Data Driven Journalism


Netflix knows what we like to watch, when, for how long, and a whole lot more. Whenever we select a program, the system recalibrates its data to personalize our experience. And again with each session.

Within this, Netflix applies a myriad of cool data techniques, and many of the challenges and decisions behind their processes explained regularly on their Tech Blog.

We looked at five lessons that data journalists can take from the Netflix experience:

Where is the best place to insert text on an image? Text mustn’t obscure the image, but it also needs to be prominent enough to grab the audiences’ attention. Similar concerns face journalists constructing visualizations.

To optimize text placement, Netflix uses a text detection algorithm to detect when there is already text within a frame and prevent overlaps.  Yet, in doing so, there is the risk of false positives. In order to limit these, the team applies a number of image transformations, outlined in the diagram below, and checks these against the text detection algorithm. By providing more images, with different features highlighted, the algorithm has a larger corpus of data on the image properties and harness this to best place text.

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Netflix is available in 21 languages and, subsequently, its instant search algorithm needs to be able to point users to relevant content in their local language. One important goal of the Netflix search is retrieving content with as few interactions as possible.;

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