Modern tools and approaches to data visualization and building dashboards

Today we will talk about building visualizations, the tools used for this, and also touch on the topic of how to build high-quality and understandable visualizations.

Data visualization is a key part of any workflow in data science, but it is often seen by many as an inconvenient extra step in the analysis report.

Taking this position is a mistake: high-quality visualization costs thousands of words, and in this article, we will understand why this is so.

Data visualization

Data visualization is the presentation of data in a form that ensures the most efficient human work to study it. Data visualization is widely used in scientific and statistical research (in particular, forecasting, data mining, business analysis), in educational design for teaching and testing, in news reports and analytical reviews. Data visualization is concerned with information visualization, infographics, scientific data visualization, exploratory data analysis, and statistical graphics.

You should use visualizations if you need to:

  • Provide the user with information in a visual form;
  • Compactly describe the patterns inherent in the original data set;
  • Reduce the dimension or compress the information;
  • Restore gaps in the data set;
  • Find noises and outliers in the data set.


The dashboard, or in other words, the analytical panel, originated as a synthesis of powerful mathematical analytics tools and an optimal graphical representation of the analysis results. The management of the companies wanted to see the key performance indicators, trends, dependencies, and other metrics in a clear compact form, as well as interactively change various parameters. In addition to visual data visualization, the main goals achieved with dashboards are related to comparing a particular indicator over time or evaluating it relative to other indicators.

At the moment, the most popular tools for building dashboards are:

  • Google Data Studio
  • Power BI
  • Tableau
  • Plotly
  • Redash

Data visualization rules

When talking about visualizations we should mention the rules for building them. The main ones are:

Correct chart type

The main goal of visualization is to simplify and speed up the perception of information. The chosen chart format and type should help, not hinder, this.

It is equally important to ensure that generally accepted standards are not violated. The time axes (years, months, quarters) should always be positioned horizontally from left to right, this is intuitive. If they are placed vertically from top to bottom, it will be very difficult to understand.

Remember that the poorly chosen type and format of the visualization immediately reduce the credibility of the information provided.

Easy data comparison

One of the main goals of visualization is a convenient and visual comparison of two or more indicators.

So to make your charts valuable and useful, show the relationship between the data. If you break the same type of information into many separate graphs, visualization becomes meaningless.

It is the quick understanding of the highest and lowest values, trends, and correlations that is the main advantage of visualization compared to a regular table or text. Diagrams should convey your ideas much faster and more clearly. If this is not the case, change the chart type.

The name and signature

Make sure that your chart always has a fully understandable name and all the necessary captions, otherwise there is a risk of misinterpretation.

The period and units of measurement should always be clear. Don’t expect the user of your chart to guess this from the context. To make sure that the data is interpreted correctly, put yourself in the shoes of the reader who sees your chart for the first time. Everything should be very clear, the reader should not have a single doubt about the interpretation of the data presented.

Remember that your task when creating graphs and charts is to simplify the perception of data, and not to cause unnecessary questions.

Summing up the above, it is worth noting that data visualization is a powerful tool used in exploratory data analysis to graphically verify the results obtained. Properly constructed visualizations improve the perception of the explored data, help to find hidden patterns, find noise and outliers in the data, speed up the process of perception of new information and give a fuller and deeper understanding of the issue under study.

If you also want to get a better understanding of your data and need consultancy, feel free to contact us at any time!