But what is Data Visualization? Read the full text to learn more!
Or see more at:
Data marketing : knowing everything about the data-driven structure
Data Driven culture and the Web Analytics universe: the importance of data measurement
A little about Data Visualization
Data Visualization, Data Viz or Data Visualization is the process of presenting data and information in a visually appealing and understandable way.
In this context, graphs, tables, maps and other visual elements are used to represent complex data in a clear and concise manner. By transforming raw data into visual representations, Data Visualization makes it easier to identify patterns, trends and can provide valuable insights when the reader is data literate.
Acculturating data, in turn, involves transforming raw data into meaningful, processable and understandable information for different audiences. In the case of teams, it is important to assess the need and level of understanding of reading information to apply Data Viz according to reality.
Benefits of an elaborate data visualization
By transforming complex information into clear visual representations, we can identify patterns, trends, and insights that would otherwise be difficult to perceive. By understanding its benefits, the integration of Data Visualization with a business front becomes more realistic.
Among the benefits, we have three that are crucial to starting this data journey. They are:
1. Quick understanding
By visualizing data, you can understand it more quickly and intuitively than by simply analyzing numbers and tables. Visualization allows our brains to process complex information more efficiently, making it easier to identify patterns and relationships between different data sets.
2. Effective communication
Charts and visuals are more impactful and memorable than large blocks of text. By using data visualization in presentations, reports or even blog posts, you can convey information clearly and persuasively, capturing your audience’s attention and making it easier to understand the data.
3. Informed decision making
Data visualization enables deeper, more informed analysis, resulting in more informed decisions. By exploring different perspectives and visualizing correlations between variables, you can identify opportunities, solve problems, and make strategic decisions with greater confidence.
Tool examples
There are several tools available to perform Data Visualization, each with its own characteristics and functionalities, and to understand them and create an integration it is necessary to study them and observe the specific needs of the project, the level of technical skill and the integration with other data analysis tools that may be used.
However, the most common ones on the market are Tableau, Power BI, GDS, D3.js and Plotly. Get to know them:
Tableau : Tableau is a widely used platform for data visualization. It offers a user-friendly vietnam phone number example interface and advanced features for creating interactive charts, dashboards, and dynamic reports.
Power BI : Developed by Microsoft, Power BI is a powerful tool for data visualization and business analysis. It allows you to create interactive and personalized visualizations, as well as offering advanced sharing and collaboration features.
Google Data Studio : Google Data Studio is a free tool that lets you create custom reports and data visualizations using multiple data sources. It's a popular option for teams who want to share and collaborate in real time.
D3.js : Known for being a widely used JavaScript library for creating custom and interactive data visualizations, D3.js is a more technical option that offers full flexibility to create custom charts, although it requires programming knowledge.
Plotly : Plotly is a data visualization library that supports multiple programming languages, including Python, R, and JavaScript. It offers a wide range of interactive and customizable charts, as well as sharing and collaboration features.
Professionals qualified to create narratives in data
For these tools, some people need to be more skilled, as in the case of D3.js. However, there is a range of professionals who can benefit from this use, given the understanding, communication and way of making decisions.
Data Analysts: These are responsible for collecting, cleaning, and analyzing large data sets. They use data visualization tools to create visualizations that help them identify patterns, trends, and insights in the data.
Data Scientists: Work with complex data and apply statistical techniques and algorithms to extract valuable information. They use data visualization tools to communicate their findings and insights in a clear and impactful way.
Business Analysts: Use data visualization tools to help make strategic decisions. They create visualizations that highlight key metrics, identify market trends, and provide insights to drive business performance.
Marketers: Can use data visualization tools to analyze and present campaign performance metrics such as conversion rates, return on investment (ROI), and audience segmentation. This helps them make informed decisions and optimize their marketing strategies.
Managers and Executives: Re-use data visualization tools to track business performance and gain real-time insights. Clear, intuitive visualizations help identify problem areas, growth opportunities, and make strategic decisions.
How to interpret and explore information with Data Visualization
Data Visualization: Choosing the Best Chart Type
Knowing which professionals are qualified to use it and which tools can help them during their Data Viz journey, we have put together some suggestions on how to interpret and explore this information.
Choose the right type of chart
Each data set requires a specific type of chart to highlight the relevant information. There are different types of charts in this case, so it is important to understand the characteristics of each one and choose the most appropriate one to represent your data clearly and accurately. Let's look at some examples:
Bar Graphs : Bar graphs are useful for comparing different categories or groups. They consist of horizontal or vertical bars that represent quantities. Use bar graphs when you want to compare values between different categories or show changes over time.
Pie Charts : Pie charts are ideal for showing the proportion of different parts in relation to the whole. They are made up of sectors that represent percentages or proportions. Use pie charts when you want to highlight the contribution of each part to a whole.
Scatter Plots : Scatter plots are used to display the relationship between two numerical variables. They consist of points scattered on a Cartesian plane. Use scatter plots to identify patterns, trends, or correlations between variables.
Line Graphs : Line graphs are used to visualize changes or trends over time. They are made up of points connected by straight lines. Use line graphs when you want to show the evolution of a variable over a specific period of time.
Area Charts : Area charts are similar to line charts, but the areas below the lines are filled. They are useful for showing the relative contribution of different categories to a whole. Use area charts to highlight percentage changes or proportions over time.
Histogram Charts : Histogram charts are used to visualize the distribution of a continuous data set. They consist of adjacent bars that represent the frequency or count of data at specific intervals. Use histogram charts to identify the shape and dispersion of data.
Simplify and focus
Avoid the temptation to overload your visualizations with too many elements. Simplify the presentation of your data by removing unnecessary information and highlighting only the key points. A clean, focused visualization is more effective at conveying your message and allowing viewers to interpret the data correctly.
Use colors strategically
Colors can be used to emphasize important information and create visual hierarchy in charts. However, it is essential to choose colors that are easy to distinguish and do not cause confusion. Avoid using too many vibrant colors, as this can distract your audience and compromise the readability of your data.
Conclusion
Finally, we have Data Visualization as a powerful tool for interpreting and exploring complex information.
By transforming data into clear, engaging visualizations, you can:
Accelerate understanding;
Improve communication;
Make more informed decisions.
Therefore, don't forget to go through team acculturation to broaden understanding in creating a storyline about the data and its reading to simplify visualizations and use them strategically.
With the ability to interpret and explore information through Data Visualization, it is possible to be even better prepared to face the challenges of an increasingly data-driven world.