Data and Entertainment: A match made by science

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The entertainment business is one of the few industries where data scientists are in high demand, as it should. I love film, I love music, and I love art, and if I am able to help out a studio, a musician, or an artist with data, it’s a match made in heaven. With that said, and despite its reputation for art, the entertainment business has recently placed a strong emphasis on science.

With streaming services, production studios, and legacy media businesses collecting vast amounts of data on production patterns, user viewing habits, and post-production planning, data scientists are in a unique position to assist organizations in making sense of the data. It’s a profession that demands equal parts technical prowess, analytical acumen, and creativity — and a passion for movies and television doesn’t hurt, too!

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The ultimate goal of data science in the entertainment sector is to extract meaningful and actionable insights from collected data, which is similar to the purpose of data science in other fields. However, data science’s precise applications can vary widely, and the entertainment business is one where data scientists are increasingly influencing an organization’s direction.

Data science insights may assist entertainment organizations with forecasting, operations research, subject modeling, user segmentation, and content recommendations on the surface.

Data is used by streaming providers like Amazon and Netflix to determine which shows are approved and promoted. At the Wharton Customer Analytics Initiative Conference in 2015, Dave Hastings, Netflix’s head of product analytics, observed, “You don’t make a $100 million investment these days without an awful lot of analytics.”

Meanwhile, data scientists at 20th Century Fox have employed AI to examine movie trailers in order to figure out what moviegoers could enjoy. The role of data science in entertainment has only risen in the years thereafter.

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Further, data scientists may assist entertainment companies in making better decisions by providing data-driven solutions rather than the traditional way of depending on human experience and intuition.

Netflix’s data analytics division, for example, has assisted the company with business and technical issues such as budgeting, location selection, set construction, and actor scheduling.

Netflix’s data scientists have also developed algorithms that allow production executives to make important decisions based on data-driven “what-if” scenarios rather than their best predictions.

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Our role as Data Scientists in Entertainment

Most data scientists bring technical skills like probability and statistics, data visualization, machine learning and AI, and Python and SQL understanding to the table. While these abilities may aid in the analysis of large amounts of data, the entertainment industry expects data scientists to be creative in their approach to data sets and be able to successfully convey findings to others without technical expertise.

“Learning what is a meaningful challenge to solve, how to ask smart questions with data, and solving problems creatively are all related and adjacent abilities,” said Alyssa Zeisler, the Wall Street Journal’s Research and Development Chief and Senior Product Manager, Editorial Tools. “Newcomers should concentrate on how to articulate ideas and communicate them in ways that a stakeholder is likely to grasp, whether that stakeholder prefers numbers and anecdotes.”

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In other words, connecting pertinent dots to tell a story and convincing people in authority that the insights are meaningful and worth acting on is a big part of a data scientist’s work. According to Daryl Kang, Lead Data Scientist at Forbes, who feels that familiarity and affection for the subject matter translates to improved motivation at work, which can lead to more effective advocacy for data-supported suggestions, being passionate about the industry can also help.

What I can do for your organization, as a data scientist is use data and machine learning to build recommendation engines, define performance and success metrics, developing and communicating recommendations rooted in data to non-technical members of the organization, design and develop algorithms and statistical models, and machine learning pipelines, and analyzing behavioral data and identifying opportunities for growth.

If this is something that interests you connect with me on Linkedin at, and let's start a conversation.

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Jay Burgess

Jay Burgess

I use data to help people and companies make better decisions. I also like writing about leadership. #BusinessIntelligence #ecommerce #datascientist #ai #sales