Selected Presentations of the the past years
Many thanks to all speakers, visitors and sponsors.
Why data science often fails | Marcel Kling & Dr. Carsten Bange
You might have seen presentations and advice on success factors for data science. But we think that many are not specific enough to really help you in building successful data science teams or executing successful data science programs. Marcel and Carsten collected the pitfalls and success factors they think really matter – beyond the usual high level (project) management advice.
Key Takeaways from the Data Journeys at Zalando, Porsche and VWFS | Thamm, Borek, Alzner & Kumar
Many large companies in Germany and Europe are now starting their data transformation in order not to lose the edge to companies in the US and China. The decisive question is here how to start and implement the Data Journey in large organizations. One crucial step is the transition from the Data Strategy and Data Lab phases to to a Factory environment. Whereas in the first two phases Data Prototypes are drafted and built, the difficulty in the latter phase is the operation of Data Products and the management of their portfolio. Besides organizational challenges, also the governance of the data itself is a point that needs to be optimized on the way to a thriving data driven organization. In addition to that recent events have highlighted the importance of software development and operation skills which leads to the Data Ops phase of the Data Journey where the focus lies on running the product and maintaining it. Learn how to set the right environment to launch your data projects in an agile way that allow you to fail fast and move on for a quicker route to success. Find out how to create a modern data culture that will revolutionize company operations to get the most out of working with your data assets and drive your business forward. Hear about recommendations concerning software tools, frameworks and cloud platforms. Benefit of the challenges and successes as well as the experience of data & ai projects from VWFS, Zalando and Porsche.
Quantum Machine Learning puts AI on Steroids | Christian Nietner
Currently, we are at the beginning of a new era of computing, which will have a massive impact on society as a whole – it is called Quantum Computing. This technology will greatly influence the development and boost the performance of AI. Furthermore, it will allow for the first time to solve very complex problems e.g. in the fields of material and medical research or cryptography. First industry players like Volkswagen, Daimler or Airbus have already successfully applied this technology to business relevant optimization problems. Right now there is applicable quantum hardware accessible provided by D-Wave, Google or IBM. Although these hardware structures are rather small at the moment there is proof that these quantum systems already outperform classical ones. The reason for this computation power lies in the quantum effects that are utilized on the hardware level. In contrast to classical computers that work with bits which are either 1 OR 0, a quantum computer works with so called qubits that are 1 AND 0 at the same time. This counter intuitive behavior of simultaneously being in two different states can be used to perform calculations in parallel in a way that is impossible to do with a classical computer. Just imagine you want to read all books in a library: the classical way is to read each book one after another whereas the quantum way is to read all books at the same time. Using this intrinsic property quantum computers allow to e.g. process large amounts of data and train more complex neural networks very fast. Due to the rapid technological development and the versatile use cases that can be adressed, all industries should start thinking about how they can benefit from the potential of quantum computing. In my talk I will give a short introduction into quantum computing, summarize the current status quo of the field and show some use cases focusing on quantum machine learning.
Christian Nietner, Founder & CTO, Avanetix
How Machine Learning is turning the Automotive Industry upside down | Jan Zawadzki
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, 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 traditional OEMs. Technology-first companies like Waymo or Tesla threaten to overtake established OEMs with billion-dollar market capitalization. Autonomous vehicles produce terabytes of data every day. This data can be immensely valuable in developing machine learning-driven functions. However, substantial challenges remain in the way of using this data. Visit this talk to hear about these challenges to help turn the automotive industry from a mechanical engineering to a software industry.
Jan Zawadzki, Project Lead Data Science, Carmeq GmbH
How to Transform a Global Company into a Data Driven Leader | Alexander Borek & Alexander Thamm
Each Data Transformation Journey is different, but there are some solution patterns that can help in traditional industries. The presentation will explore the following questions on how to drive the change towards a truly data driven enterprise:
- How can you implement data at the heart of your business to enhance your customer’s experience of your brand?
- How to set the right environment to launch your data projects in an agile way that allow you to fail fast and move on for a quicker route to success?
- How can you create a modern data culture that will revolutionise company operations to get the most out of working with your data assets and drive your business forward?
- How can you keep the right balance between investments to incorporate new technologies (e.g. Artificial Intelligence and Blockchain Technologies) and the basic groundwork to make sure your company is ready to absorb them?
