Analysis of the connections and differences between dataization, informationization, digitization and intelligence (building collections)
Release Date: 2020-01-03
The concepts of dataization, informationization, digitization, and intelligence have emerged endlessly. However, there is no authoritative definition in the industry. Everyone has different opinions, and there is a tendency of "a hundred flowers blooming and a hundred schools of thought". Especially non-IT professionals are often very confused about these concepts.
In this article, the relevant definitions of dataization, informatization, digitization, and intelligence are introduced, combining the definition of the organization and the development trend of the industry, to analyze the connections and differences between the four, so that readers can better understand the relationship between them and help Digital transformation and upgrading of enterprises.
数据、信息、知识、智慧四只者之间的关系 1. The relationship between data, information, knowledge and wisdom
IBM DIKW data value system
As can be seen from the figure, data is the lowest level concept in the knowledge hierarchy, and data is the source of information, knowledge and wisdom. Different roles of enterprises have different information needs, and need to meet the information needs of supervisors at all levels.
Data: It is the use of conventional keywords to abstract the quantity, attributes, locations and interrelationships of objective things, and to save, transfer and process them artificially or naturally in this field.
Information: It is time-sensitive, has a certain meaning, and has a logical, processed, and valuable data flow for decision-making.
Knowledge: Mining information through methods such as induction, deduction, and comparison through people's participation, and precipitating its valuable part, and combining it with the existing human knowledge system, this valuable information is transformed into knowledge.
Wisdom: Based on the existing knowledge, the ability of humans to analyze, compare, and deduct solutions based on the information obtained during the movement of the material world. As a result of this ability, the valuable part of the information is mined out and made part of the knowledge architecture.
数据与数据化 2. Data and dataization
2.1 Definition of data
In the era of big data, you can realize the importance of data, but do you really understand the definition of data? The definitions of the agencies are as follows:
As early as 1946, the term data was used for the first time to clearly denote "transmittable and storable computer information."
According to Wikipedia, the meaning of data is no longer limited to the computer field, but refers to all qualitative or quantitative descriptions.
国际数据管理协会（DAMA） 2. International Data Management Association (DAMA)
DAMA considers data to represent facts in formats such as text, numbers, graphics, images, sound, and video.
This means that data can represent facts, but it should be noted that data ≠ facts-only data that meets a series of specific requirements such as accuracy, completeness, timeliness, etc. under certain requirements can express specific facts.
美国质量学会（ASQ） 3. American Quality Association (ASQ)
ASQ defines data as "a set of facts collected"; Laura Sebastian, a senior data quality architect in the United States, believes that "data is an abstract representation of selected attributes such as real-world objects, events, and concepts, Express and understand their meaning, collection, and storage through well-definable conventions. "
Objects to be described by data, including objects (persons, things, locations, etc.), time, and concepts. Among them, the data describing people, places, and things is often called master data. Because master data is typically used in multiple business processes and systems. Standardization of master data and synchronization of master data are very important for system integration and sharing.
国际标准化组织（ISO） 4. International Organization for Standardization (ISO)
ISO defines data as "reinterpretable information expressed in a formal manner suitable for communication, interpretation or processing."
Data is essentially a representation method, an artificially created symbolic form, an interpretation of the object it represents, and at the same time it needs to be interpreted.
The representation and interpretation of data to things must be authoritative, standard, and universal. Only in this way can the purposes of communication (transmission, sharing), interpretation, and processing be achieved.
To ensure that the way data is expressed and interpreted is authoritative, general, and standard, we must develop a series of standards around the data.
新牛津美语字典（NOAD） 5. New Oxford American Dictionary (NOAD)
NOAD defines data as "facts collected together for reference and analysis." Seventeenth-century philosophers used data to represent "known or assumed facts that are the basis of reasoning and calculations."
The above two definitions mean that the data can support analysis, reasoning, calculation and decision-making.
