Crunching the numbers NR14

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Crunching the numbers NR14

  1. 1. Crunching The numbersCrunching The numbers The secrets behind big dataThe secrets behind big data journalism projectsjournalism projects Jennifer LaFleur, Center forJennifer LaFleur, Center for Investigative ReportingInvestigative Reporting Andy Lehren, The New York TimesAndy Lehren, The New York Times July 2014July 2014
  2. 2. It’s all about the story  Great data stories are not just a bunch ofGreat data stories are not just a bunch of numbersnumbers  Like any investigation, you are trying toLike any investigation, you are trying to find the best storyfind the best story  If you have all the data, you have all theIf you have all the data, you have all the anecdotes to choose from. Find the mostanecdotes to choose from. Find the most compelling.compelling.  Find anecdotes that fit with overarchingFind anecdotes that fit with overarching themesthemes
  3. 3. The New York Marathon
  4. 4. Combining dataCombining data  Put together more than one data set to getPut together more than one data set to get sparks to flysparks to fly  Look for as many data sets as possibleLook for as many data sets as possible that might tell the talethat might tell the tale  Helps with figuring out holes, verifyingHelps with figuring out holes, verifying
  5. 5. The Snowden FilesThe Snowden Files Smartphones, leaky apps, locations
  6. 6. Stories do not stop with dataStories do not stop with data  All the traditional tools applyAll the traditional tools apply  Documents, interviewing, freedom ofDocuments, interviewing, freedom of information requestsinformation requests  Storytelling and narratives: How you tellStorytelling and narratives: How you tell the tale remains importantthe tale remains important  Data can allow you to tell those tales withData can allow you to tell those tales with more authoritymore authority
  7. 7. Amazing thingsAmazing things … can be amazing …… can be amazing … …or wrong…or wrong  Verify everythingVerify everything  Outliers – extremes – are often the bestOutliers – extremes – are often the best stories. They may also be wrongstories. They may also be wrong  Bad data? Incomplete data? Changes inBad data? Incomplete data? Changes in the way data was recorded?the way data was recorded?  The limits of data: It can tell you what’sThe limits of data: It can tell you what’s possible to report on. And the limits of howpossible to report on. And the limits of how far you can go.far you can go.
  8. 8. Military SuicidesMilitary Suicides  Worse than previously reported. MilitaryWorse than previously reported. Military playing with statistics, underplaying severity.playing with statistics, underplaying severity.  Those who kill themselves areThose who kill themselves are disproportionally those whodisproportionally those who never served in anever served in a war zone.war zone.  Disproportionally young, white males.Disproportionally young, white males.  Often with troubled backgrounds.Often with troubled backgrounds.  Problems tracking soldiers harming efforts toProblems tracking soldiers harming efforts to prevent suicidesprevent suicides  Military loses track of reservistsMilitary loses track of reservists
  9. 9. Data trollingData trolling  Magic unicorn?Magic unicorn?  There is no one button to hit and findThere is no one button to hit and find storiesstories  This can be very frustrating.This can be very frustrating.  Have an idea for a story before you diveHave an idea for a story before you dive into datainto data  Always exceptionsAlways exceptions
  10. 10. Inspiration from Johan MueleggInspiration from Johan Muelegg ?
  11. 11. Finding documentsFinding documents
  12. 12.  USOC data of all US Olympic competitors for three decadesUSOC data of all US Olympic competitors for three decades  Database built from scraped athlete biographiesDatabase built from scraped athlete biographies  Congressional bills hot wiring citizenshipCongressional bills hot wiring citizenship  Determined athletes whose Olympic dreams were thwartedDetermined athletes whose Olympic dreams were thwarted  Country medal counts for each Olympics to show effectsCountry medal counts for each Olympics to show effects Reporting The Story
  13. 13. World-class athletes compete at the Olympics not for their native land Documenting country hopping
  14. 14. Newsroom organizationNewsroom organization  Getting support for projectsGetting support for projects  Teamwork: Collaborating for efficiency,Teamwork: Collaborating for efficiency, skillsskills  Convincing editors a project is worthwhileConvincing editors a project is worthwhile  A lot of us had to do work on our own timeA lot of us had to do work on our own time to build our skillsto build our skills

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