The Art of data visualization: techniques and tools for effective communication

Dashboard by Leanardo Diffusion

Data visualization is an effective form of communication that allows you to see and understand trends, anomalies, and patterns in your data, as well as present your data to a non-technical audience without confusion. In the world of Big Data, data visualization is essential for analyzing massive amounts of information and making data-driven decisions.

Why data visualization matters

Data visualization is important because it helps tell a story with data, turning spreadsheets of numbers into eye-catching charts and tables. Some benefits of data visualization are:

• Easy sharing of information. Data visualization allows you to convey the main message quickly and clearly, without having to read long reports or complex tables.
• Interactive exploration of opportunities. Data visualization allows you to explore data dynamically, filtering, sorting, and zooming in on different aspects. This can lead to new relationships or insights hidden in the data.
• Display of patterns and relationships. Data visualization makes it easy to recognize patterns and relationships in data, such as correlations, distributions, trends, or outliers. This can help identify the causes or consequences of a phenomenon or predict its future behavior.

How to choose the right data visualization technique

Choosing the right data visualization technique depends on the type of data you want to represent and the story you want to tell with the data. There is no universal rule, but some general principles are:

• Pick the chart type according to the variable type. For example, pie, bar, or column charts can be used for categorical variables; for quantitative variables, you can use line, area, or scatter graphs; Maps or cartograms can be used for geographical variables.
• Select the number of variables according to the complexity of the chart. For example, for a pie or bar chart, it is better to limit yourself to a few categories; for a line or scatter chart, multiple series or dimensions can be shown; For a map, you can use colors or symbols to represent different variables.
• Choose color, shape, and size according to the meaning. For example, for color, you can use divergent, sequential, or qualitative scales depending on the type of data; For shape and size, different symbols can be used to distinguish groups or categories.

What are the most used data visualization tools and software

There are many data visualization tools and software, ranging from simple to complex, intuitive to difficult. Not every tool is suitable for every person who wants to learn data visualization techniques, and not every tool can scale to an industrial or enterprise level. Some of the most used data visualization tools and software are:

• Tableau: is one of the most popular and powerful software for data visualization. It allows you to create interactive and customizable dashboards with different types of charts and maps. It can connect to different data sources and supports predictive and prescriptive analytics.
• Google Charts: is a free JavaScript library that allows you to create simple and interactive charts to embed in a web page. You can use it with Google Sheets or other online or offline data. It offers different types of charts and maps and supports animation and customization.
• Dundas BI: is an integrated business intelligence platform that offers data visualization, advanced analysis, and reporting solutions. It allows you to create interactive and customizable dashboards with different types of charts and maps. It can connect to different data sources and supports predictive and prescriptive analytics.
• Power BI: is a cloud service from Microsoft that offers data visualization, advanced analytics, and reporting solutions. It allows you to create interactive and customizable dashboards with different types of charts and maps. It can connect to different data sources and supports predictive and prescriptive analytics.

How to evaluate the quality of a data visualization

The integrity and quality of the data, the design and technical execution of the visualization, adherence to conventions and standards, and the visualization’s ethical and social implications are all factors that affect how well a data visualization is done. You can follow some suggestions to gauge the caliber of data visualization:

• Verify the axis assignment. Any chart’s foundation should be at “0” for both the X and Y axes. The graph might convey the proportions or differences between the data incorrectly if the axis is truncated.
• Inspect the manipulation of the scale. The graph’s scale should be suitable for the data set and not overly compressed or expanded. A graph’s ability to highlight or conceal patterns or outliers in the data depends on how the scale is set.
• Verify the data selection. The chart’s data should be complete and representative of the context and goal of the visualization. The graph may present a skewed or incomplete picture of reality if the data were chosen arbitrarily or selectively.
• Examine the graph’s suitability. The type of chart you select should be suitable for the variety and quantity of variables to be represented. If the chart is incorrect for the data, it may confuse or mislead the user.
• Verify the calculations. In order for the chart to be accurate and make sense given the data and visualization, the calculations used must be accurate as well. The graph may present false or contradictory data if the calculations are flawed or inconsistent.
• Check for correlation and causation. A cause-and-effect connection between two variables is not always implied by their correlation. The chart may be deceptive or speculative if it implies causality without providing support or an explanation.
• Check for adherence to rules and guidelines. The chart should adhere to accepted conventions and standards for the presentation of data, including the use of meaningful colors, shapes, and sizes, the addition of clear labels and legends, and the avoidance of pointless or distracting elements. The chart may be unclear or unintelligible if it deviates from conventions or standards.
• Verify that the graph is easy to understand. For users to interact with and explore the chart, it should be simple, clear, and interesting. The chart may discourage or irritate the user if it is too challenging to read or use.

What are the future challenges and trends of data visualization

Data visualization is an ever-evolving and innovative field, facing several challenges and opportunities for the future. Some future challenges and trends of data visualization are:

• Big Data. The volume, variety, and velocity of data are constantly growing, requiring visualization tools and techniques that can handle the scale, complexity, and diversity of data sources and formats. Big Data poses challenges for traditional data analysis and visualization tools, which may not be able to effectively process, integrate, and represent massive amounts of information.
• Artificial Intelligence and Machine Learning. Artificial intelligence and machine learning are technologies that automate and optimize data analysis and visualization, such as discovering patterns, generating insights, or personalizing user experiences. AI and machine learning offer opportunities to create more advanced, interactive, and intelligent visualizations, but also challenges to ensure transparency, ethics, and accountability of data-driven decisions.
• Mobility and Social. Mobility and social are phenomena that influence the production, dissemination, and consumption of data and visualizations. Mobility refers to the ability to access data and visualizations from any device, place, and time, thanks to wireless connectivity and cloud applications. Social refers to the ability to share, comment, and collaborate on data and views with other users, thanks to social platforms and online media. Mobility and social media offer opportunities to broaden audiences and the impact of

data visualization, but also challenges to privacy, security, and reliability of data and interpretations. By utilizing data visualization effectively, you can unlock valuable insights and make informed decisions based on data-driven evidence.