Implement Iterative Data Visualization
Explore the iterative data visualization process to create impactful data stories. This lesson guides you through crafting visuals, gathering stakeholder feedback, and updating designs to refine your analysis. Understand how to cycle through problem definition, exploratory analysis, and visualization creation to enhance your data storytelling and present clearer narratives.
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:
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.
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.