Data visualization is the graphical representation of data and findings. By using visual components like charts, graphs, and maps, data visualization tools provide attainable way to see and comprehend trends, outliers, and patterns in data.

Bar charts

Bar graphs visually illustrate data in a manner that facilitates the comparison of values across various categories The primary method for visually comprehending the data in bar charts lies in the length of the bars. Additional attributes and values can be communicated through the use of color, size, stacking, and order. Generating bar charts in Tableau is a straightforward process; you just need to drag and drop the desired measure onto either the Rows or Columns shelf and the dimension defining the categories onto the corresponding Rows or Columns shelf.

You’ve created a horizontal bar graph, facilitating an effortless comparison of sales across departments.

Line charts

Line charts link associated points in a visual representation to depict movement or correlation between those linked points. The key elements for conveying the data include the position of the points and the connecting lines. Moreover, size and color can be employed to convey supplementary information. A prevalent type of line chart is a Time Series, which illustrates the progression of values over time. In Tableau, crafting a Time Series only demands a date and a measure.

Filled maps

Choropleth maps, also known as filled maps, utilize color-filled regions like countries, states, counties, or ZIP codes to represent locations. The filled area’s color serves as a means to encode values, typically reflecting aggregated measures and occasionally dimensions.

The choropleth map colors each state with a singular hue to signify the proportional total of sales within that state.

Symbol Maps

Symbol maps differ from filled maps in that they don’t represent areas with solid colors. Instead, they use shapes or symbols as marks at specific geographic locations. The size, color, and shape of these symbols can also convey additional dimensions and measures.

Heat maps

Heat maps depict the distribution and density of values across a geographic area. Rather than displaying individual points or symbols, the marks merge, revealing intensity in regions with high concentrations. You have control over color, size, and intensity.

Gantt Charts

Gantt charts prove highly beneficial in comprehending sequences of events with durations, particularly when there is a relational aspect. Visually, they provide valuable insights into event overlap, dependencies, and variations in duration.

Treemaps

Treemaps employ nested rectangles to showcase components of a whole, particularly in hierarchical structures. They prove particularly valuable when dealing with hierarchies and dimensions characterized by high cardinality, indicating a large number of distinct values.

Area charts

An area chart or area graph visually represents numerical data.Consider a line chart and proceed to shade the space beneath the line. In the presence of multiple lines, overlay the shaded areas to create what you can visualize as an area chart.

Pie charts

Pie charts are also suitable for illustrating relationships between parts and the whole. To generate a pie chart in Tableau, switch the mark type to Pie. This action will introduce an Angle shelf, enabling you to represent a measure. The dimension(s) you assign to the marks card, usually on the Color shelf, will delineate the individual slices of the pie.

Box and whisker plots

Box and whisker plots, also known as box plots, provide supplementary statistical context to distributions.

The method for constructing a box and whisker plot is derived from the five statistics provided below.

1. Minimum value: The smallest value in the dataset

2. Second quartile: The value below which the lower 25% of the data are contained

3. Median value: The middle number in a range of numbers

4. Third quartile: The value above which the upper 25% of the data are contained

5. Maximum value: The largest value in the dataset

Histograms

Another option for representing distributions is to utilize a histogram. It bears resemblance to a bar chart, yet the bars illustrate the frequency of occurrences for a specific value. For instance, investigators examining standardized test results for signs of grade manipulation might generate a histogram of student test scores.

Scatterplot

A scatterplot is a fundamental type of visualization for comprehending the correlation between two measures.

Dual Axis Chart

Dual-axis combination charts, also known as Combo Charts, are a powerful chart type for presenting correlated information efficiently by consolidating views. This chart format is established with a common axis, like an X-axis for date, and two distinct axes, such as Y-axes for two different measures.

Choose the Right Chart Type for Your Data

Magnitude 

Magnitude illustrates the comparative size or value of two or more distinct items. When assessing sales across various regions, you are examining magnitude. Chart types that convey magnitude encompass bar charts, packed bubble charts, and line charts.

What types of question can this chart answer?

• Which dimension member boasts the highest measure?

 • Are there any exceptional dimensions?

• What is the extent of the gap between the lowest and highest measures among these dimensions?

Deviation

Deviation charts illustrate the extent to which a value diverges from a designated baseline, like the average or median. When identifying items with unusually high or low profit margins, a deviation chart would be employed. Bullet charts, bar charts, and combination charts are viable options for presenting deviation. Additionally, the statistical significance of the deviation can be determined using a Z-score.

What types of question can this chart answer?

•  To what extent does this measure deviate from the norm?
•  How significant are the variances in this measure?
•  Is there a discernible pattern to the deviations?

Distribution

When analyzing the occurrence of events within a population, you are examining the distribution. Optimal for showcasing the number of survey respondents by age or the frequency of incoming calls by day, distribution charts offer a suitable choice. Histograms, population pyramids, Pareto charts, and box plots fall under the category of distribution charts.

What types of question can this chart answer?

• Are events concentrated around a specific probability?

• Which demographic purchases the highest quantity of items?

• What are the peak periods during our workday?

Ranking

At times, it is essential not only to portray the magnitude of a value but also the comparative ranking of all members within a dimension. Displaying the top ten salespeople or highlighting underperforming states involves the use of a ranking chart. Typically, ranking charts are represented by bar charts that incorporate rank calculations, top N sets, or key progress indicators.

What types of question can this chart answer?

