data.stage festival.stage tech.stage work.shop(0) work.shop(1)
09:00
Key Takeaways from the Data Journeys at Zalando, Porsche and Volkswagen Financial Services (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 | Kshitij Kumar, VP, Data Infrastructure, Digital Foundation, Zalando
09:00 - 09:30
Keynote
Key Takeaways from the Data Journeys at Zalando, Porsche and Volkswagen Financial Services (EN)What makes a successful data driven company is to step from the lab into the factory. Only if the scaling of data use cases into AI products is accomplished, a company can claim to have successfully implemented AI and machine learning into the organization. As part of the digitization strategy, a company-wide global data strategy is the first step in this direction. Many large companies in Germany and Europe are now starting their data transformation in order not to lose the edge to companies in the USA and China.
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.
Ganzheitliche Daten & Analytics Strategie – Analytics & BI strukturieren, organisieren & skalieren (DE)
Dr. Sebastian Derwisch, Data Scientist, BARC GmbH | Jacqueline Bloemen, Senior Analyst | BARC GmbH
09:45 - 10:15
Data Strategy
Ganzheitliche Daten & Analytics Strategie – Analytics & BI strukturieren, organisieren & skalieren (DE)Die neue (Advanced Analytics) und die alte (BI) Welt der Datenanalyse sind vielfältig verwoben. Bei isolierter Betrachtung dieser Welten werden Abhängigkeiten im Laufe der Integration analytischer Lösungen zu Hindernissen. Um einen Nutzen mit Daten und Analytics zu generieren braucht es kreatives, agiles und interdisziplinären Arbeiten über Silos hinweg. Wie kommt man zu einer koordinierten Vorgehensweise angesichts der Herausforderungen in den Bereichen Daten, BI & Advanced Analytics aus? Antworten liefert eine ganzheitliche Daten & Analytics Strategie.
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.
Redis as a Recommendation Engine (EN)
Martin Forstner, Solution Architect, Redis Labs
09:45 - 11:15
Exclusive Workshop
Redis as a Recommendation Engine (EN)Redis is an in-memory database system which addresses a variety of use cases. The range reaches from simple caching over message processing to real-time analytics and recommendations. Redis’ scalability and performance is allowing to reach millions of operations per second at a sub-millisecond latency. In addition, Redis provides several data structures and modules (like real sets, sorted sets, full text search, bloom filters, HyperLogLogs, random forests and other machine learning models) to be leveraged for the implementation of recommendation engines.
BI Meets AI- Sisense Business Intelligence Applications Platform Powered by AI (EN)
Inna Tokarev Sela, Head of AI, Sisense
09:45 - 11:15
Exclusive Workshop
BI Meets AI- Sisense Business Intelligence Applications Platform Powered by AI (EN)The workshop will include the demonstration of the current AI-powered functionalities offered by Sisense, including Sisense Narratives, Boto, Insight Miner. Then we will proceed with the overview of the Sisense AI roadmap for 2019 and beyond, including Semantic Graph, NLP, Assisted Data Preparation and Modeling.
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.
This is what we have learned from data strategies? (EN)
Janne Pullinen, Data Business Designer | SOLITA
10:15 - 10:45
Data Strategy
This is what we have learned from data strategies? (EN)This is what we have learned about success: 1. Tech is easy culture is hard 2. Focus on core business instead of one off’s or startup labs. 3. Data development is not support function or IT project. 4. Data asset is source and use case independent and cumulates in constantly increasing pace With best clients where these are true we are seeing 300% + improvements in core business KPI’s. Changes are radical and path is not easy but it is fun and rewarding.
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
How Machine Learning is turning the Automotive Industry upside down (EN)
Jan Zawadzki, Project Lead Data Science, Carmeq GmbH
10:45 - 11:15
Data & AI in Automotive
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.