What is the big in (Big) data? And how lean can your data be… | Dat Tran
Deep Learning, Artificial Intelligence, Tensorflow, Spark, Flink, Big Data, Smart Data, Hadoop, IoT, Agile, Cloud… There has been a lot of hype around those buzzwords and no they are not names of any Pokemons. In this talk, I want to make some sense out of the buzz nowadays. I will share some of my war stories as well as my own experiences in building up a data science team at the largest price comparison service in Germany.
Dat Tran, Head of Data Science, idealo.de
Data is the new Oil (EN) | Valentine Gogichashvili
Data has become the magic word across the world. “Data is the new oil”. When we talk about data, we should not forget, that, as oil, it should be first harvested, then refined, and only then it can be used, to fuel the engines of the statistical and machine learning systems to generate insights, understand what our customers want, and make correct decisions faster than others.
We at Zalando – one of the biggest online fashion retailers in Europe – are working hard on making data be that oil for us. I will cover the problems of harvesting and understanding data and will talk on where we at Zalando succeed and where we still need to do more progress on making data – the fuel of our future.
Valentine Gogichashvili, Head of Engineering, Zalando SE
How data connects cultures, markets & business and generates massive potentials | Dr. Stefan Meinzer
The national sales companies of the automotive industry have the closest contact to the dealers and the customers in the automotive industry. They push on sales and customer interaction. However, due to intercultural differences or language barriers the development of business models is still challenging and thereby requires high human efforts. On the other hand, there is one language that is unique around the world – DATA. Based on a concrete use case, we explain how the BMW Group uses the existing international knowledge to define new data driven business models that will drive customers’ centricity to the next level. With latest machine learning methodologies, skilled data scientists and international automotive experts we let data bundle the strengths to shape the future.
Dr. Stefan Meinzer, Head of regional analytics services for EMEA, BMW AG
Necessary Cultural Changes during Migration from Data Warehouse2Cloud Data Platform | Sean Gustafson
At AutoScout24 or ImmoScout24 we generate a lot of data. Our cross-company data lake and a unified cloud-base data platform provide all company-internal data to analysts, data scientists and product managers in an efficient manner. With the right team, it is relatively easy to technically implement a data platform in the cloud. However, such a platform will not be successful until a suitable culture change also takes place in the company. A new approach to data and new trust must be found. This presentation describes technical, organizational and cultural challenges in migrating from an on-premise data warehouse and Hadoop cluster to a scalable, secure and flexible data platform in the AWS cloud.
Sean Gustafson, Product Owner – Data Platform, Scout24 Group
Blockchain – StatusQuo & Potential | Thomas Schmiedel & Sebastian Heinz & Marco Plaul
The blockchain technology has developed into one of the most important disruptors of our times, with potential for changing the economy as a whole in the near future. We will therefore discuss the following questions in the panel: What is the Blockchain and how does it work? And what are Smart Contracts and how can they be used by companies? What are the biggest advantages of the Blockchain? Which industries can benefit most from the Blockchain? How can companies develop new business models based on the blockchain? What are the biggest challenges in using blockchain technologies for companies?
Thomas Schmiedel, Data Scientist, Data Reply
Sebastian Heinz, CEO, Statworx
Marco Plaul, Data Scientist, Alexander Thamm GmbH
Working with GPS data in IoT environment from the data science perspective | Dubravko Dolic
One of the most common data types to collect in IoT scenarios is GPS data. At Continental we meet those data in many different Use Cases. As Data Scientist we have to handle huge masses of GPS data so we had to develope some methods to handle those data. In this talk we show show some of them. Among other it can be shown how to check for quality in GPS data, calculate Kernel densities for Truck tracks, Optimize flawed data using Kalman Filtering and presenting Spatiotemporal data for different use Cases. Most of the examples will be done using R and Shiny but Examples in Python and QGIS are also available. The audience will recieve a good overview how to work with Geospatial data even when the data size is huge.
Dubravko Dolic, Lead Architect Advanced Analytics, Continental Reifen Deutschland GmbH
Understanding furnishing styles from images | Christian Nietner
RoomAR is a new bleeding edge software product that is targeting retailers and manufacturers in the furniture and home furnishing industry. The software visualizes virtual products in the real world using the RoomAR augmented reality mobile app. The user experience is enriched by personalized product recommendations. With the help of deep learning and computer vision algorithms, the personal interior design style and the actual living situation of users are considered. In this talk we present our approach on how to use machine learning algorithms to understand individual furnishing styles from images. We show how this work is the basis for a new visual recommender system for interior products and outline the first step on the road to a personal digital interior designer.
Christian Nietner, Founder & CTO, RoomAR