The visualization of data - For beginners and advanced data users

Polyteia is well aware of the complexity that the world of data holds and how challenging it can be to get started and to continuously develop your own data skills. With our new series, we offer insights on two levels: For beginners and for experienced data analysts. For beginners, the aim is to provide access to the world of data with practical relevance. Advanced data analysts are given the opportunity to expand their knowledge. The aim is to strengthen and expand data skills in the public sector.

Data visualizations are an important part of data work. They help to understand data and make data-based decisions. The first section of this edition of our series explains for beginners how data can be visualized and for what purpose these visualizations can be used. If you already have experience with data visualization, you can skip directly to the second section and get a more detailed insight into the creation of visualizations and their differences.

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For beginners: Understanding data through visualization

The brain reaches its cognitive limits when it comes to understanding complex amounts of data. Therefore, patterns and anomalies in data can remain undetected and prevent us from deriving any benefit from the data. The human brain is able to absorb information better in a visual form. Visualizations help to process abstract data and convert it into a comprehensible form. Large amounts of data from numbers and texts are converted into a graphical representation during data visualization in order to identify correlations, patterns and trends more easily and make data-based decisions. This can facilitate and accelerate decision-making processes in public administrations.

The required data is often found in different data sources and can be summarized in a visualization to create a meaningful graphic. These can be displayed in different types of diagrams. For example, there are bar charts, histograms, pie charts and geographical maps. However, before you start visualizing data, you should set yourself a goal as to which question the visualization should ultimately answer. Because not every type of chart is suitable for every data visualization. If you want to know what the ratio of age groups in your city looked like in May 2021, you should opt for a pie chart. On the other hand, do you want to know the growth of your population over several years? Then a line chart would be more suitable for identifying a possible trend for further growth in the future. The insights gained from this will enable you to plan preventively for your municipality and, for example, to increase housing construction.

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💡 Tips for data visualization

• Avoid pie charts with more than five categories or a total not equal to 100 percent for clarity.
• Limit the number of rows and columns in tables to maintain clarity.
• Be careful when using colors with inherent meaning (e.g. red = negative, green = positive). These can evoke certain associations and meanings that may not be universal.
• Choose a color palette that takes into account people with color vision deficiencies.
• Prioritize comprehensibility: A simple diagram with a clear focus on the content is better than a complicated representation.

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For data analysts: Data visualization techniques

Data visualizations are used to bring data into an easily understandable form and make it possible to grasp patterns, trends and relationships at a glance. The human brain is better able to absorb and process information in a visual form. People collect and process information in two ways: Unconscious and logical processing. Logical processing should play a key role in the evaluation of complex data volumes. Although this is much slower than unconscious processing, information is consciously perceived and evaluated. Meanings can be identified or certain conclusions can be drawn. Unconscious processing automatically captures a lot of information and collects and processes it unconsciously. This is how you solve simple arithmetic problems or recognize symbols. Information processing of statistics and large numbers is influenced by both types of processing. Due to unconscious processing, people repeatedly allow prejudices to flow into what is actually objective information and are thus influenced by irrelevant figures, for example.

Data visualization tools can be used to create precise and meaningful visualizations. They allow effective conclusions to be drawn from large amounts of data, facilitating decision-making and the compilation of information from internal and external data sources. Visualizations are crucial for gaining deeper insights and knowledge from raw data. After cleansing, filtering, merging and aggregating data, it can be visualized in graphs. Among other things, Online Analytical Processing (OLAP) technology enables data to be aggregated, filtered and visualized in various ways. However, before a graphic is created, an objective must be defined. This is because only the right visualization technology can ensure that the data is presented appropriately and thus provide effective insights. A visualization concept should therefore be developed in advance, including the target group. The two questions of which story is to be told and which trend is to be analysed must also be answered beforehand. It is also important to consider whether data should be compared, a combination of data or a time frame should be presented. Only then can you choose the right type of presentation for your data.

A distinction is made between two types of visualization: Static visualization and interactive visualization. Static visualization - such as an infographic - enables a single view of specific data sets. In contrast, interactive visualization allows users to interact with the graphics and diagrams. There are numerous open source, Python or JavaScript libraries that support the creation of interactive, static and animated visualizations. Geographic information systems (GIS) enable the visualization, analysis and interpretation of geographic data, which is particularly useful in urban planning. Temporal visualization techniques are used to visualize time-related data and trends, such as time series diagrams, Gantt charts and calendar heat maps. These techniques help to identify seasonal patterns, trends and changes over time. AR and VR technologies allow users to visualize and experience data in an immersive and interactive environment. These technologies are increasingly being used in areas such as education, medicine, architecture and entertainment to visualize complex data and concepts.

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Develop your data skills further

We have already provided insights into other data-related topics in our series:

• The World of Data
• Working with data platforms

If you would like to further develop your data skills, we would like to invite you to take a look at our free Data Academy learning platform. It supports administrative staff of all levels of knowledge in developing and expanding their own data skills. The Data Academy's interactive online courses cover a wide range of topics, including data visualization, data transformation, data platforms, data governance, artificial intelligence and much more. You can register quickly and easily for Polyteia's free Data Academy learning platform via this link.

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