How do you fit enterprise stories around the many other demands you face to write dailies, file web updates, tweet and shoot video? This session focuses on taking advantage of the plethora of local data available online to spot and develop quick-turnaround stories. Learn how to develop a data state of mind, find newsworthy data and begin to analyze data sets. Spot the enterprise stories in the numbers. Trainer Dave Umhoefer directs the O’Brien Fellowship in Public Service Journalism at Marquette University, where he teaches investigative reporting. @GovWatcher

1. Set your reporting free
Dig for data every day
Dave Umhoefer
O’Brien Fellowship in Public Service Journalism
NewsTrain September 27, 2019
Milwaukee, WI
This Photo by Unknown Author is licensed under CC BY

2. Why Do Data?
Make puzzles into pa@erns

3. Have more
“Aha!
moments”

4. Data doesn’t
lie, die,
disappear or go
silent

5. Data puts
you in charge

6. There’s no
be>er myth
buster or fact
checker

7. Data prepares
you to put
breaking news
in context

8. Look smart:
Get behind
the news

9. Data
analysis
turns
anecdotes…
Incident types: Armed robbery
Incident locaDon: 1500 block of West
Wells St.
Approximate Dme: 3:12 a.m.
VicDms: One male Marque@e student
Physical injuries: No physical injuries
Three suspects approached the vicDm on
foot, displayed a weapon and demanded
property.

10. …into
evidence
and
authority
By Jack Ryan

11. A Data “state
of mind”

12. Public
domain

13. “I’m not math savvy”
You are not alone
“Numbers in the
Newsroom” book
Help from colleagues
Public domain

14. “I don’t have the Dme to do this”
Start small and simple
Get data off deadline

15. “What if I screw up?”
Special precauDons
QuesDon surprising findings
Ge^ng expert help

16. “To my editor, ‘Data’ is
just a character on TV”
Break a story
Keep some secrets
NoDerivs 2.0 Generic (CC BY-ND 2.0)

17. “I’m uncomfortable requesDng data”
IRE Dp sheets: “data negoDaDon”
and “open records”
Ask informally first
Make modest requests a regular habit
Make friends in IT department

18. Successful failures
Even missing or bad data is a story
PracDce is valuable

19. Who’s used data to make a story…
Or make it be@er?
Public domain FreeStockPhotos

20. Today’s road
Find data
When to build your own
Picking your next data story

21. Campaigns and Elec>ons
Local school in the news
Housing and Neighborhoods

22. Back to the
“data state
of mind?”
How do we
get there?

23. How are we defining
“data?”
Millions: traffic >ckets
Ten: Annual # of drunk-
driving crashes for a
decade.
Pixabay public domain
Data State of Mind…

24. Check the news: Dollar amounts,
numerical claims, grades
Compare: Joe’s Diner got a B- health
grade. What’s average? High?
Contrast over >me: Past inspecDons?
Context: Neighboring restaurants?
Data State of Mind…

25. Peek Behind the Curtain
Ask for the full data behind the
numbers in reports, research.
Quick follow up stories
Fact check report’s conclusions
Data State of Mind…

26. Too many ideas start
with generali>es, stop at
one noun.
Data State of Mind…

27. “I want to inves>gate bridges.”
“I want to inves>gate unsafe bridges”
“I want to learn what percentage of
bridges are unsafe”
*“I want to know if the number of
unsafe bridges has increased”

28. POSE:
Precise and compelling ques>ons
whose answer is…
News no ma>er what you discover
Data State of Mind…

31. Narrow your Google search by Site type or
a specific URL

33. That looks like this in a Simple Google Search

34. na
ov
e:

37. And…you’ve got Census data

38. Exercise #1: Google Advanced Search
• 5-10 minutes: Play around with “marijuana arrests”
search
• Try site:com
• Try other filetypes (.csv .pdf .ppt)
• Try a search on another drug
• What results do you get? Story ideas?

40. For everything, a government form

41. Data trails in annual reports
Comprehensive Annual Financial Reports

42. DIY Databases
Turning paper records
and jumbled text into
gold

43. Records on MU police website

44. Data entry and checking

46. Other Ways to Sniff out Data
Records Reten>on Schedules

47. Rescuing data
When websites get scrubbed or taken
down

48. EXERCISE #2 Sor-ng and Filtering
• Clinton vs. Trump
• 2016 presidenDal vote in Wisconsin
• County by county elecDon results.

49. Freeze top row

50. SorDng

51. SorDng…

52. On Your Own: Sort and Filter
Sort from smallest to largest
Find County least suppor>ve of Trump. What %?
Gaps between low and high

53. Now let’s restrict it to counDes with vote
totals over 50,000
Filtering funcDon

54. Filtering

55. Filtering

56. Filtering

59. Exercise #3: Formulas
• How did 3rd party or independent candidates do
in the 2016 presidenDal vote in WI counDes?

65. Newsworthy Numbers on the Beat

66. Campaigns and Elec>ons
Finding Trumpland
Who misleads (the most)?
Compare lawmakers: ideology, net worth
Geography of donors
Who votes?

67. Local school in the news?
Rank by:
• Grad, adend rates
• Experience of staff
• Test scores
• Free lunch %
College classes back?
Enrollment trends
• Tui>on compared
• Student sa>sfac>on
• Crime stats

68. Housing and Neighborhoods
$$ on potholes, policing
Foreclosed/vacant proper>es
Absentee landlords
Ownership history
Track code viola>ons, crime
by address

70. FINAL Q and A:
What’s one data-aided story off your
beat you can try now?
What quesDons do you have about
how to do that story?

71. Contact Me:
DAVE UMHOEFER
David.Umhoefer@marque@e.edu
(414) 288-5956
@GovWatcher