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09:00
How Volkswagen scales value from data across brands: best practices from Porsche and VWFS (EN)
Ingo Alzner, Plattform Manager BI & Big Data, Porsche AG | Dr. Alexander Borek, Global Head of Data & Analytics, Volkswagen Financial Services AG | Alexander Thamm, CEO, Alexander Thamm GmbH
09:00 - 09:30
Keynote
09:15
09:30
Coffee Break
09:30 - 09:45
Coffee Break
09:30 - 09:45
Coffee Break
09:30 - 09:45
09:45
Automatisierte Prognose von Sonderausstattungen in der Automobilindustrie (DE)
Steffen Fehrmann, Senior Manager, Daimler AG | Stefan Ott, Vertriebsplaner und Projektleiter, Daimler AG
09:45 - 10:15
Data & AI in Automotive
Automatisierte Prognose von Sonderausstattungen in der Automobilindustrie (DE)Die Steuerung von Lieferanten in der Automobilindustrie ist sehr komplex und maßgeblich von der Variantenvielfalt bestimmt. Es gilt diese Varianten, die insbesondere durch die vom Kunden wählbaren Sonderausstattungen beeinflusst sind, präzise für die Zukunft zu prognostieren. Dieser Vortrag berichtet von einem PoC der es zum Ziel hatte diese Prognosen zu automatisieren.
Analytics Strategy - Structure, Organize & Scale Analytics, Data & BI (EN)
Dr. Sebastian Derwisch, Data Scientist, BARC GmbH
09:45 - 10:15
Data Strategy
This Time it's Personal: Data Science in Clinical Care (EN)
Johannes Starlinger, Health Data Researcher, Charité - Universitätsmedizin Berlin
09:45 - 10:15
Data Assistance
This Time it's Personal: Data Science in Clinical Care (EN)Vast amounts of data are routinely collected in clinical care for every single patient. While there\'s a great urge to use this data for large scale outcomes research, it\'s access, extraction, cleansing, engineering, and analysis are often much less straight forward than you would think. This talk gives an overview of some of the peculiarities of data within the clinical domain, points to important resources for working with such data, and highlights some of the projects in the Data Science in Perioperative Care lab at Charité.
TBD
09:45 - 11:15
Exclusive Workshop
TBD
09:45 - 11:15
Exclusive Workshop
10:00
10:15
How to get applied Big Data & AI really applied, or: How to achieve trust towards AI in historically grown companies! (EN)
Dr. Stefan Meinzer, Head of advanced analytics EMEA, BMW AG
10:15 - 10:45
Data & AI in Automotive
How to get applied Big Data & AI really applied, or: How to achieve trust towards AI in historically grown companies! (EN)While all companies struggle for the best data scientists, the departments around data science are getting bigger and bigger and the technological possibilities are constantly growing, one question often remains unanswered: How do companies effectively leverage the new opportunities and anchor AI in day-to-day business processes? A key element is trust in the complex outputs of Big Data. The presentation will focus on Visual Analytics as a core element for creating the necessary trust.
TBD
10:15 - 10:45
Data Strategy
The Data Janitor returns (EN)
Daniel Molnar, Data Engineer, Shopify
10:15 - 10:45
Data Assistance
The Data Janitor returns (EN)This talk is for the underdog. If you’re trying to solve data related problems with no or limited resources, be them time, money or skills don’t go no further. This talk is opinionated and updated to GDPR, deep learning and all the hype.
10:30
10:45
Predictive Road Condition (EN)
Dr. Michael Unterreiner, Projektleiter, Porsche AG
10:45 - 11:15
Data & AI in Automotive
Predictive Road Condition (EN)The driving situations, the dynamic condition of the vehicle and the road condition are of high relevance in order to return the vehicle from critical situations, in particular with adverse weather-related road conditions, into a safe condition. The subject of this presentation is to determine road conditions and to derive a prediction of these with black data.
TBD
10:45 - 11:15
Data Strategy
TBD
10:45 - 11:15
Data Assistance
11:00
11:15
Coffee Break
11:15 - 11:45
Coffee Break
11:15 - 11:45
Coffee Break
11:15 - 11:45
11:30
11:45
Data-driven acquisition strategy for B2B sales (EN)
Dominik Hülsmeier, Digitalisierungsverantwortlicher Strom- und Wärmeerzeugung, SWM Services GmbH
11:45 - 12:15
Energy
Data-driven acquisition strategy for B2B sales (EN)The aim of the analysis was to support B2B sales in such a way that their competence is clearly, quickly and easily understood at the customer appointment. For this purpose, it was necessary to create an interactive document for the customer meeting. The document enabled the key account manager to create a sector-specific just-in-time analysis of the customer\\\'s energy efficiency together with the customer.
