10 minutes

15 Data Visualization Techniques (for Analysis & Presentation)

The world produces more data than ever before, and it is forecasted that this will only increase exponentially over the next decade. Thus, learning how to interpret & visualize data is becoming a must-have skill in all careers.

What is Data Visualization?

Data visualization is the process of turning datasets into charts, graphs, diagrams and other visuals. It can be used for analyzing data or presenting data.

What is data visualization


Why is Data Visualization Important?

Data is being produced in every field: finance, business, marketing, education, gaming, sports - you name it, and the amount of data we’re producing each year is exponentially increasing:

importance of data visualization


In 2021, we generated 79 trillion gigabytes of data, meaning out of the 4.66b people who have internet connectivity, each person is generating 15,900 gigabytes of data each year.

To put that into perspective, a spreadsheet of 1000 rows and 10 columns takes up about 0.0003 GB of data. So to reach 15.9 GB, we generate 53 million times that amount in the span of a year.

We've started collecting data in every field imaginable, and this makes learning data visualization a highly important skill to have.

What is the purpose of data visualization?

The two main purposes of data visualization are data analysis and data presentation. Humans are visual creatures. It's much easier for our brain to process information from a bar chart (like the above), than a table full of numbers.

Data visualization allows us to easily see patterns, trends, correlations and distribution. It can also highlight outliers or important points in the data using contrasting colors. A good visualization not only presents information, but tells a story.

Types of data visualizations

There are 3 types of data visualizations;

  1. Charts diagrams and graphs: These are the most common types of data visualizations which allow you to see correlations, trends and patterns.
  2. Summaries: Extremely useful for showing outliers, anomalies, top rankings and summaries. Allows exploration of data through querying and tags (e.g. interactive pivot table).
  3. Interactive web applications: Useful for displaying each individual item in the dataset.

15 useful data visualization techniques

data visualization techniques

There are hundreds of different graphs and charts, but we’ll focus on the core ones which you’ll use for day-to-day stuff. These are: 

  • Pie charts
  • Bar charts
  • Tape diagrams
  • Pictographs
  • Scatterplots
  • Time series
  • Area charts
  • Bubble graphs
  • Line Charts
  • Radar Charts
  • Venn Diagrams
  • Heatmaps
  • Box & Whisker Plots
  • Bullet Graphs
  • Maps

For an explanation of each one, skip down below.

How to Choose the Right Graphs

Here's a flowchart showing you when to use each graph/chart:

In general, when deciding on which graph to use and what design and color choices to make, it comes down to 3 things:

A) What is the purpose of your visualization?

B) What type of data do you want to show? E.g. categorical vs. numerical variables?

C) Who is your audience?

data visualization guide

Types of graphs/charts

Pie charts

Divided into many parts which represent a whole. It’s best used when you can divide the circle into 2 parts (you might be able to get away with 3). Any more than that is a mistake, because our eyes are bad at comparing parts of a circle.

An alternative to pie charts are ‘donut charts’ which serve the same purpose, but have a different design.

Examples of Pie Charts: Good & Bad

bar chart

Bar charts

The most common type of chart you’ll see, and often the most useful. 

Y-Axis: numeric measurement (e.g. test score, IQ, height, weight) 

X-Axis: categorical value (e.g. gender, country, favorite color).

It's perfectly fine to switch these around.

The human eye is much better at comparing the lengths of bars than the segments of a circle so bar charts are often preferred, especially when the change is small.

It’s not recommended to use a bar chart when there are too many values to show.

Examples of Bar Charts: Good & Bad

side by side bar chart

Side-by-side bar charts

Basically 2 bar charts stacked side-by-side. Useful when you want to show a third binary variable like male/female.

Sliced bar charts

The bars are sliced into different sections which make up a whole. You can imagine each bar as similar to a pie chart.

Useful when you have a variable like “country” that can be sliced into smaller subsets like “state.”


Clustered bar charts

Clustered bar charts allow you to add another category to the visualization, although it’ll take longer for the brain to process this information, so only use this when you want to compare all the variables. Otherwise, just use two separate bar charts.

Tape diagram



Tape diagrams

It’s like a bar chart, but shows ratios instead of exact values. Better for comparisons where exact values aren’t needed.

Examples of tape diagrams: Good and bad

pictograph


Pictographs

A fancier looking bar chart where symbols/images are used instead of bars. They tend to be more memorable, but suffer from the same issues as tape diagrams: they don’t really show exact values.

Examples of Pictographs: Good and bad

scatterplots

Scatterplots

Unlike bar charts where you’re comparing a category vs. numeric variable, scatterplots compare 2 numeric variables against each other (e.g. IQ vs test score). 

  • Useful for showing the relationship between 2 variables (correlations)
  • Useful for showing outliers
  • Useful for seeing clusters (where the bulk of the data lies)

Examples of scatterplots: Good and bad

ti


Time series

Exactly the same as a scatterplot, except the x-axis variable is always “time.” Useful for showing trends over time. 



