{"id":36574,"date":"2018-11-13T20:01:38","date_gmt":"2018-11-14T01:01:38","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/the-tension-between-people-and-data-at-netflix\/"},"modified":"2018-11-13T20:29:24","modified_gmt":"2018-11-14T01:29:24","slug":"the-tension-between-people-and-data-at-netflix","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-tension-between-people-and-data-at-netflix\/","title":{"rendered":"The tension between people and data at Netflix"},"content":{"rendered":"

Netflix is one the largest media companies in the world, growing from a small DVD rental company to a subscription-based streaming platform and media behemoth. In 2017, Netflix reported having over 117 million subscribers in more than 190 countries. Every day, Netflix\u2019 subscribers stream more than 140 million hours of content[1]<\/a> from the company\u2019s extensive content library, which includes both licensed and original content. Moreover, due to its digital status, Netflix has a large amount of data about its users and their behavior. This data has enabled the company to utilize machine learning to produce shows that customers want to watch. However, in the TV and filmmaking industry, many projects are based on the artistic visions of creatives and relations between the different components of the ecosystem. Therefore, Netflix\u2019s dependency on data is creating a tension between people and data, as the company strives to dominate the global media scene.<\/p>\n

Machine learning is when computers analyze data and learn from it using statistical probabilities[2]<\/a>. Machine learning is changing the media scene, as content generators are utilizing the predictive power of computing to cluster their consumes and accurately predict their engagement with the content. Netflix has adopted machine learning early on in its journey to develop original content, as its algorithms learn from the plethora of user\u2019s viewing behaviors that the company gathers; from how users pick a show to watch, to when do they pause, rewind, stop or binge watch[3]<\/a>. Combining user behavior data with viewing preferences, Netflix proved it could engineer the perfect show, as the company was able to have an 80% success rate for original shows, almost double the success rate of traditionally produced TV shows[4]<\/a>.<\/p>\n

With the great success of\u00a0 \u2018House of Cards’ one of Netflix’ earliest original shows, the company expanded its dependency on machine learning to produce original content. The company\u2019s investment in original content increased significantly, from almost 24 shows produced in 2015, to 700 shows in 2018 and an expected budget $8 billion[5]<\/a>. However, the rise of original content production gradually turned Netflix into a Hollywood powerhouse, strengthening its position in an ecosystem based on relations. This presented a challenge to the company, as on one hand it rose to prominence by being data driven, while on the other hand it has become a leader in an industry based on people and relations. As a result, tension between the Los Angeles based content team and the Silicon Valley based technology team started becoming more prevalent. For example in 2017, after Netflix released the show \u2018GLOW\u2019 about woman wrestling in the 1980s, the data team concluded that the performance of the show was not meeting expectations and therefore did not merit a renewal for a second season. However, the content team argued that the relations with the creators of the show are important for Netflix\u2019s position and future projects, and subsequently won the argument[6]<\/a>. A similar argument about data, relations, and artistic independence happened within the company regarding the show \u2018Lady Daynimite,\u2019[7]<\/a> and as the company expands its original content and strengthen its position as a production empire, this tension will only grow.<\/p>\n

To address this issue in the short term, Netflix could reach out more to the different components of the ecosystem to raise awareness on the role of data in the content creation and selection process. However, in the medium term, Netflix could\u00a0 start explicitly integrating quantitative benchmarks in the production contracts, becoming more transparent with creators and enabling them to know the metrics that will qualify their show or films for renewal or further investment. On the other hand, to address the tension between data and creativity in the short run, I recommend that Netflix divides its original content budget into two segments: data driven content and people driven content. The data driven content is a continuation of the content highly shaped by machines to optimally serve the needs of the customers. On the other hand, the people driven content could be a smaller part of the budget, dedicated for creative visionaries who will take risks and produce fresh content, with a higher tolerance for failure. Yet the question that still stands is: how can a data driven company successfully develop a strong position in a relations based industry? Could Netflix utilizes data and machine learning, while at the same time give creatives the freedom to produce groundbreaking shows?<\/p>\n

(729 word)<\/p>\n

 <\/p>\n

 <\/p>\n

[1]<\/a> Netflix, Inc. 2017 Annual Report, p. 1, [https:\/\/s22.q4cdn.com\/959853165\/files\/doc_financials\/annual_reports\/0001065280-18-000069.pdf] accessed November 2018.<\/p>\n

[2]<\/a> Chris Meserole, \u201cWhat is Machine Learning?\u201d The Brookings Institute, October 4, 2018, [https:\/\/www.brookings.edu\/research\/what-is-machine-learning\/], accessed November 2018.<\/p>\n

[3]<\/a> Bernard Marr, \u201cNetflix Used Big Data To Identify The Movies That Are Too Scary To Finish,\u201d Fortune, April 18, 2018, [https:\/\/www.forbes.com\/sites\/bernardmarr\/2018\/04\/18\/netflix-used-big-data-to-identify-the-movies-that-are-too-scary-to-finish\/#42d62ca93990], accessed November 2018.<\/p>\n

[4]<\/a> Orcan Intelligence, \u201cHow Netflix Uses Big Data,\u201d Medium (blog), January 12, 2018, [https:\/\/medium.com\/swlh\/how-netflix-uses-big-data-20b5419c1edf<\/a>] accessed November 2018.<\/p>\n

