Displaying data graphically – how to visualize your company key figures effectively

The world is data-driven and the ability to visualize data effectively is therefore essential. With the right visualization, complex information can be presented in an understandable way and informed decisions can be made. Data visualization includes a variety of chart types, such as bar charts, line charts, pie charts and column charts, each with their own benefits and uses.

This blog post provides a comprehensive overview of the various methods and tools for visualizing data. We look at how charts such as histograms and scatter plots can be used to identify trends and patterns in data. One example of this is the visualization of data series from a CSV file, which can be displayed using different charts.

The design of the visualizations, including the axis labels and the choice of colors, plays a decisive role in the comprehensibility of the information presented. Infographics and diagrams help to present complex data in an appealing and clear way.

We will also look at the evolution of data visualization, from simple tables and statistics to modern, interactive infographics and visualizations made possible by big data and advanced software tools.

Whether to support business decisions or to illustrate research results - this article shows how you can create impressive visualizations with the right chart types and tools. Discover the art of data visualization and use its many possibilities to transform data into valuable experiences.

What is data visualization?

Data visualization is the art and science of presenting data in graphical form in order to present complex information and correlations in an understandable and clear way. This method makes it easier to recognize trends, patterns and outliers in data series and therefore easier to interpret.

Definition of

Data visualization refers to the presentation of data using visual elements such as charts, graphs and infographics to make information easier to understand.

Basic concepts

• Diagrams: Graphical representations of data that make it possible to recognize correlations and trends.
• CSV file: A commonly used file format in which data is stored in tabular form and which often serves as a starting point for creating charts.
• Chart types: Different types of charts, such as bar charts, line charts, pie charts and column charts, each offering specific benefits and suitable for different applications.

Examples of known data visualizations

• Bar charts: Ideal for visualizing the frequency or comparison of values between different categories.
• Line charts: Show the progression of data over a certain period of time and are useful for recognizing trends.
• Pie charts: Illustrate the percentage distribution of a whole over different components.
• Histograms: Used to visualize the distribution of a data set and are widely used in statistics in particular.

Data visualization is a crucial method for putting the vast amount of information available to us today into an understandable and usable context. Whether in science, business or everyday life, the ability to visualize data is invaluable. We go into more detail about the types below.

Advantages of data visualization

Data visualization offers numerous advantages that help to make complex information understandable and to make informed decisions. Here are some of the most important benefits:

Improved data comprehensibility

• Easier interpretation: Graphical representations such as bar charts and line charts help to quickly recognize trends and patterns in the data.
• Quick overview: Charts provide a quick overview of large amounts of data and make it easier to gain important information. Chart types such as bar charts are particularly helpful.

Support in the decision-making process

• Better basis for decision-making: Visual processing of data enables well-founded decisions. Pie charts and histograms help with resource planning and the presentation of results.
• Identification of patterns: Scatter plots show relationships and correlations between data points, which facilitates decision-making.

Effective communication

• Clarity: Visual representations are often clearer and more concise than tables or pure columns of figures. Infographics and diagrams improve comprehensibility.
• Engagement: Diagrams and infographics arouse the reader's interest and encourage more intensive engagement with the information.

Efficiency and time savings

• Fast analysis: Tools such as Excel and Tableau allow you to quickly create charts and graphs from a CSV file. This saves time and increases efficiency.
• Automation: Many data visualization tools offer automation functions that increase efficiency and save time.

Facilitated communication of complex data

• Clear presentation: Diagram types such as box plots and bar charts illustrate complex data structures and make it easier to recognize correlations. This is particularly advantageous for big data.

Example

A company that displays its sales data in a bar chart can quickly identify which products are selling best and where improvements are needed. A line chart can also show the development of sales figures over time.

The diverse possibilities of data visualization allow complex information to be made understandable and usable. This is invaluable in all areas - from science and business to everyday decision-making.

Types of data visualizations

There are many different types of data visualizations, each with their own strengths and specific use cases. Here we would like to show you the most common chart types and their typical uses:

Diagram for categories: Bar chart

• Definition: Bar charts represent data with rectangular bars, where the length of the bars represents the values.
• Use: Ideal for displaying frequencies or comparing different categories.
• Example: A company can use bar charts to compare the sales figures of different products. The x-axis shows the products and the y-axis the sales figures.

Diagram for data curves: Line diagram

• Definition: Line charts show data points connected by lines to represent trends over time.
• Use: Suitable for displaying data trends over time.
• Example: The development of share prices can be effectively illustrated with a line chart. The x-axis shows the time period and the y-axis the share prices.

Diagram to visualize proportions: Pie chart

• Definition: Pie charts (or pie charts) show percentage distributions as segments of a circle.
• Use: Well suited for visualizing parts of a whole.
• Example: A company can use a pie chart to show the market share of its products.

Diagram for comparing data categories: Bar chart

• Definition: Similar to bar charts, but the bars are aligned vertically.
• Use: Useful for comparing data categories, especially if the number of categories is manageable.
• Example: The monthly revenue of a company can be displayed in a bar chart. The x-axis shows the months and the y-axis the revenue.