Placing Data in the Center of Everything we do (EN)
Malcolm Micallef, Data Platform Manager | Tipico
10:45 - 11:15
Data Strategy
Placing Data in the Center of Everything we do (EN)Our vision is to place data at the centre of everything we do at Tipico. All routine actions performed, customer engagements made, and decisions taken are powered by big data & analytics. We will explore the organisational and technological evolution of Tipico’s Data tribe through Tipico’s journey to becoming the data driven enterprise of tomorrow.
Infrastructure matters - Building the right foundation for your data value chain (EN)
Eva Murray, Head of BI & Tableau Zen Master, Exasol | Carsten Weidmann, Technical Alliance Manager, Exasol
10:45 - 11:15
Data Assistance
Infrastructure matters - Building the right foundation for your data value chain (EN)Infrastructure can make or break your BI & AI capabilities. If you go too far, you may not be able to show a return on your investment. Planning too conservatively can stifle development and progress, leaving you one step behind the competition. In our talk we share use cases and recommendations for optimising your data value chain and give you practical tips for taking your environment to the next level.
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...
Industry Exchange: Mobility (EN)
Andrea Gillhuber, Head of Strategy & Engineering
11:45 - 12:45
Special
Industry Exchange: Mobility (EN)Do you work in the area of Mobility? Then our Industry Exchanges are perfect for you! Take the opportunity and talk to experts about current market trends, problems, opportunities and much more. Our sessions enable an interactive exchange with like-minded people. Andreas Gillhuber, Head of Strategy and Engineering at Alexander Thamm GmbH, is hosting the exchange.The exchange consists of a welcome & introduction round, a presentation by the host as well as an interactive game to promote discussion and exchange. The session will be closed with a feedback round. 
Industry Exchange: Finance & Insurance (EN)
Magnus Metz, Senior Account Developer & Team Lead Sales, Alexander Thamm GmbH
11:45 - 12:45
Special
Industry Exchange: Finance & Insurance (EN)Do you work in the area of Finance or Insurance? Then our Industry Exchanges are perfect for you! Take the opportunity and talk to experts about current market trends, problems, opportunities and much more. Our sessions enable an interactive exchange with like-minded people. Magnus Metz, Team Lead  Sales at Alexander Thamm GmbH, is hosting the exchange.The exchange consists of a welcome & introduction round, a presentation by the host as well as an interactive game to promote discussion and exchange. The session will be closed with a feedback round. 
12:00
12:15
BrainWaves – Energy Transition Through Data Science The Agile Way (EN)
Jana-Vanessa Dering, Data Scientist, EWE AG
12:15 - 12:45
Energy
BrainWaves – Energy Transition Through Data Science The Agile 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
How to make 5 billion predictions in 2 days (EN)
Sebastian Wernicke, Chief Data Scientist, ONE LOGIC
12:15 - 12:45
Artificial Intelligence
How to make 5 billion predictions in 2 days (EN)MARKANT is the largest trade and industry collaboration for European food retail, working with over 14,000 industry partners and approximately 150 trade partners. Together with ONE LOGIC, MARKANT is developing a centralized forecasting platform for their trade and industry partners. The goals is to obtain precise sales forecasts up to 26 weeks in advance. With more than 200 article-location-combinations, this means that 5.5 billion forecasts have to be calculated every week within just 2 days’ time. Additionally, the statistical models must be able to take into account events like promotions and external effects like holidays. The results are optimized production, logistics, and sales processes, leading to significant savings and a reduction of environmental footprint.
Critical success factors ramping up cloud data platform and DataOps capabilities in an energy company (EN)
Ville Viitanen, Business Analytics Manager | Fortum
12:15 - 12:45
Technology Infrastructure
Critical success factors ramping up cloud data platform and DataOps capabilities in an energy company (EN)Fortum has many years of experience of running analytics capabilities in cloud environment. During 2018 Fortum started the program of modernizing its customer information related data platform. By enabling agile means to utilize the data assets, Fortum has been able to develop cost efficiency, shorten the deployment cycles and speed-up the development pace.
