data.stage festival.stage tech.stage work.shop(0) work.shop(1)
08:30
Doors Open
08:30 - 09:30
08:45
09:00
09:15
09:30
Opening of the Data Festival 2020
Alexander Thamm, CEO, Alexander Thamm GmbH | Dr. Carsten Bange, CEO, BARC GmbH
09:30 - 09:45
09:45
10:00
10:15
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
10:30
10:45
How to scale Data function in a fast-growing Organization?
Dr. Sébastien Foucaud (HRS Group)
10:45 - 11:15
How to scale Data function in a fast-growing Organization?In a fast-growing company like HRS, knowing how to use Data Analytics, Data Science and Machine-Learning is not only about hiring the right data people and work on the right problem but also how to setup the function and spread best knowledge and practices related to data and associated technology across the organization. In this talk I will use concrete examples of problems faced in setting up a large data function and how we are solving it at HRS.
2119 - the Data Science Escape Room @ Bayer
Rico Horn (Bayer Business Services), Klaas Bollhoefer (Birds on Mars)
10:45 - 11:15
2119 - the Data Science Escape Room @ BayerBayer & Birds on Mars created a unique experience for Bayer employees and executives - the Data Science Escape Room...
Building ML Platform & executing ML Project: Revenue impact within 6 months
Andreas Jaeck (ProsiebenSat.1 Media), Dr. Rostyslav Shevchenko (ProSiebenSat.1 Digital Data GmbH)
10:45 - 12:15
Building ML Platform & executing ML Project: Revenue impact within 6 monthsThis presentation is in 2 parts. Data Engineering: First, we introduce a cloud-based architecture for training and operating ML-models at scale. This framework is based on continuous integration/deployment, loose coupling and generic building blocks...
11:00
11:15
The Keys to a Real Data-Driven Organization
Marius Reichard (Continental AG)
11:15 - 11:45
The Keys to a Real Data-Driven OrganizationBecoming a successful data driven organization requires a well-defined strategy in terms of roles, trainings and a community. In this session Marius Reichard will present the keys how to establish a data-driven organization, which actually works and does not only look great on paper!
11:30
11:45
Where can you get Data for your Smart City Analytics Cases?
Joachim Bürkle (DB Systel), Timo Maibach (Benz + Walter GmbH)
11:45 - 12:15
Where can you get Data for your Smart City Analytics Cases?In a smart city, modern technologies from the fields of energy, mobility, urban planning, administration and communication are networked in such a way that the quality of life for the residents increases. At the same time, the sustainability of the city benefits...
12:00
12:15
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
12:30
12:45
13:00
13:15
13:30
13:45
Panel – Explainable AI and Beyond
Alexander Thamm (Moderation), Dr. Andreas Stadie, Jan Meller, Jan R. Seyler, Dr. Sébastien Foucaud
13:45 - 14:45
Creating a GDPR-compliant Customer Analysis System on Google Cloud Platform
Dr. Christoph Stockhusen (Otto Group data.works GmbH)
13:45 - 15:15
Creating a GDPR-compliant Customer Analysis System on Google Cloud PlatformAt Otto Group, Europe\'s largest fashion retailer, we provide our subsidiaries (OTTO, Bonprix, About You, Witt, Baur, etc.) with a GDPR-compliant customer analysis system with roughly 104 million customer accounts, their billions of orders, and their surfing behaviour. This system allows our subsidiaries to better address their customers and create a more personalized customer experience based on the group-wide available data of our customers while still preserving their privacy...
14:00
14:15
14:30
14:45
The Do's and Don'ts of Delivering AI Projects: A Practitioners GuideArtificial Intelligence (AI) offers vast opportunities across industries and sectors. While traditional project or software management techniques have been around for decades, AI is new territory. According to a Gartner study, 85% of AI Projects are doomed to fail...
Deep Learning for Forgery Detection in Videos (Deepfakes, Face2Face)With recent developments in AI, it is now possible to create high quality fake videos that look extremely realistic. This has positive applications in computer graphics, but on the other hand this can also have dangerous implications on society as in political propaganda and for public shaming. Even if we have a reliable detector(classifier) for one forgery, it is still unsure that it will work on a different forgery. This works aims to address this problem, transferability among different forgeries. My talk will focus on how to build a single model that detects most of the forgeries surfacing the internet.
15:00
15:15
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
15:30
15:45
Application of AI to insurance
Dr. Andreas Nawroth (Munich Re)
15:45 - 16:15
Application of AI to insuranceThe talk will provide a motivation for Deep Learning approaches in the insurance industry. Basic steps and problems in the application of Artificial Intelligence will be shown. The historical review of the current developments will show the fast progress of the field and potential applications. An appropriate framework for a production ready AI solution will be introduced. The talk will be concluded by serval examples of Deep Learning in the insurance industry.
Chocolate&drinks: How to build product weather indices for data-driven marketingAdvertisers have one common objective: Reach the customer with the right product message at the right time at the right place. Obviously, this goal is related to weather as customers’ interest varies by weather...
16:00
16:15
Using Machine Learning to Connect Rough Sleepers to Local Services
Harrison Wilde (University of Warwick / Alan Turing Institute), Austin Nguyen (TripAdvisor), Lushi Chen (The University of Edinburgh)
16:15 - 16:45
Using Machine Learning to Connect Rough Sleepers to Local ServicesHomelessness and rough sleeping comprise a global issue that subjects a population to a host of societal and health-related pressures spanning abuse, illness, poverty and alienation. How can machine learning help the homeless?
16:30
16:45
17:00
17:15
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
17:30
17:45
Panel - AI Ethics: the good, the bad, the ugly
Carsten Bange (Moderation), Juan B. Moreno, Thomas Zeutschler, Jutta Meier
17:45 - 18:45
Recipe Recommendations @Thermomix
Bora Kiliclar (Vorwerk & Co. KG), Simon Weiß (Alexander Thamm GmbH)
17:45 - 18:45
Recipe Recommendations @ThermomixVorwerk & Co. KG and Alexanderthamm GmbH are bringing personalized recipe recommendations to the Thermomix digital ecosystem Cookidoo®. In this talk we\\\'ll give an overview of how we achieve meaningful personalized recipe recommendations and share insights as well as challenges of how the feature is provided to Vorwerks customers. Lastly, we\\\'ll give an unique outlook of challenges specific to the food recommendation problem such as: How do we avoid negative feedback loops leading to primarily unhealthy recipes being recommended? How to achieve recommendations that are in the users comfort zone and skill level but still new and exciting?
18:00
18:15
18:30
18:45
19:00
19:15
19:30
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20:45
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22:00
22:15
22:30
22:45
23:00
23:15
23:30
23:45