• What is the count of underperforming individuals in the company?

• How much revenue is contributed by our top ten customers?

• What is the valuation of our ten lowest revenue properties?

Part-to-Whole

Part-to-Whole charts illustrate the proportion of a whole that each individual part represents. For instance, when displaying the contribution of each region to overall sales or the cost distribution of various shipping modes for a specific product, a part-to-whole chart would be employed.

Part-to-Whole charts can take the form of pie charts, area charts, stacked bar charts, or treemaps.

What types of question can this chart answer?

• What is the contribution of this value to the overall total?

• How does the cost distribution evolve each year?

• Do various items contribute varying amounts to sales based on region?

Change over time

Displaying the evolution of a measure over time is a fundamental aspect of data visualizations. Numerous alternatives exist for examining changes over time, such as line charts, slope charts, and highlight tables. To depict changes over time effectively, understanding the target value for change and being proficient in handling Date fields in Tableau is crucial.

What kind of question does this chart answer?

• What variations has this measure undergone in the previous year?

• At what point in time did this measure experience a change?

• How rapidly did this measure undergo alterations?

Correlation

At times, you may have two variables and aim to understand the connection between them. For instance, you might investigate the correlation between classroom size and school graduation rate, or explore the relationship between lung capacity and endurance. It’s essential to note that correlation doesn’t always imply causation. Correlation can be visualized through scatter plots or highlight tables, and Tableau’s analytics objects can be utilized to demonstrate the strength of the correlation.

What types of question can this chart answer?

• Is there a relationship between these two measures, and if so, how robust is it?

• Do certain measures exhibit a stronger correlation than others?

• What is the strength of the relationship between these measures?

Spatial

Spatial charts depict exact locations and geographical patterns within your data. Examples include illustrating airport terminals with the highest foot traffic or mapping sales across the entire country. Spatial maps encompass filled maps, point distribution maps, symbol maps, and density maps.

What types of question can this chart answer?

• What city boasts the highest sales?

• What is the distance of our customers from distribution centers?

• How many individuals arrive at each gate?

Advantages of Data visualization

Here are some major advantages of data visualization:

1. Clarifies intricate data
2. Unveils patterns and trends
3. Assists in decision-making
4. Enhances retention and engagement
5. Augments accessibility
6. Enables real-time monitoring
7. Pinpoints areas requiring attention or enhancement
8. Facilitates predictive analysis
9. Elevates storytelling
10. Boosts productivity
11. Supports risk management

Here’s an in-depth examination of some of the primary benefits.

Clarifies intricate data

Data visualization converts extensive and intricate datasets into a visual format, simplifying the comprehension and interpretation of the data. It enables individuals to perceive data in a more digestible and accessible manner.

Unveils patterns and trends

Graphs, charts, and other visual formats aid in uncovering patterns, correlations, and trends in the data that might not be as evident in raw numerical form. The capacity to swiftly identify and comprehend these patterns can accelerate decision-making, resulting in time and resource savings.

Assists in decision-making

By emphasizing crucial insights, data visualization facilitates quicker and more efficient decision-making. Businesses can rapidly evaluate their performance, competitive landscape, customer behavior, and market trends, empowering them to make well-informed strategic decisions.

Enhances retention and engagement

Graphic information is more captivating and memorable than unprocessed data. A thoughtfully crafted visualization has the ability to narrate a compelling tale about the significance of the data, rendering it a superb instrument for presentations, reports, and communication with stakeholders.

Augments accessibility

Not every individual possesses expertise in data. Data visualization broadens data accessibility to a diverse audience, spanning from executives to operational teams, thereby elevating overall data literacy within the organization.

Enables real-time monitoring

With the emergence of interactive dashboards, businesses can actively monitor their operations in real-time. This capability proves beneficial for tasks such as tracking sales performance, overseeing supply chains, and optimizing operational efficiency.

Pinpoints areas requiring attention or enhancement

Data visualization can spotlight areas in which a business can enhance its performance. This might involve identifying departments falling short of targets, pinpointing underperforming products, or recognizing processes in need of streamlining.

Facilitates predictive analysis

Sophisticated visualization tools empower businesses to forecast future trends by analyzing historical data. This capability proves valuable for predicting sales, demand, and other crucial business metrics.

Elevates storytelling

Through data visualization, businesses can craft more compelling narratives. This proves particularly valuable in persuading stakeholders, training teams, or attracting customers. Visual data stories are inherently compelling, engaging, and easily comprehensible.

Boosts productivity

With instant insights derived from visualized data, teams can take swift action, circumventing the delays associated with data confusion or misinterpretation. This has the potential to significantly enhance productivity within a business.

Supports risk management

Data visualization proves invaluable for organizations grappling with complex scenarios rife with risks and uncertainties. The visual simplification of data aids in discerning potential areas of risk, facilitating a more comprehensive understanding. It equips organizations to navigate the intricate landscapes of data they operate within, ensuring optimal utilization of the information they generate and collect.

The benefits of data visualization extend from refining decision-making processes to enhancing communication on a significant scale. It’s crucial to note that effective data visualization demands careful consideration of how data is represented. This involves thoughtful choices regarding the most impactful visual representations, the strategic use of color and size, and the thoughtful grouping or sequencing of data to effectively convey the intended message or argument.

Written By:

Ms. Bhuvaneshwari.G is a faculty & Data Scientist of Nikhil Analytics.