When Will AI Kill Data Scientists? Your Ticket to survival in Data science (EN)
Filip Vitek, Head of Data Science, TeamViewer
11:45 - 12:15
Artificial Intelligence
When Will AI Kill Data Scientists? Your Ticket to survival in Data science (EN)We are living the era of booming demand for Data Science. In expectation of next industrial wave, where AI will
R, Knime, AWS and Data Lake – Setup and Applications of Automatization and AI @Siemens Financial Services (EN)
Julia Herbinger, Risk Controller, Siemens Financial Services | Dominik Milewski, Risk Controller, Siemens Financial Services
11:45 - 12:15
Technology Infrastructure
R, Knime, AWS and Data Lake – Setup and Applications of Automatization and AI @Siemens Financial Services (EN)From manual, time-consuming excel processes to automated and fast processes created with R and Knime and visualized by interactive Shiny Apps. Within Siemens Financial Services we started to self-enable our functional departments to develop their own applications to automate their processes with open-source tools like R and Knime...
12:00
12:15
BrainWaves – Energy Transition Through Data Science The Agil Way (EN)
Jana-Vanessa Dering, Data Scientist, EWE AG
12:15 - 12:45
Energy
BrainWaves – Energy Transition Through Data Science The Agil Way (EN)The energy revolution brings with it many challenges and opportunities for all involved. The increasing use of renewable decentralised energy sources requires innovative solutions for power grids and markets. The basis for this is data that can be collected and processed in the energy system and beyond. As part of the
TBD
12:15 - 12:45
Artificial Intelligence
TBD
12:15 - 12:45
Technology Infrastructure
12:30
12:45
Lunch Break
12:45 - 14:15
Lunch Break
12:45 - 14:15
Lunch Break
12:45 - 14:15
13:00
13:15
13:30
13:45
14:00
14:15
GDPR: one year later
14:15 - 15:15
Panel
TBD
14:15 - 14:45
Machine Learning
Visually Understanding Deep Neural Networks with the help of Dimensionality Reduction (EN)
Matthias Anderer, Geschäftsführer, Matthias Anderer GmbH
14:15 - 14:45
Neural Networks
Visually Understanding Deep Neural Networks with the help of Dimensionality Reduction (EN)Given the (toy) example of visually comparing image trademarks we study the following approach: Understanding the learned features within the different layers of a neural network can be hard. In this talk we will visually showcase the results of transforming the outputs of several layers of a state of the art imagenet architecture with UMAP...
14:30
14:45
How Machine Learning is turning the Automotive Industry upside down (EN)
Jan Zawadzki, Project Lead Data Science, Carmeq GmbH
14:45 - 15:15
Machine Learning
How Machine Learning is turning the Automotive Industry upside down (EN)The automotive industry has mobilized the global economy for decades. German automobile manufacturers (OEMs) alone employ more than 1 million people worldwide and generate sales of more than USD 500 billion. Since a Google + Stanford team won the Darpa Self-Driving Vehicles Challenge 2006 with the help of machine learning, among other things, the industry has been undergoing rapid change. Machine learning opens up brand-new business models, from autonomous driving to smart production to personal assistance in the car. However, the use of machine learning requires a different infrastructure than that found in OEMs. Technology-first companies like Waymo or Tesla threaten to overtake established OEMs with billion-dollar market capitalization. OEMs fear being degraded to pure hardware suppliers. Autonomous vehicles produce terabytes of data every day. This data can be immensely valuable in developing machine learning-driven functions. OEMs encounter the following problems: 1. data storage 2. data transfer 3. Expense of Sensors 4. Training Data Acquisition 5. Verification of Neural Networks The automotive industry must develop from a mechanical engineering to a software industry. It needs support in this process. Finally, I would like to encourage the audience to research these problems and to apply to car manufacturers to work on solutions.
TBD
14:45 - 15:15
Neural Networks
15:00
15:15
data.networking(1)
Lead topic: GDPR
15:15 - 15:45
Special
Automated Machine Learning (EN)
Karl Schriek, Head of AI / Leading Machine Learning Engineer, Alexander Thamm GmbH
15:15 - 15:45
Machine Learning
TBD
15:15 - 15:45
Neural Networks
15:30
15:45
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00
16:00
16:15
16:30
16:45

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09:30
Coffee Break
09:30 - 09:45
Coffee Break
09:30 - 09:45
Coffee Break
09:30 - 09:45
09:45
Automatisierte Prognose von Sonderausstattungen in der Automobilindustrie (DE)
Steffen Fehrmann, Senior Manager, Daimler AG | Stefan Ott, Vertriebsplaner und Projektleiter, Daimler AG
09:45 - 10:15
Data & AI in Automotive
Automatisierte Prognose von Sonderausstattungen in der Automobilindustrie (DE)Die Steuerung von Lieferanten in der Automobilindustrie ist sehr komplex und maßgeblich von der Variantenvielfalt bestimmt. Es gilt diese Varianten, die insbesondere durch die vom Kunden wählbaren Sonderausstattungen beeinflusst sind, präzise für die Zukunft zu prognostieren. Dieser Vortrag berichtet von einem PoC der es zum Ziel hatte diese Prognosen zu automatisieren.
