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Big Data in Media – The Infallible Way to Be Competitive

As digital reality unfolds, the media and entertainment industries face new challenges every day. Today, customers are more demanding since they have a wide range to choose from in this increasingly competitive field. Additionally, the use of data science in different facets of modern life requires more creativity from those in the media and entertainment industries. Big data in media is utilized for various purposes, from increasing revenue to boosting views. Besides, the benefit of data science applications is evident for significant gaming or broadcasting companies, media, etc. The audience’s opinions matter the most in the media and entertainment industry. As a result, the customer’s decision and the organization’s action are directly connected. This article on LinkedIn by Alekhya Bhamidipati discusses how the media and entertainment industry is leveraging big data in media.

Insights of Big Data in Media

Big data in media can aid entertainment organizations with forecasting, operations research, subject modeling, audience segmentation, and content recommendations. For instance, analytics determines the shows on streaming services like Netflix and Amazon. Meanwhile, data scientists at 20th Century Fox are using AI to examine movie trailers to see what viewers might enjoy. By providing data-driven insights, data scientists help businesses make better decisions. For instance, Netflix’s data analytics supports business and technical decisions, including budget planning, location finding, set building, actor schedules, and production execution

Data Scientists’ Role In the Industry

The entertainment business expects data scientists to approach data sets creatively and communicate their findings to others without technical expertise. In other words, convincing people in charge that the insights are valuable and necessary to take further action is a significant portion of a data scientist’s work.

Duties and responsibilities of entertainment industry data scientists include:

  • Constructing recommendation engines using data and machine learning
  • Defining performance and success metrics
  • Communicating data insights to non-technical team members
  • Designing and creating machine learning pipelines and statistical models
  • Analyzing data and identifying opportunities

Furthermore, the author also shares some use cases in the media and entertainment industry.

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