The Concept and Function of 1. Data Visualization

Through the "visualization" way, complex data through graphical means to effectively express, accurate and efficient, concise and comprehensive transmission of certain information, and even we help to discover certain laws and characteristics, mining the value behind the data.

The role of data visualization is mainly in the following areas:

Improve the comprehensibility of the data. Data visualization can transform abstract numbers and words into figurative graphics and colors, making data easier to be recognized and understood by the human visual system. Data visualization can also take advantage of different chart types and layouts to highlight the focus and relationships of the data, helping readers quickly capture the meaning and value of the data.

Enhance the memorability of data. Data visualization can use human visual memory and image association to combine data with visually impactful graphics and colors to improve data memorability. Data visualization can also use storytelling techniques to connect data with emotional and logical stories to enhance data memorability.

Increase the credibility of the data. Data visualization can improve the credibility of data by using objective data sources, clear data labeling, reasonable data processing, and transparent data display. Data visualization can also improve the credibility of the data by using appropriate chart types, avoiding misleading visuals, and providing context for the data.

Enhance the actability of data. Data visualization can enhance the actionability of data by using dynamic interactions that allow readers to filter, sort, compare, and aggregate data. Data visualization can also enhance the actability of data by using guiding visual cues that allow readers to analyze, interpret, evaluate, and reason about the data.

Application scenarios and value of 2. data visualization in competitor research

Competitor research refers to the collection, analysis and evaluation of competitors' relevant information to understand competitors' strategies, strengths, weaknesses, opportunities and threats, so as to provide a basis and reference for their own strategic decisions. The process and results of competitor research often involve a large amount of data and information, such as competitor fundamentals, market share, product characteristics, pricing strategies, marketing activities, customer satisfaction, financial status, innovation capabilities, and trends.

The application scenarios of data visualization in competitor research mainly include the following aspects:

Data collection. Data visualization can help competitor researchers quickly obtain and organize data from a variety of data sources, such as networks, databases, reports, questionnaires, interviews, etc. Data visualization can also help competitor researchers to clean, transform, merge and other pre-processing of data for subsequent analysis and presentation.

Data analysis. Data visualization can help competitor researchers conduct multi-dimensional, multi-level, multi-angle analysis of data, such as descriptive analysis, correlation analysis, causal analysis, predictive analysis, etc. Data visualization can also help competitor researchers discover patterns and characteristics in the data, such as trends, patterns, anomalies, and differences.

Data presentation. Data visualization can help competitor researchers present data to decision makers and stakeholders in an intuitive, vivid and persuasive way, such as charts, dashboards, data screens, storyboards, etc. Data visualization can also help competitor researchers highlight the focus and relationships of the data, convey the meaning and value of the data, and make recommendations and actions for the data.

The value of data visualization in competitor research is mainly in the following aspects:

Improve the efficiency of competitor research. Data visualization can reduce the time and cost of competitor researchers in data collection, analysis and presentation, and improve the efficiency of competitor research. Data visualization can also use automation and intelligent technology to achieve real-time data updates and dynamic display, improve the efficiency of competitor research.

Improve the quality of competitor research. Data visualization can increase the understanding and memory of data by competitor researchers and improve the quality of competitor research. Data visualization can also improve the credibility of competitor research and increase the persuasiveness and authority of data by using objective and transparent data presentation.

Increase the impact of competitor research. Data visualization can enhance the impact of competitor research and increase the appeal and resonance of data by using vivid and attractive data presentation. Data visualization can also enhance the feasibility of competitor research and increase data participation and feedback through the use of guided and interactive data presentations.

Design Principles and Steps for 3. Data Visualization

The design of data visualization is a complex and interesting process, which involves data selection, processing, analysis, presentation and other aspects, as well as visual, cognitive, aesthetic, communication and other fields. In order to ensure that the design of data visualization can achieve the desired purpose and effect, we need to follow some basic principles and steps.

