There are few industries that have been disrupted more by the digital age than media and entertainment. For decades, media organizations acted as wholesalers for content, which was a vehicle monetized mostly through advertising. With little focus on the consumer experience, the world of broadcasting, outdoor advertising, publishing and entertainment remained largely unchanged until the early 2000s. Then came digital.
As digital took off, the rise of FAANG companies (Facebook, Amazon, Apple, Netflix, Google) heightened consumer expectations around smarter, personalized experiences, making data and AI table stakes for success. Brands have shifted their ad budgets to digital channels such as connected TV, mobile and search advertising to more definitively target their ad spend, while also driving compliance with increasing privacy regulations.
Driving better data, analytics and AI outcomes for consumers, advertisers and employees is now a board-level initiative for most media and entertainment companies. The problem? Traditional data architectures weren’t built to support AI/ML use cases, especially across broad teams of data engineers, data scientists and analysts, while supporting the scale and agility media and entertainment companies need to support evolving customer demands.
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