data.stage

festival.stage

tech.stage

work.shop(0)

  • Coffee Break
    10:15 - 10:45
  • Lunch Break
    12:15 - 13:45
  • Coffee Break
    15:15 - 15:45
  • 17:15 - 17:45

work.shop(1)

  • Coffee Break
    10:15 - 10:45
  • Lunch Break
    12:15 - 13:45
  • Coffee Break
    15:15 - 15:45
  • 17:15 - 17:45
data.stage festival.stage tech.stage work.shop(0) work.shop(1)
10:15
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
10:30
10:45
11:00
11:15
11:30
11:45
12:00
12:15
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
12:30
12:45
13:00
13:15
13:30
13:45
14:00
14:15
14:30
14:45
15:00
15:15
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
15:30
15:45
16:00
16:15
16:30
16:45
17:00
17:15
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
17:30

data.stage

  • Coffee Break
    10:15 - 10:45
  • Lunch Break
    12:15 - 13:45
  • Coffee Break
    15:15 - 15:45
  • 17:15 - 17:45

festival.stage

  • Coffee Break
    10:15 - 10:45
  • Lunch Break
    12:15 - 13:45
  • Coffee Break
    15:15 - 15:45
  • 17:15 - 17:45

tech.stage

  • Coffee Break
    10:15 - 10:45
  • Lunch Break
    12:15 - 13:45
  • Coffee Break
    15:15 - 15:45
  • 17:15 - 17:45

work.shop(0)

