Information is power. However, to access information, you have to process raw data and interpret it to gain insights and make decisions. One way to read data is through visualization that uses various techniques to represent data visually.

Data visualization involves graphics using visual objects such as charts, bars, or lines to encode information. Data visualization through graphical representation allows you to make accessible data comparisons for fast decision-making. The techniques involved represent information in intuitive formats and create an integrated analysis of multiple data sources. Sometimes, you’d need someone to work on visual configuration to help arrange your data in an appeasing way.

Businesses can use data visualization to gain specific insights on performance and determine the areas that require attention. Through data visualization, it becomes easier to handle large data formats and uncover hidden data patterns. This way, your business can unearth critical information from metadata sources and create room for creating analysis reports.

Data visualization allows business owners to gain acumens into how business information is interpreted and determine how they can improve business performance. Hence, you can make information make sense and see trends that may be converted into intuitive and actionable business strategies.

Data visualization is one of the significant foundations of data science. You can use various graphical representations to portray your data and create a base for analysis. You can incorporate different methodologies to represent data based on the type of data. Let’s look at some standard techniques used in data visualization.

Bar Charts

This data visualization technique represents categorical data as bars for easy comparison. The bar size depends on how big or small the numbers are, making it easy to scan for information. Bar charts are helpful when you want to compare more than two values within a particular data category.

Bar charts also make it easier to understand how two or more categorical data sets relate to each other. It is also imperative to know that bar charts work best when there are few flat data sets. This way, comparing data parts of the whole category becomes smooth and efficient.

However, bar charts are only effective for discrete and not continuous data. Thus, it becomes harder to compare categorical data with only one value. The good thing about bar charts is that you can use various colors and labels to compare and identify relationships smoother.

Line Charts

Line charts are influential in determining the change undergone by a variable. This way, line charts become effective for identifying and comparing trends. This is achieved by plotting time on the x-axis and your variable on the y-axis, making it easier to show data results for a continuous variable.

When using line charts, it is imperative to use different colors for more straightforward information analysis. Line charts are your go-to when you need to understand various trends, patterns, and data fluxes. Line charts make it easier to compare related data sets which are different and with various series.

You can make data projections, but it would be hard to gain an in-depth assessment of your data. Make sure to use different lines and colors for different data and avoid comparing more than four data categories.

Pie Charts

Pie charts represent various data sets as part of a whole. It makes it easier to compare relative values and values that represent parts of a whole data category. They are helpful in quick metric scanning, but they can’t be used for precise data comparison.

When designing a pie chart, it is imperative to ensure that all values add up to 100%. It is effective when comparing five data categories as it may be hard to compare pie slices for multiple data. The pie slices should be in order and arranged according to size.