Tim Ganther

This video blog post serves as a test item to evaluate the integration of multimedia within data-driven publishing workflows. It demonstrates how short video content can be embedded into a standard blog layout to enhance user engagement and content flexibility. This item does not reflect real data or represent an official publication.

Block editor test

Integrating kubeapps with Keycloak and Pinniped on the cloud can provide a secure and streamlined experience for managing applications in a Kubernetes environment.

Ever wondered why we are missing out on getting from point A to point B more quickly in urban areas, such as with intermodal travel? Learn how the Telekom Data Intelligence Hub (DIH) team is using a next-generation European standards-based data sharing and exchange technology referred to as dataspace to speed up personal travel.

In this video, we want to show you how to create a data offer by using the example of finding the best hubs for shared bikes. Along the way, we give you many valuable tips and insights on how to work with the Data Intelligence Hub for the most seamless experience.

Boy, Germany’s auto business is in a pickle … and probably all of us! Automotive has been a strong and reliable engine of growth, jobs, wealth and innovation for the German and European economy. Now, it suffers from its own success: too many cars everywhere, ever longer traffic jams, air pollution, noise (see “Stuck in traffic,” link): On top of it, digitalization and new tech like electrification and autonomous driving are transforming the global auto industry and shifting revenues from selling hardware to providing services (see “Service shift,” link). Now, the government and the Bundestag, have become involved to support key stakeholders to accelerate digital solutions toward a new, better performing mobility system.

Today, data is broken. If “time is money” (Franklin, 1749), then data analytics is a disaster because more than 80% of the time budget of a data analytics project is spent with data processing and refinement – not with results (see “Data is broken,” link). Figure 1 shows a metaphorical example of the problem: At an executive-level “data” tends to be thought of as a product that can be grabbed off the shelf, like bottled water. However, for a data scientist it more like a puddle of water, raw water. Nobody would drink from a puddle. The water would have to be “treated,” analyzed for harmful ingredients and nutrients, labeled and bottled to an exact quantity. This is not cheap. The magnitude of the price difference between raw and bottled water speaks for itself. The price of one liter of branded, bottled water at retail, buys one thousand liters of raw water in Berlin. These numbers make it crystal clear how much value is created from raw to bottled water. The gist of our analogy is this: Data needs refinement and in order to economize on the process it takes – no surprise – a factory. We use factories for everything else. Henry Ford invented the modern factory and with it, automotive as an industry. His factory turned auto-making from a hand-made affair to mass production through automation (Womak et al. 1990). Now we need data factories to stop wasting analytics time on data, to make data analytics scalable.