The design principles of data visualization are as follows:

Goal-oriented. The design of data visualization should be goal-oriented, clarifying the purpose and audience of data visualization, as well as the information and value to be conveyed by data visualization. The design of data visualization should be based on the target and audience, select the appropriate data, charts, colors, layout and other elements, as well as the appropriate language, style, story and other techniques to achieve the goal of data visualization.

Concise and clear. The design of data visualization should be based on the principle of simplicity and clarity, avoiding the use of too many data, charts, colors, text and other elements, resulting in data visualization of congestion and confusion. The design of data visualization should remove irrelevant data and information, highlight the focus and relationship of the data, and make the meaning and value of data visualization clear at a glance.

True and accurate. The design of data visualization should be based on truthfulness and accuracy, ensure the objectivity and transparency of the source, labeling, processing, display and other aspects of the data, and avoid the use of erroneous, outdated, biased, and misleading data and information, resulting in distortion and misunderstanding of data visualization. The design of data visualization should use appropriate charts, proportions, scales, colors and other elements to ensure the integrity and correctness of the data, so that the credibility and authority of the data visualization is beyond doubt.

Interesting and useful. The design of data visualization should aim to be interesting and useful, increase the attractiveness and influence of the data, stimulate the emotion and resonance of the data, and promote the participation and feedback of the data. The design of data visualization should use vivid graphics, colors, animations, sound effects and other elements to increase the beauty and interest of the data, so that the appeal and resonance of data visualization is unparalleled. The design of data visualization should use interactive methods to increase the operability and customizability of data, and make the participation and feedback of data visualization impeccable.

The design steps for data visualization are as follows:

Data collection. Data collection is the first step in the design of data visualization, which involves obtaining and organizing data from a variety of data sources, such as networks, databases, reports, questionnaires, interviews, etc. The process of data collection should select the relevant data types, ranges, dimensions, metrics, etc., as well as the appropriate data format, quality, quantity, etc., based on the target and audience of the data visualization to ensure the validity and availability of the data.

Data processing. Data processing is the second step in the design of data visualization, which involves the pre-processing of data such as cleaning, transformation, and merging for subsequent analysis and presentation. The process of data processing should be based on the target and audience of data visualization, select the appropriate data tools, methods, techniques, etc., to ensure the accuracy and consistency of the data.

Data analysis. Data analysis is the third step in the design of data visualization, which involves multi-dimensional, multi-level and multi-angle analysis of data, such as descriptive analysis, correlation analysis, causal analysis, predictive analysis and so on. The process of data analysis should select appropriate data models, algorithms, metrics, etc. based on the target and audience of the data visualization to ensure that the data is meaningful and valuable.

Data presentation. Data presentation is the fourth step in the design of data visualization, which involves presenting data to decision makers and stakeholders in an intuitive, vivid and convincing way, such as charts, dashboards, data screens, storyboards, etc. The process of data presentation should be based on the target and audience of data visualization, select the appropriate chart type, color, layout, text and other elements, as well as the appropriate language, style, story and other techniques to ensure that the data is understandable, memorable, credible, and actionable.

Example of a competitor survey report 4. data visualization

To better illustrate the application and value of data visualization in competitor research, we use Champ Consulting as an example to present a data visualization competitor research report, evaluate it and make recommendations for improvement.

Champu Consulting is a company specializing in consulting services in the fields of strategy, management, innovation, and digitalization. Its customers include leading companies and public institutions in various industries. The main competitors of Champ Consulting are the following:

McKinsey & Company (McKinsey & Company): McKinsey & Company is the world's largest strategic consulting firm. It provides consulting services on strategy, organization, operations, digitalization, and innovation to 90% of the world's largest companies and government agencies.

Boston Consulting Group (Boston Consulting Group): Boston Consulting Group is one of the world's most well-known strategic consulting firms. It provides strategy, management, innovation, and digital consulting services to leading companies and public institutions in various industries around the world.

Bain & Company is one of the world's most popular strategy consulting firms, providing strategy, management, operations, digitalization, innovation and other consulting services to leading companies and public institutions in a wide range of industries around the world.