Diagram for recognizing patterns: Histogram

• Definition: Histograms show the distribution of a continuous data set in the form of adjacent bars.
• Use: Ideal for displaying the distribution of data and recognizing patterns.
• Example: The distribution of body sizes in a population can be visualized with a histogram. The x-axis shows the size classes and the y-axis the frequency.

Diagram for the representation of variables: Scatter diagram

• Definition: Scatter plots show data points on a two-dimensional plane to identify correlations between two variables.
• Use: Useful for representing relationships between two quantitative variables.
• Example: A scatter chart can be used to show the relationship between advertising expenditure and sales. The x-axis shows advertising expenditure and the y-axis shows sales.

Diagram for identifying outliers: Boxplots

• Definition: Boxplots visualize the distribution of data using quartiles and medians, often with outliers plotted.
• Use: Well suited for displaying the distribution and identifying outliers.
• Example: The distribution of test grades for a class can be displayed using a box plot. The y-axis shows the grades and the x-axis the different classes.

Diagram for displaying complex data: Infographic

• Definition: Infographics combine diagrams, text and images to present information in a clear and understandable way.
• Use: Suitable for presenting complex data in an easily understandable form.
• Example: Company reports often use infographics to visualize complex information.

These different chart types offer a variety of ways to graphically display data and gain the desired experience. The choice of the right chart depends on the specific requirements and the type of data to be displayed.

In addition, modern, specialized data visualization tools such as Qlik make it easy to create and automate charts. The right visualization method can help to present data efficiently and effectively, which is particularly important in the analysis of big data and in the creation of reports and presentations.

Data visualization makes it possible to present data in an understandable form that is both informative and visually appealing. It is an indispensable tool in modern data analysis and communication.

Tools and software for data visualization

Choosing the right tools and software for data visualization is crucial to effectively analyze and display data. Here are some of the most common tools and software solutions used to create charts and infographics:

Qlik Sense

• Functionality: Qlik Sense is a self-service data visualization software that enables users to create interactive and dynamic dashboards. It offers a powerful associative engine that seamlessly connects data from different sources.
• Benefits: Fast data integration and analysis, interactive dashboards, user-friendly interfaces, integration of AI and machine learning.
• Example: A company can use Qlik Sense to integrate sales, marketing and financial data and create dashboards that are updated in real time.

Tableau

• Functionality: Tableau is a specialized data visualization software that enables interactive and dynamic charts and dashboards. It supports a variety of chart types and data sources.
• Advantages: Interactive visualizations, easy handling, powerful data analysis.
• Example: Tableau can be used to transform extensive big data sets into clear and interactive dashboards.

Google Data Studio

• Functionality: Google Data Studio enables the creation of interactive reports and dashboards. It integrates seamlessly with other Google services and supports numerous data sources.
• Advantages: Free, cloud-based, user-friendly.
• Example: A company can use Google Data Studio to create real-time updated reports and charts for management.

Power BI

• Functionality: Power BI from Microsoft is a powerful tool for creating interactive visualizations and dashboards. It supports various chart types and offers extensive data analysis functions.
• Advantages: Integration with Microsoft services, interactive reports, extensive analysis options.
• Example: Power BI can be used to merge sales data from different sources and display it in dynamic reports.

Python libraries (Matplotlib, Seaborn)

• Functionality: With libraries such as Matplotlib and Seaborn, Python offers powerful tools for creating diagrams and graphics. These libraries are particularly useful for data-driven analyses and scientific visualizations.
• Advantages: Flexibility, customization options, integration with other Python tools.
• Example: A data scientist can use Matplotlib to visualize complex data analysis and display the results in a scatter plot.

R packages (ggplot2)

• Functionality: With the ggplot2 package, the R programming language offers a powerful tool for data visualization. It enables the creation of high-quality diagrams and graphics.
• Advantages: Highly customizable, powerful graphical representations, widely used in statistics and data science.
• Example: A statistician can use ggplot2 to visualize the results of a statistical analysis in appealing diagrams.

These tools and software solutions offer a wide range of options for visualizing data effectively and gaining the desired insights. Choosing the right tool depends on the specific requirements and the type of data to be visualized. By using the right software, data can be presented efficiently and effectively, which is particularly important when analyzing big data and creating reports and presentations.

Best practices for effective data visualization

In order to visualize data effectively, it is important to follow certain best practices. These help to present the data in a clear and understandable way and ensure that the visualizations convey the intended information.

Selecting the right diagram type

• Suitable chart type: Choose the chart that best suits the data and information to be displayed. For example, bar charts are good for comparing categories, while line charts show trends over time.
• Example: A bar chart is ideal for comparing the sales figures of different products, while a line chart can be used to visualize the sales trend over several years.

Use of clear and comprehensible axes and labels

• Labels: Make sure that the x-axis and y-axis are clearly labeled and the units are clearly indicated. This makes it easier to understand the information displayed.
• Example: In a bar chart showing the monthly revenue of a company, the months on the x-axis and the revenue on the y-axis should be clearly labeled.

