Matt Wynn, developer and watchdog reporter at the Omaha World-Herald, helps journalists get started with data journalism in this presentation for Lincoln, Nebraska, NewsTrain on April 9, 2016. It includes reasons to do data journalism, ways to find newsworthy data sets on any beat, examples of data sets from several beats, and exercises. It is accompanied by a handout: "Data-Driven Enterprise off Your Beat." NewsTrain is a training initiative of Associated Press Media Editors. More info: http://bit.ly/NewsTrain

1. Data-driven
enterprise off
your beat
MattWynn | @mattwynn

2. Why
data?
Because
agencies track
more than they
know what to do
with, and all of it
tells a story.

3. Why
data?
Because it lets
us hold agencies
accountable.

4. Why
data?
Because it gives
us the big
picture and
anecdotal
examples.

5. Why
data?
Because it lets
us prove things
agencies would
never admit.

6. Why
data?
Because
sometimes, the
story is the
volume, and
there’s no other
way to know.

7. Why
data?
Because officials
occasionally do
some weird
stuff.

8. Why
data?
Because it
differentiates
your stories from
the rest of the
pack and
provides insights.

9. Why
data?
Because if we’re
going to do page
views, let’s do
some page
views.
omahacrimereport.com
350,000 page views
dataomaha.com/salaries
1.3 million page views

11. What’s on
the docket?
- Ways to find data on
any beat
- An overview of a
few “classic CAR”
stories
- Some hands-on
exercises to give
you skills to take
home

12. Finding data

13. Finding data: Smart searching

14. Finding data: Smart searching, part two
https://dc2.education.ne.gov/tc_lookup/

15. Finding data: Let the sun shine
Make official requests standard operating procedure.
Don’t know how? Use our handy-dandy generator:
http://dataomaha.com/media/news/records/

16. Finding data: Annual and board reports

17. Finding data:
Roll your own
Sometimes you
have to make your
own data.
Be smart about it
from the outset.
Styling doesn’t help!
Every fact, a field.

18. Ideas for any newsroom

19. General government
● Tax assessments - Who’s highest?
Who’s gone up the most/least?
● Salaries - What departments are
growing or shrinking? Do salaries
generally make sense?
● Overtime - Who’s getting how
much, and how’s that compare to
base pay?
● Inspections - Weights and
measures, safety. Government
keeps us safe and businesses
honest, and what they find can be
fascinating.
● Purchase cards - What’s going on
government-issued credit cards?

20. Cops and courts
● Court records - What judge is harshest?
What charge shows up most in your
area? Do punishments compare?
● Crime logs - Where do burglaries
happen? When? Do reports compare to
UCR?
● Jail/prison logs - Who’s been in longest?
Most often? How has population risen
over time and why?
● Sex offenders - Where are they, and are
they actually where they say they’ll be?
Are they near schools or bus stops or
parks or day cares?
● Police discipline - Does it happen, is it
public, and does it go far enough or too
far?

21. Education
● Test scores - Where are they highest?
Among which students? Who has grown
the most? Who is most over- (or under-!)
performing their demographics?
● Enrollment - What schools are up or
down? Where are certain populations
rising or falling?
● Campus crime - First, is it accurate?
Then, what’s it say? How do you
compare to similar institutions?
● Teacher rosters - Where do the newbies
go? What about the experienced? Are
they moved around?
● Repair requests - What’s going wrong,
and how long does it take to fix it? Are
some schools getting treated better?

22. Grab bag - Midwest edition
● Tornado sirens - Do they cover
everywhere they should?
● Pet names - Most popular by
ZIP code? Rarest? Most
common? Type or breed?
● Potholes - Where are they --
and how quickly are they filled
when there are complaints?
● Potholes, part 2 - Lawsuits and
claims against your city.
● Gas-pump inspections - How
accurate are the pumps in your
area?

23. Let’s get our hands dirty

24. 10 minutes
with Excel
●Sorting
●Filtering
●A brief discussion
of functions
●A far-too-brief
introduction to
pivot tables

25. Excel 101: Filtering

26. Excel 101: Sorting

27. Excel 101: Functions
● There are *tons* of functions
to manipulate data.
● Statistical methods, basic
calculations, styling,
“concatenation,” etc., etc., etc.
● Can be useful for cleaning
data
● Also good for teasing out the
real story. For example, how
things have changed over
time

28. Excel 101: Functions

29. Excel 101: Let’s do it live
- Crack open nadc_donation.csv
- Who gave the biggest donation from your town?
- What is the biggest donation to a
politician/cause in your town?
- Does it change if you add together in-kind and
cash donations?
- What questions do we still want to answer?

30. Excel 101: Pivot tables

31. To recap
- Data are increasingly
required to do our jobs
well.
- There are many good
stories you can do easily.
- Filtering, sorting,
functions and pivot
tables are enough to
cause a lot of trouble.

32. Questions?
How can I help you with your
next data story?
Matt Wynn
@mattwynn
matt.wynn@owh.com