What is data visualization (data visualization)?

The importance of data visualization

The human brain processes visual information much more easily than numerical or textual information. Well-designed visualization allows for the rapid assimilation of large amounts of information, facilitates data comparison, identification of key findings and supports data-driven decision making. In the world of big data, where we are dealing with huge volumes of information, visualization becomes an essential tool for its effective exploration and communication.

Types of data visualization

There are many different types of charts and visualizations, and choosing the right one depends on the type of data and the goal you want to achieve:

• Line charts (Line charts): Ideal for showing trends and changes over time.
• Bar/Column charts (Bar/Column charts): Used to compare values between different categories.
• Pie charts (pie charts): Show the proportion of each part to the whole (percentage distribution). They should be used with caution, especially with multiple categories.
• Scatter plots: Used to study relationships and correlations between two numerical variables.
• Heatmaps: Represent values in a table using colors, making it easier to identify areas with high or low values.
• Geospatial maps (Geospatial maps): Visualize data associated with a geographic location.
• Network/graph diagrams (Network diagrams): Show relationships and connections between objects.
• Infographics: combine different types of visuals, icons and text to present information in an attractive and easy-to-understand form.

Data visualization tools

There are many tools available on the market to support the creation of data visualizations, from simple spreadsheets to advanced BI platforms and software libraries:

• BI platforms: Tableau, Microsoft Power BI, Qlik Sense, Looker (Google Cloud). They offer extensive capabilities for creating interactive dashboards and reports.
• Spreadsheets: Microsoft Excel, Google Sheets. Have built-in basic charting functions.
• Development libraries: matplotlib, Seaborn, Plotly (for Python), D3.js, Chart.js (for JavaScript), ggplot2 (for R). They give great flexibility to developers and data analysts to create custom visualizations.

Principles for creating effective visualizations

For visualization to be effective, it should be:

• Clear: Simple, clear, with appropriate labels and legends.
• Accurate: Faithfully reflecting the data without being misleading.
• Adequate: Selected appropriately for the type of data and purpose of presentation.
• Aesthetic: Pleasing to the eye, but without excessive distracting embellishments.
• Contextual: Presented with the necessary context and interpretation.

Summary

Data visualization is a powerful tool for exploring, analyzing and communicating the information hidden in data. Using the right visualization techniques and tools allows for a better understanding of phenomena, faster decision-making and more effective knowledge transfer within an organization. It is an essential skill for analysts, data scientists and managers in today’s data-driven world.