Regardless of the currency you hold, all decisions should be based on data. More importantly, the authenticity of the data should be verified to improve the accuracy of decision-making.
Decision-making involves data, information, knowledge and other related contents. What is the internal logical relationship between them? Only by understanding the relationship between them can we make better decisions.
The interaction process, transformation sequence and transformation pattern between data, information, knowledge and understanding are necessary.
The relationship between data, information and knowledge
There is a parallel relationship between data, information and knowledge from an individual perspective, an inclusion relationship from a scope perspective, a hierarchical relationship from a social perspective, a chain relationship from a transformation perspective and an intersection relationship from a logical perspective.
Parallel relationship: If data, information and knowledge are regarded as separate entities, the relationship between the three is purely parallel, independent of each other, interdependent, with clear connotations and boundaries. As the most basic relationship between data, information and knowledge, the parallel relationship is manifested in that they can exist as independent entities in reality.
Inclusion relationship: Inclusion relationship holds that data covers information, information covers knowledge, and knowledge determines decision-making. For example, "30" is a piece of data, "Today's rainfall is 30mm" is a piece of information, "Today it rains, the rainfall is 30mm, due to gravity, water vapor liquefies when it is cooled" belongs to knowledge, "Beijing's rainfall today is 30mm, and traffic police are added to the city's main roads." This view holds that information comes from data, data is the basis of information, and there is no information that is separated from data. There is a similar relationship between information and knowledge, and between knowledge and decision.
Chain relationship: "Collect data → aggregate data to form information → deeply analyze information to obtain knowledge → activate knowledge to form decisions → apply logical reasoning to form decisions → execute decisions to realize value"
Data with specific purposes and that are interrelated can form information. In-depth understanding and mining of information, from data to decision-making is a gradual, causal process, where data is transformed into information, information is refined into knowledge, and knowledge is activated into decisions.
Cross-relationship: Although data, information, knowledge, and decision-making can all exist objectively as individuals, they overlap in content. For example, information comes from data, but not all data has information value;
Decision-making is the transformation and sublimation of knowledge. Although it comes from social practice, information without universality and regularity cannot become knowledge.
Basic process of decision making
This is a cyclical process. After determining the target requirements, tasks should be planned according to the target requirements, followed by information collection and data processing, and analysis and evaluation of the results. If the target requirements can be met, a report will be generated; if not, the next work cycle will need to be re-entered, and this cycle will be repeated until a report is finally generated.
1. Target needs. You must identify the focus and prioritize the issues to be solved or in a certain area.
2. Mission planning. Mission planning can maximize the use of existing resources to meet target needs. There are many factors such as the problems that need to be solved and the solutions to the problems. The quality of mission planning determines the efficiency of intelligence work.
Task planning can refer to the following steps: set task goals, break down task goals into several sub-goals, set keywords for each sub-goal (usually more than 10), and determine where and how to search to obtain relevant search data.
3. Information collection. Information collection should focus on data sources. Different task objectives and sub-objectives of different task objectives have different requirements for data sources. For example, data surveys, market surveys, information event surveys, etc. have significantly different requirements for data sources. Select and determine the appropriate data source based on the characteristics and requirements of data collection for different survey objects. After the data source is determined, it is necessary to select an appropriate search method. Proper selection of search methods is of great significance to improving search efficiency and achieving satisfactory search results. You can comprehensively consider setting keywords, using web page snapshots and web page caches, using multiple data sources simultaneously, using crawler software, and utilizing third-party auxiliary work to select an appropriate search method. Search
The choice of method has a direct guiding role in which search tool or tools to adopt.
4. Data processing: The purpose of data processing is to extract and derive valuable and meaningful data from large, messy and difficult data. It is the process of converting data into information.
The main steps of data processing include: validation - ensuring that the data is correct and relevant, sorting - arranging the data in a certain and/or different order, summarizing - extracting the key points from the data, aggregation - combining multiple data, analysis - collecting, organizing, analyzing, interpreting and presenting data, reporting - listing detailed information or summary information or calculation results, classification - classifying the data into different categories. Different professional tools can be selected for different stages of data processing.
5. Analysis and evaluation. The analysis and evaluation process is the process of using appropriate analysis methods, extracting information from data, summarizing the information, and finally forming a conclusion. Commonly used analysis methods include: association analysis, comparative analysis, logic tree analysis, correlation analysis, etc., not limited to the analysis methods described in this article.
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