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
Felix Bauer, Managing Director & CEO, Aircloak GmbH | Dr. Sébastien Foucaud, Vice-President Data Science, XING | Marcel Kling, Director Customer Data & Advanced Analytics, Lufthansa AG | Herbert Maier, Managing Director, Commerzbank | Sebastian Kraska, Attorney at Law, External Data Protection Officer, IITR Datenschutz GmbH | Moderation: Alexander Thamm, CEO, Alexander Thamm GmbH
14:15 - 15:15
Panel
Verwandeln Sie Daten in Produkte – Neue Möglichkeiten für Startups, Mittelstand und Konzerne (DE)
Timo Tautenhahn, Senior Solution Consultant, Tableau
14:15 - 14:45
Data Visualization
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
Explainable AI - Opening the Machine Learning Black Box (EN)
Fabian Müller, Head of Data Science, Statworx
14:45 - 15:15
Machine Learning
Explainable AI - Opening the Machine Learning Black Box (EN)Despite their widespread adoption, machine learning models are considered to be black boxes. When using them, it is often argued, that if a model performs well, we should trust it and simply ignore the question why it made a certain prediction. But this argumentation is short-sighted. Understanding a model is crucial when decisions are made based on model predictions. This talk will highlight the importance of understanding predictions made by machine learning and will give an overview of state-of-the-art methods to open the black box.
Data sovereignty as a key capability of data economics - implemented according to IDS by Deutsche Telekom (EN)
Prof. Dr. Boris Otto, Geschäftsführender Institutsleiter, Fraunhofer-Instituts für Software- und Systemtechnik ISST und stellvertretender Vorstandsvorsitzender, International Data Spaces Association | Sven Löffler, Business Development Executive, IoT & Data Economy | T-Systems
14:45 - 15:15
Data Economics
Data sovereignty as a key capability of data economics - implemented according to IDS by Deutsche Telekom (EN)Data is the new gold of the digital age, the basic raw material. We live and work in a world in which physical products are increasingly becoming digital services. More and more German companies see the exchange of data as an essential component of their business model. We are driving this development forward and enabling our customers to create innovation, added value, services and products. To this end, we provide a trustworthy and secure platform for the procurement and exchange of data based on the reference architecture of the International Data Spaces Association as well as analysis tools and secure working environments from a single source.
15:00
15:15
Using Machine Learning at an Industrial Scale (EN)
Karl Schriek, Head of AI / Leading Machine Learning Engineer, Alexander Thamm GmbH
15:15 - 15:45
Machine Learning
Using Machine Learning at an Industrial Scale (EN)In this presentation, we will look at the current state of industrialization of Machine Learning solutions. Algorithms (even extremely sophisticated ones that can develop themselves) ultimately make up only a small part of each Machine Learning system. Successfully setting up such a system requires a huge architectural and engineering effort. We will look at what makes Machine Learning inherently difficult to do at an industrial scale and then explore recent developments that have made it much easier to do so. Some key talking points will include designing modular workflows; supporting those workflows with a microservices approach; and prototyping at scale.
