What does “data visualization” mean?
The data visualization comprises a complete story of a business with a beginning, middle, and end. Visualization makes us understand everything we need to know about our business story from the first to the last. Data in its raw form is nothing but a story, so as humans we are connected with stories.
Data Visualization is defined as:
A graphical representation of data or information using common graphics such as charts, plots, animations, infographics, etc., Data visualization helps us to easily understand and analyze the outcome of the data. It paves the way for employees or business owners to present their data or information to a non-technical audience without any further confusion.
It’s a world of big data, and these data visualization technologies are used to analyze massive amounts of data or information. We can select the right graphical presentation based on the story we are trying to tell.
Where to put these data visualizations? These data visualizations are everywhere: on Twitter, on the news, and so on.
What Is the Need to Visualize the Data?
By nature, we humans are visual beings. We are drawn to shapes, colours, patterns, and so on. It provides a quick and effective way for information to be communicated universally. It helps businesses identify the factors affecting customer behaviour, point out the areas to be improved, help to understand when or where to place the products and help to predict the volume of sales.
The data visualization tells the story that is hidden behind the numbers without words. The other benefits include
i) the ability to process information and make decisions quickly.
ii) can maintain the interest of the audience with the information provided.
iii) data is much more understandable and more accessible.
The main reason why we need to visualize the data is that it helps the audience see, interact with, and understand the data better. The better to convey the points visually, the better to leverage the information. The visualization sits right in the middle between the analysis and the storytelling.
The importance of data visualization is that
(i) To analyze the data:
Analyzing a report helps us focus on the area that needs attention. These reports help the analyst to understand the key points of their business. Visually representative data helps companies increase their profit through better analysis and decisions.
(ii) Improvement in Decision Making:
Humans tend to process data more visually than in any tabular form or report. It enables decision-makers to take action based on the data collected, which both accelerates decision-making and business growth.
(iii) It saves time:
The raw data compels us to do the math. Why not have the math done for you? It can be done with this data visualization. When it’s represented in visual form, it’s easy to collect the information we need.
(iv) Using numbers to compare performance:
When we compare the data, the visualization has an impact. In the graphical representation, when the line chart is falling from a cliff, it likely gets our attention. When the visualization is properly used, any mystery behind the data will be eliminated, and the comparison will be clear and can be an advantage in decision-making.
(v) Bringing a Story to Life:
When the data is visualized, it tells a story that comes to life. The right visualization tells the story of a business in a few words.
What Makes a Good Data Visualization?
Data visualization is a form of art, and its effectiveness is dependent on execution. If it is done right, it will be effective. Have you ever opened a spreadsheet just to find out-of-context numbers and asked yourself, “Everyone is confident around me, and I didn’t understand that these numbers are that important? The truth is that this is what everyone thinks with these numbers. This data visualization makes the data analysis more accessible. This complex data can be understood by everyone and feel confident in their decision-making.
A graphical representation defines good data visualization, and it serves the purpose. If this visualization can be interpreted by a user and asks the question of how and what the information is displayed, it states that you are on the right path.
Now many ask or wonder, “How to pick up the right visualization to serve the purpose?”
What Is The Right Visualization For My Data?
It’s not that easy to find the right visualization for the data and the information. The most important task is to match our data with the right visualization so that the users can attain the values they need. If you are wondering where to start, let us ask these questions ourselves.
When we find out the answer to these questions, it helps us focus on which type of visualization is right for which type of category we should drill down for our data.
Data visualization categories
- Hierarchical
- Temporal
- Multidimensional
- Network
- Geospatial
I) HIERARCHICAL DATA VISUALIZATION
These datasets represent a very unique data network. These are the collections of data with which each dataset is connected to a parent dataset. They are used to display the cluster of information that flows from a single origin point.
There is a time and place for using hierarchical data visualizations, as they are more complex and challenging for the users to read. Due to its linear path, it’s the simplest hierarchical visualization.
Examples:
• Tree Diagram
• Sunburst diagram
• Diagram rings
II. TEMPORAL DATA VISUALIZATION
These data visualizations are one-dimensional and linear, which is used to represent time series commonly.
These data visualizations give a sense of familiarity. These are common in newspapers for showing information on the fluctuation of housing markets over a quarter or to visualize the gain or loss of company reports.
The main advantage of using temporal visualization is that it makes the user understand how and when to interpret the data when we look at it.
Examples:
• Line Chart
• Bar Chart
• Scatter plots
• Time series
• Gantt chart
• Timelines
III) MULTIDIMENSIONAL DATA VISUALIZATION
These data visualizations have multiple dimensions. These visualizations tend to be the more vibrant and eye-catching types of visualization due to their dimensionality.
If the data needs to be filtered, these visualizations are the best to use because they can break our data in several ways.
Examples:
• Pie charts
• Scatter plots
• Histogram
• Venn diagrams
IV) NETWORK DATA VISUALIZATION
These visualizations help us to show the relationship between the nodes and links without words.
Examples:
• Matrix charts
• Word clouds
• alluvial diagram
• Node-link diagram
V) GEOSPATIAL DATA VISUALIZATION
These visualizations are the earliest forms of visualization. They overlay maps with data points. These data visualizations are used for navigation before the computational analysis.
