The What and Why of Data Visualization

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What is data visualization?

In the big data era, the sheer volume and variety of data is actually a powerful ingredient for businesses to seek valuable insights. However, managing and interpreting the data could be incredibly complicated and time-consuming. This is where data visualization comes into play.

So, what exactly is data visualization?

Data visualization is the graphical representation of information and data. It converts complex datasets into visual elements like charts, graphs, maps and infographics, making it easier to interpret and understand intricate information quickly. We will be able to identify trends, outliers, patterns and correlations in the data. But before we delve deeper into the method, let’s first learn about the history of data visualization to how it has evolved to meet changing needs.

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History of Data Visualization

Throughout history, humans have used visualizations to educate, communicate, and tell stories. Visuals have always played a crucial role in how we learn and understand information. The development of data visualization showcases a progression from simple charts to the advanced interactive tools available today. This evolution highlights an ongoing effort to make data clearer, more accessible, and easier to act upon.

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The Dawn of Human Visualization

Before the 17th century, data visualization was mainly confined to maps, showing landmarks, cities, roads, and resources. It wasn’t until 1644 that Michael Florent Van Langren, a Flemish astronomer, created what is considered the first visual representation of statistical data. He plotted a one-dimensional line graph displaying twelve different estimates of the longitude difference between Toledo and Rome, attributed to various astronomers. This graphical approach, as opposed to a table, strikingly highlighted the wide variations in these estimates.

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The Foundation of Statistical Charts

The 18th century marked the beginning of thematic mapping, encompassing geologic, economic, and medical data. This era introduced abstract graphs of functions, measurement errors, and empirical data collections. William Playfair, often regarded as the father of many popular graph types we use today, such as line, bar, and pie charts, emerged during this period.

Statistical graphics soon found their way into public health, famously used by Dr. John Snow during London’s cholera outbreak in 1855. By plotting cholera cases as dots on a map, Snow demonstrated that the majority of cases were clustered around a specific water pump on Broad Street. This compelling visual evidence persuaded city officials to remove the pump handle, effectively curbing the epidemic.

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The Rebirth of Data Visualization

The latter half of the 20th century saw a renaissance in data visualization, driven by the advent of computer processing. In the early 1980s, Edward Tufte’s seminal work, The Visual Display of Quantitative Information became a cornerstone in data visualization education. In that book, there are some guides for enhancing the visual quality of routine

Attractive displays of statistical information should:

• have a properly chosen format and design
• use words, numbers, and drawing together
• reflect a balance, a proportion, a sense of relevant scale
• display an accessible complexity of detail
• often have a narrative quality, a story to tell about the data
• are drawn in a professional manner
• avoid content-free decoration, including chartjunk.

With the rise of computers, data visualization became much more advanced. Spreadsheets changed the game by letting people create graphs and charts directly from data tables without doing it all by hand. This made it easier and faster to create, update, and tweak visualizations. Soon after, different software tools for businesses came along, offering new ways to create and style charts.

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21st Century Business Intelligence (BI) Dashboards

Today, BI dashboards are crucial tools for organizations, making it easier to interact with and understand data. They display important metrics and key performance indicators all on one screen. These clear and well-designed visuals give a full picture of how an organization is doing, helping leaders make informed, data-driven decisions and plan strategically. They also promote a data-driven culture by showing how business performance stacks up against goals and making complex data relationships easier to grasp.

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Why Data Visualization is Important?

Data visualization is essential because it transforms raw data into something that people can easily see, interact with, and understand. This ability to make complex information accessible bridges the gap between technical and non-technical audiences, ensuring that insights are shared and understood across the organization.

As technology advances and the needs of data analysis evolve, the role of data visualization continues to grow in importance across industries. The more effectively you present data visually, the better equipped you are to draw meaningful conclusions and make informed decisions. When done right, it empowers individuals and organizations to fully leverage the information available. These are some examples how effective data visualization can help individuals or organization.

1. Enhanced Understanding: Intricate datasets are simplified, and the patterns and correlations that might be hidden in raw data are highlighted. For instance, line graphs can highlight upward or downward trends over time, while scatter plots can reveal relationships between variables.

2. Efficient Data Analysis: Easier to spot outliers and anomalies that might indicate errors or unique insights. For example, a spike in a bar chart might signal an unusual event worth investigating further.

3. Improved Decision-Making: Facilitate data-driven decisions empowered with a clear, evidence-based insights.

4. Effective Communication: Convey complex information concisely to a wider audience, including those without a technical background. A persuasive tool in presentations and reports to support arguments or recommendations.

5. Enhanced Collaboration: Organizations that leverage data visualization foster a data-driven culture. With a common reference point for team discussions, employees at all levels can communicate findings and collaborate on solutions, promoting transparency and accountability.

6. Better Data Retention: Combining analysis with storytelling makes data more relatable and compelling. People are more likely to remember and recall information presented visually. It also facilitates quick comparisons between different datasets or variables, which aids in retention and understanding.

7. Time Savings: Visual tools and dashboards allow for real-time data analysis and monitoring. This enables quick responses to changes and timely adjustments to strategies, enhancing overall efficiency.

Not only learning but mastering these skills enables individuals and organizations to harness their data more effectively and achieve their objectives.

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Author

This chapter was authored by Gerald Bryan, an analytics consultant at Supertype with extensive experience in enterprise AI consulting in Indonesia, having worked with companies such as Adaro Group, Central Bank of Indonesia, Bursa Efek Indonesia, and Toyota Astra Motor. He also developed Sectors (a financial market intelligence platform), responsible for the data gathering and its ETL pipelines.

Gerald is a former Apple Developer Academy @Binus Scholar, with one user-centric product available on the App Store. He also holds the Microsoft Certified Data Analyst Associate certification, with a focus on using PowerBI for data visualization and storytelling.

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Contributors

• Evelyn Ong, project owner at Supertype

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References

1. Edward Tufte - The Visual Display of Quantitative Information
2. Tableau - The Historty of Data Visualization
3. Yellowfin - The Fascinating History of Data Visualization