Discussing Risk: Designing Resilient Media Supply Chains Panel

Discussing Risk: Designing Resilient Media Supply Chains Panel

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Tag: vidispine

Discussing Risk: Designing Resilient Media Supply Chains Panel

This panel of supply chain experts examine the state of business, operational and technical risk at media companies. With the pandemic accelerating established technology transitions such as the move to virtualized infrastructures, have media companies effectively managed the security risks that this transition implies. And what about the increasing use of data to drive media operations, will that pose new challenges that media professionals have not been accustomed to deal with? This session is laser focused on every type of risk encountered by media companies in today’s volatile media world. It is an ideal session for media technology professionals wanting to grow their awareness of new types of risks that could radically affect their work as well as how to manage them effectively.

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Vidispine – an Arvato Systems Brand – How advanced AI improve rating prediction

Ratings forecasts are critical for broadcasters in defining their prices and sales models, models that are becoming increasingly complex as broadcasters look to offer cross platform campaigns to com-pete with newcomers disrupting the market. Advertisers want to know how and when they will hit their target (GRPs or impressions). Broadcasters want to know that they can deliver on their contracts but also without massively over delivering and losing potential revenue. At the same time, the accurate prediction of ratings, for a human, takes years of experience and practice and proves a significant succession planning challenge for broadcasters.

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Vidispine – an Arvato Systems Brand – Performance Prediction in a MAM system

In this presentation, we will look at how artificial intelligence can learn to produce highly accurate duration estimations, even for complex workflows. The presentation has a technical focus, starting from the initial collection of requirements and working through the modelling process and evaluation, before looking at how such a solution could be deployed. Finally, we’ll look at how such a solution could be integrated into workflows to automate decisions such as “red line” workflows, or pre-emptively scale systems for load.

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