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 data stories:

Easy: Picking a good visualization to answer a data question
Hard: Discovering the core message of your data story that will move your audience to action

Easy: Knowing who is your target audience
Hard: Knowing what motivates your target audience at a personal level by understanding their everyday frustrations and career goals

Easy: Collecting questions your audience wants to answer
Hard: Delivering answers your audience can act on

Easy: Providing flexibility to slice and dice data
Hard: Balancing flexibility with prescriptive guidance to help focus on the most important things

Easy: Labeling visualizations
Hard: Explaining the intent and meaning of visualizations

Easy: Choosing dimensions to show
Hard: Choosing the right metrics to show

Easy: Getting an export of the data you need
Hard: Restructuring data for high-performance analytical queries

Easy: Discovering inconsistencies in your data
Hard: Fixing those inconsistencies

Easy: Designing a data story with a fixed data set
Hard: Designing a data story where the data changes

Easy: Categorical dimensions
Hard: Dates

Easy: Showing data values within expected ranges
Hard: Dealing with null values

Easy: Determining formats for data fields
Hard: Writing a human-readable definition of data fields

Easy: Getting people interested in analytics and visualization
Hard: Getting people to use data regularly in their job

Easy: Picking theme colors
Hard: Using colors judiciously and with meaning

Easy: Setting the context for your story
Hard: Creating intrigue and suspense to move people past the introduction

Easy: Showing selections in a visualization
Hard: Carrying those selections through the duration of the story

Easy: Creating a long, shaggy data story
Hard: Creating a concise, meaningful data story
 
Easy: Adding more data
Hard: Cutting out unnecessary data

Easy: Serving one audience
Hard: Serving multiple audiences to enable new kinds of discussions

Easy: Helping people find insights
Hard: Explaining what to do about those insights

Easy: Explaining data to experts
Hard: Explaining data to novices

Easy: Building a predictive model
Hard: Convincing people they should trust your predictive model

Easy: Visual mock-ups with stubbed-in data
Hard: Visual mock-ups that support real-world data

Easy: Building a visualization tool
Hard: Building a data storytelling tool