbots, automation tools for sustainability reasons, upgrading old and conflicting systems.
An important element of digital transformation is technology and also enabling innovation. The new technologies are built using cloud architectures and approaches.
Technological advances in the software and hardware industry make product production and service delivery more efficient by:
- accelerating production cycles and time to deliver goods to customers;
- interactive, multi-channel IT solutions, constant connectivity;
- customized solutions using digital data.
Modern IT tools allow manufacturers and service providers to better track consumer behavior and explore their preferences. IT systems for business management in the front-end part, designed for customers, as well as social networks, improve access to information and support consumer choice. Access to information and improved consumer choice lead to demanding and proactive customers and improve customer experience and satisfaction. Consumers perform a more systematic evaluation and comparison of offers; they rely not only on the information provided by sellers and manufacturers. Various studies support the assumption that consumers are more confident in reviews and reviews from other users than from corporations: 78% of users trust review recommendations, compared with only 14% trusting branded advertising (Ernst &Young, 2011).
These characteristics restructure commercial practices and redefine strategic relationships in both the B2C (business-to-customer) and B2B (business-to-business) environments. The application of digital technologies gives an advantage to the first ones on the market in attracting attention from consumers and building trust in the products and services offered. The two fundamental drivers in this case are the proper implementation of digital marketing and cybersecurity, which give the best competitive advantage.
IT leaders included in the MIT Sloan CIO Symposium series last September agreed that consumer behavior has changed rapidly in many ways since the beginning of the pandemic. Rodney Zemmel, a global leader at McKinsey Digital, says consumers are “accelerating digital in almost every category.” McKinsey data show the accelerated transition to streaming and online services such as online grocery shopping, cashless transactions, distance selling. Improving the customer experience and satisfaction has become a crucial goal and crucial part of the digital transformation (MIT, 2020).
5.The Impact of Digital Transformation on Business Processes
In order to remain competitive, existing businesses need to step up efforts to improve and adapt new business processes, while engaging in strategic and complementary cooperation.
The business processes are defined as a set of logically related tasks performed to achieve a defined business outcome. This definition is similar to: “The logical organization of people, materials, energy, equipment, and procedures into work activities designed to produce a specified end result (work product)”. Business processes have two important characteristics (Davenport & Short, 1990):
- They have customers; that is, processes have defined business outcomes, and there are recipients of the outcomes. Customers may be either internal or external to the firm.
- They cross organizational boundaries; that is, they normally occur across or between organizational subunits. Processes are generally independent of formal organizational structure.
Common examples of processes meeting these criteria include (Davenport & Short, 1990):
- developing a new product;
- ordering goods from a supplier;
- creating a marketing plan;
- processing and paying an insurance claim;
- writing a proposal for a government contract.
Business processes in an organization are subject to change. This change can be done for efficiency reasons, may be imposed by external factors – technology change, state regulation, competitors’ actions, or may be the result of internal processes in the company that are not controlled by management. Any such change may result in positive or negative effects on the organization’s performance, as the task of management is to control these processes and make adjustments to improve the quality of work.
The identified need for change of business processes is only the first step towards the improvement of processes, whether the desired change is organizational (operational improvement), technological (implementation of new software systems) or a combination of both. In both cases, changes are required tо affect the organization’s activities in a different way.
Any improvement in activity is related to change – change in established practice, responsibilities, tools and technology. When seeking improvement of the process, the following options are possible:
- a radical change in the sequence of activities, which affects the whole process (reengineering);
- а change in the characteristics of the activities from which the process is composed without affecting its consistency (refinement).
And while reengineering means a radical change to existing business processes in the organization to gain competitive advantages, business process improvement focuses on improving business process parameters without resorting to substantial change in established business practices.
With a variety of modern technological solutions, the improvement and optimization of the activity can be accomplished in a variety of ways. There are situations in which these ways overlap and complement or situations in which they are mutually exclusive. The issue of management decision and judgment is in which case what strategy to take in order to maximally effect the change.
Data integration throughout the firm is strategic important decision. Consolidated data from divisions and departments in the business organization, including key business processes, are immediately available to any authorized user. The biggest advantage of corporate systems and storing data in a centralized database is the chance to reduce costs and increase the ability to transmit information throughout the organization.
The most common, most popular business processes that are automatically included in a typical enterprise system are (Laudon & Laudon, 2018):
“Financial and accounting processes, including general ledger, accounts payable, accounts receivable, fixed assets, cash management and forecasting, product-cost accounting, cost-center accounting, asset accounting, tax accounting, credit management, and financial reporting;
Human resources processes, including personnel administration, time accounting, payroll, personnel planning and development, benefits accounting, applicant tracking, time management, compensation, workforce planning, performance management, and travel expense reporting;
Manufacturing and production processes, including procurement, inventory management, purchasing, shipping, production planning, production scheduling, material requirements planning, quality control, distribution, transportation execution, and plant and equipment maintenance;
Sales and marketing processes, including order processing, quotations, contracts, product configuration, pricing, billing, credit checking, incentive and commission management, and sales planning.”