Modern Data Strategies – Making the Move from Batch to Stream (EN)
Alex Herdt, Central Regional Sales Manager - EMEA, Attunity | Christopher Knauf, Director of Sales, Attunity
15:15 - 15:45
Data Strategy
Modern Data Strategies – Making the Move from Batch to Stream (EN)Understanding the data strategies of big enterprises is crucial to developing your business environment – using customer case studies centring around the Attunity Platform you’ll learn how to: Design a modern information architecture to serve customer expectations (Generali)Utilise CDC tools to access real-time data (Both Case Studies)Extract the SAP data & build a Streaming Engine (Conrad Electronics)
A Process-Oriented Approach for the Economic Evaluation of a Data Science and Machine Learning Platform (EN)
Dr. Björn Höfer, Manager Advanced Analytics & Data Science, Telefónica Germany | Frederik Ström, Sr. Business Developer EMEA, dataiku
15:15 - 15:45
Technology Infrastructure
A Process-Oriented Approach for the Economic Evaluation of a Data Science and Machine Learning Platform (EN)Data science teams and businesses at large can greatly benefit from the introduction of modern platforms for data science and machine learning. At the same time, the introduction of such a platform represents an investment of a considerable amount of time and money and bears the risk of a lock-in effect. Hence, a diligent evaluation is crucial. However, it is a challenging task. There are many technical dependencies. In addition, many potential benefits can be realized only after a phase of implementation and learning. While a POC style of evaluation can efficiently reduce uncertainties around the technical dependencies, it cannot easily be translated into monetary benefits justifying the investment and the risk. We present an approach based on a breakdown of the data science process that allows a benefit estimation depending on the kind of data science projects an analytics unit performs. Once the process steps are defined, the platform-driven efficiency gains per step can be estimated based on the POC experience and projected to whole project work of the analytics unit.
15:30
15:45
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00
16:00
Fuck-up Session: why do projects fail? (EN)
Stefan Bader, Director Parking, Continental AG | Julia Davin, Co-Founder, Masterplan Engineering | Dr. Ferdinand Kiermaier, Team Lead Advanced Analytics in Procurement & Supply Chain - CoE Center of Expertise, BASF SE | Matthew Lehar, Senior Data Scientist, Telefónica Germany NEXT GmbH | Alexander Schnell, Data Scientist, W&H Dentalwerk | Moderation: Alexander Thamm, CEO, Alexander Thamm GmbH
16:00 - 17:00
Special
16:15
16:30
16:45

data.stage

festival.stage

tech.stage

work.shop(0)

work.shop(1)

data.stage festival.stage tech.stage work.shop(0) work.shop(1)
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.
Ganzheitliche Daten & Analytics Strategie – Analytics & BI strukturieren, organisieren & skalieren (DE)
Dr. Sebastian Derwisch, Data Scientist, BARC GmbH | Jacqueline Bloemen, Senior Analyst | BARC GmbH
09:45 - 10:15
Data Strategy
Ganzheitliche Daten & Analytics Strategie – Analytics & BI strukturieren, organisieren & skalieren (DE)Die neue (Advanced Analytics) und die alte (BI) Welt der Datenanalyse sind vielfältig verwoben. Bei isolierter Betrachtung dieser Welten werden Abhängigkeiten im Laufe der Integration analytischer Lösungen zu Hindernissen. Um einen Nutzen mit Daten und Analytics zu generieren braucht es kreatives, agiles und interdisziplinären Arbeiten über Silos hinweg. Wie kommt man zu einer koordinierten Vorgehensweise angesichts der Herausforderungen in den Bereichen Daten, BI & Advanced Analytics aus? Antworten liefert eine ganzheitliche Daten & Analytics Strategie.
10:00
10:15
10:30
10:45
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
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
Verwandeln Sie Daten in Produkte – Neue Möglichkeiten für Startups, Mittelstand und Konzerne (DE)
Timo Tautenhahn, Senior Solution Consultant, Tableau
14:15 - 14:45
Data Visualization
14:30
14:45
15:00
15:15
15:30
15:45
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00
data.stage festival.stage tech.stage work.shop(0) work.shop(1)
09:00
Key Takeaways from the Data Journeys at Zalando, Porsche and Volkswagen Financial Services (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 | Kshitij Kumar, VP, Data Infrastructure, Digital Foundation, Zalando
09:00 - 09:30
Keynote
Key Takeaways from the Data Journeys at Zalando, Porsche and Volkswagen Financial Services (EN)What makes a successful data driven company is to step from the lab into the factory. Only if the scaling of data use cases into AI products is accomplished, a company can claim to have successfully implemented AI and machine learning into the organization. As part of the digitization strategy, a company-wide global data strategy is the first step in this direction. Many large companies in Germany and Europe are now starting their data transformation in order not to lose the edge to companies in the USA and China.