  • Coffee Break
    10:15 - 10:45
  • Lunch Break
    12:15 - 13:45
  • Coffee Break
    15:15 - 15:45
  • 17:15 - 17:45

work.shop(1)

  • Coffee Break
    10:15 - 10:45
  • Lunch Break
    12:15 - 13:45
  • Coffee Break
    15:15 - 15:45
  • 17:15 - 17:45
data.stage festival.stage tech.stage work.shop(0) work.shop(1)
08:30
Doors Open
08:30 - 09:30
08:45
09:00
09:15
09:30
Opening of the Data Festival 2020
Alexander Thamm, CEO, Alexander Thamm GmbH | Dr. Carsten Bange, CEO, BARC GmbH
09:30 - 09:45
09:45
10:00
10:15
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
Coffee Break
10:15 - 10:45
10:30
10:45
How to scale Data function in a fast-growing Organization?
Dr. Sébastien Foucaud (HRS Group)
10:45 - 11:15
How to scale Data function in a fast-growing Organization?In a fast-growing company like HRS, knowing how to use Data Analytics, Data Science and Machine-Learning is not only about hiring the right data people and work on the right problem but also how to setup the function and spread best knowledge and practices related to data and associated technology across the organization. In this talk I will use concrete examples of problems faced in setting up a large data function and how we are solving it at HRS.
2119 - the Data Science Escape Room @ Bayer
Rico Horn (Bayer Business Services), Klaas Bollhoefer (Birds on Mars)
10:45 - 11:15
2119 - the Data Science Escape Room @ BayerBayer & Birds on Mars created a unique experience for Bayer employees and executives - the Data Science Escape Room...
Building ML Platform & executing ML Project: Revenue impact within 6 months
Andreas Jaeck (ProsiebenSat.1 Media), Dr. Rostyslav Shevchenko (ProSiebenSat.1 Digital Data GmbH)
10:45 - 12:15
Building ML Platform & executing ML Project: Revenue impact within 6 monthsThis presentation is in 2 parts. Data Engineering: First, we introduce a cloud-based architecture for training and operating ML-models at scale. This framework is based on continuous integration/deployment, loose coupling and generic building blocks...
11:00
11:15
The Keys to a Real Data-Driven Organization
Marius Reichard (Continental AG)
11:15 - 11:45
The Keys to a Real Data-Driven OrganizationBecoming a successful data driven organization requires a well-defined strategy in terms of roles, trainings and a community. In this session Marius Reichard will present the keys how to establish a data-driven organization, which actually works and does not only look great on paper!
11:30
11:45
Where can you get Data for your Smart City Analytics Cases?
Joachim Bürkle (DB Systel), Timo Maibach (Benz + Walter GmbH)
11:45 - 12:15
Where can you get Data for your Smart City Analytics Cases?In a smart city, modern technologies from the fields of energy, mobility, urban planning, administration and communication are networked in such a way that the quality of life for the residents increases. At the same time, the sustainability of the city benefits...
12:00
12:15
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
Lunch Break
12:15 - 13:45
12:30
12:45
13:00
13:15
13:30
13:45
Panel – Explainable AI and Beyond
Alexander Thamm (Moderation), Dr. Andreas Stadie, Jan Meller, Jan R. Seyler, Dr. Sébastien Foucaud
13:45 - 14:45
Creating a GDPR-compliant Customer Analysis System on Google Cloud Platform
Dr. Christoph Stockhusen (Otto Group data.works GmbH)
13:45 - 15:15
Creating a GDPR-compliant Customer Analysis System on Google Cloud PlatformAt Otto Group, Europe\'s largest fashion retailer, we provide our subsidiaries (OTTO, Bonprix, About You, Witt, Baur, etc.) with a GDPR-compliant customer analysis system with roughly 104 million customer accounts, their billions of orders, and their surfing behaviour. This system allows our subsidiaries to better address their customers and create a more personalized customer experience based on the group-wide available data of our customers while still preserving their privacy...
14:00
14:15
14:30
14:45
