“Learn data, and you can tell stories that more people don’t even know about yet but are eager to hear.” - Nathan Yau, FlowingData
In our previous blogs, we have ventured a lot into the details of areas like data science, machine learning, artificial intelligence and big data, we have shed light on their various technological procedures, methods, strategies as well as working processes.
In each of these technologies, one element which remained constant was Data. Data is considered as the 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 salesman got reliable statistics, now he wants to share it with his colleague. Prior to this step, his team has some pre-planned steps such as;
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 must have invested for a specific product and so many,
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 salesman and his team attempt to work on it out.
The present situation, the salesman is thinking to arrange a large volume of data to have a discussion with his team, but lots of confusion and apprehension surrounds him, like, what to do with collected data, should he write all the information, draw a picture, or better, use a chart?
Whichever be the apprehensions he faces, he wants that the information should be absorbing and accurate in order to make sure his team understands the facts and figures very clearly and retains the information to satisfy any queries which they have.
In the choice of what type of visualization, he should use the picture of visualization that must be not purely artistic nor it should be fully personalized to represent data. Simply, the salesman needs a simple introduction of data (information) plus design to prepare and visualize information.
The wrong choice of visualization could lead to both, indifference and confusion. From this example, we can clearly understand the importance of Data Visualization.
(Also read: Tableau and Power BI for data visualization)
Data visualization is the technique to assist people in understanding the significance of data by arranging it in the visual context. When text-based data is exposed in any data visualization tool, a viewer can easily recognize any patterns, trends, and correlations in 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.
Data visualization is not limited to standard charts and graphs made in Microsoft Excel spreadsheet, the number of ways is available to display data such as dials and gauges, geographic maps, infographics, heat maps, bar and pie charts, etc.
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. For example, a data scientist can deploy various types of data visualizations using R programming.
In short to craft a good data visualization, a well-sourced, complete and clean data is required to visualize, then a right chart is chosen, now design and customize visualization according to simple preferences, there is no requirement to add any elements that distract from the data, and the last visualization is complete and ready to publish in front of viewers.
We have data or sort of information 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;
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.
Types of data visualization
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.
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.
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 we need to visualize data for large statistics.
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.
If you 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.
It comprises of some specific features due to which 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.
There are multiple ways to process information inside the human brain, charts and graphs might be a decent tool to visualize large amounts of complex data in a more accessible way, it is far better than to peruse over spreadsheets or reports.
In simple words, data visualization is a smart, easy way to communicate concepts in a specific manner and can be experimented with different situations by doing slight manipulations and adjustments.
It helps in identifying areas that lack concentration and development, analysing which factors impact consumer behaviour, direct in understanding the right place for a product, estimating sales and volumes, etc. Some examples of Data Visualization tools include FusionCharts, Highcharts, Tableau and Qlikview.
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.
(Read also: Advance data visualization in R programming)
In our previous blog, we have also discussed the dashboard for the data visualization presentation(Tableau).
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