Media Analytics

Drive Production Decisions

Increasingly, data analytics is driving production decisions in media, sports, and entertainment. Strategic planning requires industry leaders to develop a single, unified view of any given product or person in order to understand the variables that drive ratings and viewership. Empowered with this information, decision makers fine-tune strategies ranging from ad pricing to movie marketing, script development and talent choices.

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Manage Data Proliferation


Unfortunately, data proliferation is challenging productive analysis. It is particularly difficult for companies to harness the abundance of data within and external to their organization. Products (e.g., TV shows) and people (e.g., actors) are constantly being viewed, reviewed, followed and rated by a multitude of external sources. In addition, manual methods of preparing both external and internal data sources for analytics are slow and not scalable. Teams of data scientists must be dedicated to pulling, combining and cleaning internal data, then integrating third-party sources to enrich that information with social media data, ratings, reviews and other externally created attributes.

Easily Enrich Data

Tamr automates the preparation of all enterprise data sources, whether internal or external, to create a complete view of a product or person. Leveraging machine learning, Tamr easily enriches internal data with hundreds of data sources throughout the digital supply chain – from iTunes to, from RottenTomatoes to AllFlicks, from AMC to Fandango.

Tamr’s workflow will:

  • Dramatically enhance time-to-value in preparing data for analytics — reducing the time needed to spin up new analyses from months to merely days or weeks
  • Enable significant scaling with complete accuracy — allowing analysts to analyze all relevant data, not just a small subset, and ultimately leading to better decisions
  • Create repeatability in the analytics process – enabling analysts to answer questions continuously, even as data changes, by building a reusable data infrastructure
  • Reduce the burden on IT and empower your analysts – quickly guiding the matching process without requiring the need to write complex scripts or business logic