In the process of digital transformation of a business organization, business models, business processes and the organizational structure are changing:
- Management models: Informed management decisions, with complete information about the effectiveness in all divisions of the business organization.
- Customer-driven business processes: all operations departments can focus more on the customer and respond more effectively to product demand.
- Improved organizational structure: A more disciplined approach to business throughout the company, regardless of the physical location and/or place in the organizational structure.
In some sectors, digital transformation primarily involves production processes. This is particularly typical for manufacturing companies. Reducing costs by digitizing the processes of developing, testing and producing new products is of paramount importance. Mobile applications are more important to improving the production processes and internal communications of employees than to interact with customers who are mostly not end-users. Large databases and information processing are more focused on production (Bradley et al., 2015). The digitization of production processes opens up many opportunities for expanding business and for its internationalization / globalization.
The digitization of production processes, however, opens up many opportunities for the expansion of business and for its internationalization in traditional economic sectors (Schwertner, 2017).
The industry’s traditional value chain of original equipment manufacturers (OEMs), suppliers, retailers and the aftermarket has been disrupted by new, digitally astute entrants in both the existing and extended value chain. New technologies propel business model innovations that challenge and extend the standard value chain in offering new products and services to the consumer (Ernst &Young, 2011).
The digital trends lead to a growing relevance of new entrants in traditional segments and creation of new segments.
The speed of this transformation is governed by the advances in connectivity technology, changes in consumer behavior, the emergence of new business models, and by environmental trends and regulatory practices. The impact has been seen mostly in the aftersales stage of the value chain. However, digitization is also having a significant transformational impact on R&D, procurement, assembly, marketing, and sales. In the parts segment, 10 to 15% of all global revenue will be generated online by 2025, and for parts and service retailing, China will be the most attractive market for revenue growth in digitization (Schwertner, 2017).
Players in the industry must also respond to fundamental consumer expectations around security and data privacy. The ability to manage and secure consumer data is a challenge faced by most industries in this increasingly digital world.
Digital transformation requires an in-depth analysis of the current state of business processes and business model in the organization. The analysis precedes the development of a digital transformation strategy. The analysis should answer questions grouped into several areas: Digital Transformation Attitude; Managing Support for Digital Business Strategy; The degree of use of digital technologies in the work of staff; The degree of use of digital communication channels; Digital infrastructure; Digital tools that meet customer needs and internal processes; Investments in digital solutions – what resources can be allocated to processes of digital transformation (Schwertner, 2017).
The analysis should focus on a number of key areas: users, suppliers and partners, investors, organization staff and organization leadership. The analysis should show how an organization’s digitalization will create more value for consumers, how it will help investor relations, how it will enhance interaction with partners, how it will change corporate culture, and how will be effective the change in organization and process to digital transformation. (Schwab, 2016).
Digital transformation also includes the provision of services of the same high quality through all access channels at any time using cloud services and mobile applications. Service provision companies (cloud services, consulting services, outsourced services, IT services, and others) must cover the full cycle: from requirements and needs analysis, architecture creation, through product installation and configuration to the creation of business processes according to the specifications of customers. Cloud solutions are based on an integrated IT platform – SaaS, iPaaS and others. In recent years, there has been the largest growth in cloud services, averaging over 20% per year. This trend is easy to explain, as this type of services minimize operational risk and increase the quality of processes, while improving cost efficiency for companies. Service provision companies implement solutions with integrated IT platforms to achieve optimized processes, transparency and traceability. These firms can be representatives of various industries: automotive and industrial production, financial and banking services, food and beverage producers, retail chains and more. There is a rapid penetration of digitalization in sectors such as logistics, media, trade, automotive and high-tech manufacturing.
The sectors subject to regulation are extremely conservative, with changes taking place slowly and consistently. One such example is the banking sector. Financial institutions are working harder than ever to remain profitable during a pandemic and competitive with a variety of alternatives. Modern technology introduces innovations that disrupt this conservative industry. They avoid regulatory pressure through new models and even new currencies.
Upcoming regulations and standards in Europe, various payment service initiatives in Asia, open banking, as well as the need for real-time payments and contactless cards, are driving modernization and digitalization across the industry at a rapid pace.
Another sector in which digitalisation is being prepared is agriculture, where transformation requires a change in education and training.
During a pandemic, global supply chains are disrupted or severely hampered. The business problems associated with using global supply chains are:
- Greater geographic distances and time differences;
- Additional costs for transportation, inventory, and local taxes and fees;
- Varying performance standards;
- Foreign government regulations;
- Cultural differences.