TBD
09:45 - 11:15
Exclusive Workshop
TBD
09:45 - 11:15
Exclusive Workshop
10:00
10:15
TBD
10:15 - 10:45
Data Strategy
10:30
10:45
TBD
10:45 - 11:15
Data Strategy
TBD
10:45 - 11:15
Data Assistance
11:00
11:15
Coffee Break
11:15 - 11:45
Coffee Break
11:15 - 11:45
Coffee Break
11:15 - 11:45
11:30
11:45
12:00
12:15
TBD
12:15 - 12:45
Artificial Intelligence
TBD
12:15 - 12:45
Technology Infrastructure
12:30
12:45
Lunch Break
12:45 - 14:15
Lunch Break
12:45 - 14:15
Lunch Break
12:45 - 14:15
13:00
13:15
13:30
13:45
14:00
14:15
TBD
14:15 - 14:45
Machine Learning
14:30
14:45
TBD
14:45 - 15:15
Neural Networks
15:00
15:15
data.networking(1)
Lead topic: GDPR
15:15 - 15:45
Special
TBD
15:15 - 15:45
Neural Networks
15:30
15:45
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00

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09:00
How Volkswagen scales value from data across brands: best practices from Porsche and VWFS (EN)
Ingo Alzner, Plattform Manager BI & Big Data, Porsche AG | Dr. Alexander Borek, Global Head of Data & Analytics, Volkswagen Financial Services AG | Alexander Thamm, CEO, Alexander Thamm GmbH
09:00 - 09:30
Keynote
09:15
09:30
Coffee Break
09:30 - 09:45
Coffee Break
09:30 - 09:45
Coffee Break
09:30 - 09:45
09:45
Analytics Strategy - Structure, Organize & Scale Analytics, Data & BI (EN)
Dr. Sebastian Derwisch, Data Scientist, BARC GmbH
09:45 - 10:15
Data Strategy
This Time it's Personal: Data Science in Clinical Care (EN)
Johannes Starlinger, Health Data Researcher, Charité - Universitätsmedizin Berlin
09:45 - 10:15
Data Assistance
This Time it's Personal: Data Science in Clinical Care (EN)Vast amounts of data are routinely collected in clinical care for every single patient. While there\'s a great urge to use this data for large scale outcomes research, it\'s access, extraction, cleansing, engineering, and analysis are often much less straight forward than you would think. This talk gives an overview of some of the peculiarities of data within the clinical domain, points to important resources for working with such data, and highlights some of the projects in the Data Science in Perioperative Care lab at Charité.
TBD
09:45 - 11:15
Exclusive Workshop
TBD
09:45 - 11:15
Exclusive Workshop
10:00
10:15
How to get applied Big Data & AI really applied, or: How to achieve trust towards AI in historically grown companies! (EN)
Dr. Stefan Meinzer, Head of advanced analytics EMEA, BMW AG
10:15 - 10:45
Data & AI in Automotive
How to get applied Big Data & AI really applied, or: How to achieve trust towards AI in historically grown companies! (EN)While all companies struggle for the best data scientists, the departments around data science are getting bigger and bigger and the technological possibilities are constantly growing, one question often remains unanswered: How do companies effectively leverage the new opportunities and anchor AI in day-to-day business processes? A key element is trust in the complex outputs of Big Data. The presentation will focus on Visual Analytics as a core element for creating the necessary trust.
TBD
10:15 - 10:45
Data Strategy
The Data Janitor returns (EN)
Daniel Molnar, Data Engineer, Shopify
10:15 - 10:45
Data Assistance
The Data Janitor returns (EN)This talk is for the underdog. If you’re trying to solve data related problems with no or limited resources, be them time, money or skills don’t go no further. This talk is opinionated and updated to GDPR, deep learning and all the hype.
10:30
10:45
Predictive Road Condition (EN)
Dr. Michael Unterreiner, Projektleiter, Porsche AG
10:45 - 11:15
Data & AI in Automotive
Predictive Road Condition (EN)The driving situations, the dynamic condition of the vehicle and the road condition are of high relevance in order to return the vehicle from critical situations, in particular with adverse weather-related road conditions, into a safe condition. The subject of this presentation is to determine road conditions and to derive a prediction of these with black data.
