Data visualization is a crucial aspect of many organizations’ business strategies due to the ever-increasing volume of data and its value for a business. The first step in making sense of data is to visualize it. Data analysts employ various data visualization approaches, such as charts, diagrams, and maps, to translate and present complex data and relationships in a straightforward manner. 

The proper technique and its implementation are frequently the only ways to make data intelligible. On the other hand, poorly chosen strategies may not allow data to reach its full potential and may even make it irrelevant.

Importance Of Data Visualization 

Data visualization is considered an essential aspect of data analytics since it aids in interpreting Big Data in the current situation by displaying complex numerical and factual numbers. It is valuable for businesses since it arranges the raw data into a more understandable visual format and delivers data most effectively. 

Furthermore, visual information provides a quick and effective means to communicate a message to a large audience. It is currently utilized practically in every industry to boost sales with current customers while also targeting new areas and demographics for potential customers.

Major Uses Of Data Visualization

  • Helps analyze organizational data with presentable outcomes 
  • As part of the data reduction process, it plays a significant role in mixing sectors
  • It assists in the data cleansing process by detecting erroneous and missing values
  • And aids in preprocessing of data mining process 

What Are The Top Data Visualization Techniques?

Data visualization uses images to convey a clear understanding of what raw data means in a universal and effective manner. You can visualize data using several different approaches.

The following are a few top data visualization techniques that businesses can use:

  • Box Plots 

A box plot, often known as a box and whisker plot, shows the distribution of data in quartiles.

  • A box is drawn from the primary quartile to the third quartile of the data set, and a line inside the box addresses the median.
  • The “whiskers,” or lines, are then drawn from the box to the base (lower extreme) and greatest (upper extreme) (upper extreme).
  • Individual focuses aligned with the whiskers are used to address outliers.
  • This type of outline is beneficial for quickly determining whether the information is balanced or tilted and providing a visual representation of the data set that can be interpreted.

Box plots, in simple terms, represent a five-number summary of a collection of data that includes the lower quartile, minimum score, median, upper quartile, and maximum score.

  • Heat Maps 

A heat map represents data in a completely different way, and it’s a graphical representation of data in which different colors correspond to different values. The viewers will be able to understand the trend more rapidly due to the difference in color representation.

This data visualization technique can be used :

  • To visualize correlation tables
  • For displaying data with missing values

The information is communicated through a two-dimensional table in both circumstances. For example, if you need to figure out when a store makes the most sales, you can use a heat map with the day of the week on the vertical axis and the time of day on the horizontal axis.

After that, you can specify trends in the data by shading in the matrix with colors that correspond to the number of deals at each time of day. This allows you to determine the specific times your store experiences the most sales by shading in the matrix with colors that correspond to the number of deals at each time of day.

  • Word Cloud & Network Diagrams For Unstructured Data 

Because semi-structured and unstructured data necessitate novel visualization approaches, the collection of massive data poses challenges. The word frequency within a collection of text is represented in a word cloud by its general size in the cloud.

This method is used to display high or low recurrence words in unstructured data. The network diagram is another visualization approach that can be used for semi-structured or unstructured data.

Connections are depicted as nodes and ties in the network diagram. They are employed in various applications, such as social network analysis and item deal mapping across geographical boundaries.

  •  Wedge Stack Graph 

Wedge stack graphs are a type of data visualization that uses a radial structure to display hierarchical data. These graphs can be used to display data with multiple levels of frequency. When you request a stacked graph with wedges, the graph type changes to stacked walls. The Wedges graph type is unaffected by the size of the object or the number of side indications.

  •  Treemaps 

The size of the rectangles used for each category in Treemap is proportional to its entire fraction, indicating hierarchical data in a nested structure.

A zone in a leaf hub rectangle corresponds to a predetermined data element.

Depending on the option, the leaf hub is colored, enlarged, or both by the chosen credits. They make use of space by displaying a large number of items on the screen simultaneously.

Conclusion 

We live in a hyper-connected digital age that is far removed from the pen and paper or even copy-and-paste mentality of the past and as a result.  Therefore, businesses should use digital tools to help them make the best possible decisions while gathering crucial data in the most efficient, effective way to make a roaring visualization successful. Various data visualization techniques can be used for an effective presentation, and choose the one that suits your requirements.