By Internet suppliers, manufacturers, and partners communicate easier using e-mail, faxes, or phone calls, but those communication methods open possibility of making errors. Implementation of web-based supply chain management systems gives all participants a way to make data and information more easily available through browsers and portals. Internet enables move from sequential supply chains to concurrent supply chains.
Traditionally, customers purchase whatever products are available. Although colors, sizes, and prices may vary somewhat, generally a manufacturer decides what to produce by forecasting what the potential demand might be through a push-based model. That is quickly changing to a pull-based model in which the customer tells the manufacturer ahead of time what he/she wants to buy. One of the best examples of this new pull-based model is Dell Computer’s build-to-order business model. Dell doesn’t build a computer until it receives a customer order. Then it builds the computer to the customer’s specifications (Laudon & Laudon, 2018). Figure 2 below shows the differences between the push-based business model (manufacturer-driven business model), where the manufacturer decides what to produce and pull-based supply chain business models (customer-driven business models), where the customer tells the manufacturer ahead of time what he/she wants to buy.
Figure 2. Build-to-stock/push-based vs. demand-driven/pull-based Models
Managing by customer-driven processes that cross organizational boundaries is an intuitively appealing idea that has worked well in the companies that have experimented with it (Davenport & Short, 1990). The customer-driven model of business processes is effective in the time of pandemic.
The Implementation of SCM (Supply Chain Management) systems facilitates efficient customer response, allows the business to be driven more by customer demand, enables move from push-based, sequential models to pull-based, concurrent models of production. Internet enables move from sequential supply chains to concurrent supply chains. Complex networks of suppliers can adjust immediately.
6.The Impact of Digital Transformation on Business IT Infrastructure
The introduction of a single integration platform for business, which supports all types of business integration, both between different businesses B2B (Business to Business) and from business to government B2G (Business to Government), between different applications A2A (Application to Application), exchange electronic document management (EDI), integration with external software solution providers through API management, electronic invoicing and other services insure a competitive advantage. Such an integrated platform ensures seamless integration of all systems in companies, applications, data storages and cloud services.
Structured and unstructured databases are used to store data. The database itself is defined as an organized set of data stored for a long time and designed for use by various applications through effective access and support tools (Martin, 1983). The big IT companies Oracle, SAP, IBM offer in their portfolio of services and products, cloud services and database management technologies. Database as a service (DBaaS) is an architectural and operational approach that allows IT vendors to provide database functionality as a service to one or more users (Schwertner et al., 2018).
Data usage is already the largest business in the world. About 1.4 trillion. dollars from the combined value of Alphabet (Google’s parent company) and Facebook, i.e. 1.9 trillion dollars come from consumer data and their use by companies. Access to capital is no longer the biggest problem for startups, but access to data is proving to be a bigger problem. One possibility is for the collection of data by individual users from any source. A second way to strengthen the position of those who give their data is through collective action – especially important when so much of their value in the network comes not from their individual personal data, but from their interactions with others. Governments are involved in the protection of personal data and its regulation by the GDPR in the European Union and the CPA in California. Although the main goal of both is data protection, both are a big step forward in terms of data rights themselves. The European Commission seems much more inclined to intervene than the United States in technology regulations and plans to take another step in this direction with a data bill in 2021.
6.1. Big Data in Business
The Cloud technology, mobile, big data analysis, internet of thing, and industrial internet of things are solutions for digital firms. Big Data analysis means the processing and use of large amounts of data that exceed the capacity of the software tools of traditional databases (relational databases) used to store, manage and analyze data. Big Data stems from the rapid increase in the amount, speed and variety of digital data generated in real time, as a result of the increasingly important role of information technology in business. Data management makes it possible to generate information that can be used in the time period before certain decisions are implemented.
The entry of smartphones and tablets into the business increases the levels of connectivity and interaction between users. This is partly due to social networks. With the development of smartphones, the ability to collect a variety of data generated by high levels of activity of users and employees is increasing. That’s why more and more companies are implementing cloud technologies to handle the huge amount of data. According to Flexera report for 2020 worldwide, the percentage of companies using cloud services reaches more than 90%, 93% of enterprises have a multi-cloud strategy; 87% have a hybrid cloud strategy (Weins, 2020).
