Tips to Make Effective Charts With an Analytics Platform

Here are some tips to help you create the perfect chart for your data. 

Today, with all the new technologies and an increase in storage capacity, we are capturing data at a high rate. But having multiple sources of data can get overwhelming: analytics platforms simplify data processing. Data analysis is the process of examining and understanding data to find answers. Visual representation through the creation and customization of charts can help you understand data quickly and gain clarity. However, finding the right representation is key to understanding complex subjets: you need to know which graph to use to extract your data. To be able to do so, you need to understand how charts work.

Here are some tips to help you create the perfect chart for your data. 

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Identify The Information You Want Your Chart to Convey

 

When you have large amounts of data to discover and understand, graphical representations can be much more effective than symbolic representations. 

One of the struggles in the creation of a chart is understanding what type of graph to use. On analytics platforms, you can create as many types of charts as there are types of data. It should be chosen based on what type of data is being analyzed. To create an effective graph, you need to understand why you need it, and what you want to extract from it. 

Before making your chart, identify the information you want your data to support, or the result you want to get. It is crucial to keep it simple – an overly complex graphic will only bring confusion. Simplicity is clarity. 

 

Identify the Type of Data You Want to Display on Your Chart

 

Before you decide what type of chart to display your data with, you should first consider what type of data you are working with. Data can be discrete or continuous. Identifying whether your data is discrete or continuous will help you choose the best type of chart for the information you want to display.

Discrete data refers to individual and countable items. It is not measured, but counted. The number of people in a room is an example of discrete data. Bar graphs, line graphs, and pie charts are helpful in displaying this type of data.

On the other hand, continuous data is measured. Height, weight, temperature, and length are examples of continuous data. It refers to change over time.

 

Examples of Charts You Can Use to Visualize Your Data

 

Line Graphs

 

Line charts are used to track changes over short and long periods. Use a line chart rather than a bar chart for small changes. You can compare trends by representing them on the same diagram – there will be different lines. 

 

Bar Graphs

 

A bar graph is another type of chart that shows the relationships between different data sets. Bar graphs are best used to compare values across categories: the higher or longer the bar, the greater the value. On bar charts, data can be represented on a horizontal or vertical bar. 

 

Pie Charts

 

If you want to understand how different parts explain the whole, you may use a pie chart (or a circle chart). Indeed, this type of graph shows a percentage distribution – the circle is divided into sectors that each represents a proportion of the whole. One limitation of pie charts is that they can get crowded if there are too many segments. Small percentages (that can be very important) are also tricky to show. 

 

Venn Diagrams

 

A Venn diagram is a chart that shows all possible logical relationships in a finite collection of sets of data. Each set is represented by a circle. Circles that overlap have things in common, whereas circles that do not overlap do not have those commonalities. Venn diagrams are primarily used to show the common factors or the differences between groups.

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Remove Clutter From Your Chart

 

Your chart needs to be a story that people can read. To improve the quality and the impact of your graph, start removing all unnecessary elements. This can include background colors that make the data hard to read, grid lines, color effects, 3D elements, double Y-axis, or even decimals. Choose the font and size carefully, as some elements are more important than others.

 

Use Text to Give Clarity to Your Chart

 

Although creating graphs is mostly about visualizing the data, you should not ignore the power of words. Text can help tell the story of your data and help to understand its meaning.

A title should be clear and used to help arouse interest. Use complete sentences instead of an accumulation of keywords. Rephrasing the titles of the graphs into questions can also be a way to better understand the answers that the data provides.

 

Improve Your Chart by Using Colors

 

To choose the right chart for your information you want to convey, you can start customizing it more. Using the right colors will make charts easier to read, and therefore, will make data easier to understand. 

Do not use color to make your charts look better. Using the wrong palette of colors will only distract your audience. Colors must be chosen thoughtfully to convey the right information. 

Choose the right colors for the feelings you want to communicate. You can use colors that will represent the theme of your data. For example, if the theme is sustainable energy, you could use a green palette. Think about the elements you want to put forward: colors can help you highlight the most important elements and make it easier to read a complex chart. 

 

Make Your Chart Interactive 

 

Sometimes, when demonstrating data, you may encounter a situation where you end up with a cluttered chart. When creating a graph to compare multiple sets of data, it can seem complicated and chaotic. 

It is possible to use dynamic elements to make a chart interactive, so you can choose a particular product and analyze its performance and trend. An interactive graph allows the user to control which data sets should be shown or hidden in the graph. They come in many shapes and forms: you could create a drop-down list, option buttons, or scroll bars that allow you to select an item to display the corresponding data. 

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