Data Storytelling: What's Easy and What's Hard
Putting data on a screen is easy. Making it meaningful is so much harder. Gathering a collection of visualizations and calling it a data story is easy (and inaccurate). Making data-driven narrative that influences people...hard.
Here are 25 more lessons we've learned (the hard way) about what's easy and what's hard when it comes to telling stories with data. We also included links to our π Data Storytelling Lessons where they might help make things a little less hard for you.
Easy β Picking a good visualization to answer a data question β¦π How to Choose the Right Chart
Hard β Discovering the core message of your data story that will move your audience to action β¦π Story Elements
Easy β Knowing who is your target audience
Hard β Knowing what motivates your target audience at a personal level by understanding their everyday frustrations and career goals
Easy β Collecting questions your audience wants to answer
Hard β Delivering answers your audience can act on
Easy β Providing flexibility to slice and dice data
Hard β Balancing flexibility with prescriptive guidance to help focus on the most important things β¦π Explore vs. Explain
Easy β Labeling visualizations
Hard β Explaining the intent and meaning of visualizations β¦π Relatable and Specific
Easy β Choosing dimensions to show
Hard β Choosing the right metrics to show β¦π Metrics: Your Story Characters
Easy β Getting an export of the data you need
Hard β Restructuring data for high-performance analytical queries
Easy β Discovering inconsistencies in your data
Hard β Fixing those inconsistencies
Easy β Designing a data story with a fixed data set
Hard β Designing a data story where the data changes β¦π Explore vs. Explain
Easy β Categorical dimensions
Hard β Dates and times
Easy β Showing data values within expected ranges
Hard β Dealing with null values
Easy β Determining formats for data fields
Hard β Writing a human-readable definition of data fields
Easy β Getting people interested in analytics and visualization
Hard β Getting people to use data regularly in their job β¦π Data Personality Profiles
Easy β Picking theme colors
Hard β Using colors judiciously and with meaning β¦π Color and Contrast
Easy β Setting the context for your story
Hard β Creating intrigue and suspense to move people past the introduction β¦π Narrating Data Stories
Easy β Showing selections in a visualization
Hard β Carrying those selections through the duration of the story
Easy β Creating a long, shaggy data story
Hard β Creating a concise, meaningful data story β¦π Story Structure
Easy β Adding more data
Hard β Cutting out unnecessary data
Easy β Serving one audience
Hard β Serving multiple audiences to enable new kinds of discussions β¦π Data Personality Profiles
Easy β Helping people find insights
Hard β Explaining what to do about those insights β¦How to Ensure Your Actionable Insights Lead to Action
Easy β Explaining data to experts
Hard β Explaining data to novices β¦π Relatable and Specific
Easy β Building a predictive model
Hard β Convincing people they should trust your predictive model
Easy β Visual mock-ups with stubbed-in data
Hard β Visual mock-ups that support real-world data
Easy β Building a visualization tool
Hard β Building a data storytelling tool