The pandemic does not allow to work in offices and to learn at schools and universities. All developed countries have reduced or closed the possibility to buy goods in stores, employees have to work mostly from home. In addition, some of nonessential businesses are closed or reduced. The only alternative to do these activities is online – internet and cloud services. Many industries are seriously hurt by these circumstances. The usage of the cloud will increase due to these situations. Additional resources are needed meet the growing usage of the cloud services. These are not only data center’s resources but also network capacity and frontend devices like computers and smart phones. Some organizations will be not able to increase their hardware capacity, so they will be forced to migrate from their data centers to cloud because of personnel problems, narrow data centers, no appropriate hardware on the market and supply delays. The scaling of data centers is a complex project work and also needs investments. In many cases the enterprises and organization can also find that the public and public-hybrid cloud solutions are reliable and cheaper solution for their IT activities. This process is accelerated by the pandemic, but it is also a trend in the last years.
In 2016, about three quarters of companies based in the European Union (EU) and with a team of at least 10 employees have a website, and almost half use social media. However, only 10% of them state that they also analyze Big Data, according to Eurostat (European Statistical Office). The most popular sources for analysis are portable geolocation data used by 47% of businesses and data generated by social media (45%). One third (33%) of companies’ report analyzing their own Big Data from smart devices and sensors, and 25% have used other Big Data sources (Schwertner, 2017). Of the EU Member States for which such information is available, Big Data analysis is used by at least 15% of companies in Malta and the Netherlands (19% each), Belgium (17%), Finland and the United Kingdom (15% each), in Bulgaria 7%. On the other hand, only 6% of companies analyze Big Data in Germany and Poland, and in Cyprus this percentage is 3% (Eurostat, 2016).
Big Data is a complex multidimensional notion. At first glance it looks like analyzing big amounts of data and producing information, but Big Data analysis is very difficult task. The components of the big data are mostly unstructured data – advertisements, films, images, and other graphics objects, geospatial descriptions, text documents in various formats, and possibly in different languages. The sources of these data lie mostly outside the organizational structures of the analyzing organization and are collected using networks, media tools, geolocation devices. This chaotic and unstructured amount of data is hard to analyze, to find the connections between the different documents and to derive relevant and useful information that can be used for making decisions.
The next problem is the short lifecycle of these data and the flood of new pieces of data and documents produced by the mass media, trade, industry and business. The data is changed very quickly and replaced by tons of new documents. All these facts lead to the necessity of new technology methods for data processing known as Big Data.
The Big Data technologies use the follow analysis methods:
- A/B testing – In this method, a control group of items is compared to other test groups in which one or more indicators have been changed. The aim is to clarify what changes improve the targets. With this method you can find the optimal combination of indicators to achieve a certain goal – for example, the best perception of a new marketing offers by consumers. The Big Data allow a huge number of iterations to be performed and a statistically reliable result to be obtained. For example, Amazon and Zynga use A/B testing.
- Association rule learning – This is a set of methods for identifying relationships, associative rules for relationships between variables in large volumes of data. These methods are used in data mining solutions.
- Classification – A group of methods that allow to predict the behavior of consumers in a particular market segment – for example, to make a purchase decision, the volume of consumption, to refuse to use a product. Used in data mining solutions.
- Cluster analysis – A statistical method for classifying objects into groups based on similar common features that were not previously known. Used in data mining solutions.
- Crowdsourcing – A method of collecting data from a large number of sources.
- Data fusion and data integration – A group of methods that allow to analyze the comments of users of social networks and to compare with sales results in real time.
- Data mining – A group of methods that allow the detection of meaningful correlations, dependencies, repetitive patterns, trends and anomalies in data sets. Data mining tools are used to implement Big Data projects, which aim to predict a pattern of user behavior or, for example, to determine which group of users will best perceive a new product, what qualities are characteristic of the most successful employees and others.
- Ensemble learning – Developed for the purposes of machine learning, this method includes a number of predicative models, thanks to which it achieves high quality of the derived predictions.
- Genetic algorithms – In this method, possible solutions are presented in the form of “chromosomes” that can combine and mutate, and like the process of evolution in nature, the most adapted survive. This is a heuristic search algorithm used to solve optimization and modelling problems by random selection, combining and variations of target parameters and using mechanisms analogous to natural selection.
- Machine learning – A field of artificial intelligence aimed at creating algorithms for self-learning based on empirical data.
- Natural language processing (NLP) – Methods for recognition and processing of natural language, borrowed from computer science and linguistics.
- Network analysis – A group of methods for analyzing connections between nodes in networks. They are applied to data from social networks, allowing to analyze relationships between individual users, companies, communities and others.
- Optimization – A group of numerical methods for redesigning complex systems and processes in order to improve one or more of their indicators. They are used to support strategic decision-making – for example, the composition of the product line placed on the market, to conduct investment analysis and others.
- Pattern recognition – Methods with elements of self-learning applied to predict patterns of behavior.
- Predictive modelling – Methods that allow a mathematical model to be created for a predetermined probable scenario for the development of certain events. A typical example of the application of Predictive modelling is the analysis of data from a CRM system in order to predict the possible conditions under which some subscribers of a company will give up its services and start using those of competitors.
