Real-time analytics is becoming increasingly important for us
I have cought up with Dr. Volker Stümpflen, Head of Data Strategy and Operations at Mediengruppe RTL, to talk about the use of real-time analytics. He is primarily responsible for the development and expansion of data science activities and the realisation and monetisation of specially analysed data models with a focus on user behaviour. He has extensive experience in large database systems and applications, based on his previous activity as Director of Data Science, with a focus on data-driven risk management in the financial investment sector. His other stations included the long-standing management of the Biological Information Systems Group at the Helmholtz Centre in München, with a focus on semantic big data systems in biomedicine. He was also founder and CEO of Clueda AG, a multi-award-winning fintech startup (including the Best in Big Data Award, winner in the Disruptive Technology Challenge Big Data category).
Absolutely! Two or three years ago it was different. Even though our core business of linear TV continues to run very well, RTL is developing into a four-screen provider across all terminals. This is still going very well, but we are preparing for the future. Data, analytics and machine learning play a much greater role here. The reason is very simple: We want to understand our users better, get closer to them. Ultimately, our aim is to offer content and market advertising opportunities to as specific target groups as possible. To this end, it is important that we gain insights and act more data-driven.
Our approach is that we want to bring analytical thinking into the entire organisation. For example, we decided on self-service BI with appropriate visualisation tools as a method. The department is able to – largely independently – find new insights and make decisions based on them.
Absolutely, this is a central component of our strategy. We are implementing a cloud strategy based on this. This will allow us to define and provide a uniform data lake in the future. This makes it even easier for the departments to monitor and control their business using analytical methods.
I see two aspects. On the one hand, it is a question of implementing rather complex analyses that are rarely time-critical. We then use Hadoop-based infrastructures in batch operation. A second area, however, is the ability to perform real-time analytics using streaming technologies. Here we also have a look at tools such as Spark, Kafka or cloud-based services from Google, for example. So we already have initial approaches in operation and want to expand this further.
Technically speaking, real time is relatively new to us.
