Data visualization is a part of modern data processing, and in this article, we explore what data visualization means, why it is essential, and the challenges associated with it.
The Significance of Data Visualization
The increasing volume of data has created a need for data visualization. Massive and multidimensional datasets can be challenging to comprehend, but visualization makes data understandable and usable. Information design aims to achieve just that - making data accessible. Businesses widely use data visualization, aiding in the detection of anomalies and inconsistencies in data. Visualization makes data more comprehensible, facilitating decision-making.
Historical Examples of Data Visualization
Historical examples demonstrate that data visualization has been effectively used in different eras. One particularly fascinating example comes from Dr. John Snow in 19th-century London. During a cholera epidemic, Snow used visualization to address the situation. He created a map that marked all cholera cases in the city. Surprisingly, the map revealed that most cases were concentrated in a specific area, particularly around a specific water pump.
This visualization helped Snow draw the conclusion that cholera was spreading through this water pump, and he succeeded in shutting it down. Thanks to this simple yet effective visualization, Snow managed to halt the epidemic and save many lives. [1]
Tools
Many programming languages offer many tools for data visualization. For example, Python[2] is a popular programming language in the field of data science and it offers several libraries for data visualization, such as Matplotlib[3], Seaborn[4], and Plotly[5]. In Java[6], visualization can be performed with, for example, JFreeCart[7], JavaFX[8], XChart[9], and so on. Those libraries are suitable for people with programming skills, while the following ones are mainly intended for business and data analysts, as well as for individuals who do not want to rely on programming.
Google Sheets [10] is a web-based spreadsheet tool with basic data visualization capabilities, suitable for individuals and small teams.
Looker[11] is a business intelligence platform designed for in-depth data exploration and visualization, ideal for data-driven organizations.
Microsoft Power BI[12] is a comprehensive data visualization platform that seamlessly integrates with Microsoft's ecosystem, offering strong collaboration features.
Qlik Sense[13] is a powerful data visualization and business intelligence tool known for its associative data model and interactive visualizations, making it a top choice for data exploration.
Tableau[14] is a renowned data visualization and business intelligence tool with a user-friendly interface for creating interactive dashboards and reports.
Distorting Data Through Visualization
Data visualization is not always impartial. Impure or incomplete data can distort results. Using visualization with poor data can lead to misinterpretations. It is crucial to be aware of how data is processed in visualization.
Visualization can also be used to deliberately mislead. Graph scales may be misleading or the presentation may be intentionally too complex to understand. Parts of the data may also be included or omitted that do not necessarily give a correct picture of the data. The visualization can also be presented in a distorted context.
Future Prospects
Data visualization is an evolving field, and in the future, new technologies, such as quantum computers, have the potential to impact computing and data visualization by significantly speeding up data processing, handling complex data analysis tasks, optimizing data representations, enabling more accurate simulations and predictions, and addressing big data challenges.
Data visualization is an effective means to make complex data understandable and usable. Simultaneously, it is crucial to be aware of the challenges and potential distortions in data visualization that can affect decision-making and results.
Observation and Visualization: John Snow and the Broad Street Pump, https://inferentialthinking.com/chapters/02/1/observation-and-visualization-john-snow-and-the-broad-street-pump.html
Python, https://www.python.org/
Matplotlib, https://matplotlib.org/
Seaborn, https://seaborn.pydata.org/
Plotly, Website: https://plotly.com/
Java, https://www.java.com/
JFreeChart, http://www.jfree.org/jfreechart/
JavaFX, https://openjfx.io/
Google Sheets, google.com/sheets/about
Looker, https://www.looker.com/
Microsoft Power BI, https://powerbi.microsoft.com/
Qlik Sense, https://www.qlik.com/us/products/qlik-sense
Tableau, https://www.tableau.com/