- Regression – A group of statistical methods for finding patterns between the change of a dependent variable and one or more independent ones. It is used for forecast analyses and in data mining. The “linear regression” method is widely used, which determines the influence of one numerical parameter on another. For example, what is the average value of sales volume when the marketing budget changes by 100 euros. Another option is “logical regression” – when the dependent variable can take only two values (0 and 1) – this is one of the most common methods for analyzing the probability of occurrence of an event depending on the values of some parameters.
- Sentiment analysis – These are methods for assessing consumer sentiment based on natural language recognition technologies. They allow messages related to a specific topic or subject (for example, for a given product) to be extracted from the general information flow, as well as to assess whether the opinions expressed on the topic are positive or negative, what is the degree of their emotionality.
- Signal processing – A group of methods borrowed from radio engineering, which aim to recognize a signal against the background of noise and analyze this signal.
- Spatial analysis – A group of methods borrowed from statistics for spatial data analysis – topology of the area, geographical coordinates, geometry of objects. The source of large volumes of such data is often the Geographic Information Systems (GIS) of large organizations.
- Supervised learning – Methods based on machine learning technologies that allow to find functional relationships in the analysed data sets.
- Simulation – Modelling the behavior of complex systems that are often used to predict and test different scenarios for planning purposes.
- Time series analysis – A group of methods borrowed from statistics and the theory of digital signal processing. Used to analyze repetitive series of data. Typical applications – tracking the securities market, tracking the number of patients with a disease and others.
- Unsupervised learning – Methods based on machine learning technologies that allow to identify hidden functional relationships in the analysed data sets. These methods have common features with Cluster Analysis.
- Visualization – Methods for graphical presentation of the results of Big Data analyses in the form of diagrams or animated images in order to simplify the interpretation and easier understanding of the results (Stephenson, 2013).
The situation is further complicated by the variability of the data. Their composition and structure are constantly changing when launching new services, installing advanced sensors, conducting new marketing companies and others.
Big Data is the focus of attention in many organizations, but its successful implementation is in the process of improvement. The data has a huge potential for improving the functioning of business organizations in all areas of their activities: from improving customer service, optimizing the ongoing internal processes in the organization, performance analysis and risk assessment. Unlocking this potential requires the use of specialized tools through which the data can be presented in an easy to perceive, analyze and predict graphical form, which serves as a basis for smarter management and business decisions. The key to faster integration of Big Data is the use of Business Intelligence (BI) tools. Analysts believe that BI technology is already a strategically important tool and a necessary tool for most businesses. By summarizing all the information accumulated through the various communication channels, business analysis systems provide a comprehensive view of the relationship between the business and its customers. Better service, optimization and acceleration of processes in the organization and finding the right channels for communication with different customer groups are just some of the benefits that can be achieved by introducing business analysis systems in the corporate infrastructure of organizations. Business analysis tools can reveal trends in customer behavior, thus making business organizations much more flexible and innovative in finding more successful approaches to retaining and attracting new customers.
Banks have long consolidated huge arrays of customer data, and modern technology allows flexible work with them. This creates the potential for optimization and additional monetization. The areas of application of such developments are many – from marketing to security, but one of the main advantages for banks in the application of Big Data is the sale of additional services to customers.
6.2. Mobile Platforms
The mobile broadband networks provide improved connectivity and the ability for digital technologies to be used everywhere. The deployment of mobile networks starts in 2007, but they surpassed fixed line broadband in just one year. Today, mobile broadband networks are the main carrier of broadband Internet access. This wider deployment of mobile technologies and access to networks, devices and applications eliminates time and space constraints in the use of digital technologies, thus dramatically changing everyday life and work environment. Mobile technologies allow people to use their personal devices for work, which increases the speed of work and changes work processes. Although only 50% of connections are currently made through third generation (3G) and fourth generation (4G) networks, the deployment of fifth generation (5G) technology by 2021-2022 is able to cover the growing demand for ubiquitous and instant access to applications. 5G networks use denser signals through smaller antennas and cloud solutions to deliver 50 to 100 times higher speeds than 4G networks. Thus, 5G promises to be of paramount importance for building the infrastructure of a number of industries. According to the GSMA (Global System for Mobile Communications – GSMA) by 2025, 1.2 billion people will have access to 5G networks. Switching to these solutions will change the mobile services provided to consumers and create new business models, as well as possible problems for countries and industries that fail to invest smartly during the transition period. Unlike previous evolutions of technology – 2G in the early 1990s, 3G in the early millennium and 4G less than a decade ago – the new 5G network standards will not only lead to faster data transfer speeds, but will also allow the connection of any sensors, devices, including cars, appliances and huge agricultural machinery. 5G will be a revolution not only for consumer services but for entire industries.