09:15
09:30
Coffee Break
09:30 - 09:45
Coffee Break
09:30 - 09:45
Coffee Break
09:30 - 09:45
09:45
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.
Redis as a Recommendation Engine (EN)
Martin Forstner, Solution Architect, Redis Labs
09:45 - 11:15
Exclusive Workshop
Redis as a Recommendation Engine (EN)Redis is an in-memory database system which addresses a variety of use cases. The range reaches from simple caching over message processing to real-time analytics and recommendations. Redis’ scalability and performance is allowing to reach millions of operations per second at a sub-millisecond latency. In addition, Redis provides several data structures and modules (like real sets, sorted sets, full text search, bloom filters, HyperLogLogs, random forests and other machine learning models) to be leveraged for the implementation of recommendation engines.
BI Meets AI- Sisense Business Intelligence Applications Platform Powered by AI (EN)
Inna Tokarev Sela, Head of AI, Sisense
09:45 - 11:15
Exclusive Workshop
BI Meets AI- Sisense Business Intelligence Applications Platform Powered by AI (EN)The workshop will include the demonstration of the current AI-powered functionalities offered by Sisense, including Sisense Narratives, Boto, Insight Miner. Then we will proceed with the overview of the Sisense AI roadmap for 2019 and beyond, including Semantic Graph, NLP, Assisted Data Preparation and Modeling.
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.
This is what we have learned from data strategies? (EN)
Janne Pullinen, Data Business Designer | SOLITA
10:15 - 10:45
Data Strategy
This is what we have learned from data strategies? (EN)This is what we have learned about success: 1. Tech is easy culture is hard 2. Focus on core business instead of one off’s or startup labs. 3. Data development is not support function or IT project. 4. Data asset is source and use case independent and cumulates in constantly increasing pace With best clients where these are true we are seeing 300% + improvements in core business KPI’s. Changes are radical and path is not easy but it is fun and rewarding.
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
How Machine Learning is turning the Automotive Industry upside down (EN)
Jan Zawadzki, Project Lead Data Science, Carmeq GmbH
10:45 - 11:15
Data & AI in Automotive
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.
Placing Data in the Center of Everything we do (EN)
Malcolm Micallef, Data Platform Manager | Tipico
10:45 - 11:15
Data Strategy
Placing Data in the Center of Everything we do (EN)Our vision is to place data at the centre of everything we do at Tipico. All routine actions performed, customer engagements made, and decisions taken are powered by big data & analytics. We will explore the organisational and technological evolution of Tipico’s Data tribe through Tipico’s journey to becoming the data driven enterprise of tomorrow.
Infrastructure matters - Building the right foundation for your data value chain (EN)
Eva Murray, Head of BI & Tableau Zen Master, Exasol | Carsten Weidmann, Technical Alliance Manager, Exasol
10:45 - 11:15
Data Assistance
Infrastructure matters - Building the right foundation for your data value chain (EN)Infrastructure can make or break your BI & AI capabilities. If you go too far, you may not be able to show a return on your investment. Planning too conservatively can stifle development and progress, leaving you one step behind the competition. In our talk we share use cases and recommendations for optimising your data value chain and give you practical tips for taking your environment to the next level.
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...
Industry Exchange: Mobility (EN)
Andrea Gillhuber, Head of Strategy & Engineering
11:45 - 12:45
Special
Industry Exchange: Mobility (EN)Do you work in the area of Mobility? Then our Industry Exchanges are perfect for you! Take the opportunity and talk to experts about current market trends, problems, opportunities and much more. Our sessions enable an interactive exchange with like-minded people. Andreas Gillhuber, Head of Strategy and Engineering at Alexander Thamm GmbH, is hosting the exchange.The exchange consists of a welcome & introduction round, a presentation by the host as well as an interactive game to promote discussion and exchange. The session will be closed with a feedback round. 