[5]<\/a> Todd Spangler, \u201cNetflix Eying Total of About 700 Original Series in 2018,\u201d Variety, February, 2018, [https:\/\/variety.com\/2018\/digital\/news\/netflix-700-original-series-2018-1202711940\/]<\/p>\n

[6]<\/a>,7 Shalni Ramchandran, Joe Flint, \u201cAt Netflix, Who Wins When It\u2019s Hollywood vs. The Algorithm?\u201d The Wall Streer Journal, November 10, 2018, [https:\/\/www.wsj.com\/articles\/at-netflix-who-wins-when-its-hollywood-vs-the-algorithm-1541826015], accessed November 2018.<\/p>\n

<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

Netflix is one the largest media companies in the world, growing from a small DVD rental company to a subscription-based streaming platform and media behemoth. In 2017, Netflix reported having over 117 million subscribers in more than 190 countries. Every […]<\/p>\n","protected":false},"author":11761,"featured_media":0,"comment_status":"open","ping_status":"closed","template":"","categories":[346],"class_list":["post-36574","hck-submission","type-hck-submission","status-publish","hentry","category-machine-learning","hck-taxonomy-organization-netflix","hck-taxonomy-industry-media-and-broadcasting","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nThe tension between people and data at Netflix - Technology and Operations Management<\/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-rctom\/submission\/the-tension-between-people-and-data-at-netflix\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The tension between people and data at Netflix - Technology and Operations Management\" \/>\n<meta property=\"og:description\" content=\"Netflix is one the largest media companies in the world, growing from a small DVD rental company to a subscription-based streaming platform and media behemoth. In 2017, Netflix reported having over 117 million subscribers in more than 190 countries. Every […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-tension-between-people-and-data-at-netflix\/\" \/>\n<meta property=\"og:site_name\" content=\"Technology and Operations Management\" \/>\n<meta property=\"article:modified_time\" content=\"2018-11-14T01:29:24+00:00\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data1\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\\\/\\\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/the-tension-between-people-and-data-at-netflix\\\/\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/the-tension-between-people-and-data-at-netflix\\\/\",\"name\":\"The tension between people and data at Netflix - Technology and Operations Management\",\"isPartOf\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\"},\"datePublished\":\"2018-11-14T01:01:38+00:00\",\"dateModified\":\"2018-11-14T01:29:24+00:00\",\"breadcrumb\":{\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/the-tension-between-people-and-data-at-netflix\\\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/the-tension-between-people-and-data-at-netflix\\\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/the-tension-between-people-and-data-at-netflix\\\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Submissions\",\"item\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/submission\\\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"The tension between people and data at Netflix\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/#website\",\"url\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/\",\"name\":\"Technology and Operations Management\",\"description\":\"MBA Student Perspectives\",\"potentialAction\":[{\"@type\":\"性视界Action\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\\\/\\\/d3.harvard.edu\\\/platform-rctom\\\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"en-US\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"The tension between people and data at Netflix - Technology and Operations Management","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-tension-between-people-and-data-at-netflix\/","og_locale":"en_US","og_type":"article","og_title":"The tension between people and data at Netflix - Technology and Operations Management","og_description":"Netflix is one the largest media companies in the world, growing from a small DVD rental company to a subscription-based streaming platform and media behemoth. In 2017, Netflix reported having over 117 million subscribers in more than 190 countries. Every […]","og_url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-tension-between-people-and-data-at-netflix\/","og_site_name":"Technology and Operations Management","article_modified_time":"2018-11-14T01:29:24+00:00","twitter_card":"summary_large_image","twitter_misc":{"Est. reading time":"4 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-tension-between-people-and-data-at-netflix\/","url":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-tension-between-people-and-data-at-netflix\/","name":"The tension between people and data at Netflix - Technology and Operations Management","isPartOf":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website"},"datePublished":"2018-11-14T01:01:38+00:00","dateModified":"2018-11-14T01:29:24+00:00","breadcrumb":{"@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-tension-between-people-and-data-at-netflix\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-tension-between-people-and-data-at-netflix\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/the-tension-between-people-and-data-at-netflix\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/d3.harvard.edu\/platform-rctom\/"},{"@type":"ListItem","position":2,"name":"Submissions","item":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/"},{"@type":"ListItem","position":3,"name":"The tension between people and data at Netflix"}]},{"@type":"WebSite","@id":"https:\/\/d3.harvard.edu\/platform-rctom\/#website","url":"https:\/\/d3.harvard.edu\/platform-rctom\/","name":"Technology and Operations Management","description":"MBA Student Perspectives","potentialAction":[{"@type":"性视界Action","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/d3.harvard.edu\/platform-rctom\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"en-US"}]}},"_links":{"self":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/36574","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission"}],"about":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/types\/hck-submission"}],"author":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/users\/11761"}],"replies":[{"embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/comments?post=36574"}],"version-history":[{"count":0,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/hck-submission\/36574\/revisions"}],"wp:attachment":[{"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/media?parent=36574"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/d3.harvard.edu\/platform-rctom\/wp-json\/wp\/v2\/categories?post=36574"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}