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Top 5 most effective ways to visualize data

Data, Tips
Welcome to the world of visual data storytelling, choose your visual wisely

What are Data Visualizations?

  • Show trends
  • Display comparisons
  • Make the numbers relatable and accessible
  • Tell a story about the data presented

Initial questions to ask before diving into visualizing data:

What is your end user asking for the data?

Who is your audience? Are they data savvy? Or data newbies?

How’s the data integrity? Is the data of good quality?

Which visual am I going to use?

Let’s dive-into our top 5 data visualization methods:

  1. Visualization Method: Pie Charts

Use Case (s): Display what makes up the whole picture “ literally a piece of the pie”percentage share of categories

Best practices: 

  • Categories with less than 5 points
  • Total should always equal 100%

Examples:

  • Demographic data such as: Age, Gender, HHI
  • Device Types
  • Regional data
  • Social Media platforms 

2. Visualization Method: Bar Graphs

Use Case(s): Show data distribution, compare values, 

Best Practices: 

  • Order data from left to right to show consistency

Examples:

  • Show temporal growth ( month-over-month, year-over-year)
  • Category performance (Product lines: Socks, Shoes, Jewelry)

3. Visualization Method: Line Graphs

Use Case(s): Show historical trends, trends over time, understanding relationships between data sets

Best Practices:

  • Limit the number of lines, too many can be crowded
  • Ensure the data displayed is to scale ( nothing like showing a trend lines with exponential differences)

Examples:

  • Show trends over time (with whole numbers)

4. Visualization Method: Scatterplots

Use Cases(s): Evaluate the relationship between multiple data sets (correlation)

Best Practices:

  • Have dependent and independent variables to plot
  • Ensure each data set is color coded differently

Examples:

  • Distribution of population data

5. Visualization Method: Box Plots

Use Case(s): Views distribution in quantiles, and usually outlines the lower and upper quartile values. 

Best Practices: for the data savvy audience, box plots help to visualize data points relative to quartiles and outliers

Examples:

  • Identifying volumes of data points within the 75, 50, 25 percentiles and identifying outliers

Where to build the best data visualization tools?

Where to find data visualizations:

For those who work with large volumes of data, Python and React have great libraries, for small to medium volumes, Excel can do the job too.

Here are some recommended libraries and a link to Matplotlib and Pandas examples:

React libraries: ChartsJS, Highcharts, eCharts, Platly, D3

Python packages: Matplotlib  Pandas, Chaco, PyX, Bokeh

What data visualization works well for you needs? Email us! Or @thedatadame on social media