Cheatsheet for Charts: Pair the Right Chart with the Right Set of Data

Sep 28, 2020 | Article

Data Visualization: A brief Overview

What is Data Visualization?

Living in this age of Big Data, you would definitely have come across the term “Data Visualization.” It is used by all kinds of people: academics, economists, administrators, CEO’s – you name it! Owing to this popularity, it has so many definitions that when you google “Definition of Data Visualization,” you get about 8,14,00,000 results. Whew! That’s a lot to handle, but don’t worry, we got your back. Simply put, Data Visualization or Data Viz is any visual representation that helps in understanding, organizing, and analyzing data. It helps your numbers tell a meaningful story and helps your readers understand it.

Why do we need it?

A picture is worth a thousand words! This is the mantra behind data viz. According to Danish Physicist Tor Nørretranders, both our conscious and sub-conscious systems process the most information in a given time through vision. This means we are more likely to grasp details from a picture than a text. To illustrate this further, look at the two images below, which lists the processing capacity of our senses.

bandwidth of the senses, Tor Nørretranders, Sub-conscious bandwidth, Conscious bandwidth, sensory system
bandwidth of the senses, Tor Nørretranders, Sub-conscious bandwidth, Conscious bandwidth, sensory system

Even though both images convey the same data, comparing and identifying the largest value is much quicker in the visual representation when compared to the tabular form. So, it goes without saying that when Data is visualized, it becomes easier to understand. Understanding paves the way to better communication, which in turn results in effective utilization of the information.  

How does it help us?

A huge incentive of using visuals over text is the sheer amount of data insights it provides. A quicker understanding of data leads to swift actions, which are very important for key decision-makers. Spotting trends and patterns help companies stay ahead of the competition. Finding and correcting errors becomes easier, making the business productive.  With its help, Business stakeholders can effectively analyze their reports and focus on the areas requiring attention. In short, Data viz helps in understanding information much faster and in recognizing patterns that might otherwise be difficult to see with the text-based data.

Matching Data to Charts

What information do we need to select a chart?

Data Viz, like any other tool, can be very useful when used right. To get the most out of your data, you need to match your data with the right type of visual chart. How do we do that? By asking yourself the following questions:

1. Who is my audience?
2. What insights do I want my readers to gain?
3. What should be the range of my axis?
4. Should I display values over time or among groups?
5. What information many categories do I need?
6. How many data points do I need for each category?

The Cheatsheet

Once you have the answers to the above questions, the next step is to use that knowledge to identify the best chart for you. We have made a table that shows you which visual chart is to be used for which type of visualization task and in which case. Just refer to the following table to find the ideal partner for your data:

Comparing Data

When to Use

Chart Type

  • To compare individual  or precise values
  • The value involves various units of measure
  • Displaying quantitative information is more important than trends

Table

table, icon
  • To display and compare the rank of values and focus on the extremes
  • Short data category labels
  • Items on the chart have less than seven categories
  • Example: Revenue per landing page, Sales by year, etc.

Column chart

column chart, icon
  • To show if the data values have attained a particular goal
  • Items on the chart have over seven but less than 15 categories
  • Display negative numbers
  • Long data category labels
  • Example: Website visitors per country, Customers won per role, etc.

Bar chart

bar chart, icon
  • To compare many different items and show the composition of each item being compared
  • To display visual aggregate all of the categories in a group when the size of individual categories is not important
  • Example: Sales by region, Sales by product, etc

Stacked chart

stacked chart, icon
  • To compare multiple items or groups on various attributes
  • To visualize comparisons of quality data
  • The number of attributes should be at least three but less than 10
  • Example: Comparing the features of two or three cars, Rainfall by month, etc.

Radar chart

Tracking data over a period of time

When to Use

Chart Type

  • To show how a category changes over time
  • To display a continuous dataset having a high number of data points
  • Trend based visualizations
  • Example: Web traffic over time, number of purchase returns by month, etc.