However, if we want to ensure that the data can support analysis, reasoning, calculation and decision-making, we must guarantee the truth and accuracy of the data, which is the most basic requirement.
2.2 Definition of data
Dataization: Data represents the description of a certain thing, and it can guide the business by recording, analyzing, and reorganizing the data. The core connotation of dataization is a deep understanding and essential use of big data.
2.3 Application of data
The most intuitive data is the variety of reports and reports. Dataization is to organize the digitized information in an organized manner, and provide powerful data support for decision-making through intelligent analysis, multidimensional analysis, and query backtracking.
信息化定义 3.Informatization definition
3.1 Definition of Informatization
Informatization: Digitization of information. Informatization refers to the construction of computer information systems, processes and data in traditional businesses are processed through information systems, and technology is applied to individual resources or processes to improve efficiency.
3.2 Definition of Enterprise Informatization
Enterprise informationization refers to the use of computer technology, network technology and database technology to control and integrate the management of various information in the production and operation activities of enterprises based on the optimization and reconstruction of business processes. Realize the sharing and effective use of internal and external information. In this way, improving the economic efficiency and market competitiveness of enterprises will involve the innovation of corporate management concepts, the optimization of management processes, the reorganization of management teams and the innovation of management methods.
3.3 Information Construction
(1) Information development status
In the information age, enterprises have constructed systems that support the business environment, such as OA, HR, financial systems, procurement systems, knowledge management systems, ERP, etc., and formed a shaft-type "chimney". Due to heterogeneous systems or inconsistent data standards, when data is aggregated as a whole, there is always a one-sided distortion of the data. The accuracy of macro data statistics is not good, the personalized analysis of data cannot meet the needs, and the connection between data and equipment at the micro level cannot be communicated.
(2) Characteristics of informatization
Human activities are dominated by the physical world, and a small number of behaviors are improved and enhanced by means of information technology.
Human thinking mode is still offline process thinking, and informationization serves offline physical world activities. When the active online and offline rules collide, the offline physical world is the main.
Process is the core, software system is a tool, and data is a by-product of the operation of the software system.
3.4 Informatization Construction Phase
The current enterprise information management systems in use are: OA office automation systems; CRM systems for managing customer relationships; ERP enterprise resource planning systems; MES manufacturing execution management systems.
Traditional enterprises have undergone the following four stages of informatization:
(1) Before 2000, during the construction phase of MIS system (OA, financial software, etc.), the electronicization of manual operations solved the problems of efficiency and labor saving. Information systems are basically decentralized and independent construction;
(2) From 2000 to 2010, ERP focused on the construction stage to solve communication problems between different departments. The application integration stage is ESB, and SOA talks a lot;
(3) From 2011 to 2017, IT resource integration, data analysis, and decision support were at the core. IT resource integration cloudization (IaaS), historical application system PaaS;
(4) From 2018 to the present, the concept of China-Taiwan has emerged, the structure is layered, and the new and old structures co-exist for quite some time.
数字化与数字化转型 4. Digitalization and Digital Transformation
Digitalization is the best way to promote informationization. Digitalization brings dataization.
4.1 Digital definition
Digitalization (Digitalization): Digitalization is to simulate physical systems in computer systems, embody the physical world in computer systems, use digital technology to drive organizational business model innovation, drive business ecosystem restructuring, and drive enterprise service revolution.
According to Gartner's definition, business digitalization refers to the use of digital technologies to change business models and provide new opportunities to create revenue and value. It is a process of turning to digital business.
4.2 Background of Digital Transformation
Digital survival is a new way of survival based on information technology in modern society. In the digital living environment, people's production methods, lifestyles, communication methods, thinking modes, and behavior modes all take on a new look. In 2017, the "digital economy" was officially included in the party's 19th National Congress report. In 2018, China's digital economy totaled 31.3 trillion yuan, accounting for 34.8% of GDP. In 2019, national leaders clearly stated in the congratulatory letter to the China International Digital Economy Expo 2019 that the digital economy has a profound impact on the economic and social development, global governance systems, and human civilization process of various countries, and highly summarized China's guiding principles and practical measures for developing the digital economy.