Industry Exchange: Finance & Insurance (EN)
Magnus Metz, Senior Account Developer & Team Lead Sales, Alexander Thamm GmbH
11:45 - 12:45
Special
Industry Exchange: Finance & Insurance (EN)Do you work in the area of Finance or Insurance? Then our Industry Exchanges are perfect for you! Take the opportunity and talk to experts about current market trends, problems, opportunities and much more. Our sessions enable an interactive exchange with like-minded people. Magnus Metz, Team Lead  Sales at Alexander Thamm GmbH, is hosting the exchange.The exchange consists of a welcome & introduction round, a presentation by the host as well as an interactive game to promote discussion and exchange. The session will be closed with a feedback round. 
12:00
12:15
BrainWaves – Energy Transition Through Data Science The Agile Way (EN)
Jana-Vanessa Dering, Data Scientist, EWE AG
12:15 - 12:45
Energy
BrainWaves – Energy Transition Through Data Science The Agile 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
How to make 5 billion predictions in 2 days (EN)
Sebastian Wernicke, Chief Data Scientist, ONE LOGIC
12:15 - 12:45
Artificial Intelligence
How to make 5 billion predictions in 2 days (EN)MARKANT is the largest trade and industry collaboration for European food retail, working with over 14,000 industry partners and approximately 150 trade partners. Together with ONE LOGIC, MARKANT is developing a centralized forecasting platform for their trade and industry partners. The goals is to obtain precise sales forecasts up to 26 weeks in advance. With more than 200 article-location-combinations, this means that 5.5 billion forecasts have to be calculated every week within just 2 days’ time. Additionally, the statistical models must be able to take into account events like promotions and external effects like holidays. The results are optimized production, logistics, and sales processes, leading to significant savings and a reduction of environmental footprint.
Critical success factors ramping up cloud data platform and DataOps capabilities in an energy company (EN)
Ville Viitanen, Business Analytics Manager | Fortum
12:15 - 12:45
Technology Infrastructure
Critical success factors ramping up cloud data platform and DataOps capabilities in an energy company (EN)Fortum has many years of experience of running analytics capabilities in cloud environment. During 2018 Fortum started the program of modernizing its customer information related data platform. By enabling agile means to utilize the data assets, Fortum has been able to develop cost efficiency, shorten the deployment cycles and speed-up the development pace.
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
Felix Bauer, Managing Director & CEO, Aircloak GmbH | Dr. Sébastien Foucaud, Vice-President Data Science, XING | Marcel Kling, Director Customer Data & Advanced Analytics, Lufthansa AG | Herbert Maier, Managing Director, Commerzbank | Sebastian Kraska, Attorney at Law, External Data Protection Officer, IITR Datenschutz GmbH | Moderation: Alexander Thamm, CEO, Alexander Thamm GmbH
14:15 - 15:15
Panel
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
Explainable AI - Opening the Machine Learning Black Box (EN)
Fabian Müller, Head of Data Science, Statworx
14:45 - 15:15
Machine Learning
Explainable AI - Opening the Machine Learning Black Box (EN)Despite their widespread adoption, machine learning models are considered to be black boxes. When using them, it is often argued, that if a model performs well, we should trust it and simply ignore the question why it made a certain prediction. But this argumentation is short-sighted. Understanding a model is crucial when decisions are made based on model predictions. This talk will highlight the importance of understanding predictions made by machine learning and will give an overview of state-of-the-art methods to open the black box.
