Abstract: The horizon for an universal standard of cyber-economics, digital transaction finance, administrative electronic bigdata on the internet and intranet, and paperless digital works for international business throughout the world.
The digitization of the economy is one of the most critical issues of our time. Digital technologies has transformed businesses and peoples life, and will continue to do so in the future. Here it is about digital economics and how the digital economy influences markets, the digital society and organizations. We learn about how Internet, sharing economy, social networks, BigData and mobile communications change global businesses and how to create value for humans and enterprises in the digital society.
Basic grasp consists of two parts:
1) Basic theory in digital economics, including: value creation models, digital business models and market regulations.
2) Digital economics in context, and how the digital economy influences societies, environment, poverty, privacy, strategy, financial operations and city development.
Visitor to this site gain:
1) To get knowledge in digital economics.
2) To get knowledge on how the digital economy influences its surroundings.
1) To perform an analysis of a value network.
2) To perform an analysis and to construct a business model.
3 To be able to write an independent program within a specialized interest using their skills in Data Science or BigData.
It is of little wonder that four of the five most valuable companies on the planet today specialize in information processing (Apple, Alphabet, Microsoft, and Facebook), while the fifth (Amazon) leverages data in the very heart of its business model. For those practicing management, the concept of “digital economics” evokes the inter-relationships between data, business value and managerial decision making.
One of the defining characteristics of modern economies is that value is an attribute of user experience rather than a supplier’s products or services. The globalization of value chains pushes economic agents towards more profitable activities, both upstream and downstream of production. If expert foresaw that modern enterprise would move from assembling products to aggregating services, others underlined the subsequent shift of business value from organizational services to customer experiences. Value today is seen as a synonym of the quality of our consumer experience, whether it be in leisure time or professional activities. At the heart of our perceptions is data in context, which we use to quality our physical, augmented, or virtual realities.
Data is a proxy of these human experiences. Though data is nothing new, it is increasingly ubiquitous — more data will be created this year than in the previous 5000 years of recorded history. Data is at the heart of the Fourth Industrial Revolution — advances in internet technologies and business analytics are the current foundations of sustainable competitive advantage. Digital strategies stretch beyond websites to an Internet of Things designed to capture consumer preferences, actions, and motivations. If data has no intrinsic value, our ability to transform data into individual and/or collective action has become the fulcrum of both business and society.
The ubiquity of data has changed the way we look at value. Data isn’t collected to simply describe physical objects, but to feed multi-purpose algorithms that condition the way we model the world around us. Data Science is less concerned with what we do (descriptive) than what we could (predictive) or should do (prescriptive analytics). Business information systems are no longer designed to track tangible goods, but to provide horizontal platforms that leverage the intangible assets of what we as consumers have, know, and do. Data isn’t just data, it has become the lifeblood of modern enterprise.
The lessons of digital economics cover much more than a review of hardware, software, and automation. Digital technologies don’t magically produce decisions that transform data into action, people do. Some researchers work on Prospect Theory has provided a powerful demonstration of how human bias and perception influence how we look at the data. Machine learning at best can contribute to our perceptions of value. Data Science involves understanding the nature of the problem to be solved, the quality of the data at hand, applying the appropriate methodologies, and transforming the data into action. The impact of digital economics won’t depend on producing more data, but on promoting the quality of human decision-making.
created and under developed for all nations by
Professor Dr. David A. T. P., Ph.D., Sc.D., Sophy Investments