GaiaX is a European cloud with the highest security standards and data sovereignty.Europe wants to be a real competitor on the international market. With the DIH, Deutsche Telekom is already developing a sovereign data platform and can therefore contribute expertise to GaiaX.
In modern times, digital business models are the fundamental differentiator of future competitiveness in Industry 4.0 and are becoming increasingly important. In order to better understand data-driven business models, the platform industry 4.0 provides a number of examples of why they are advantageous for the future.
At a time when trade fairs and congresses cannot be held due to the corona pandemic, digital alternatives are becoming increasingly important for maintaining economic development. For this reason, the International Data Space Association will convert the joint stand planned for the Hannover Messe into a virtual event.
Telekom Data Intelligence Hub joins the fight against COVID-19. In its #HackCarona hackathon, Data Natives calls on data scientists worldwide to join forces as a team and find solutions.
T-Systems is Germany’s leading IT-service provider for the automotive industry. Clients benefit from reliable operations of global backend systems (EU, US, China), leading innovation and super-fast 5G and edge communications.
Stop wasting time processing your data - start experimenting! Join the data sandbox to create results faster by benefiting from experiments with free data types & better data. In data analytics today, more than 80% of the time budget is spent on data processing alone. With the “data sandbox” you can generate results faster.
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.
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?
Hollywood already invented them: avatars, cyborgs, androids, clones. Today, leading politicians, managers and investors speak about the Digital Twin as a reflection of people as something perfectly natural. But where remains the data sovereignty?
Telekom Data Intelligence Hub at Europe’s biggest data science conference by Data Natives, a community of 70.000+ data experts. Telekom is presenting with partners IBM and IDSA.
On 28 and 29 October 2019, the most important personalities from politics and business met. Among others, the project Gaia-X was presented. Here, IDSA and Deutsche Telekom are working together to establish a sovereign data infrastructure for the whole of Europe.
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.
The Data Intelligence Hub supported this years AIHack4Diversity in Berlin. Under the theme "Diverse teams can often receive better results than homogeneous teams", 7 teams tried to solve the honeypot challenge.
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.
No longer missing out on innovation … Startup Autobahn presents a curated lineup of start-up stars to select automakers and suppliers in the automobility ecosystem.
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?
Every two years Daimler invites its top suppliers for two days of exhibits, keynotes, presentations and expert talks: New developements with connected, autonomous, electrified and shared automobility.
Obtaining the right data in the right amount is key to AI success. However, on their own very few companies will be able to collect sufficient amounts of data. German economics minister Altmaier sums it up: “We need data pools in Europe.” But how to pool and share data, while maintaining sovereignty?
Topic: Trustworthy data exchange in the supply chain! Get to know the potential of a networked supply chain and how to use it for your company - with live demo. Sign up now!
Everyone is talking about it, but in praxis it shows: "The subject of AI is completely new territory." Automotive IT asked experts such as Prof. Dr. Chris Schlueter Langdon of T-Systems to explain how to avoid reinventing the wheel.
Data Intelligence Hub is listed by BDI as one of the leading B2B platforms for data exchange in Industry 4.0.
Immerse yourself in the exciting world of metalworking at EMO 2019. The Data Ingelligence Hub team invites you and provides you with a free ticket.
We asked leading data scientists: How do you spend the next US$1, on more data (quantity) or better data (quality)? And how to measure the quality and quantity of data?
Deutsche Telekom is inviting you to the IDSA Summit from June 25 - 26, 2019. Read the agenda and take part at the event in Bonn.
Did you miss our live sessions? View the recordings of the live sessions here.
Learn how to pull your data from the Azure blob storage and analyze it on the Data Intelligence Hub Jupyter Notebook.
The shift to new mobility services is seen as epic and rapid. In less than two vehicle generations a new service business is estimated to be as big as the auto business.
Neural Networks are an exciting technology used in the field of machine learning these days. But there is a phenomenon that many big networks struggle with: Training Time.
What is Artificial Intelligence? How important is Open Source Software? What are the most important solutions?
Despite big investments in Data scientists, expensive IT infrastructure and tools, AI seems to be stuck in first gear. What's often missing is the right management approach - and the right recipe to orchestrate all the new ingredients ...
At the end of November the Big Data Days took place in Berlin. A short video summarizes the highlights.
Big Data is promised to be the next big business. Yet, today, most companies don’t even know how big ‘big’ is, how to size data quantities properly. No surprise that profit is missing. Start measuring!
Digitizing the production line of a Hidden Champion in 100 days? Learn more about how a Telekom Team "walked the talk", used connected sensors and Data Analytics to uncover hidden production issues and improve quality.
Why artificial intelligence would be impossible withouth data.
Watch our new video, which tells you the most important thing about the Data Intelligence Hub in only 85 seconds.
Deutsche Telekom has defined nine guidelines for dealing with artificial intelligence (AI).
“The world’s most valuable resource is no longer oil, but data”, the Economist wrote in 2017. But what can companies do with all the data they are creating?
The launch of the Data Intelligence Hub will be complemented with a “DIH University Initiative.”
Industry 4.0, artificial intelligence, cloud computing: The automation and networking of all living and working areas are establishing …
This year’s Strata Data Conference in London was the place to be for the Telekom Data Intelligence Hub.
This part of the blog focusses on the relevance of the topic and the added value of a data marketplace.
Data can be bought and sold, creating data marketplaces.…
The workbench is a self-service-tool for data scientists which helps at building, scaling and deploying machine learning and advanced analytics solutions using the current most powerful technologies.
Virtually every company, regardless of its target market, aims to leverage competitive advantages, cut costs and optimize processes…
The basis for this interplay of services is the exchange of data.
If “Time is Money”, then data analytics today is a disaster. Because at least 80% of the time budget of a data analytics project is spent on data preparation, not with analytics and results.