5 Common Types of Data Visualization in Business Analytics
We have discussed a lot about data science, machine learning, artificial intelligence, big data in the previous blogs, we have proceeded through various technological procedures, methods, strategies, working process of the above topics in detail. It was observed that everything is concentrated on Data, and yes, Data is Fuel for the present technical advancement. Again, continuing with the event, the “data-centred” tool and technique, we will talk about Data Visualization.
Let’s start with a simple case, during a market-survey, a salesperson got reliable statistics, now she wants to share that with her colleague, prior to this step, her team has some pre-planned questions like how much revenue they are going to get, in which direction they should move to get more business profits, how much amount of time and money they should invest for a specific product and so many others, such a list of question could only be investigated by market-survey under certain conditions. So, to proceed with some information extracted from the market survey and list of questions, the salesperson and her team worked out.
In the present situation, the salesperson is thinking to arrange a large volume of data to have a discussion with her team, but lots of confusion and apprehensions surrounded her, like, what to do with collected data, would I write all the information, draw a picture, or better use a chart?
She wants the information should be absorbing and accurate to make sure her team understands the facts and figures clearly. She should use the visualization that must be not purely artistic nor it should be overwhelming for her team.
Simply, the salesperson needs a simple introduction of data (information) plus design to prepare and visualize information. The wrong choice of visualization could lead to both, differences and confusion. From this example, we can clearly understand the importance of Data Visualization.
Introduction to Data visualization
Data visualization is the technique to assist people in understanding the significance of data by arranging it in the visual context. When data is exposed in a data visualization tool, a viewer can easily recognize any patterns, trends, and correlations in that data. The images used in data visualization have interactive and dynamic capabilities that allow users to manipulate them, extract data for querying and deep analysis.
Suppose a data scientist looks for writing advanced predictive analytics or machine learning algorithms, it is necessary for him to visualize the outcomes to direct the results and ensure that algorithms work fine. Other examples include a marketing team that wants to analyze the performance of an email campaign by tracking open rate, click-through rate, and conversion rate, etc.
The following aspects are essential for crafting a good data visualization
A well-sourced, complete and clean data is required
Knowledge of choosing the right chart that conveys the intended message
Designing and customization techniques
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Features and Importance of Data Visualization
If we talk about the main objective or need for data visualization, it is to understand the consequence of data available and to communicate this information precisely, coherently and efficiently. Data visualization turns large and small datasets into a pictorial form that visualizes in a simple manner and that is more comfortable for the human brain to interpret and process.
Some specific features of Data visualization which makes it is used versatile, are described below;
It is interactive and exposed trends,
It contributes a viewpoint, narrates a story and describes the process,
It applies animation and real images,
It fixes data into meaning and conserves time,
It grants access to raw data and extracts meaningful, knowledgeable insights.
5 common types of Data Visualization
We have data in the form of numbers, or statistics, there is always a story lying behind numbers, visualizing that statistics brings creation in them. Presenting and visualizing data accurately establish trust between you and your viewers, let’s have a gaze at how to select the most authentic and likeable approach to visualize data;
Bar Graphs: If you want to analyze data over time or the data is assembled in multiple categories such as various industries, variety of food, the progress of a company in the past 5 years, etc, a Bar Graph is the best choice with some characteristics or some kinds of careful suggestions, in order to make bar graph more effectively and easy to read, outline includes orders of the bars should be chronological, fix time frames label at one axis and label other quantities on other axes, data should not be placed from most to least or least to most but must be in chronology. Bar graphs include data in the form of multiple categories, we can either make individual graphs for each and every category or keep it in a single form through including multiple bars as one for each category at each time label. These bars could be assigned side by side or accumulated on top of each other. If the dataset is arranged into multiple categories but isn’t confined in time, we could use the bars’ order from most to least or least to most. This arrangement helps the viewers to get a conclusion easily.
Various types of data visualization tools
Line Chart: Line graphs are also used for presenting data over time or classified data by category as bar graphs. The only difference is that line graphs allow for refinement. If you want to present data over very long time periods or continuously changing data, the line graph could be a solid choice to consider. Most of the time it happens, we clearly don’t know how to fill data accurately in the time duration for which data is available, in that condition we are drawing nothing other than a straight line, though the rate of progress or decay between time duration is not linear up to a remarkable extent, so line graphs must be used very delicately to avoid malformation of data.
Pie chart: It is a presentation of data visualization in the circular form or circular chart. It is one of the most popular forms of data visualization, it can only be used when a smart portion of data add up to a whole. For example, 40 % of the marks are considered to pass in an exam, which could be displayed in the pie chart as it is indicating to 40 % out of the total 100 % of the marks. We can convert the percentage to proportions or proportions to the percentage for this aim, additionally, circle charts cannot be used to show an increase or decrease on their own. In case, if a pie chart could be used to present the data over time, there is a need to make a new chart for each time period and every measurement and display them together for comparison.
Quantagrams: The repeated pictogram or icon representation to show quantity is termed as Quantagrams, A very common example to show the multi-character quantities using Quantagrams is the number of people. You must have seen Quantagrams as classic male and female icons at the doors of the restroom. This technique is suitable for small numbers, small percentages or proportions, If we talk about pictograms, they are so simple and feel sound or reductive if they get used for any severe issues or a large quantity. It would appear as minimized if a severe issue is represented with simple sorted icons. We can opt Typography if need to visualize data for large statistics.
Typography: It is limited to certain cases where it can be accepted as the best solution provider, it is not restricted to provide an old text-only solution, instead, it is intelligently used to achieve a successful and effective piece of content. The data would be fit for typography if it is large or greater than 100, never be a percentage of a whole or increase or decrease in percentage, and can’t be compared to another number. In order to improve typography visualization, it can be combined with a pictogram or icon that gives the viewer a clear visual picture with the context of the subject matter of data and numbers.
In this blog, we have gone through an introductory tour of data visualization, we have seen various features, importance and common types of data visualization. In a nutshell, data visualization is meant to present data in a way to make the information easy to digest and understand at a glance, since data can be represented in multiple ways, care should be taken to choose the best chart in practice for visualization. In our previous blog, we have also discussed the dashboard for the data visualization presentation(Tableau).
For more blogs in analytics and new technologies do read Analytics Steps.