The consumption of digital media is changing significantly due to the explosive growth in the popularity of Internet content. However, people spend time in front of the TV than ever before despite the fact that online streaming services have appeared as alternatives of traditional television. As a result of the continuous digitalization of the content providers nowadays, a massive amount of data is generated containing valuable information about the consumers and the digital contents themselves. Processing all these data and gathering the relevant information has a great business value when it is about decision making at the TV operators and content creators.
In my thesis work I am defining and observing key performance indicators related to the TV consumption. I look for BI solutions that business decision makers can benefit from and I am going to investigate analytical and visualization solutions for both offline and real-time data. Using the results of the analysis one of the most important fact that the usage statistics have a significant periodicity throughout the time, and despite the fact that there are quite a lot of missing attributes in the datasets the consumption can successfully be observed both on the level of item-metadata and using their changes in time.