  {"id":4997,"date":"2017-04-05T00:51:13","date_gmt":"2017-04-05T04:51:13","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-digit\/submission\/the-paper-of-record-adapts-to-a-paperless-world\/"},"modified":"2017-04-05T01:00:17","modified_gmt":"2017-04-05T05:00:17","slug":"the-paper-of-record-adapts-to-a-paperless-world","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-digit\/submission\/the-paper-of-record-adapts-to-a-paperless-world\/","title":{"rendered":"The Paper of Record Adapts to a Paperless World"},"content":{"rendered":"<p>Before 性视界, I spent several years at <em>The New York Times<\/em>. In the face of collapsing advertising revenues and declining newsprint circulation, <em>The Times<\/em> has spent years searching for a scalable and repeatable business model (a colleague suggested that this makes it a 165-year-old <a href=\"https:\/\/steveblank.com\/2010\/01\/25\/whats-a-startup-first-principles\/\">startup<\/a>). As the firm\u2019s digital model has evolved, management has recognized the value that data\u2014a source for reporting, and a user experience and operational driver\u2014creates in the digital-first world.<\/p>\n<p><em>Note: I worked in the NYT Data Science &amp; Engineering group, but this post is sourced strictly from public sources (all linked) unless noted.<\/em><\/p>\n<h3 style=\"margin-bottom: 25.5px\">Creating Value in Journalism<\/h3>\n<p>For years, <em>The Times<\/em> has leveraged quantitative data and analysis to enhance its reporting. Nate Silver\u2019s <a href=\"https:\/\/fivethirtyeight.blogs.nytimes.com\/about-fivethirtyeight\/\">FiveThirtyEight<\/a> is perhaps the most visible example (now supplanted by <a href=\"https:\/\/www.nytimes.com\/section\/upshot\">The Upshot<\/a>). Journalists have become accustomed to mining data for reporting insights, even outside of politics. One example: <a href=\"https:\/\/www.nytimes.com\/2014\/09\/12\/business\/air-bag-flaw-long-known-led-to-recalls.html\">defective Takata airbags<\/a>. Having manually dug through 2,000+ regulatory \u201cincident reports\u201d\u00a0to identify accidents related to Takata\u2019s malfeasance, that article\u2019s author <a href=\"https:\/\/vimeo.com\/117882911#t=195\">handed off her dataset to a data scientist<\/a> who built a <a href=\"https:\/\/en.wikipedia.org\/wiki\/Logistic_regression\">logistic regression<\/a> model to find those reports\u00a0among the remaining 31,000 that were also relevant to the article.<\/p>\n<p>Beyond crunching datasets for \u201ctraditional\u201d article-writing, <em>Times<\/em> editors and journalists have also turned to data-driven \u201cautomated storytelling.\u201d In sports, their <a href=\"https:\/\/www.nytimes.com\/interactive\/2016\/upshot\/nfl-playoff-simulator-every-teams-playoff-picture.html?_r=0\">playoff predictor<\/a> allowed NFL fans to simulate season-ending games to understand teams\u2019 chances of making the playoffs, while the <a href=\"https:\/\/twitter.com\/nyt4thdownbot\">4th Down Bot<\/a> examined historical play data to suggest fourth down play-calling. Other pieces tell more typical stories with data augmented\u00a0with user input (e.g., <a href=\"https:\/\/www.nytimes.com\/interactive\/2017\/01\/18\/upshot\/some-colleges-have-more-students-from-the-top-1-percent-than-the-bottom-60.html\">examine your college\u2019s economic diversity<\/a>). And in 2013, a <a href=\"http:\/\/www.nytimes.com\/interactive\/2013\/12\/20\/sunday-review\/dialect-quiz-map.html\">popular data-driven dialect quiz<\/a> quickly became the <a href=\"http:\/\/www.huffingtonpost.com\/2014\/01\/21\/new-york-times-intern-dialect-map-popular-josh-katz_n_4636941.html\">most-read nytimes.com story of all time<\/a>. These efforts all represent important efforts to leverage data in pursuit of <em>The Times<\/em>\u2019s mission to print \u201call the news that\u2019s fit to print\u201d\u2014even when print matters less and less.<\/p>\n<div style=\"text-align: center;margin-bottom: 25.5px\"><a href=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/04\/Map.