The importance of colors and design

• Colors: Use colors sensibly to highlight data and distinguish between different data categories. However, avoid using too many colors, as this can impair clarity.
• Design: A clear and simple design helps to present the data in an understandable way. Too many design elements can be distracting.
• Example: A pie chart with clearly defined color segments can clearly illustrate the market shares of different products.

Avoidance of distortions and misinterpretations

• Scaling: Ensure that the axes are scaled correctly to avoid distortions. Incorrect scaling can display the data incorrectly and lead to misinterpretations.
• Example: A histogram for displaying the distribution of body sizes should scale the size classes evenly to ensure correct visualization.

Interactive vs. static visualizations

• Interactive visualizations: Use interactive visualizations if you want users to be able to explore the data themselves. Tools such as Tableau or Power BI offer extensive possibilities for this.
• Static visualizations: Static visualizations may be sufficient for reports and presentations. These should be clear and concise.
• Example: An interactive dashboard can help users explore the impact of different marketing strategies on sales, while a static bar chart summarizes the most important results in a report.

Use of suitable tools and software

Use suitable tools and software such as Qlik Sense or special Python libraries such as Matplotlib and Seaborn to create high-quality diagrams and infographics.

By following these best practices, you can ensure that your data visualizations convey the desired information effectively and comprehensibly. Whether in reports, presentations or interactive dashboards, thoughtful visualization helps make complex data understandable and informed decisions.

Challenges and solutions for data visualization

Data visualization comes with some challenges that need to be overcome in order to create accurate and understandable representations. Here are some of the most common challenges and corresponding solutions:

Dealing with large amounts of data

• Challenge: Large amounts of data, as is the case with big data, can be difficult to process and visualize.
• Solution: Use powerful tools and software that have been specially developed for processing large amounts of data, such as Tableau or Power BI. These tools can process data efficiently and create interactive visualizations.

Representation of complex data structures

• Challenge: Complex data structures and correlations are difficult to present in an understandable way.
• Solution: Use diagram types such as network diagrams or tree diagrams, which are particularly suitable for visualizing complex structures. Interactive visualizations can also help to make complex relationships easier to understand.

Ensuring data integrity and accuracy

• Challenge: Data errors and inaccurate representations can lead to misinterpretations.
• Solution: Check the data thoroughly for errors and use reliable data sources. Automated tools for data cleansing and processing can also help to ensure data integrity.

Balance between aesthetics and functionality

• Challenge: An appealing design can sometimes impair comprehensibility.
• Solution: Make sure that the design of the visualization conveys the information clearly and precisely. Use a simple and functional design that puts the data front and center. A well-designed bar chart or column chart can be both aesthetically pleasing and informative.

Choosing the right type of diagram

• Challenge: Choosing the right chart type to display the data can be difficult.
• Solution: Consider the type of data and the information to be conveyed. For example, a pie chart is suitable for displaying proportions, while a line chart is more suitable for time series data. Use examples and templates to find the right chart type.

Adaptation to different target groups

• Challenge: Different target groups have different needs and levels of understanding.
• Solution: Adapt the visualizations to the respective target group. Use simple and clear visualizations for a general audience and more detailed visualizations for experts. Infographics can help to make complex data understandable for a wide audience.

Use of suitable tables and infographics

• Challenge: Tables and infographics must be designed in such a way that they convey the essential information clearly and precisely.
• Solution: Use tables to clearly present detailed data series and figures. Infographics combine different types of diagrams to present information in a clear and understandable way.

Example

A company that presents its sales data in a bar chart must ensure that the data is correct and up-to-date. By using tools such as Excel or Tableau, the data can be automatically updated and presented in an appealing design. A simple design with clear labels and a suitable choice of colors can help to present the sales figures in a clear and understandable way.

By overcoming these challenges and applying the above solutions, you can ensure that your data visualizations are effective and informative. This is particularly important for making informed decisions and understanding complex data.

Closing words

Data visualization is a powerful tool for presenting complex data in an understandable and clear way. By using different chart types such as bar charts, line charts, pie charts and bar charts, important information can be presented clearly and precisely. Tools such as Qlik Sense, Tableau and Power BI offer numerous options for importing data from a CSV file or other sources and converting it into appealing visualizations.

A good visualization uses a suitable design and ensures that the data is correct and up-to-date. Infographics, tables and charts help to make the information understandable and to make informed decisions. Tables are particularly useful for presenting detailed data series and figures clearly.

By applying proven methods and using suitable software, companies and individuals can take full advantage of data visualization. From the presentation of simple series of figures to the visualization of complex data sets - the possibilities are almost unlimited. Above all, however, it is important to adapt the visualizations to the needs of the target group in order to communicate the information effectively.

Overall, data visualization is an indispensable element in modern data analysis and communication. It makes it possible not only to understand data, but also to communicate it effectively, thus making a significant contribution to decision-making in companies and other areas. A well-designed visualization can bring somewhat complex data material into an easily understandable and meaningful form that helps the viewer to quickly grasp the key findings.