Enterprises in the torrent of the digital age must also keep pace with the times and resonate at the same frequency as the times to avoid being abandoned by the times.
Digital transformation background
We believe that digital transformation is: through the in-depth use of digital technology, building a fully perceptual, fully connected, full scene, and intelligent digital world, and then optimizing the business of rebuilding the physical world, and innovating traditional management models, business models, and business models And reshape to achieve business success.
4.3 The nature of transformation
Digital transformation is essentially business transformation. Digital transformation is essentially a deep transformation and reconstruction of business, management, and business models driven by information technology. Technology is the fulcrum and business is the core.
4.4 Purpose of the transformation
In the process of digital transformation, the use of new technologies is not the purpose. The fundamental purpose of transformation is to enhance the competitiveness of products and services, so that enterprises can gain greater competitive advantages.
4.5 Challenges Facing Digital Transformation
For most enterprises, the challenges of digital transformation come from all aspects: from technology control to business innovation, from organizational change to cultural remodeling, from digital capacity building to talent training, so the success of digital transformation cannot be achieved overnight. Digital transformation is a long and arduous task, and it takes 3-5 years or more for most enterprises to achieve significant results.
智能与智能化 5. Intelligence and intelligence
5.1 Definition of Intelligence
Intelligence: The process from feeling to memory to thinking is called "wisdom". The result of wisdom produces behavior and language. The process of expression of behavior and language is called "ability", and the two are collectively called "intelligence".
5.2 Intelligent definition
Intelligent: work that enables the subject to have sensitive and accurate sensing functions, correct thinking and judgment functions, adaptive learning functions, and effective execution functions. Intelligence is a process from manual, automatic to autonomous.
5.3 Intelligent application
Artificial intelligence: The intelligence exhibited by artificially manufactured systems; it is the technology and methodologies that enable machines / systems to complete complex tasks that normally require human intelligence to complete. Artificial intelligence is the main way to achieve intelligence.
The main ways to achieve artificial intelligence: big data intelligence, swarm intelligence, cross-media intelligence, human-computer hybrid enhanced intelligence, and brain-like intelligence. In all these areas, a new generation of artificial intelligence technology is emerging.
More than 60 years after the birth of artificial intelligence technology, despite three ups and downs, it has achieved great success. Machine learning technology status:
Data sample level: manually label training data and manually screen training samples;
The level of the learning process: manually set the network structure and artificially design the model algorithm;
Environmental task level: manually preset application scenarios and manually specify execution tasks.
数据化、信息化与数字化联系与区别 6. The connection and difference between data, information and digital
The concepts of informatization and digitization tend to be systematic, while dataization involves the concept of the executive layer, and all business data is digitized. With data analysis as the starting point, discovering problems, analyzing problems, solving problems through data, breaking the traditional experience-driven decision-making method, and realizing scientific decisions. In the end, informatization, digitization, and dataization minimize the manual effort and time to achieve the problem of artificial intelligence changing work efficiency.
Focus on the establishment and management of business information
The related information of the enterprise, through the various information resources recorded, relates to the results and management of the business of each link.
Formation and invocation of object resources focusing on product areas
Based on the support and capabilities provided by information technology, business and technology can truly interact and change the traditional business operation model.
Focus on results
Organize the digitized information, and provide powerful data support for decision-making through intelligent analysis, multi-dimensional analysis, and query backtracking.
Application focused on work process
Work that enables the subject to have sensitive and accurate sensing functions, correct thinking and judgment functions, adaptive learning functions, and effective execution functions.
