Implement Iterative Data Visualization

Break down the components of the iterative data visualization processes.

What is iterative data visualization?

The iterative data visualization process is a crucial part of data storytelling. It typically involves a three-stage process, depicted in the figure below:

The three stages of the iterative data visualization process
The three stages of the iterative data visualization process

Step 1: Create visuals

This step involves creating and presenting visuals to a group of stakeholders, typically in the form of individual visualizations, slides, dashboards, and so on.

Step 2: Get stakeholder feedback

This step involves receiving and prioritizing stakeholder feedback on elements of the design. Example feedback could include:

  • Changing elements of the plot to be visually more apparent ( adjusting the color or position of the plots in a dashboard).

  • Elaborating on what a metric signifies (with a legend).

  • Fine-tuning or removing unnecessary data.

  • Adding additional data to the visualization or dashboard.

Step 3: Update the design

This step involves updating the design accordingly, to prevent a significant redesign unless necessary.

Iterative data visualization in the design lifecycle

It can be beneficial to perform iterative data visualization processes at different times. A summary illustration is depicted in the below diagram of an example design lifecycle.

Lifecycle for iterative data storytelling
Lifecycle for iterative data storytelling
  • Data acquisition, data collection, and storage: These are the stages where a data storyteller and/or data scientist gathers the datasets they will be using for the analysis and store them appropriately. This stage can optionally include data processing steps.

  • Define a problem statement: This is the stage where the data storyteller creates a problem statement from the data, such as an opportunity or pain point that must be identified using the data.

  • Exploratory data analysis: In this stage, a data storyteller starts exploring their data with visualizations.

  • Visualization generation: This stage involves creating both draft and final visuals to use as part of the storytelling.

  • Data presentation and stakeholder feedback: These are typically the last stages of the lifecycle, where the data storytelling is presented to a group of stakeholders.

Iterative elements in the lifecycle

A data storyteller may refresh and refine their data visualizations and problem statements after an initial exploratory data analysis (EDA) stage. Similarly, after receiving stakeholder feedback, a data storyteller might alter the problem statement and EDA stages to incorporate additional features that the narrative needs to include, consequently influencing the generation of the visualizations.