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-4999\" src=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/04\/Map.png\" alt=\"\" width=\"975\" height=\"726\" srcset=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/04\/Map-300x223.png 300w, https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/04\/Map-768x572.png 768w, https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/04\/Map-600x447.png 600w\" sizes=\"auto, (max-width: 975px) 100vw, 975px\" \/><\/a><br \/>\n<small>Where <em>The Times<\/em> thinks I\u2019m from (with remarkable accuracy). After 22 years living in Madison, water fountains will always be <a href=\"https:\/\/en.wikipedia.org\/wiki\/Drinking_fountain#History\">bubblers<\/a>.<\/small><\/div>\n<h3 style=\"margin-bottom: 25.5px\">Creating Value for the Reader Experience<\/h3>\n<p>Beyond crafting news, <em>The Times<\/em> leverages data to improve reading experiences and surface engaging content to readers. For example: in 2015 the newsroom launched <a href=\"http:\/\/www.niemanlab.org\/2015\/08\/the-new-york-times-built-a-slack-bot-to-help-decide-which-stories-to-post-to-social-media\/\">Blossom<\/a>, a Slack bot that recommends stories for posting on Twitter and Facebook based on past high-performing articles and their text composition. Editors retain final say over news coverage decisions, but they are not averse to improving the reader experience with input from data.<\/p>\n<p>Beyond manually-curated suggestions, <em>The Times<\/em> also operates an article recommendation engine (shown under the <a href=\"https:\/\/www.nytimes.com\/#recommendations\">\u201crecommended for you\u201d tab on nytimes.com<\/a>) that recommends articles based on readers\u2019 browsing histories. <a href=\"https:\/\/open.blogs.nytimes.com\/2015\/08\/11\/building-the-next-new-york-times-recommendation-engine\/\">As engineers discuss<\/a>, the system uses a <a href=\"https:\/\/en.wikipedia.org\/wiki\/Topic_model\">topic modeling<\/a> algorithm (<a href=\"https:\/\/en.wikipedia.org\/wiki\/Latent_Dirichlet_allocation\">LDA<\/a>) to transform an article\u2019s text into a set of <a href=\"https:\/\/en.wikipedia.org\/wiki\/Feature_(machine_learning)\">features<\/a> that describe its content, enabling comparisons to other articles. By comparing new articles to past articles that users have read, the engine surfaces engaging content for those readers. Thus, <em>The Times<\/em>\u2019s data capabilities transcend news-writing, improving the reader experience throughout.<\/p>\n<div style=\"text-align: center;margin-bottom: 25.5px\"><a href=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/04\/Recommendations.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-5000\" src=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/04\/Recommendations.png\" alt=\"\" width=\"390\" height=\"414\" srcset=\"https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/04\/Recommendations.png 390w, https:\/\/d3.harvard.edu\/platform-digit\/wp-content\/uploads\/sites\/2\/2017\/04\/Recommendations-283x300.png 283w\" sizes=\"auto, (max-width: 390px) 100vw, 390px\" \/><\/a><br \/>\n<small>Article recommendations: a rare look into Micah\u2019s psyche.<\/small><\/div>\n<h3 style=\"margin-bottom: 25.5px\">Capturing Value<\/h3>\n<p><em>The Times<\/em> continues to compete with other news outlets\u2014and companies like Facebook\u2014for attention. To capture value that data-driven journalism and product enhancements (like those above) have created for readers, they clearly hope to realize increases in digital subscriptions. Beyond that, <em>The Times<\/em> also applies the same granular readership data that underlies article recommendations (as well as other data sources) to problems in marketing and operations. Reader histories\u00a0contain powerful signals that <a href=\"https:\/\/vimeo.com\/117882911#t=1874\">identify potential subscribers and unsubscribers<\/a> and <a href=\"https:\/\/vimeo.com\/117882911#t=1934\">reveal international value capture opportunities<\/a>. And to avoid waste in <em>The Times<\/em>\u2019s \u201cdead tree\u201d business, data scientists have used newspaper sales data to <a href=\"https:\/\/vimeo.com\/117882911#t=1898\">match newspaper printing to expected demand<\/a>.