Data sovereignty as a key capability of data economics - implemented according to IDS by Deutsche Telekom (EN)
Prof. Dr. Boris Otto, Geschäftsführender Institutsleiter, Fraunhofer-Instituts für Software- und Systemtechnik ISST und stellvertretender Vorstandsvorsitzender, International Data Spaces Association | Sven Löffler, Business Development Executive, IoT & Data Economy | T-Systems
14:45 - 15:15
Data Economics
Data sovereignty as a key capability of data economics - implemented according to IDS by Deutsche Telekom (EN)Data is the new gold of the digital age, the basic raw material. We live and work in a world in which physical products are increasingly becoming digital services. More and more German companies see the exchange of data as an essential component of their business model. We are driving this development forward and enabling our customers to create innovation, added value, services and products. To this end, we provide a trustworthy and secure platform for the procurement and exchange of data based on the reference architecture of the International Data Spaces Association as well as analysis tools and secure working environments from a single source.
15:00
15:15
Using Machine Learning at an Industrial Scale (EN)
Karl Schriek, Head of AI / Leading Machine Learning Engineer, Alexander Thamm GmbH
15:15 - 15:45
Machine Learning
Using Machine Learning at an Industrial Scale (EN)In this presentation, we will look at the current state of industrialization of Machine Learning solutions. Algorithms (even extremely sophisticated ones that can develop themselves) ultimately make up only a small part of each Machine Learning system. Successfully setting up such a system requires a huge architectural and engineering effort. We will look at what makes Machine Learning inherently difficult to do at an industrial scale and then explore recent developments that have made it much easier to do so. Some key talking points will include designing modular workflows; supporting those workflows with a microservices approach; and prototyping at scale.
Modern Data Strategies – Making the Move from Batch to Stream (EN)
Alex Herdt, Central Regional Sales Manager - EMEA, Attunity | Christopher Knauf, Director of Sales, Attunity
15:15 - 15:45
Data Strategy
Modern Data Strategies – Making the Move from Batch to Stream (EN)Understanding the data strategies of big enterprises is crucial to developing your business environment – using customer case studies centring around the Attunity Platform you’ll learn how to: Design a modern information architecture to serve customer expectations (Generali)Utilise CDC tools to access real-time data (Both Case Studies)Extract the SAP data & build a Streaming Engine (Conrad Electronics)
A Process-Oriented Approach for the Economic Evaluation of a Data Science and Machine Learning Platform (EN)
Dr. Björn Höfer, Manager Advanced Analytics & Data Science, Telefónica Germany | Frederik Ström, Sr. Business Developer EMEA, dataiku
15:15 - 15:45
Technology Infrastructure
A Process-Oriented Approach for the Economic Evaluation of a Data Science and Machine Learning Platform (EN)Data science teams and businesses at large can greatly benefit from the introduction of modern platforms for data science and machine learning. At the same time, the introduction of such a platform represents an investment of a considerable amount of time and money and bears the risk of a lock-in effect. Hence, a diligent evaluation is crucial. However, it is a challenging task. There are many technical dependencies. In addition, many potential benefits can be realized only after a phase of implementation and learning. While a POC style of evaluation can efficiently reduce uncertainties around the technical dependencies, it cannot easily be translated into monetary benefits justifying the investment and the risk. We present an approach based on a breakdown of the data science process that allows a benefit estimation depending on the kind of data science projects an analytics unit performs. Once the process steps are defined, the platform-driven efficiency gains per step can be estimated based on the POC experience and projected to whole project work of the analytics unit.
15:30
15:45
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00
Beer Break
15:45 - 16:00
16:00
Fuck-up Session: why do projects fail? (EN)
Stefan Bader, Director Parking, Continental AG | Julia Davin, Co-Founder, Masterplan Engineering | Dr. Ferdinand Kiermaier, Team Lead Advanced Analytics in Procurement & Supply Chain - CoE Center of Expertise, BASF SE | Matthew Lehar, Senior Data Scientist, Telefónica Germany NEXT GmbH | Alexander Schnell, Data Scientist, W&H Dentalwerk | Moderation: Alexander Thamm, CEO, Alexander Thamm GmbH
16:00 - 17:00
Special
16:15
16:30
16:45

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