Examples:
• Density map
• flow map
• Heat map
• Cartogram
COMMONLY USED VISUALIZATIONS FOR DATA
There are several types of data visualization available. Choosing the right one from these types is the most difficult task for data analysts. In the context of business, choosing a visualization helps us to extract the value from our data. For a data set, it is hard to choose which visualization is best for the users.
Once a data visualization is picked, it’s important to make it fit into our data design. There are several data visualizations to choose from below.
- Bar Chart
- Pie or donut chart?
- Tree map
- Radar chart
- Waterfall chart
- Heat map
- Summary chart
- Table
- Line chart
- Combination chart
BAR CHART
A bar chart represents categorical data with rectangular bars of values, which are represented by heights. These bar charts have two axes; one represents categories and the other represents values. They are used for the instant comparison of charts.
When to use a bar chart?
i) When comparing a large number of categorical values:
Using bar graphs for comparison among the discrete categories when the categories are qualitative visually
ii) When comparing multiple categories or sub-categories at the same time:
Bar charts are used to compare how these entities perform against one another and how much they contribute to each subgroup.
iii) When two data sets on a single chart need to be visualized,
Using overlapping bar charts, similar data sets with different width bars can be compared on the same chart.
iv) When insights on deviations on charts are required:
Using column charts, the negative and positive values are compared, and these charts are used to analyze deviation.
When not to use a bar chart?
i) When a continuous set of data needs to be represented and compared,
ii) When it is necessary to represent time and other variables
PIE CHART
A graphical representation that displays data in a graph of circular shape. These are used to represent the sample data in different categories. Each category is represented by a slice of pie.
When to use pie charts?
i) A pie chart is appropriate for representing when the data is divided into distinct parts.
ii) When the data visualization does not need to represent time, pie charts are used.
iii) If the sample data has a few components, pie charts can be used.
iv) When each category contributes to sample data, pie charts are used to visualize it.
When not to use pie charts?
i) When representing incompatible data
ii) A pie chart should be avoided when comparing data.
iii) When there is a comparison between the slices, a pie chart should be avoided.
Types of Pie Charts:
Donut plot
3D pie graph
Pie chart with layers
TREEMAP
It is a type of visualization that represents a large amount of hierarchical data using nested rectangles. The total area is normally the sum of its internodes.
These are excellent ways to visualize the data into layers to show the relationship between them.
It is used to see patterns in the branches and display large data sets simultaneously.
RADAR CHART
These charts help us to understand the relative difference between the data items. We can easily understand and compare multiple items that require attention.
The radar charts are used to plot smaller data sets. These are used as a substitute for line charts.
The radar chart becomes overwhelming when too much data is plotted on the radar chart.
WATERFALL CHART
These charts help us to visualize when the initial value is affected by the intermediate values either positively or negatively. The cumulative value is represented in the final column.
This waterfall chart helps us to reveal the composition of a number.
If more than one number or metric needs to be visualized, these waterfall charts are not the appropriate ones.
HEAT MAP
The heat map helps us to compare the value of information with the way of colours and saturation to differentiate it. The high-value data points are represented by warm colours, and the low-value data points are represented by cool colours.
Using a heat map, rate the values on the scale. It helps us identify the relationship between the two measures.
To visualize a single metric, heat maps are not appropriate.
SUMMARY CHART
This chart helps us to display a single numeric value. These capability summary charts are displayed to compare the capability between two time periods.
It can compare two periods with a percentage change or a value change.
A negative trend can be typically indicated by red and a positive trend by green.
TABLE
It makes it easier to display the data in rows and columns. These tables allow us to display numeric values and graphics such as icons, sparklines, or bullet charts. To display large data sets, tables are not the best option.
Table types include:
i) Tables of Contents: It displays data in rows like a spreadsheet.
ii) Pivot Charts: It displays the value as plain normal text. It helps us identify the blank entries.
iii) Tables with Rankings: These display data in ascending or descending order. It allows the addition of the comparison value between the periods, as comparison values have time and place.
LINE CHART
These are the most popular visualizations of data in compact form. The data is represented by dots, which are then connected by a straight segment of lines.
These line charts help us to understand the trends and fluctuations in data. It helps to protect the data.
If the data is demonstrated in-depth, the line charts are not the best ones to visualize.
COMBINATION CHART
Combination charts are used to compare two data sets over the same dimension. These charts display values as columns on the left axis and lines on the right axis.
These combination charts are distinct from other types of data visualization. Two data sets with different numeric scales can be compared in a single dataset.
It is used to compare dissimilar data sets in the same data visualization.
HOW CAN WE LEVEL UP THE DATA VISUALIZATION?
i) Selecting the best graph
Knowing our data and determining which charts fit the best is the first step to the right visualization. There are numerous types of charts, and each is created for specific data.
Line plots show numerical trends over time.
Scatter plot – variable relationships
Histograms: Data distribution over a specific interval.
Pie chart: data point proportional distribution
ii) Keeping things simple:
As always, the best strategy is simplicity. The easier to understand the work, the better it’s perceived by others.
iii) Paying attention to texts
A picture is worth more than 1000 words, but no visualization is text-free. When visualization is created, three types of texts are often included: titles, levels, and legends. Reading a title makes the audience understand the visualization without any extra information.
