DATA INTELLIGENCE HUB - For business customers

Christoph Schlueter Langdon
Author profile

Christoph Schlueter Langdon

Chris is a professor in Data Science and Analytics; his doctorate involved Artificial Intelligence and Complex Adaptive Systems.
At the Telekom Data Intelligence Hub he is a development executive responsible for mobility, data spaces and analytics.
Prior to joining Deutsche Telekom, he founded a business analytics startup, co-founded the Peter Drucker Customer Lab at Claremont Graduate University, and the Special Interest Group on Agent-based Systems of the Association for Information Systems (AIS) / “Wirtschaftsinformatik.” He began his career as a consultant with Accenture and later joined the faculty of the University of Southern California (USC) in Los Angeles. His research has been funded by Microsoft and Intel, among others; results have been published in leading publications, such as in journals of the Institute of Electrical and Electronics Engineers (IEEE), Information Systems Research and Communications of the ACM. Over the last two decades, Chris has become known for his success with data science and analytics for Fortune Global 100 clients in the US, Europe and China.

In the Interview with Margret Steinhaus "From California to Berlin - why I came to Telekom from Silicon Valley" on the 13th of September, 2018 he talks about how he came from Silicon Valley in California to a position in the Deutsche Telekom Group, what we in Germany can learn from the mentality of Silicon Valley and how he feels at T-Systems in the IoT environment.

Mr. Schlueter Langdon, you started via a LinkedIn campaign on 1 June 2018 at T-Systems in Berlin. Can you tell us your story?

Yes, 3 things came together: First, an interview with Tim Höttges on the TV station Bloomberg. He described IoT as Deutsche Telekom's growth area. While carriers in the US are more likely to rely on video and advertising, there are opportunities with IoT in Germany because of the strength of the German industry. And I am a data science and analytics professor and have been teaching IoT and Smart or Intelligent Products for years because it is the future. Secondly, I had to teach a new course on social media analytics in the summer. During research for the course, I came across a campaign ad on LinkedIn from Ingo Hofacker, the head of T-System’s IoT division. And since I wanted to go back to Germany for family reasons, I just clicked on the ad and sent a message.

Can you give us a little insight into your current area of responsibility?

Deutsche Telekom is investing heavily in its own IoT products. A key building block and platform is the Data Intelligence Hub, DIH for short, a data hub and data marketplace with an attached workshop for Analytics and Artificial Intelligence. Everyone says, "Data is the New Oil," but just like oil, it also takes extensive and time-consuming refining to be able to leverage the data assets with analytics. And this is where I am an expert and can help to get even faster and better. Industry needs to digitize and needs partners that are essentially neutral, secure and big. I know this from my many projects in the automotive industry. And this is exactly where the DIH fits. It's tailored to our customers' needs, it's infrastructure, it removes the complexity, cost and risk of data analytics, allowing customers to focus on digitizing their core business. On top they can even earn money with your own data via DIH.

What can we learn from the mentality of the Silicon Valley in Germany? And where are we one step ahead of the Valley?

The advantage of Silicon Valley in digitization today is based on intangibles, even the name is not on maps. In terms of the intangibles, the whole of Europe could also score points: From the flair of French fashion, from the elegance of Italian design to the top quality of German cars. The stuff for success stories exists. What's missing is speed with many managers, the digital economy is moving faster. Often no decision is made, one prefers to stick with the old, and if something is dared, then it is too little and not focused enough. Often everything is tried internally instead with outside talents and specialized partners. Digitization is about software layers, you can't play at all levels, you have to focus on one layer or niche, then copy or outsource the rest.

How do you like it at T-Systems?

Very good and not at all like a large corporation. There was a professional onboarding in both Ingo Hofacker's group with a Welcome Day and my team "The Datanistas." The dynamism, decisiveness and customer orientation here feel more like a start-up. In addition, there is a closeness with German industry and the strong, medium-sized “Mittelstand” firms and thus business opportunities, which do not exist in Silicon Valley.

German original:

Articles by Christoph Schlueter Langdon

T-Systems as pioneer: Implementing IDS

IDSA has developed a blueprint to enable companies to pool and share data for competitive advantage, while maintaining data sovereignty. The Telekom Data Intelligence Hub offers a first commercial implementation.

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Berlin digital twin: Yes, intermodal traffic is faster!

How to fix a problem like the coronavirus crisis, ultimately? With the right solution, medicine and vaccine. How do you find the right medication? Testing! How do you speed up the process? Simulation! But it has to be backed up with science and a rigorous experimentation process. Like we have done to identify solutions to the traffic problem in dense urban areas such as Berlin.

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Data factories for data products

With 80 percent of the time budget spent on preparing rather than analyzing data, it looks like a fundamental flaw in the system. What to do? Maybe we can learn from the past: With his car factories, Henry Ford revolutionized the auto business. How if we started building data factories?

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What’s holding back intermodal mobility? Is there a customer value problem?

New last mile technology has arrived, think e-scooters, smart parking apps … but intermodal mobility hasn’t. Are there no benefits for the end-user? Let’s find out … with data science.

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Success with “small data” through specialized analytics

With all the attention on big data, it is easy to overlook the AI potential of small or unbalanced samples – or “small data”. Cloud trends like edge-computing open new opportunities.

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Treat data like cars

Chancellor Merkel urged German enterprises to utilize the potential for value that lies in data, Otherwise, a rude awakening is to be expected. But how can the transition into a data-oriented organization succeed? To implement product management might be a good start.

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Stuck in traffic: How bad is it … do we age faster … how can we fix it?

We spend more than four years of our lives inside a car. If we are stuck in a traffic jam for 25 percent of the time, then we age 1 year faster … unless we spend the time in a useful way… maybe new technology can help.

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Power shift to data: The auto-less auto company?

Automotive revenue is shifting to mobility services and power – and profit – will shift from the vehicle – or hardware – to data: A rerun of a story foretold in Rappaport & Halevi’s “The Computerless Computer Company” in a 1991 Harvard Business Review article?

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Space race to the future neighborhood

At the moment, roads and parking dominate the scenery of inner cities. The arrival of new forms of mobility, however, will radically alter the consumption of urban space.

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Fleet capacity utilization with heatmap analytics

There is a lot of open-source data out there, for instance on population density and means of transport. However, a differentiated picture and prognosis are only possible with dynamic data on movements.

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