The Do's and Don'ts of Delivering AI Projects: A Practitioners GuideArtificial Intelligence (AI) offers vast opportunities across industries and sectors. While traditional project or software management techniques have been around for decades, AI is new territory. According to a Gartner study, 85% of AI Projects are doomed to fail...
Deep Learning for Forgery Detection in Videos (Deepfakes, Face2Face)With recent developments in AI, it is now possible to create high quality fake videos that look extremely realistic. This has positive applications in computer graphics, but on the other hand this can also have dangerous implications on society as in political propaganda and for public shaming. Even if we have a reliable detector(classifier) for one forgery, it is still unsure that it will work on a different forgery. This works aims to address this problem, transferability among different forgeries. My talk will focus on how to build a single model that detects most of the forgeries surfacing the internet.
15:00
15:15
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
Coffee Break
15:15 - 15:45
15:30
15:45
Application of AI to insurance
Dr. Andreas Nawroth (Munich Re)
15:45 - 16:15
Application of AI to insuranceThe talk will provide a motivation for Deep Learning approaches in the insurance industry. Basic steps and problems in the application of Artificial Intelligence will be shown. The historical review of the current developments will show the fast progress of the field and potential applications. An appropriate framework for a production ready AI solution will be introduced. The talk will be concluded by serval examples of Deep Learning in the insurance industry.
Chocolate&drinks: How to build product weather indices for data-driven marketingAdvertisers have one common objective: Reach the customer with the right product message at the right time at the right place. Obviously, this goal is related to weather as customers’ interest varies by weather...
16:00
16:15
Using Machine Learning to Connect Rough Sleepers to Local Services
Harrison Wilde (University of Warwick / Alan Turing Institute), Austin Nguyen (TripAdvisor), Lushi Chen (The University of Edinburgh)
16:15 - 16:45
Using Machine Learning to Connect Rough Sleepers to Local ServicesHomelessness and rough sleeping comprise a global issue that subjects a population to a host of societal and health-related pressures spanning abuse, illness, poverty and alienation. How can machine learning help the homeless?
16:30
16:45
17:00
17:15
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
Beer Break
17:15 - 17:45
17:30
17:45
Panel - AI Ethics: the good, the bad, the ugly
Carsten Bange (Moderation), Juan B. Moreno, Thomas Zeutschler, Jutta Meier
17:45 - 18:45
Recipe Recommendations @Thermomix
Bora Kiliclar (Vorwerk & Co. KG), Simon Weiß (Alexander Thamm GmbH)
17:45 - 18:45
Recipe Recommendations @ThermomixVorwerk & Co. KG and Alexanderthamm GmbH are bringing personalized recipe recommendations to the Thermomix digital ecosystem Cookidoo®. In this talk we\\\'ll give an overview of how we achieve meaningful personalized recipe recommendations and share insights as well as challenges of how the feature is provided to Vorwerks customers. Lastly, we\\\'ll give an unique outlook of challenges specific to the food recommendation problem such as: How do we avoid negative feedback loops leading to primarily unhealthy recipes being recommended? How to achieve recommendations that are in the users comfort zone and skill level but still new and exciting?
18:00
18:15
18:30
18:45
19:00
19:15
19:30
19:45
20:00
20:15
20:30
20:45
21:00
21:15
21:30
21:45
22:00
22:15
22:30
22:45
23:00
23:15
23:30
23:45

data.stage

festival.stage

tech.stage

work.shop(0)

  • Coffee Break
    10:15 - 10:45
  • Lunch Break
    12:15 - 13:45
  • Coffee Break
    15:15 - 15:45
  • 17:15 - 17:45

work.shop(1)

  • Coffee Break
    10:15 - 10:45
  • Lunch Break
    12:15 - 13:45
  • Coffee Break
    15:15 - 15:45
  • 17:15 - 17:45
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