Evolution of Journalism & Rich Media

Evolution of Journalism & Rich Media

ONA 2015

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People are getting the news online

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Online and mobile channels necessitate new user experiences; not everyone evolving

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Some players are getting ahead with rich media and fluffier content

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Readers are looking at text less, new formats more

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And demanding more rich media

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Especially younger readers

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Data visualizations provide richness too

http://www.nytimes.com/interactive/2015/09/15/world/europe/migrant-borders-europe.html?smid=tw-nytimes&smtyp=cur&_r=0

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We’re starting to see a shift

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Visualizations dramatically impact user engagement

Notes: n=935, 328-word articleSource: Graphiq Study

No Media Stock Image Interactive Visualization

+10% mean time

on site

+36%mean time

on site

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Visualizations transform fly by users into highly engaged users

0

20

40

60

80

100%

Distribution of Engagement by Asset Type

No Media Stock Image Interactive Visualization

Fly By (<20s)

Low Engagement

(20-59s)

Engaged (60-119s)

Highly Engaged (>120s)

-31%

-11%

4%

96%

% ChangeInteractive Viz

Notes: n=935, 328-word articleSource: Graphiq Study

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But creating graphics takes time

Is Santa Monica Wealthy?

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Which Santa Monica?

Census defined geographic hierarchy

Where are the cities?

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What do you mean, wealthy?

Demography

Thousands of files with shifting schemas over time

Population, migration, ethnicity & language, transportation, energy

Santa Monica

Education

“Public” data sourced from FOIAs and local sources

Business Profile

Derived from public and licensed sources

Real Estate

Aggregated from billions of licensed data points

Government

Public + think tank sources

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Ok – acquire, extract, and transform the dataAcquisition Extraction Transformation

Key

Activ

ities

•Research data supply chain, identify sources for each attribute

•Download thousands of files from dozens of sources

•Dozens of annual FOIA requests

•Audit data quality, negotiate licensing partnerships

Exam

ple

Wee

ks

• Education quality: national scores from NCES, AP, SAT, ACT; state specific scores from 50 state reporting agencies; and FOIA requests for 30 states

62 weeks

12 (public) + 50 (licensing)

•Research file structure, taxonomy, field definitions, geography types

• Select target attributes, manually map fields (taxonomy changes each year)

•Reverse geocode entities into correct geo

• Integrate data from multiple sources and load data into single master table

• Income distribution: selected 20+ fields from 13,700 attributes across 12,000 summary American Community Survey files (ACS), manually mapped across historical file versions (field names change over time)

40 weeks

24 (public) + 16 (licensing)

•Group fields into new, user-friendly values (“% College Educated”)

• Statistical analysis, heuristic-development to tag geos with user-friendly terms (“Wealthy”)

•Develop scoring algorithms based on multiple variables (“Good schools”)

•Automate extraction and transformation

• Business profile: conducted geospatial analysis on 32M companies spanning 19K industries to develop heuristics that tagged geographies with over-indexation of key industries relative to other geographies (“Lots of Religious Institutions”)

36 weeks

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Now open up your chart editor

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Or forward to your graphics team

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Because your graphics team has more than enough time

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It’s not just challenging for journalists

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Knowledge is Power

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Everything starts with the universe’s deepest knowledge graph

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Knowledge is Power

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Knowledge is Power – Graphiq Search

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Knowledge is Power – Graphiq Feed

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Knowledge is Power – Graphiq Feed

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Big Partners Are Taking Notice

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