6.3. Autonomous DB Management Systems
It is a fact that the administration of the DBs is a very hard task. This is so, because nowadays the DBs are very complex: have many different types of data, many features and functionalities and also (as every software) more than enough bugs. The need to update and to upgrade the DBs can’t be eliminated. There also big performance and security problems related to the DBs. This makes the Data Base Administration (DBA) a serious and hard task. There are not enough experienced DBAs and the salaries increase. From other hand the number of the installed DB instances increases drastically with the digitalization of the society and the industry. All this bring a bad reputation over the DBs: they are claimed as very complex products that need expensive personal and expensive hardware in order to be used. This is a big challenge not only for the consumer but also for the producer of the DBs. One way to reconcile this problem are the cloud services. With the cloud services the care for infrastructure, installation, supporting and running of the DBs and the applications are delegated to the provider of the cloud services. But the provider has its own problems – there are too many DB instances on the Cloud and too less experiencer personal and time to support the farms with the DBs. So, the industry slowly goes to the idea for “autonomous” DBs. These means that the maintenance, update, upgrade and security of the installed and running DBs will be done automatically. Even more – the complex performance tuning will be automated. The Oracle approach to these activities will be displayed.
The Cloud gives the unseen possibility for the enterprises (also middle and small) to host their software in the Cloud Services providers. This triggers a new problem – the huge amount of this software (particularly DBs) needs support efforts – patching, updating, upgrading, bug fixing, performance tuning, problems fixing, backup and recovery and many other activities which are the contents of the DBA job – very expensive because of the need for big amount of knowledge, experience, passion and talent. Definitely there are no enough people to meet these requirements. So, for good or bad these activities should be automated as much as possible.
The autonomous Oracle DB were available only in Oracle Cloud environment. The no Oracle Cloud installations were not able to be autonomous. Since the mid of 2020 Oracle purchase autonomous DBs for on premises installation. This solution is complex and expensive. The obstacle is not only the royalty fees. The autonomous DBs of Oracle are able to run only on specialized hardware known as Oracle Exadata. This is expensive Data Base Machine – a huge complex of computational, storage and internal and external network components.
The main feature of the Oracle Autonomous Database Cloud is that it offers high level of automation including machine learning and so eliminates human efforts, human errors and manual tuning.
This includes the following (Oracle Corporation, 2019):
- No Human Activities and Efforts: Database automatically upgrades, patches, and tunes itself while running; automates security updates with no downtime window required.
- No or reduced Human Errors due the checked procedures that are programmatically fulfilled: as result SLA guarantees 99.995% reliability and availability, which minimizes costly planned and unplanned downtime to less than 30 minutes a year.
- No Manual Performance Tuning: Indexes are automatically created, data are compressed. So, the Database consumes less compute and storage resources because of machine learning and automatic compression. Combined with lower manual admin costs, Oracle offers even bigger cost savings.
These unprecedented targets at this point of the development of the software sound as pure phantasy for every DBA (Hall, 2018), but now they are reality.
6.4. Artificial Intelligence in Business
Artificial intelligence (AI) can augment or automate decisions and tasks today performed by humans, making it indispensable for digital business transformation. With AI, organizations can reduce labor costs, generate new business models, and improve processes or customer service (Gartner, 2019).
Some design principles that will help the organizations evaluate artificial intelligence applications for business results, not just operational improvements, are the following:
- Anticipate the future – In digital business, AI generates insights that lead directly to business execution. A strategic AI application can produce granular insights into what customers, markets or other entities are likely to do in specific future situations and what the enterprise can do to influence them.
- Act autonomously – AI applications provide value by automating existing manual processes, but can also go a step further by enabling autonomous operation of the business.
- Connect to the customer – Digital businesses thrive on knowledge of markets and customers. To support digital business initiatives, AI applications must get as close to customers as possible. CIOs should think about strategic AI applications that enable their organization to capture critical information to help build more intimate customer relationships overtime.
- Elevate the physical – Strategic AI applications can improve the physical world by using robots.
- Detect the invisible – Strategic AI applications can make decisions much faster than humans about increasingly complex situations.
Manage risk – Security, risk and privacy form the biggest barriers to the development of AI applications and are even more of an issue when AI applications serve a strategic business purpose. These limits reduce the risk of concept drift and prevents any damage the application could do (Gartner, 2019).
The global market for industrial robots is expected to nearly double in the next five years. Producers are forced to find ways to be more flexible due to broken supply chains. There has been a huge increase in the demand for material handling equipment and “collaborative robots” designed to interact with humans. These “cobots” are especially useful in e-commerce, which the coronavirus has given a huge boost. It is estimated that the pandemic has increased companies’ buffer stocks by about 5%. To counteract this, they stock up on robots used in warehouses. Cobots are currently helping to maintain social distance, but will continue to be useful after the pandemic.
