Data And Digital Transformation

2020 may become the first year of China's digital economy. Some data show that the proportion of digital economy in GDP in 2018 has accounted for more than 34%, and the epidemic has greatly accelerated the pace of digital transformation of various industries. Data and knowledge, as new productive factors, are increasingly becoming the most important means of production. Along with it are legal issues such as the ownership and use of data as assets. After obtaining the legal right to use the data, how to combine the data with the existing management, enable it with data, and establish a data-driven management process? We need complete executive thinking and solutions. "Data law" and "data analysis is the future" respectively give authoritative answers and best practice suggestions for imitation from the perspective of data law and data analysis application in the whole life cycle of enterprises.

The adjustment objects of data method are very complex, including data subject, data controller and data processor. In reality, the three may overlap, and the core legal relationship that needs to be adjusted is data protection and data value mining. The journal not only discusses the data and the application of data in the field of law, but also discusses the data and the application of health law in different fields. This book analyzes the whole process of data flow, such as data income, use, competition, sharing and cross-border from the legal perspective. It is a very advanced monograph in the field of data law. However, the industry is still unable to reach a consensus on the value of data. The book thinks that the value of information is difficult to determine from the personal level, or it is not as valuable as the data pool. This judgment involves the legislation on the identification and ownership of data rights, which belongs to one family of people and needs further discussion.

"Data analysis is the future" makes us realize the gap of digital economy development at the enterprise level. The informatization of Chinese enterprises has been carried out for nearly 30 years, but the data analysis of many enterprises is still limited to internal data statistics and simple chart analysis. The enterprise data in the digital economy era is far from that. This book combines the relevant tools of big data, data science and data analysis with enterprise development strategy and business management process, and constructs a systematic and complete enterprise digital management methodology, which is a rare management reference book with comprehensive use. This book fills in the blank of similar books in data analysis model development, business case development and data management process, highlights the two core elements of "people and process" in digital enterprise management, and provides rich reference materials for further study and improvement of interested readers.

In the era of digital economy, all kinds of enterprises need to upgrade the ability of "solving problems with data analysis" to "establishing competitive advantage through data analysis". Davenport's formulation breaks through the conventional understanding of enterprise's competitive advantage by management experts. To speed up the process of data analysis and business analysis, we need to speed up the process of data analysis and business analysis. It takes about five years for a middle-level data analyst, and nearly 10 years to train a data scientist. This requires the education sector and the business sector to start cooperation at the university stage to build a team of analysts who understand business, data and analytical methods. At the same time, the enterprise process needs to meet the needs of digital transformation, in order to make the analysis results "into the system, into the process". In fact, all of these should be planned from the strategic level. This is the enterprise digital strategy, which is not taught in the traditional MBA course. Its role is to establish a data-centered corporate culture and organizational structure, and support and promote the transformation of enterprises from passive decision-making based on internal data analysis to dynamic and real-time decision-making based on big data The key to enhance the core competitiveness in the era of "economy".

On the basis of being familiar with the classic statistical analysis tools, we need to learn new analysis methods and tools, such as machine learning, data and text mining and analysis. These are also the main big data artificial intelligence methods to help us extract the information, knowledge and wisdom we need from the data. The most fascinating part of this book is the integration of the latest psychology, brain science and behavioral economics. It focuses on providing systematic solutions from the perspective of usage. In particular, it devotes a third of the book to discussing how to use data to tell "good stories" and provides best practices for implementing these strategies. This part is very practical for the middle-level managers of enterprises. It is meaningless if the good data can not be used to effectively transmit information. Data analysts must understand management, understand the psychological demands of communication objects at different management levels, and design and display their own data analysis results based on empathy. This is an art of narration. From this point of view, we have a very high demand for data analysis talents. They must understand business, management and analysis methods, have the temperament of thinkers and be good at interpersonal communication. The future belongs to those data masters who have the ability to solve problems and are good at transposition thinking. The society will be led by those who know how to learn quickly and can dig out the patterns that could not be found before from the data. Our ability to do great things with data will change every aspect of our lives.

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