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 πŸŽ“ 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€¦πŸŽ“ How to Choose the Right Chart

Hard β€” Discovering the core message of your data story that will move your audience to action β€¦πŸŽ“ 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€¦πŸŽ“ Explore vs. Explain


Easy β€” Labeling visualizations

Hard β€” Explaining the intent and meaning of visualizations β€¦πŸŽ“ Relatable and Specific


Easy β€” Choosing dimensions to show

Hard β€” Choosing the right metrics to show β€¦πŸŽ“ 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€¦πŸŽ“ 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€¦πŸŽ“ Data Personality Profiles


Easy β€” Picking theme colors

Hard β€” Using colors judiciously and with meaning β€¦πŸŽ“ Color and Contrast


Easy β€” Setting the context for your story

Hard β€” Creating intrigue and suspense to move people past the introduction β€¦πŸŽ“ 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€¦πŸŽ“ 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€¦πŸŽ“ Data Personality Profiles


Easy β€” Helping people find insights

Hard β€” Explaining what to do about those insights …How to Ensure Your Actionable Insights Lead to Action


Easy β€” Explaining data to experts

Hard β€” Explaining data to novices β€¦πŸŽ“ 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

Previous
Previous

Tips for Using Photos in Data Storytelling

Next
Next

How to Ensure Your Actionable Insights Lead to Action