Basic internet knowledge

Structured data refers to data that can be stored in a relational database following a predefined data model. These data have a clear data structure and format, such as rows and columns in a table, and each field has a predefined data type (such as integer, string, date, etc.). Structured data is easy to retrieve and analyze, and can be queried and operated by SQL (Structured Query Language). Some common examples of structured data include customer information, financial data and inventory records, which are usually stored in relational databases such as MySQL and Oracle. The characteristics of structured data include:  Definable attributes: structured data has the same attributes for all data values. For example, each reservation record can have attributes such as reservation name, activity name, activity date and reservation amount. Relational attributes: Structured data tables have common values that link different data sets together. For example, you can use the fields of customer id and booking id to associate customer data with reservation data. Quantitative data: Structured data is helpful for mathematical analysis. For example, you can calculate and measure the frequency of attributes and perform mathematical operations on numerical data. Stored: Structured data is usually stored in relational databases and managed by SQL. SQL allows you to define a data model called schema, and determine preset rules (such as fields, formats and values) for data under this model. Easy to use: Structured data is easy to understand and access, and updating and modifying operations are relatively simple. Storage efficiency is high, because fixed-length storage units can be allocated to data values. Scalability: Structured data expands according to the algorithm, and with the increase of data volume, it can increase the storage and processing capacity. Analysis: Machine learning algorithms can analyze structured data and identify common patterns of business intelligence. You can use SQL to generate reports and modify and maintain data. Unstructured data refers to data with no predefined data model, usually text or multimedia content. This kind of data has no fixed format or structure, so it is difficult to process with traditional databases and data analysis tools. Examples of unstructured data include social media posts, video and audio files, documents and PDF files. The challenge of unstructured data is that advanced processing methods, such as natural language processing or image analysis, are needed to extract meaningful insights. The characteristics of unstructured data include:  There is no fixed format or structure: the format of unstructured data is diverse and irregular, including text, image, audio, video and so on. Difficult to process with traditional tools: Unstructured data need to be processed with special technologies and tools (such as natural language processing, image recognition, etc.). Stored in file system or NoSQL database: Unstructured data is usually not stored in relational database like structured data, but in file system, digital asset management system, content management system and version control system. Complex algorithms are needed for analysis: the analysis of unstructured data usually involves more complex programming operations and machine learning. These analyses can be accessed through various programming language libraries and specialized design tools using artificial intelligence. The amount of data is usually large: the storage scale of unstructured data is usually larger than that of structured data, and more funds, space and resources are needed to store these data. Generally speaking, the main difference between structured data and unstructured data lies in their organization, storage methods and the difficulty of analysis. Structured data is more suitable for direct analysis and reporting because of its tight organization and easy search. Unstructured data, on the other hand, need more advanced processing technology to extract meaningful insights because of its lack of predefined format.

The quantization characteristic of hard data is one of its most remarkable characteristics. This kind of data usually exists in digital form, which can be accurately measured and counted. For example, the annual sales of a company can be quantified as a specific monetary amount, or the defect rate of a product can be quantified as the number of defects per thousand units. This quantitative ability makes hard data highly practical in comparison, trend analysis and prediction.  The objectivity of hard data means that they are based on observable and verifiable facts and are not influenced by personal prejudice or subjective interpretation. This objectivity provides a solid foundation for decision-making, because it reduces the influence of personal feelings or preset positions on data interpretation.  Soft data are usually non-digital, and they contain rich descriptive information, such as personal opinions, emotions, attitudes and perceptions. This kind of data is often collected through interviews, questionnaires, social media and other channels, which can reveal people's real feelings and deep-seated needs. The subjectivity of soft data means that they are influenced by personal feelings and experiences, so there may be differences and uncertainties. This subjectivity makes soft data need more explanation and understanding in analysis.  The source of hard data and soft data is one of their most remarkable differences. Hard data usually comes from structured databases and statistical data, which are obtained through standardized measurement and recording processes, such as sales records, financial statements, census data, etc. The collection process of these data often follows strict protocols and standards to ensure the consistency and comparability of the data.  In contrast, soft data comes from more informal and unstructured channels, such as interviews, questionnaires, social media posts, customer feedback and so on. These data usually contain personal opinions and feelings, so they are more diverse and complex. The processing methods of hard data usually involve mathematical and statistical methods, such as regression analysis, time series analysis, hypothesis testing and so on. These methods depend on the quantitative characteristics of data and can provide accurate analysis results.  The processing of soft data needs more complex qualitative analysis methods, such as content analysis, discourse analysis, topic coding and so on. These methods aim to extract meaningful information and patterns from unstructured text and media content.  Hard data are often used in decision support and prediction models, because their quantitative characteristics make them suitable for building mathematical models and making accurate calculations. For example, in the financial field, transaction data can be used to build a risk assessment model; In the retail industry, sales data can be used to forecast inventory demand. Soft data is helpful to understand user behavior and market trends because they reveal consumers' emotions, attitudes and preferences. In marketing and brand management, soft data can help enterprises better understand their target customers and formulate more effective communication strategies.  In practical application, hard data and soft data are often combined to obtain more comprehensive analysis results. For example, in market research, sales data (hard data) can be combined with customer satisfaction survey (soft data) to evaluate product performance and market acceptance.  In this study, we deeply discussed the definition, characteristics, differences and their combined application of hard data and soft data. Hard data plays a key role in decision support and prediction model because of its quantification, objectivity, accuracy and easy analysis. Soft data, with its characteristics of qualitative data, subjectivity, fuzziness and difficulty in analysis, provides us with a deep understanding of user behavior and market trends. By comparing hard data with soft data, we find that there are significant differences in their sources, processing methods and uses. Hard data usually comes from structured databases and statistical data, which are suitable for analysis by mathematical and statistical methods, and are often used in decision support and prediction models. Soft data, on the other hand, comes from unstructured channels and needs more complex qualitative analysis methods, which is helpful to understand user behavior and market trends.  In practical application, the combination of hard data and soft data can provide a more comprehensive analysis perspective. For example, in the retail industry, the combination of sales data (hard data) and customer satisfaction survey (soft data) can help managers better understand customers' buying behaviors and preferences.  In the real estate market, the combination of housing sales price (hard data) and consumer confidence survey (soft data) can improve the accuracy of market forecast. In addition, the combination of hard data and soft data also shows great potential in user behavior analysis and market trend insight. E-commerce websites optimize user experience by analyzing users' purchase records (hard data) and online behaviors (soft data). Beverage companies adjust their product development and marketing strategies by analyzing the correlation between discussion (soft data) and sales data (hard data) on social media. In a word, hard data and soft data have their own unique values and application scenarios. In data analysis and research, their combined use can provide richer and more accurate insights and help enterprises and researchers make more informed decisions. In the future, with the progress of data analysis technology, we expect that the combination of hard data and soft data can reveal more unknown patterns and trends and provide more solid support for decision-making.