Line chart

  • To track changes over time in two or more related groups that make up one whole category
  • The emphasis is on the cumulative data volume rather than the data points
  • Example: New vs. Returning website visitors, Sales by month for two or more products, etc.

Area Chart

  • To display price movements over time in stock, derivative, currency, and any other markets
  • Scaling any section of the whole plot for a closer look is needed
  • Example: Stock prices over time, etc.

Stock Chart

Analyzing compositions and part-to-whole relationships

When to Use

Chart Type

  • To display parts of a whole in percentages
  • Show relative proportion
  • All categories add up to 100
  • Example: Customer survey, Spending in a month, etc.

Pie Chart

  • To visualize foundation-based relationships
  • Data is hierarchical
  •  Example: Food chain, Employee salary by management level, etc.

Pyramid Chart

  • To visualize stages in a process and the completion rate for each stage
  • To identify the bottlenecks in the process
  • Example: Software sales conversion, etc.

Funnel Chart

  • To visualize hierarchical data in a nested format
  • To show a wide range of datasets efficiently
  • To identify the relationship between two elements in a hierarchical data structure
  • Example: Web traffic by source, Products by revenue, etc.

Treemap

Studying the distribution of data

When to Use

Chart Type

  • To show the presence or absence of a relationship between two variables
  • Use for correlation and distribution analysis
  • Not more than two categories
  • To display data distribution and clustering trends
  • Example: Customer satisfaction by response time, etc.

Scatter plot

  • Same as scatter plot but with 3 or 4 categories
  • Example: Products purchased by age and gender, etc.

Bubble Chart

  • To display errors or margin of error in a dataset
  • Example: Variability of software sales, etc.

Error Chart

  • To show the relationship between a group and a matrix of two categories
  • To provide rating information
  • To analyze a category across a matrix of data
  • Example: Risk Matrix, Users by the time of day and region, etc.

Heatmap

Displaying project data

When to Use

Chart Type

  • To display plans and activity schedules of a project
  • Project management tool
  • Example: Planned vs. actual progress, Apartment rental schedule, etc.

Gantt chart

  • To display resource occupancy
  • Example: Server status, etc.

Resource chart

Evaluating performance data

When to Use

Chart Type

  • To display progress toward a goal comparing it to another measure and provide a rating
  • Space efficiency is required
  • Linear scale
  • Example: Planned vs. actual sales, etc.

Bullet chart

  • To show a specific data point over a range using a pointer
  • Linear scale
  • Example: server CPU utilization, etc.

Linear Gauge

  • To display a single value to estimate progress toward a goal
  • Use for displaying KPI’s on dashboards
  • Radial scale
  • Example: Speedometer, KPI dashboard, etc.

Circular Gauge

Exploring Geographical Data

When to Use

Chart Type

  • To compare a dataset by geographic region
  • To quickly spot the best and worst-performing areas, trends, and outliers
  • Specific values are not important
  • Example: Products sold by region, website visits by country, etc.

Choropleth Map

  • To show distributions and densities of a large number of discrete objects
  • Example: Manufacturing units by country, etc.

Dot Map

  • To display a numeric value on a territory
  • To compare proportions over geographic regions without the issues caused by regional area size
  • Example: sales offices by revenue, etc.

Bubble Map

  • To display a connection between two points on a geographical map
  • To show territories, routes, rivers, etc.
  • Example: Airline routes, etc.

Connector Map

data viz, data visualization, charts, visuals, right charts

The Takeaway

As you would have guessed by now, when it comes to Data Viz, there is no one-size-fits-all solution. It requires a balance between the knowledge of what to use and the art of when to use it. Using our simple but practical guide, you should now have achieved that perfect balance and be ready to whip up the best chart for your data. Right? If you are still a wee bit confused and would instead leave the charts for the pros to handle, don’t worry, we have you covered. ScaleFresh excels at taking your data and turning it into an insightful asset. We work with various Data Viz tools, including Tableau and PowerBi, and can help you navigate these un‘chart’ed territories. Let us bring to life the story behind your data with our visuals.

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