TBD
10:45 - 11:15
Data Strategy
TBD
10:45 - 11:15
Data Assistance
11:00
11:15
Coffee Break
11:15 - 11:45
Coffee Break
11:15 - 11:45
Coffee Break
11:15 - 11:45
11:30
11:45
Data-driven acquisition strategy for B2B sales (EN)
Dominik Hülsmeier, Digitalisierungsverantwortlicher Strom- und Wärmeerzeugung, SWM Services GmbH
11:45 - 12:15
Energy
Data-driven acquisition strategy for B2B sales (EN)The aim of the analysis was to support B2B sales in such a way that their competence is clearly, quickly and easily understood at the customer appointment. For this purpose, it was necessary to create an interactive document for the customer meeting. The document enabled the key account manager to create a sector-specific just-in-time analysis of the customer\\\'s energy efficiency together with the customer.
When Will AI Kill Data Scientists? Your Ticket to survival in Data science (EN)
Filip Vitek, Head of Data Science, TeamViewer
11:45 - 12:15
Artificial Intelligence
When Will AI Kill Data Scientists? Your Ticket to survival in Data science (EN)We are living the era of booming demand for Data Science. In expectation of next industrial wave, where AI will
R, Knime, AWS and Data Lake – Setup and Applications of Automatization and AI @Siemens Financial Services (EN)
Julia Herbinger, Risk Controller, Siemens Financial Services | Dominik Milewski, Risk Controller, Siemens Financial Services
11:45 - 12:15
Technology Infrastructure
R, Knime, AWS and Data Lake – Setup and Applications of Automatization and AI @Siemens Financial Services (EN)From manual, time-consuming excel processes to automated and fast processes created with R and Knime and visualized by interactive Shiny Apps. Within Siemens Financial Services we started to self-enable our functional departments to develop their own applications to automate their processes with open-source tools like R and Knime...
12:00
12:15
BrainWaves – Energy Transition Through Data Science The Agil Way (EN)
Jana-Vanessa Dering, Data Scientist, EWE AG
12:15 - 12:45
Energy
BrainWaves – Energy Transition Through Data Science The Agil Way (EN)The energy revolution brings with it many challenges and opportunities for all involved. The increasing use of renewable decentralised energy sources requires innovative solutions for power grids and markets. The basis for this is data that can be collected and processed in the energy system and beyond. As part of the
TBD
12:15 - 12:45
Artificial Intelligence
TBD
12:15 - 12:45
Technology Infrastructure
12:30
12:45
Lunch Break
12:45 - 14:15
Lunch Break
12:45 - 14:15
Lunch Break
12:45 - 14:15
13:00
13:15
13:30
13:45
14:00
14:15
GDPR: one year later
14:15 - 15:15
Panel
TBD
14:15 - 14:45
Machine Learning
Visually Understanding Deep Neural Networks with the help of Dimensionality Reduction (EN)
Matthias Anderer, Geschäftsführer, Matthias Anderer GmbH
14:15 - 14:45
Neural Networks
Visually Understanding Deep Neural Networks with the help of Dimensionality Reduction (EN)Given the (toy) example of visually comparing image trademarks we study the following approach: Understanding the learned features within the different layers of a neural network can be hard. In this talk we will visually showcase the results of transforming the outputs of several layers of a state of the art imagenet architecture with UMAP...
14:30
14:45
How Machine Learning is turning the Automotive Industry upside down (EN)
Jan Zawadzki, Project Lead Data Science, Carmeq GmbH
14:45 - 15:15
Machine Learning
How Machine Learning is turning the Automotive Industry upside down (EN)The automotive industry has mobilized the global economy for decades. German automobile manufacturers (OEMs) alone employ more than 1 million people worldwide and generate sales of more than USD 500 billion. Since a Google + Stanford team won the Darpa Self-Driving Vehicles Challenge 2006 with the help of machine learning, among other things, the industry has been undergoing rapid change. Machine learning opens up brand-new business models, from autonomous driving to smart production to personal assistance in the car. However, the use of machine learning requires a different infrastructure than that found in OEMs. Technology-first companies like Waymo or Tesla threaten to overtake established OEMs with billion-dollar market capitalization. OEMs fear being degraded to pure hardware suppliers. Autonomous vehicles produce terabytes of data every day. This data can be immensely valuable in developing machine learning-driven functions. OEMs encounter the following problems: 1. data storage 2. data transfer 3. Expense of Sensors 4. Training Data Acquisition 5. Verification of Neural Networks The automotive industry must develop from a mechanical engineering to a software industry. It needs support in this process. Finally, I would like to encourage the audience to research these problems and to apply to car manufacturers to work on solutions.
TBD
14:45 - 15:15
Neural Networks
15:00
15:15
data.networking(1)
Lead topic: GDPR
15:15 - 15:45
Special
Automated Machine Learning (EN)
Karl Schriek, Head of AI / Leading Machine Learning Engineer, Alexander Thamm GmbH
15:15 - 15:45
Machine Learning
TBD
15:15 - 15:45
Neural Networks
15:30
15:45
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00
16:00
16:15
16:30
16:45

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