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Data visualization enables the telling of stories by arranging data into an easily-understandable format and emphasizing patterns and outliers. A good visualization communicates a narrative by filtering out irrelevant data and emphasizing the important information.

1. Introduction 

Data visualization is the process of turning raw data into visual representations, such as a map or chart graphs, in order to make it easier for the human brain to interpret and extract insights from them.

The basic goal of data visualization is to make it easier to see patterns, trends, and outliers in large amounts of data by displaying them graphically.

The phrase is often used in conjunction with other terminologies such as information graphics, information visualization, and statistical graphics to describe simple visual representations of complex data and complex information.

It is required to display data after it has been acquired, processed, and modeled, which is why data visualization is a step in the data science process.

Data visualization is also a component of the larger data presentation architecture (DPA) field. This is concerned with the effective visual communication identification and retrieval of data as well as the modification, formatting, and transmission of that data.

Data Visualization

2. History of Data Visualization 

For generations, people have used visual representations of data to make sense of it, from maps in the 17th century to pie charts in the early 1800s.

Charles Minard’s depiction of Napoleon’s invasion of Russia is one of the most often quoted examples of statistical graphics.

Napoleon’s retreat from Moscow was shown on a map that indicated the size of Napoleon’s army as well as its route, as well as the temperature and time scales that accompanied it.

As far as data visualization is concerned, it’s all about technology. Large volumes of data may be processed in a fraction of the time it used to take thanks to computers.

Today, data visualization is a fast expanding combination of science and art that is set to transform the business intelligence landscape in the next few years.

3. Importance of Data Visualization 

Data visualization is a simple visual content creation using visual elements.  These are effective means of delivering information to a large number of people throughout the world via the use types of visual data.

Additionally, the practice can aid businesses in determining which factors influence customer behavior, and identifying areas that require improvements.  Thus, additional attention, making data more memorable for stakeholders, determining when and where specific products should be placed, and forecasting sales volumes, among other things.

Among the other advantages of data visualization are the following:

  • The capacity for rapid information absorption, insight enhancement, and decision-making
  • Greater awareness of the next measures necessary to strengthen the company
  • An enhanced capacity for retaining an audience’s attention via the use of understandable information
  • A simple delivery of information that enhances the potential for everyone concerned to give thoughts
  • Obviate the need for data scientists, since data is now more approachable and intelligible
  • Enhanced capacity to move fast on discoveries and, as a result, achieve achievement with more speed and fewer errors

4. Data visualization and big data

It is now more crucial than ever to be able to view the creating visuals representation of your data. Thanks to the growing popularity of big data and data analysis initiatives in the data-driven era.

As more organizations turn to machine learning for assistance, they are receiving a large amount of data that may be difficult and time-consuming to filter through and analyze.

There is a method to expedite this process and provide this information to company owners and other individuals who need it in a timely manner. Visualization is a technique for accomplishing both of these goals.

If you’re dealing with a large amount of data, you may want to consider presenting it in a different style than you’re used to, such as using pie charts, histograms, and business graphs, for example.

The information shown instead is more sophisticated, such as heat maps and fever charts. “Big data visualization” is the term used to describe this. Raw data from many sources must be processed and transformed into graphical representations.  Thus, people can use to swiftly make conclusions. This requires sophisticated computers, which are required to do this task.


5. Examples of data visualization

Excel spreadsheets were the go-to tool for creating tables, bar graphs, and pie charts back in the early days of data visualization. Although basic methods of visualization are still widely used, more complex ones are also accessible, such as the ones listed below:

  • infographics
  • bubble clouds
  • bullet graphs
  • heat maps
  • fever charts
  • time-series charts

Some other popular techniques are as follows.

Line Charts-  Using this method is one of the most frequent and simple methods. Line graphs show the ebb and flow of several variables across time.

Area Charts –  Using this method, you can display multiple values in a time series, or a sequence of data collected at regular intervals over a long period of time.

Scatter Plots –  The link between two variables is shown using this method. Data points are represented by dots on an x- and y-axis in a scatter plot.

Treemaps – Hierarchical information may be shown using this way. Each category’s rectangle size is proportionate to its share of the total. When comparing distinct sections of a whole, treemaps work best when they include numerous categories.

Pyramids of the population  In this method, the social story of a population is represented by a stacked bar graph. It is ideally suited for displaying the population’s dispersal.


6. Why Data Visualization is Important for Marketing

The use of data visualization in marketing analytics has grown in popularity. Business development may be seen more clearly using this method. A look at five of the most important advantages of using data visualization in marketing

  • Analyses of Patterns and Trends

Using visuals and reports, we may comprehend numerous patterns and trends analyses such as sales analysis, market research analysis, customer analysis, defect analysis, cost analysis, and forecasts. These assessments serve as a rock-solid foundation for marketing and sales.

  • Enhances Productivity and Revenue

Any business’s primary objective is to boost productivity and revenue, and graphical information plates play a critical part in doing this. Visual information that is timely and well-organized enables decision-makers to respond fast.

  • Demonstrates Creativity

Data visualization enables us to artistically depict data and analysis. To comprehend this data, we use a variety of graphs, colors, patterns, and interactive graphics.

  • Enhances Reporting

The most apparent use of data visualization is in reporting. The results, which are presented in a variety of graphs and images, are both striking and effectively articulated. Visualizations increase report engagement when compared to more conventional ways.

  • Communication That Is Both Effective and Timely

In marketing, communication is critical. Confidence and motivation are boosted by effective communication reports and images. Additionally, they benefit workers, customers, and upper management. Additionally, effective visualizations save time and make reports simpler to interpret. After all, time is a valuable commodity!


7. Data visualization tools and vendors

8. Conclusion

When it comes to data visualization, it’s all about theory and practice. As you learn this skill, keep in mind best practices and your own unique approach. Data visualization is here to stay, so build a strong foundation of analysis, narrative, and exploration skills that you can use to any tool or platform.

If you want to get any support regarding Data Visualization our team Digifix is happy to help you. 

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