6.1 Business data
Business dataization: build a professional information system to realize the dataization of enterprise business management. Business-related forms and information flows are stored digitally, but simple digital storage has not yet reached the stage of dataization. Information can only be used through internal indicators (also known as modelling) to achieve business data availability, analysis, It can be improved, and it can only be called business data when entering the operation link. The benefit of business dataization is the realization of more detailed operations. In the process of business dataization, metadata plays a core driving role.
6.2 Data Commercialization
Data commercialization refers to the establishment of an enterprise's data center, forming the accumulation of data assets, supporting data governance and data services, and designing data service applications in conjunction with enterprise business development to provide data value to the enterprise. Business production data, data feedback business. Emphasize the transformation of data into advisory information to help customers achieve business goals, emphasize the application of data, and focus more on making data valuable.
6.3 Intelligence is the ultimate goal
Intelligence is the ultimate goal of informatization, digitization, and dataization, and it is also an inevitable trend of development.
6.4 The difference between digital and information
Digitization is a high-level stage of informatization. It is an extensive and in-depth application of informatization. It is an extension from collecting and analyzing data to forecasting data and operating data. Digitalization is no longer an informatization support, but it is just an air tower.
Comparison of information and digital
Digitalization does not depart from informationization. Digitization is to solve the problem of information islands between information systems in information construction, and to realize the interconnection and interconnection of data between systems. Then carry out multi-dimensional analysis on these data, digitally model the operation logic of the enterprise, and guide and serve the daily operation of the enterprise.
6.5 Digital Construction
(1) Application of digital methodology
Value-oriented, changing the application of data from top to bottom, breaking the "data chimney" between systems. Through the construction of a series of scenario application services based on the data warehouse and platform, it supports the corporate governance model from flat to three-dimensional, and describes the management and control elements in multiple dimensions. Wider.
Data is no longer just linked, but can achieve collusion between information and equipment, that is, micro-digital processing, which can realize the conversion of analog information and digital information, and also can aggregate digital information into intelligent macro data that conforms to human thinking habits, and achieve Comprehensive support for macro applications.
(2) Digital characteristics
Most human activities and interactions are performed in the digital world, and a small amount of decision-making command information is returned to the physical world to command equipment and machines to complete operations.
Data is the projection of the digital world of the physical world, the foundation of everything, and information systems are the processes and tools for generating data.
The concepts of informatization and digitization tend to be systematic, while dataization involves the concept of the executive layer. All business is data-based, and all data is business-oriented. With data analysis as the starting point, discovering problems, analyzing problems, solving problems through data, breaking the traditional experience-driven decision-making method, and realizing scientific decisions. In the end, informatization, digitization, and dataization minimize the manual effort and time to achieve the problem of artificial intelligence changing work efficiency.
This article explains in detail the connotations of dataization, informationization, digitization, and intelligence, so that readers can better understand and master related concepts in the field of data. Data analysis, informationization, digitization, and intelligence are compared and analyzed from their respective perspectives, and the previous connections and differences between the four are drawn.
In conclusion, digital transformation is a long-term and arduous task. Digitalization is the best way to promote informationization. Digitalization brings dataization. Artificial intelligence is the main way to achieve intelligence. Business production data and data feed back the business, thereby driving digital transformation. (Special thanks to Mr. Cai Chunjiu for his professional guidance!)
 National Standard GB / T 36073-2018 Data Management Capability Maturity Evaluation Model
 CCSA TC601 Big Data Technology Standards Promotion Committee, Institute of Cloud Computing and Big Data, China Academy of Information and Communications Technology · White Paper on Data Asset Management Practices (4.0)
 The DAMA Guide to the Data Management Body of Knowledge
 Boris Otto · Data Governance
 Weber K, Otto B, Oterle H (2009). One Size Does Not Fit All --- A Contingency Approach to Data Governance
 New Oxford American Dictionary (NOAD)
 International Organization for Standardization (ISO)
 American Quality Association (ASQ)
Source: Data Craftsman Club