Area charts

For comparison of multiple scatterplots. 

bubble chart

Bubble charts

It’s like a scatterplot, but contains more variables. The size of the circles indicate a third variable whilst the colors of the circles can indicate a fourth variable.

Useful for stuff which has fewer data points, but more variables to compare.

Bubble charts can be confusing to readers and take longer for the brain to process, so oftentimes it’s better to use a scatter plot + bar chart to show the same data.

Line charts

Despite the looks of it, it’s more similar to a bar chart than a scatter plot. 

The x-axis is usually a categorical value whilst the y-axis is usually a numeric value. 

It’s less memorable than a bar chart, but better at highlighting changes along the x-axis. Most times, the x-axis will be date related (e.g. Monday, Tuesday, Wednesday or Jan, Feb, March).

rda

Radar charts

Shows the many traits of one thing. Helpful for highlighting strengths & weaknesses. You can overlay different radar charts on top of each other for comparisons too.

Examples of radar charts: Good and bad

Venn diaht

Venn diagrams

Mainly for showing overlaps in demographics. Highlights similarities and differences between 2-3 things.

Examples of venn diagrams: Good and bad

heatma

Heatmaps

Color coded information which shows you where all the action/volume is happening at. The darker the color usually means the more volume.

Box & Whisker Plot

Box & whisker plots

  • The bottom line represents the lowest value (MIN)
  • The upper line represents the highest value (MAX)
  • The lower box represents the 25th percentile (Q1)
  • The upper box represents the 75th percentile (Q3)
  • The middle shows the 50th percentile (median or Q2)

They are useful for seeing distributions in data. For presentation purposes, you’ll likely have to explain what each part means to the audience as most people haven’t seen these before.

Examples of Box & Whisker plots: Good and bad

bullet graph

Bullet graphs

These are mainly used for showing performance reports in business and marketing.

  • The middle line represents the true value.
  • The dark perpendicular line represents the goal
  • The color coded bars represent different ranges such as bad, satisfactory, good, great

Putting multiple bullet graphs together can allow businesses to see where they’re underperforming and helps in decision making.

Maps

If one of your variables is location, it opens the door up for all kinds of data visualization on maps.

  • Choropleth maps - Like a heatmap, the locations are color coded to represent values.
  • Dot maps - plotting each point to the map
  • Connection maps - Shows how two places are connected
  • Bubble maps - it’s like a bubble chart where the size of the circle represents the value.

Type 2: Interactive data

Interactive data is powerful tool because it allows you to update the data in real time rather than have static images of graphs/charts. It's a crucial element to storytelling and allows you to show the journey of how to get from point A to point B.

For data analysis purposes, having interactive data makes it really easy to query and facet through your data without typing in SQL queries. It also makes it super easy to find outliers, anomalies, top rankings and summaries of the data. Here are some examples of interactive data:

  1. An online, interactive pivot table tutorial.
  2. Interactive sliced bar chart (showing the cost of several holiday destinations)
  3. AI generated insights on PPC marketing data

These were all made using Polymer Search.

Type 3: Interactive gallery view

Interactive gallery view is like an online catalogue shop that displays each item separately.

Think of it like Youtube thumbnails, Netflix titles and Steam store's games.

You can instantly turn a spreadsheet into a gallery view using Polymer Search.

Example: FlixGem

Interactive Gallrey View

The advantage of this method is it'll amaze your audience, as most people don't even know this is possible. It works best if you're sharing your data online.

The best type of data to use for this is one where you want to showcase each individual item, and each item contains several characteristics e.g. the above example uses: movie title: genre - director - writer - IMDb score - show length and more.

Data visualization tips

  • Learn to pick suitable graphs/charts for your data.
  • Learn color theory
  • Use all the easy-to-learn data visualization tools at your disposal
  • Animate or interact with the data if you want to tell a story
  • Learn from example

Tools for data visualization

There are dozens of tools on the market with each having their pros & cons.

Beginner friendly tools:

Polymer Search:

  • Super easy to create interactive graphs/charts
  • Limited choices of graphs/charts
  • Very easy to create interactive data
  • Best tool for turning a spreadsheet into interactive gallery view
  • Free and paid plans

Diagrams.net

  • Best tool for informational diagrams like pyramid charts and flow charts
  • Free to use

Excel:

  • Has a wide variety of graphs/charts and informational diagrams
  • These are not interactive
  • Impossible to create interactive summaries or data
  • Difficult to create more complex graphs/charts like sliced bar charts.
  • Free to use

More advanced tools:

Tableau:

  • Can handle large amounts of data (big data)
  • Allows scripting languages such as R and Python
  • Good for creating graphs/charts/diagrams
  • Good for creating interactive dashboards
  • Bad at creating creating interactive patterns
  • Expensive

PowerBI:

  • Powerful tool for creating beautiful, interactive graphs, charts and diagrams.
  • Affordable pricing
  • Requires learning the DAX language

Posted on
December 13, 2021
under Blog
December 13, 2021
Written by
Ash Gupta
Former Tech Lead for Machine Learning at Google AdWords (6 years) and a quant developer on Wall Street. Co-Founder & CEO of Polymer Search.

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