The modern world is full of data, coming out of abundant sources people use every day, and it is expected to increase further as the number of internet and mobile phone users is increasing day by day. A survey indicates that about 2.5 quintillion bytes of data are created every single day. What a massive amount! This data has to be channelized and reduced to a more readable form almost every day. Imagine manually managing all the data an organization makes in a day, including employees records, sales charts, customer information, technical reviews data, etc. How difficult, terrifying, and time-consuming the situation would be!
With the tremendous amount of data, it is equally essential to prepare resources to gather, store, analyze and reduce data in meaningful insights. In the absence of capable resources, tools, techniques, and manpower, handling such a vast amount of data is almost impossible. A well-thought-out data analytics strategy is a must to help in any decision-making. Visualization is one of the essential aspects of the data analytics process. With effective visualization, one can easily see through the data stored in hundreds of files in a small document. Visualization helps communicate the insights in an effective manner to all stakeholders, saving time. Today, there are many powerful data visualization tools like Tableau and QlikView that one can use for effective visualization. In fact, Tableau eLearning is one of the effective ways of entering this rewarding domain.
All data experts are required to develop a strong understanding of effective visualization, its types, and how to create appealing reports, dashboards, etc. The current article lists down some of the best visualization elements required frequently.
What is visualization?
Visualization is an integral part of data analytics, and each data expert uses one or another type of visualization in a project to communicate their findings. Visualization can be defined as representing reduced data in terms of graphs, plots, maps, and charts. It is beneficial as one can easily identify trends, patterns, or deeper insights in raw data. This can help businesses make timely and informed decisions. Visualization can help achieve diverse business objectives such as examining the markets, sales team forecasting, pricing, stock analysis, and so on.
Types of Visualizations
Since visualization involves the graphical representation of data, it is important to know common types of visualization. There are ample options to represent data in a visual format. It depends on one’s creativity to select the best-suited graphs. Listed below are some of the most commonly used visualization types.
- Column Chart
- Bar Chart
- Scatter and Line Plot
- Pie Chart
- Heat Map
This chart is a widely used graphical representation wherein the data is depicted in the form of vertical rectangular columns. The rectangle height is proportional to the value of the data being plotted. It is very simple to create—however, a compelling way to represent the data. The rectangular columns are usually colored in different shades to make the plot more appealing.
Anything that can be counted is suitable for the column chart. They are effective in comparing the values of any two categories. Column charts are frequently used for comparison of the change in anything over time, such as increase or decrease in sales over a year. Column charts are created in three forms; Stacked, Standard, and Percentage, wherein stacked column charts are more popular.
A bar chart represents categorical data in the form of horizontal rectangular bars whose length represents the value of the category. It is similar to column charts, with the only difference being horizontal bars instead of vertical columns to represent data. A bar chart is capable of representing any category of elements like months in a year, members in a family, employees in an organization, electronic items in an electronic store, etc. Bar charts tend to channelize data in both organized and unorganized manners. Bar charts can be arranged in any pattern, such as ascending and descending orders. Ascending and descending ordered charts are commonly known as Pareto charts.
Scatter and Line Plot
A scatter plot depicts each point in the data set by a dot. Wherein line plot is similar to scatter plot with the only difference being points on plots are connected by a line. A Scatter plot helps to find clusters in data or see the overall trend of data. One can fit a curve to establish the correlation between parameters. A line plot is used to understand the local trend of the data. Three more names acknowledge the line graph; line plot, line graph, and curve chart. It’s primarily used for showing the trends in given data over time.
A pie chart represents the data in a circular form. It is suitable for categorical variables. Circle, also known as pie, is divided into slices of different colors to represent different categories. The area of each slice represents the number of elements in a particular category which is usually converted in terms of the percentage of the total number of elements. When one sums up the values of all of the slices, it should add up to 100%. The pie chart is the most common and is used extensively in demographics, engineering, marketing, finance, and so on.
A heat map is a graphical representation of the raw data wherein values are represented in terms of colors. Engineers, marketers, researchers are some of the roles that use heat maps.
Heat maps are generally used to interpret web page visitors’ behavior in analytics. It can be used for understanding customer engagement with the services provided on web pages of banks, big organizations, NGOs, government facilities, companies, and hospitals. With heat maps, one can access info on what sections of web pages visitors have clicked on, how long they read the specific sections of websites, etc. Heat maps are further divided into scroll maps, move maps and click maps. All maps give insights into various visitors’ behavior on web pages.
Another good use of heat maps is engine data analysis, wherein one can assess how much percentage of time an engine has been operating in a particular operating range. It is crucial to understand how close the engine is running to its design points.
Undoubtedly after reading the article, one can quickly sum up that visualization is very critical to represent and communicate findings to customers. Without appealing visualizations, all efforts and resources put on data analytics of massive data is worthless. The article covered some of the exciting ways of visualization among many. It is not an exhaustive list but just a glance at visualization types. One can include these visualizations in their reports either static or dynamic, to garner more customer attention.