turning data into narrative

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Turning Data Into Narrative: Strategies for finding and sharing stories embedded within sets of data. Daniel X. O’Neil @juggernautco

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Turning Data Into Narrative: Strategies for finding and sharing stories embedded within sets of data.

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Page 1: Turning Data Into Narrative

Turning Data Into Narrative: Strategies for finding and sharing stories embedded within sets of data.

Daniel X. O’Neil@juggernautco

Page 2: Turning Data Into Narrative

@juggernautco

Strategies for finding…

• Search is your friend• Advanced search is your best friend• Don’t default to FOIA• Don’t deal with Public Information Officers• The hidden web still exists• Data is often more structured than you think• It takes an abundance of data types to tell a

story

Page 3: Turning Data Into Narrative

@juggernautco

FOIA is not your friend.

• The Internet is your friend.• Example: Dallas crime reports• Here’s there statement about getting data from them on

their public Web site:– Open Records requests must be made in writing. They may be:– 1.Hand-carried to the Records Section, Dallas Police

Headquarters, 1400 S. Lamar Street, Dallas, TX – 2.Faxed to 214-671-4636– 3.E-mailed to [email protected] – 4.Mailed by US Postage to - Dallas Police Open Records, 1400

S. Lamar Street, Dallas, TX. 75215

Page 4: Turning Data Into Narrative

@juggernautco

Page 5: Turning Data Into Narrative

@juggernautco

Page 6: Turning Data Into Narrative

@juggernautco

Page 7: Turning Data Into Narrative

@juggernautco

Page 8: Turning Data Into Narrative

@juggernautco

…and sharing stories…

• Knowing more than anyone else is still the only way to do this

• Surfacing from the hidden Web is doing everybody a favor

• Information is not knowledge. Publishing data without context is not super-useful

• Most data is boring. Why? Because data is made by people, and most people are boring most of the time

Page 9: Turning Data Into Narrative

@juggernautco

Page 10: Turning Data Into Narrative

@juggernautco

Ten Databases

• Building permits• Business licenses• Historic preservation list• Sanborn maps (1929 and 1950)• County assessor • County recorder of deeds• Original photography• Google search for news coverage• New York Times archive• Walgreens surplus property

Page 11: Turning Data Into Narrative

@juggernautco

Page 12: Turning Data Into Narrative

@juggernautco

…embedded within sets of data

• It’s got to be the other way around• We’ve got to embed our data into our stories

rather than find stories embedded in our data• I don’t want to search for anything• I’d rather know everything• Every object should have a page on the

Internet (so let’s get to work)

Page 13: Turning Data Into Narrative

@juggernautco

We need a machine.

• A generic context engine• To evenly distribute information• And tell me what the information

means• I know: that sounds like a “reporter”• But people used to think that

“search engine” sounded a lot like “librarian”, too

• We need humans and machines

Page 14: Turning Data Into Narrative

@juggernautco

It’s easy.

• Find dataset• Review dataset• Describe what the data means• Find another dataset• Describe what the other dataset

means• Describe what the first dataset means

in the context of the second dataset• Repeat• Let’s do this thing.