The greater and longer-term impact of a pandemic can be better understood through the data that companies generate from their activities and from the algorithms they apply to make current decisions.
Digital Transformation is more complex than plain automation of processes – it transforms the processes themselves, business models and customer expectations. Through comprehensive digital connections between systems, people, places and objects, the digital businesses create value and generate revenue. Every company today can develop a strategy and use digital technologies to create a profitable position for their business as well for their industry. The potential that is unlocked through digital transformation is the next step in the development of the global and national economy.
The new phenomena are also used to achieve exchange of communications between the companies and external parties – suppliers, distributors and customers. It is more complex than the simple exchange of messages or orders. It makes possible to study and to analyze the current market situation and customer demands and to reveal the trends of future changes in order to adapt and coordinate the efforts of the companies with the anticipating state of the market, customer needs and technologies.
Digital Transformation triggers the application of specific new digital technologies. New computer technologies with deep scientific origin get on the stage: Big Data, Analysis and Decision based on Artificial Intelligence methods, Self-Learning approaches and Algorithms and many others.
From the point of view of the IT technologies (hardware, software, networks) this leads to centralization of the hardware and applications in the Cloud Data Centers – that unleash totally new capability for corporations in terms of availability, effectiveness and/or efficiency. This increases the demand on specialized hardware like Data Base Machines that capsulate hardware components in a unit what makes the processing faster and the maintenance easier.
The integration of all these technologies transforms the society that will live in a totally changed world. The producing of goods, running the businesses, leading the organizations is moving toward intensive usage of new technologies and supporting applied IT methods (software, hardware, processes) and growing digital communication channels. This new world shaped through Digital Transformation is enormously multicomponent and complicated – we need constantly to analyze, assess and develop it to ensure the stability and the power of our societies.
BCG, (2016). Nine Technologies are transforming Industrial Production. Boston Consulting Group. [Online]. Available: https://www.bcg.com/capabilities/operations/embracing-industry-4.0-rediscovering-growth.aspx
Bradley, J. and Loucks, J. and Macaulay, J. and Noronha, А. and Wade, M., (2015). How Digital Disruption Is Redefining Industries. Global Center for Digital Business Transformation, Digital Vortex. [Online]. Available: https://www.cisco.com/c/dam/en/ us/solutions/collateral/industry-solutions/digital-vortex-report.pdf
Davenport, T. and Short, J., (July 15, 1990). The new Industrial Engineering: Information Technology and Business Process Redesign. MIT Sloan Management Review, Magazine summer 1990, Research Highlight [Online]. Available: https://sloanreview.mit.edu/article/ the-new-industrial-engineering-information-technology-and-business-process-redesign/
Deloitte analysis. (June 2016). Industry 4.0 and manufacturing ecosystems. Exploring the world of connected enterprises, Deloitte University Press. [Online]. Available: https://www2.deloitte.com/content/dam/Deloitte/za/Documents/energy-resources/ZA_Deloitte-Industry4.0-manufacturing-ecosystems-Jun16.pdf
Ernst &Young, (2011). The digitization of everything – How organizations must adapt to changing consumer behavior, Ernst &Young LLP, Published in the UK, [Online]. Available: https://www.the-digital-insurer.com/wp-content/uploads/2014/04/200-EY_Digitisation_of_everything.pdf
Eurostat, (2016). Businesses’ use of big data analysis in the EU Member States. [Online]. Available: https://ec.europa.eu/eurostat/
Gartner, (2019). 6 Design Principles for Artificial Intelligence in Digital Business. Smarter with Gartner. [Online]. Available: https://www.gartner.com/smarterwithgartner/6-design-principles-for-artificial-intelligence-in-digital-business/
Geissdoerfer, M. and Savaget, P. and Evans, S., (2017). The Cambridge Business Model Innovation Process. Procedia Manufacturing. 8: 262–269. Doi:10.1016/j.promfg.2017.02.033. ISSN 2351-9789.
