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When machine learning goes wrong

We read (and have written) a lot about how companies have used machine learning successfully - but what about when companies have used machine learning unsuccessfully? There are a lot of examples of the latter. We've all been there. Like anything in life, data science is something we get better …

What might happen to opt-in rates in a GDPR world

At the risk of quoting the 21st Secretary of Defense, one of the big "known unknowns" of GDPR is how it will impact opt-in rates. For many of the data use cases that define data-driven TV, this is a critical success factor: addressable advertising relies on matching viewer data …

Realtime feature detection in a browser with TensorFlow JS

At Dativa, we've been doing AI and machine learning for a long time. When we started, it was hard, requiring massive computing power and a lot of time. At the beginning of 2019, we are in a situation where many of these technologies are mature to the stage where …

TV Analytics 101: Data Mining

Many TV businesses have vast amounts of archived data going back years. It might be great to have a large volume of data to work with, but it also means you need a complicated process to figure out what data the company has in a report that lists these …

TV Analytics 101: Mobile app analytics

Apps are now must-have for broadcasters and MVPDs alike-but how to extract the most significant value out of them? Like the other elements of a TV business, high-quality analytics are essential for all aspects of day-to-day and long-term decision-making for your apps. Analytics should be part of the build …