Sharing your findings with the world is just like telling any good story — sometimes it’s more about the storyteller than the story itself.
All too often, truly meaningful and interesting data projects fall through the cracks because they lack a cohesive narrative or don’t convince the audience why they should care. Remember, it’s up to you to decide how to best leverage your data to tell your story in a way that is compelling, interesting, and true to you. Here are some guiding questions to get you started:
Your data story can and should change based on your intended audience. The contextualizing information you provide, anecdotes you share, or images you include in a professional journal would be completely different from those you’d choose to share to a group of high school science students. Consider the following questions:
- What is your relationship to your audience?
- Are you their peer? Did you used to be in their shoes? Do you have anything in common?
- What can you do to understand your audience?
- Create an audience profile for one of your readers/users
- Have you interviewed them? Learned their likes/dislikes?
- What is your ideal medium?
- Your ideal medium is the format through which you implement your product or disseminate your findings, such as:
- Digital (web, smart phone applications, social media, etc.)
- Formal Print (reports, conferences, PowerPoint/Keynote presentations)
- Informal Print (staff meetings, flyers, etc.)
- Video
- What do you want them to take away?
- Is your purpose to share something generally exciting (informational) or do your results inform a specific decision or action (decisional)?
- If informational: highlight the findings that are most shocking/interesting to you and your audience
- If decisional: present the findings in a way that obviously supports some change or recommendation
- This often requires you to contextualize your information — what else should your audience know to reach your conclusion?
| General Tips |
|---|
| - Use a word editing app like Hemingway to improve the readability of your writing - Hemingway will highlight lengthy or run-on sentences, remove overly dense writing, offer alternatives for weak adverbs and phrases as well as poor formatting choices. |
| - Connect to your audience emotionally — how can you make this more personal? |
| - Visualize your story with a storyboard (see MIT’s guide to finding a story in your data) |
| - Find the right balance between words/explanation and figures/tables/images - This will largely depend on who your intended audience is and what medium you are using — digital products should be more visual while reports or prints should rely more on words |
| - Similarly, balance your quantitative data with qualitative data — too much dry facts or too many numbers may work against a compelling data story - Anecdotes, stories, and contextualizing comments also count |
| - Start with your ultimate goal: What message do you want the audience to walk away with? |
Finding the ‘best’ way to visualize your data takes time and experience — if you’re a beginner, focus your efforts on learning from others and refining your methods to master the art of translating data to diagrams.
| If you just need a quick chart or table, check out these online tools — they are simpler to use than the advanced data visualization guides and may be more appropriate for your specific project: |
| - Google Charts (interactive charts & simple data tools) - DataWrapper (charts, tables, and maps) - Infogram (beginner-friendly, collaborative, focuses on design thinking principles) |
For more complex data projects, choosing the right visualization is more than just deciding between a pie chart vs. a bar graph — it’s about understanding your audience’s learning style and design preferences, leaning in to your creative side, and asking for lots of feedback.
Here are some resources to help you understand all types of data visualization, how to create them, and which choices are most appropriate for your data:
- Beginner: This article summarizing general Data Visualization strategies and common methods used in different professions and sectors
- Beginner: Tableau’s Data Visualization for Beginners: a Definition & Learning Guide with helpful examples
- Beginner: This Step-by-Step Guide to Data Visualization and Design written for beginners
- Beginner-Intermediate: Kaggle’s Data Visualization Course teaches you how to implement some more basic, powerful data visualization techniques (line charts, scatter plots, and distributions) and how to choose the right one
- Intermediate-Advanced: The Data Visualization Catalogue has a comprehensive list of charts that are separated by what data visualization function they employ
- All levels: Coursera often has free online Data Visualization Courses — check to see if one is available!
Getting your message out there requires you to actively share and distribute what you discovered or created.
Important Note: While it may seem as if we believe success is a necessary requirement to any “good” data project, this could not be further from the truth. No data scientists is free from failure, and data projects with less-than-ideal or confusing outcomes — besides being incredibly common — are immeasurably valuable to share with others. As a community, we will never learn from each other’s experiences if we do not communicate our failures.
Across the agency, there are a few existing groups and initiatives that exist to help you leverage your department’s resources to publicize your findings. Take advantage of the resources available to you, ask for help from those who’ve done this before, and be proud of yourself for completing your project!