Telling stories with data

Denise Malan | denise@ire.org | @DeniseMalan
So you want to engage users
with your data?
Where to start?
Planning
Cleaning
Analysis
Design
Bulletproofing
Launch
Maintenance
Planning:
● Set goals — but be flexible
● Focus, focus, focus
● Set clear roles for all staff involved
○ Data manager - we...
More about focus
What story are you trying to tell?
Follow the data (story might not be what you
thought)
Provide context
Simpler is always better
“Macgyver gets lazy”

Credit: xkcd
Data cleaning
● Enlist the experts to help you understand
the data
● Scrub it til you can’t scrub anymore
● Anticipate wea...
Analysis and project design
Think of your project as a news story
Headline
Lede and nut graf
Supporting details
“Sidebars”...
Bulletproofing
● Double-check your cleaning
● Have someone not involved with the
project test it
● Show it to your sources...
What are the caveats?
● No dataset is perfect
● Be upfront about what you can learn and
what you CAN’T learn from this dat...
Credit: xkcd
Launch
Marketing: Make sure people know about it
Newsrooms: consider doing story about
database
Nonprofits, governments: n...
Maintenance
Be available: Designate a point person for
users to contact with questions, concerns,
corrections, etc.
Be up ...
Example
http://www.caller.com/data/islanddevelopment/
Questions?
Denise Malan
denise@ire.org
@DeniseMalan
479.422.2405
Telling Stories with Data
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Telling Stories with Data

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Telling Stories with Data

  1. 1. Telling stories with data Denise Malan | denise@ire.org | @DeniseMalan
  2. 2. So you want to engage users with your data? Where to start?
  3. 3. Planning Cleaning Analysis Design Bulletproofing Launch Maintenance
  4. 4. Planning: ● Set goals — but be flexible ● Focus, focus, focus ● Set clear roles for all staff involved ○ Data manager - weeds ○ Project manager/designer - trees ○ Department leader - forest ● Set timeline (be realistic) ● How will you market the project? ● Plan NOW for maintenance
  5. 5. More about focus What story are you trying to tell? Follow the data (story might not be what you thought) Provide context
  6. 6. Simpler is always better “Macgyver gets lazy” Credit: xkcd
  7. 7. Data cleaning ● Enlist the experts to help you understand the data ● Scrub it til you can’t scrub anymore ● Anticipate weaknesses and criticisms of the data ● Get a clearer picture of your focus and adjust if necessary
  8. 8. Analysis and project design Think of your project as a news story Headline Lede and nut graf Supporting details “Sidebars” belong on another page or in another project
  9. 9. Bulletproofing ● Double-check your cleaning ● Have someone not involved with the project test it ● Show it to your sources or experts ● Try to break it
  10. 10. What are the caveats? ● No dataset is perfect ● Be upfront about what you can learn and what you CAN’T learn from this data ● Anticipate questions, criticisms and possible conclusions people might try to draw
  11. 11. Credit: xkcd
  12. 12. Launch Marketing: Make sure people know about it Newsrooms: consider doing story about database Nonprofits, governments: notify media
  13. 13. Maintenance Be available: Designate a point person for users to contact with questions, concerns, corrections, etc. Be up to date: Don’t let the project get stale Evaluate performance
  14. 14. Example http://www.caller.com/data/islanddevelopment/
  15. 15. Questions? Denise Malan denise@ire.org @DeniseMalan 479.422.2405

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