<\/p>\n<p>Of course, on all of these fronts <em>The Times<\/em> isn\u2019t the only game in town. <a href=\"http:\/\/www.fivethirtyeight.com\/\">FiveThirtyEight<\/a> (under ESPN ownership) remains committed to data-driven journalism, and appears to have an edge in <a href=\"http:\/\/fivethirtyeight.com\/features\/why-fivethirtyeight-gave-trump-a-better-chance-than-almost-anyone-else\/\">election predictions<\/a> (<a href=\"https:\/\/d3.harvard.edu\/platform-digit\/submission\/fivethirtyeight-and-the-big-data-fail-election-2016\/\">shortcomings aside<\/a>). The Jeff Bezos-owned <em>Washington Post<\/em> has also beefed up its data and algorithmic expertise, using data to <a href=\"https:\/\/developer.washingtonpost.com\/pb\/blog\/post\/2016\/02\/08\/bandito-a-multi-armed-bandit-tool-for-content-testing\/\">optimize headlines<\/a> (with <a href=\"https:\/\/en.wikipedia.org\/wiki\/Multi-armed_bandit\">multi-armed bandits<\/a>) and leaning on algorithms to <a href=\"https:\/\/www.wired.com\/2017\/02\/robots-wrote-this-story\/\">write short news stories<\/a>. Both players\u2014and plenty of others\u2014will be worth watching as the digital media landscape evolves.<\/p>\n<h3 style=\"margin-bottom: 25.5px\">Where We Came From; Where We\u2019re Going<\/h3>\n<p>Building these analytic capabilities necessitated significant investments in technical infrastructure and knowhow. My tenure overlapped with major engineering advancements in the logging technology required to gather the granular user data that powers efforts described above. And in early 2014, a private <em>Times<\/em> <a href=\"http:\/\/www.niemanlab.org\/2014\/05\/the-leaked-new-york-times-innovation-report-is-one-of-the-key-documents-of-this-media-age\/\">Innovation Report<\/a> leaked to the press, wherein company executives identified the challenge\u2014and progress made\u2014in hiring data- and tech-savvy newsroom staff.<\/p>\n<p>But perhaps the biggest challenge to adapting to the \u201cBig Data\u201d age is cultural. <em>The Times<\/em> has traditionally enforced rigid separation between journalistic and business staff in both the built environment (housing the newsroom in a distinct physical space) and in the org chart (an arrangement known colloquially as \u201cThe Wall\u201d or \u201cthe separation of church and state\u201d). As recent progeny of the Innovation Report <a href=\"https:\/\/www.nytimes.com\/projects\/2020-report\/\">indicate<\/a>, <em>The Times<\/em> has built better products by carefully softening these barriers, building bridges between business-focused analytics staff and the newsroom while eschewing conflicts of interest.<\/p>\n<p>In all, this is clearly an interesting time for the <em>Times<\/em>. Management has invested heavily in analytics capabilities with the aim of improving the firm\u2019s products. Still, despite incredible progress, digital operations remain <a href=\"https:\/\/www.nytimes.com\/projects\/2020-report\/\">$300 million short<\/a> of their 2020 revenue goal. Time will tell if data will be the difference-maker in <em>The Times<\/em>\u2019s business model search.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Amidst a digital transformation, The New York Times recognizes data\u2019s value in a digital-first world.<\/p>\n","protected":false},"author":444,"featured_media":4998,"comment_status":"open","ping_status":"closed","template":"","categories":[1175,1377,1378],"class_list":["post-4997","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-journalism","category-the-new-york-times","category-who-uses-these-tags-anyway"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-digit\/assignment\/data-and-analytics-as-digital-assets\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>The Paper of Record Adapts to a Paperless World - Digital Innovation and Transformation<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-digit\/submission\/the-paper-of-record-adapts-to-a-paperless-world\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Paper of Record Adapts to a Paperless World - 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