Hall, T., (January 2018). Oracle Database 18c is NOT an Autonomous Database. [Online]. Available: https://oracle-base.com/blog/2018/01/03/oracle-database-18c-is-not-an-autonomous-database/
IDC, (2020). New IDC Spending Guide Shows Continued Growth for Digital Transformation in 2020, Despite the Challenges Presented by the COVID-19 Pandemic. [Online]. Available: https://www.idc.com/getdoc.jsp?containerId=prUS46377220
ITU, (2013). Harnessing the Internet of Things for Global Development. [Online]. Available: https://www.itu.int/en/action/broadband/Documents/Harnessing-IoT-Global-Development.pdf
Kane, G. and Palmer, D. and Phillips, A. and Kiron, D. and Buckley, N., (June 2015). Strategy, Not Technology Drives Digital Transformation. MIT Sloan Management Review and Deloitte University Press, [Online]. Available: http://sloanreview.mit.edu/projects/ strategy-drives-digital-transformation/
Khan, S., (2016). Leadership in the Digital Age – a study on the effects of digitalization on top management leadership (PDF). Stockholm Business School. [Online]. Available: https://su.diva-portal.org/smash/get/diva2:971518/FULLTEXT02.pdf
KPMG, (2018). The CIO role is transforming as organizations strive to be customer-centric in an environment of increased risk and aim to make a success of digital. Harvey Nash / KPMG CIO Survey 2018. [Online]. Available: https://home.kpmg/xx/en/home/ insights/2018/06/harvey-nash-kpmg-cio-survey-2018.html
Laudon, K. and Laudon, J., (2018). Management Information Systems: Managing the Digital Firm, 15th ed. Pearson Education, Inc.
McKinsey Global Institute, (May 2017). What’s now and next in analytics, AI, and automation. [Online]. Available: https://www.mckinsey.com/featured-insights/digital-disruption/whats-now-and-next-in-analytics-ai-and-automation
MIT, (2020). MIT Sloan CIO Symposium series event. [Online]. Available: https://mitcio.com/agenda-2/
MIT, (2013). Digitally Mature Firms are 26% More Profitable than Their Peers. MIT Initiative on Digital Economy. [Online]. Available: http://ide.mit.edu/news-blog/blog/digitally-mature-firms-are-26-more-profitable-their-peers
Oracle Corporation, (2019). The World’s #1 Database Is Now the World’s First Self-Driving Database. [Online]. Available: https://www.oracle.com/database/autonomous-database/feature.html
PricewaterhouseCoopers (PwC), (2020). Passing the agility stress test - How ЕМЕА private businesses are building resilience to manage the COVID-19 pandemic and beyond. Europe, Middle East and Africa (EMEA) Private Business Survey 2020. [Online]. Available: https://www.pwc.com/gx/en/services/entrepreneurial-private-business/emea-private-business-survey.html z
Schwab, K., (2016). The Fourth Industrial Revolution: what it means, how to respond. World Economic Forum, Insight Report. [Online]. Available: https://www.weforum.org/agenda/ 2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/
Schwertner K., (June 2017). Digital Transformation of Business. Proceedings of International Scientific Conference “Business and Regional Development”, Trakia University, vol.15, pp.388-393, ISSN 1313-7069. [Online]. Available: http://tru.uni-sz.bg/tsj/TJS_Suppl.1_Vol.15_2017/65.pdf
Schwertner K. and Zlateva, P. and Velev, D., (June 2018). Digital Technologies of Industry 4.0 in Management of Natural Disasters. Conference Proceedings of 2nd International Conference on E-commerce, E-Business and E-Government (ICEEG 2018), The Hong Kong Polytechnic University, Conference Proceedings Citation Index (ISI Web of Science), ACM Digital Library, ISBN: 978-1-4503-6490-4, pp.95-99, doi>10.1145/3234781.3234798 [Online]. Available: https://dl.acm.org/doi/10.1145/3234781.3234798
Stephenson, D. (2013). 7 Big Data Techniques That Create Business Value. The TouchPoint by Firmex. [Online]. Available: https://www.firmex.com/resources/blog/7-big-data-techniques-that-create-business-value/
Waghorn, T., (2011). Jeremy Rifkin's Third Industrial Revolution. Forbes magazine. [Online]. Available: https://www.forbes.com/sites/terrywaghorn/2011/12/12/jeremy-rifkins-third-industrial-revolution/
Weins, K., (May 2020). Cloud Computing Trends: 2020 State of the Cloud Report, IT Industry Trend. Flexera blogs, [Online]. Available: https://www.flexera.com/blog/industry-trends/trend-of-cloud-computing-2020/
Weske, M. (2012). Chapter 1: Introduction. Business Process Management: Concepts, Languages, Architectures. Springer Science & Business Media. pp. 1–24. ISBN 9783642286162.
Wikipedia, (2017). Digitization. [Online]. Available: https://en.wikipedia.org/wiki/Digitization
World Economic Forum, (January 2016). The Future of Jobs. Employment, Skills and Workforce Strategy for the Fourth Industrial Revolution. Report. [Online]. Available: https://www.weforum.org/reports/the-future-of-jobs
World Economic Forum, (2016). White Paper. Digital Transformation of Industries: In collaboration with Accenture Digital Enterprise. [Online]. Available: https://www.accenture.com/_acnmedia/accenture/conversion-assets/wef/pdf/accenture-digital-enterprise.pdf